From e3f11b85334047ba947fc6e733cadd3a65e6be29 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 5 Feb 2025 09:02:06 -0500 Subject: [PATCH] update build platform Signed-off-by: Vladimir Mandic --- .eslintrc.json | 1 + .vscode/settings.json | 3 + CHANGELOG.md | 4 +- dist/face-api.esm-nobundle.js | 2 +- dist/face-api.esm.js | 14 +- dist/face-api.esm.js.map | 4 +- dist/face-api.js | 14 +- dist/face-api.node-gpu.js | 2 +- dist/face-api.node-wasm.js | 2 +- dist/face-api.node.js | 2 +- dist/tfjs.esm.js | 14 +- dist/tfjs.version.js | 2 +- package.json | 36 +- src/NeuralNetwork.ts | 1 + typedoc/assets/hierarchy.js | 1 + typedoc/assets/icons.js | 2 +- typedoc/assets/icons.svg | 2 +- typedoc/assets/main.js | 10 +- typedoc/assets/navigation.js | 2 +- typedoc/assets/search.js | 2 +- typedoc/assets/style.css | 2806 +++++++++-------- typedoc/classes/AgeGenderNet.html | 40 +- typedoc/classes/BoundingBox.html | 22 +- typedoc/classes/Box.html | 22 +- typedoc/classes/ComposableTask.html | 4 +- .../ComputeAllFaceDescriptorsTask.html | 8 +- .../ComputeFaceDescriptorsTaskBase.html | 4 +- .../ComputeSingleFaceDescriptorTask.html | 8 +- .../classes/DetectAllFaceLandmarksTask.html | 10 +- typedoc/classes/DetectAllFacesTask.html | 10 +- .../classes/DetectFaceLandmarksTaskBase.html | 4 +- typedoc/classes/DetectFacesTaskBase.html | 4 +- .../DetectSingleFaceLandmarksTask.html | 10 +- typedoc/classes/DetectSingleFaceTask.html | 10 +- typedoc/classes/Dimensions.html | 4 +- typedoc/classes/FaceDetection.html | 18 +- typedoc/classes/FaceDetectionNet.html | 32 +- typedoc/classes/FaceExpressionNet.html | 40 +- typedoc/classes/FaceExpressions.html | 6 +- typedoc/classes/FaceLandmark68Net.html | 42 +- typedoc/classes/FaceLandmark68TinyNet.html | 42 +- typedoc/classes/FaceLandmarkNet.html | 42 +- typedoc/classes/FaceLandmarks.html | 22 +- typedoc/classes/FaceLandmarks5.html | 22 +- typedoc/classes/FaceLandmarks68.html | 36 +- typedoc/classes/FaceMatch.html | 8 +- typedoc/classes/FaceMatcher.html | 18 +- typedoc/classes/FaceRecognitionNet.html | 32 +- typedoc/classes/LabeledBox.html | 24 +- typedoc/classes/LabeledFaceDescriptors.html | 8 +- typedoc/classes/NetInput.html | 30 +- typedoc/classes/NeuralNetwork.html | 28 +- typedoc/classes/ObjectDetection.html | 18 +- typedoc/classes/Point.html | 4 +- typedoc/classes/PredictedBox.html | 28 +- typedoc/classes/Rect.html | 22 +- typedoc/classes/SsdMobilenetv1.html | 32 +- typedoc/classes/SsdMobilenetv1Options.html | 8 +- typedoc/classes/TinyFaceDetector.html | 42 +- typedoc/classes/TinyFaceDetectorOptions.html | 8 +- typedoc/classes/TinyYolov2.html | 44 +- typedoc/classes/TinyYolov2Options.html | 8 +- typedoc/classes/draw.DrawBox.html | 4 +- typedoc/classes/draw.DrawBoxOptions.html | 10 +- typedoc/classes/draw.DrawFaceLandmarks.html | 6 +- .../draw.DrawFaceLandmarksOptions.html | 16 +- typedoc/classes/draw.DrawTextField.html | 10 +- .../classes/draw.DrawTextFieldOptions.html | 14 +- typedoc/classes/tf.Tensor-1.html | 180 +- typedoc/classes/tf.Variable.html | 184 +- .../classes/tf.io.CompositeArrayBuffer.html | 12 +- typedoc/enums/Gender.html | 6 +- typedoc/enums/draw.AnchorPosition.html | 10 +- typedoc/enums/tf.Rank.html | 16 +- typedoc/functions/allFaces.html | 2 +- typedoc/functions/allFacesSsdMobilenetv1.html | 2 +- typedoc/functions/allFacesTinyYolov2.html | 2 +- typedoc/functions/awaitMediaLoaded.html | 2 +- typedoc/functions/bufferToImage.html | 2 +- typedoc/functions/computeFaceDescriptor.html | 6 +- typedoc/functions/createCanvas.html | 2 +- typedoc/functions/createCanvasFromMedia.html | 2 +- typedoc/functions/createFaceDetectionNet.html | 2 +- .../functions/createFaceRecognitionNet.html | 2 +- typedoc/functions/createSsdMobilenetv1.html | 2 +- typedoc/functions/createTinyFaceDetector.html | 2 +- typedoc/functions/createTinyYolov2.html | 2 +- typedoc/functions/detectAllFaces.html | 2 +- typedoc/functions/detectFaceLandmarks.html | 6 +- .../functions/detectFaceLandmarksTiny.html | 6 +- typedoc/functions/detectLandmarks.html | 6 +- typedoc/functions/detectSingleFace.html | 2 +- typedoc/functions/draw.drawContour.html | 2 +- typedoc/functions/draw.drawDetections.html | 2 +- .../functions/draw.drawFaceExpressions.html | 2 +- .../functions/draw.drawFaceLandmarks-1.html | 2 +- typedoc/functions/euclideanDistance.html | 2 +- typedoc/functions/extendWithAge.html | 2 +- .../functions/extendWithFaceDescriptor.html | 2 +- .../functions/extendWithFaceDetection.html | 2 +- .../functions/extendWithFaceExpressions.html | 2 +- .../functions/extendWithFaceLandmarks.html | 2 +- typedoc/functions/extendWithGender.html | 2 +- typedoc/functions/extractFaceTensors.html | 8 +- typedoc/functions/extractFaces.html | 6 +- typedoc/functions/fetchImage.html | 2 +- typedoc/functions/fetchJson.html | 2 +- typedoc/functions/fetchNetWeights.html | 2 +- typedoc/functions/fetchOrThrow.html | 2 +- typedoc/functions/fetchVideo.html | 2 +- typedoc/functions/getContext2dOrThrow.html | 2 +- typedoc/functions/getMediaDimensions.html | 2 +- typedoc/functions/imageTensorToCanvas.html | 2 +- typedoc/functions/imageToSquare.html | 2 +- typedoc/functions/inverseSigmoid.html | 2 +- typedoc/functions/iou.html | 2 +- typedoc/functions/isMediaElement.html | 2 +- typedoc/functions/isMediaLoaded.html | 2 +- typedoc/functions/isWithAge.html | 2 +- typedoc/functions/isWithFaceDetection.html | 2 +- typedoc/functions/isWithFaceExpressions.html | 2 +- typedoc/functions/isWithFaceLandmarks.html | 2 +- typedoc/functions/isWithGender.html | 2 +- typedoc/functions/loadAgeGenderModel.html | 2 +- typedoc/functions/loadFaceDetectionModel.html | 2 +- .../functions/loadFaceExpressionModel.html | 2 +- typedoc/functions/loadFaceLandmarkModel.html | 2 +- .../functions/loadFaceLandmarkTinyModel.html | 2 +- .../functions/loadFaceRecognitionModel.html | 2 +- .../functions/loadSsdMobilenetv1Model.html | 2 +- .../functions/loadTinyFaceDetectorModel.html | 2 +- typedoc/functions/loadTinyYolov2Model.html | 2 +- typedoc/functions/loadWeightMap.html | 2 +- typedoc/functions/locateFaces.html | 4 +- typedoc/functions/matchDimensions.html | 2 +- typedoc/functions/minBbox.html | 2 +- typedoc/functions/nonMaxSuppression.html | 2 +- typedoc/functions/normalize.html | 2 +- typedoc/functions/padToSquare.html | 4 +- typedoc/functions/predictAgeAndGender.html | 6 +- .../functions/recognizeFaceExpressions.html | 6 +- typedoc/functions/resizeResults.html | 2 +- typedoc/functions/resolveInput.html | 2 +- typedoc/functions/shuffleArray.html | 2 +- typedoc/functions/sigmoid.html | 2 +- typedoc/functions/ssdMobilenetv1-1.html | 6 +- typedoc/functions/tf.add.html | 8 +- typedoc/functions/tf.avgPool.html | 16 +- typedoc/functions/tf.batchNorm.html | 16 +- typedoc/functions/tf.browser.draw.html | 6 +- typedoc/functions/tf.browser.fromPixels.html | 8 +- .../functions/tf.browser.fromPixelsAsync.html | 6 +- typedoc/functions/tf.browser.toPixels.html | 6 +- typedoc/functions/tf.cast.html | 8 +- typedoc/functions/tf.clipByValue.html | 6 +- typedoc/functions/tf.concat.html | 6 +- typedoc/functions/tf.conv2d.html | 18 +- typedoc/functions/tf.depthwiseConv2d.html | 20 +- typedoc/functions/tf.div.html | 8 +- typedoc/functions/tf.exp.html | 6 +- typedoc/functions/tf.expandDims.html | 6 +- typedoc/functions/tf.fill.html | 8 +- typedoc/functions/tf.io.browserFiles.html | 4 +- .../functions/tf.io.browserHTTPRequest.html | 4 +- .../tf.io.concatenateArrayBuffers.html | 8 +- typedoc/functions/tf.io.copyModel.html | 6 +- typedoc/functions/tf.io.decodeWeights.html | 6 +- .../functions/tf.io.decodeWeightsStream.html | 2 +- typedoc/functions/tf.io.encodeWeights.html | 10 +- typedoc/functions/tf.io.fromMemory.html | 6 +- typedoc/functions/tf.io.fromMemorySync.html | 6 +- typedoc/functions/tf.io.getLoadHandlers.html | 2 +- .../tf.io.getModelArtifactsForJSON.html | 8 +- .../tf.io.getModelArtifactsForJSONSync.html | 4 +- .../tf.io.getModelArtifactsInfoForJSON.html | 4 +- typedoc/functions/tf.io.getSaveHandlers.html | 2 +- typedoc/functions/tf.io.getWeightSpecs.html | 4 +- typedoc/functions/tf.io.http.html | 16 +- typedoc/functions/tf.io.isHTTPScheme.html | 2 +- typedoc/functions/tf.io.listModels.html | 6 +- typedoc/functions/tf.io.loadWeights.html | 4 +- typedoc/functions/tf.io.moveModel.html | 6 +- .../functions/tf.io.registerLoadRouter.html | 2 +- .../functions/tf.io.registerSaveRouter.html | 2 +- typedoc/functions/tf.io.removeModel.html | 6 +- .../functions/tf.io.weightsLoaderFactory.html | 8 +- typedoc/functions/tf.io.withSaveHandler.html | 6 +- .../functions/tf.io.withSaveHandlerSync.html | 6 +- typedoc/functions/tf.matMul.html | 8 +- typedoc/functions/tf.maxPool.html | 16 +- typedoc/functions/tf.mul.html | 8 +- typedoc/functions/tf.pad.html | 8 +- typedoc/functions/tf.relu.html | 6 +- typedoc/functions/tf.reshape.html | 8 +- typedoc/functions/tf.scalar.html | 6 +- typedoc/functions/tf.separableConv2d.html | 18 +- typedoc/functions/tf.sigmoid.html | 6 +- typedoc/functions/tf.slice.html | 10 +- typedoc/functions/tf.slice3d.html | 4 +- typedoc/functions/tf.softmax.html | 6 +- typedoc/functions/tf.stack.html | 6 +- typedoc/functions/tf.sub.html | 8 +- typedoc/functions/tf.tensor-2.html | 8 +- typedoc/functions/tf.tensor1d-1.html | 4 +- typedoc/functions/tf.tensor2d-1.html | 6 +- typedoc/functions/tf.tensor3d-1.html | 6 +- typedoc/functions/tf.tensor4d-1.html | 6 +- typedoc/functions/tf.tidy.html | 8 +- typedoc/functions/tf.tile.html | 6 +- typedoc/functions/tf.transpose.html | 6 +- typedoc/functions/tf.unstack.html | 6 +- typedoc/functions/tf.zeros.html | 6 +- typedoc/functions/tinyFaceDetector-1.html | 6 +- typedoc/functions/tinyYolov2-1.html | 6 +- typedoc/functions/toNetInput.html | 4 +- .../utils.computeReshapedDimensions.html | 2 +- typedoc/functions/utils.getCenterPoint.html | 2 +- typedoc/functions/utils.isDimensions.html | 2 +- typedoc/functions/utils.isEven.html | 2 +- typedoc/functions/utils.isFloat.html | 2 +- typedoc/functions/utils.isTensor.html | 2 +- typedoc/functions/utils.isTensor1D.html | 2 +- typedoc/functions/utils.isTensor2D.html | 2 +- typedoc/functions/utils.isTensor3D.html | 2 +- typedoc/functions/utils.isTensor4D.html | 2 +- typedoc/functions/utils.isValidNumber.html | 2 +- .../functions/utils.isValidProbablitiy.html | 2 +- typedoc/functions/utils.range.html | 2 +- typedoc/functions/utils.round.html | 2 +- typedoc/functions/validateConfig.html | 2 +- typedoc/hierarchy.html | 2 +- typedoc/index.html | 156 +- typedoc/interfaces/IBoundingBox.html | 4 +- typedoc/interfaces/IDimensions.html | 4 +- typedoc/interfaces/IFaceDetecion.html | 4 +- typedoc/interfaces/IFaceLandmarks.html | 4 +- typedoc/interfaces/IFaceMatch.html | 4 +- typedoc/interfaces/IPoint.html | 4 +- typedoc/interfaces/IRect.html | 4 +- .../interfaces/ISsdMobilenetv1Options.html | 6 +- typedoc/interfaces/ITinyYolov2Options.html | 6 +- typedoc/interfaces/draw.IDrawBoxOptions.html | 8 +- .../draw.IDrawFaceLandmarksOptions.html | 14 +- .../draw.IDrawTextFieldOptions.html | 12 +- typedoc/interfaces/tf.io.IOHandler.html | 6 +- typedoc/interfaces/tf.io.LoadOptions.html | 45 +- typedoc/interfaces/tf.io.ModelArtifacts.html | 48 +- .../interfaces/tf.io.ModelArtifactsInfo.html | 24 +- typedoc/interfaces/tf.io.ModelJSON.html | 40 +- .../interfaces/tf.io.ModelStoreManager.html | 16 +- typedoc/interfaces/tf.io.RequestDetails.html | 8 +- typedoc/interfaces/tf.io.SaveConfig.html | 14 +- typedoc/interfaces/tf.io.SaveResult.html | 14 +- typedoc/interfaces/tf.io.TrainingConfig.html | 12 +- .../tf.io.WeightsManifestEntry.html | 16 +- typedoc/modules/draw.html | 19 +- typedoc/modules/tf.Tensor.html | 2 +- typedoc/modules/tf.browser.html | 6 +- typedoc/modules/tf.html | 54 +- typedoc/modules/tf.io.html | 47 +- typedoc/modules/utils.html | 16 +- typedoc/types/AgeAndGenderPrediction.html | 2 +- typedoc/types/BatchNorm.html | 2 +- typedoc/types/ConvWithBatchNorm.html | 2 +- typedoc/types/DefaultTinyYolov2NetParams.html | 2 +- typedoc/types/Environment.html | 2 +- typedoc/types/FaceDetectionFunction.html | 2 +- typedoc/types/FaceDetectionOptions.html | 2 +- typedoc/types/FileSystem.html | 2 +- typedoc/types/ITinyFaceDetectorOptions.html | 2 +- typedoc/types/MobilenetParams.html | 2 +- typedoc/types/NetOutput.html | 2 +- typedoc/types/NetParams.html | 2 +- typedoc/types/TMediaElement.html | 2 +- typedoc/types/TNetInput.html | 2 +- typedoc/types/TResolvedNetInput.html | 2 +- typedoc/types/TinyYolov2Config.html | 2 +- .../types/TinyYolov2ExtractBoxesResult.html | 2 +- typedoc/types/TinyYolov2NetParams.html | 2 +- typedoc/types/WithAge.html | 2 +- typedoc/types/WithFaceDescriptor.html | 2 +- typedoc/types/WithFaceDetection.html | 2 +- typedoc/types/WithFaceExpressions.html | 2 +- typedoc/types/WithFaceLandmarks.html | 2 +- typedoc/types/WithGender.html | 2 +- .../types/draw.DrawFaceExpressionsInput.html | 2 +- .../types/draw.DrawFaceLandmarksInput.html | 2 +- typedoc/types/draw.TDrawDetectionsInput.html | 2 +- typedoc/types/tf.NamedTensorMap.html | 2 +- typedoc/types/tf.Tensor1D.html | 4 +- typedoc/types/tf.Tensor2D.html | 4 +- typedoc/types/tf.Tensor3D.html | 4 +- typedoc/types/tf.Tensor4D.html | 4 +- typedoc/types/tf.Tensor5D.html | 4 +- typedoc/types/tf.TensorLike.html | 4 +- typedoc/types/tf.io.IOHandlerSync.html | 4 +- typedoc/types/tf.io.LoadHandler.html | 4 +- typedoc/types/tf.io.OnProgressCallback.html | 4 +- typedoc/types/tf.io.SaveHandler.html | 4 +- typedoc/types/tf.io.WeightData.html | 2 +- typedoc/types/tf.io.WeightGroup.html | 4 +- .../types/tf.io.WeightsManifestConfig.html | 4 +- typedoc/variables/FACE_EXPRESSION_LABELS.html | 2 +- typedoc/variables/env.html | 2 +- typedoc/variables/nets.html | 2 +- typedoc/variables/tf.ENV.html | 2 +- typedoc/variables/tf.image.html | 2 +- typedoc/variables/tf.version.html | 2 +- typedoc/variables/version.html | 2 +- types/face-api.d.ts | 2 +- 310 files changed, 2772 insertions(+), 2891 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 typedoc/assets/hierarchy.js diff --git a/.eslintrc.json b/.eslintrc.json index c430a49..83b1d62 100644 --- a/.eslintrc.json +++ b/.eslintrc.json @@ -29,6 +29,7 @@ "@typescript-eslint/explicit-module-boundary-types": "off", "@typescript-eslint/no-var-requires": "off", "@typescript-eslint/no-empty-object-type": "off", + "@typescript-eslint/no-require-imports": "off", "camelcase": "off", "class-methods-use-this": "off", "default-param-last": "off", diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..3662b37 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "typescript.tsdk": "node_modules/typescript/lib" +} \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md index 7ad3dfc..6d89c58 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,9 +14,7 @@ ### **1.7.14** 2024/09/10 mandic00@live.com - -### 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C{constructor(t,e,r,a,i=!1){super({x:t,y:e,width:r,height:a},i)}};var Fo=.5,Do=.43,Eo=.45,$=class{constructor(t,e,r=new v(0,0)){let{width:a,height:i}=e;this._imgDims=new A(a,i),this._shift=r,this._positions=t.map(s=>s.mul(new v(a,i)).add(r))}get shift(){return new v(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new v(this.imageWidth,this.imageHeight)))}forSize(t,e){return new this.constructor(this.relativePositions,{width:t,height:e})}shiftBy(t,e){return new this.constructor(this.relativePositions,this._imgDims,new v(t,e))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,e={}){if(t){let i=t instanceof D?t.box.floor():new C(t);return this.shiftBy(i.x,i.y).align(null,e)}let{useDlibAlignment:r,minBoxPadding:a}={useDlibAlignment:!1,minBoxPadding:.2,...e};return 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se(r.as2D(r.shape[0],-1),e.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:e,paramMappings:r}=this.extractClassifierParams(t);this._params=e,this._paramMappings=r}extractClassifierParams(t){return Cr(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:e,classifierMap:r}=Pe(t);return this.faceFeatureExtractor.loadFromWeightMap(e),Ir(r)}extractParams(t){let e=this.getClassifierChannelsIn(),r=this.getClassifierChannelsOut(),a=r*e+r,i=t.slice(0,t.length-a),s=t.slice(t.length-a);return this.faceFeatureExtractor.extractWeights(i),this.extractClassifierParams(s)}};var Nr=["neutral","happy","sad","angry","fearful","disgusted","surprised"],it=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: 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N(o,e),{params:{entry_flow:u,middle_flow:f,exit_flow:b},paramMappings:e}}function Br(o,t,e){return n.add(n.conv2d(o,t.filters,e,"same"),t.bias)}function er(o,t,e=!0){let r=e?n.relu(o):o;return r=S(r,t.separable_conv0,[1,1]),r=S(n.relu(r),t.separable_conv1,[1,1]),r=n.maxPool(r,[3,3],[2,2],"same"),r=n.add(r,Br(o,t.expansion_conv,[2,2])),r}function Ro(o,t){let e=S(n.relu(o),t.separable_conv0,[1,1]);return e=S(n.relu(e),t.separable_conv1,[1,1]),e=S(n.relu(e),t.separable_conv2,[1,1]),e=n.add(e,o),e}var Ee=class extends I{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyXception - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=n.relu(Br(i,e.entry_flow.conv_in,[2,2]));return 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Me=class extends I{constructor(t=new Ee(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let r=t instanceof tt?this.faceFeatureExtractor.forwardInput(t):t,a=n.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=se(a,e.fc.age).as1D(),s=se(a,e.fc.gender);return{age:i,gender:s}})}forwardInput(t){return n.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:n.softmax(r)}})}async forward(t){return this.forwardInput(await F(t))}async predictAgeAndGender(t){let e=await F(t),r=await this.forwardInput(e),a=n.unstack(r.age),i=n.unstack(r.gender),s=a.map((m,p)=>({ageTensor:m,genderTensor:i[p]})),c=await Promise.all(s.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,d=l?"male":"female",b=l?f:1-f;return 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Ht){super("FaceLandmark68Net",t)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function Or(o){let t=[],{extractDenseBlock3Params:e}=_e(o,t),r={dense0:e("dense0",!0),dense1:e("dense1"),dense2:e("dense2")};return N(o,t),{params:r,paramMappings:t}}function Hr(o){let t=[],{extractWeights:e,getRemainingWeights:r}=L(o),{extractDenseBlock3Params:a}=ye(e,t),i=a(3,32,"dense0",!0),s=a(32,64,"dense1"),c=a(64,128,"dense2");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{dense0:i,dense1:s,dense2:c}}}var Ce=class extends I{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("TinyFaceFeatureExtractor - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(112,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=xe(i,e.dense0,!0);return 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E={conv32_down:a,conv32_1:i,conv32_2:s,conv32_3:c,conv64_down:m,conv64_1:p,conv64_2:u,conv64_3:f,conv128_down:l,conv128_1:d,conv128_2:b,conv256_down:y,conv256_1:h,conv256_2:g,conv256_down_out:T,fc:x};return N(o,t),{params:E,paramMappings:t}}function j(o,t){let e=Vr(o,t.conv1);return e=nr(e,t.conv2),e=n.add(e,o),e=n.relu(e),e}function ce(o,t){let e=Ne(o,t.conv1);e=nr(e,t.conv2);let r=n.avgPool(o,2,2,"valid"),a=n.zeros(r.shape),i=r.shape[3]!==e.shape[3];if(r.shape[1]!==e.shape[1]||r.shape[2]!==e.shape[2]){let c=[...e.shape];c[1]=1;let m=n.zeros(c);e=n.concat([e,m],1);let p=[...e.shape];p[2]=1;let u=n.zeros(p);e=n.concat([e,u],2)}return r=i?n.concat([r,a],3):r,e=n.add(r,e),e=n.relu(e),e}var jt=class extends I{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("FaceRecognitionNet - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(150,!0),"float32"),i=J(r,[122.782,117.001,104.298]).div(255),s=Ne(i,e.conv32_down);s=n.maxPool(s,3,2,"valid"),s=j(s,e.conv32_1),s=j(s,e.conv32_2),s=j(s,e.conv32_3),s=ce(s,e.conv64_down),s=j(s,e.conv64_1),s=j(s,e.conv64_2),s=j(s,e.conv64_3),s=ce(s,e.conv128_down),s=j(s,e.conv128_1),s=j(s,e.conv128_2),s=ce(s,e.conv256_down),s=j(s,e.conv256_1),s=j(s,e.conv256_2),s=ce(s,e.conv256_down_out);let c=s.mean([1,2]);return n.matMul(c,e.fc)})}async forward(t){return this.forwardInput(await F(t))}async computeFaceDescriptor(t){var i;if((i=t==null?void 0:t.shape)!=null&&i.some(s=>s<=0))return new Float32Array(128);let e=await F(t),r=n.tidy(()=>n.unstack(this.forwardInput(e))),a=await Promise.all(r.map(s=>s.data()));return r.forEach(s=>s.dispose()),e.isBatchInput?a:a[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return jr(t)}extractParams(t){return Gr(t)}};function gf(o){let t=new jt;return t.extractWeights(o),t}function 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i(m,p,u){let f=e(m,`${u}/depthwise_conv`),l=a(m,p,1,`${u}/pointwise_conv`);return{depthwise_conv:f,pointwise_conv:l}}function s(){let m=a(3,32,3,"mobilenetv1/conv_0"),p=i(32,64,"mobilenetv1/conv_1"),u=i(64,128,"mobilenetv1/conv_2"),f=i(128,128,"mobilenetv1/conv_3"),l=i(128,256,"mobilenetv1/conv_4"),d=i(256,256,"mobilenetv1/conv_5"),b=i(256,512,"mobilenetv1/conv_6"),y=i(512,512,"mobilenetv1/conv_7"),h=i(512,512,"mobilenetv1/conv_8"),g=i(512,512,"mobilenetv1/conv_9"),T=i(512,512,"mobilenetv1/conv_10"),x=i(512,512,"mobilenetv1/conv_11"),E=i(512,1024,"mobilenetv1/conv_12"),B=i(1024,1024,"mobilenetv1/conv_13");return{conv_0:m,conv_1:p,conv_2:u,conv_3:f,conv_4:l,conv_5:d,conv_6:b,conv_7:y,conv_8:h,conv_9:g,conv_10:T,conv_11:x,conv_12:E,conv_13:B}}function c(){let 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yt(o,t){return n.tidy(()=>{let e=o.shape[0],r=n.reshape(vt(o,t.box_encoding_predictor),[e,-1,1,4]),a=n.reshape(vt(o,t.class_predictor),[e,-1,3]);return{boxPredictionEncoding:r,classPrediction:a}})}function Kr(o,t,e){return n.tidy(()=>{let r=H(o,e.conv_0,[1,1]),a=H(r,e.conv_1,[2,2]),i=H(a,e.conv_2,[1,1]),s=H(i,e.conv_3,[2,2]),c=H(s,e.conv_4,[1,1]),m=H(c,e.conv_5,[2,2]),p=H(m,e.conv_6,[1,1]),u=H(p,e.conv_7,[2,2]),f=yt(t,e.box_predictor_0),l=yt(o,e.box_predictor_1),d=yt(a,e.box_predictor_2),b=yt(s,e.box_predictor_3),y=yt(m,e.box_predictor_4),h=yt(u,e.box_predictor_5),g=n.concat([f.boxPredictionEncoding,l.boxPredictionEncoding,d.boxPredictionEncoding,b.boxPredictionEncoding,y.boxPredictionEncoding,h.boxPredictionEncoding],1),T=n.concat([f.classPrediction,l.classPrediction,d.classPrediction,b.classPrediction,y.classPrediction,h.classPrediction],1);return{boxPredictions:g,classPredictions:T}})}var z=class{constructor({minConfidence:t,maxResults:e}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=e||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Tt=class extends I{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:e}=this;if(!e)throw new Error("SsdMobilenetv1 - load model before inference");return n.tidy(()=>{let r=n.cast(t.toBatchTensor(512,!1),"float32"),a=n.sub(n.div(r,127.5),1),i=Jr(a,e.mobilenetv1),{boxPredictions:s,classPredictions:c}=Kr(i.out,i.conv11,e.prediction_layer);return Zr(s,c,e.output_layer)})}async forward(t){return this.forwardInput(await F(t))}async locateFaces(t,e={}){let{maxResults:r,minConfidence:a}=new z(e),i=await F(t),{boxes:s,scores:c}=this.forwardInput(i),m=s[0],p=c[0];for(let x=1;x{let[E,B]=[Math.max(0,g[x][0]),Math.min(1,g[x][2])].map(O=>O*h),[V,U]=[Math.max(0,g[x][1]),Math.min(1,g[x][3])].map(O=>O*y);return new D(u[x],new Nt(V,E,U-V,B-E),{height:i.getInputHeight(0),width:i.getInputWidth(0)})});return m.dispose(),p.dispose(),T}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return Xr(t)}extractParams(t){return Ur(t)}};function Jo(o){let t=new Tt;return t.extractWeights(o),t}function gl(o){return Jo(o)}var Qr=class extends Tt{};var to=.4,eo=[new v(.738768,.874946),new v(2.42204,2.65704),new v(4.30971,7.04493),new v(10.246,4.59428),new v(12.6868,11.8741)],ro=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],oo=[117.001,114.697,97.404],no="tiny_yolov2_model",ao="tiny_yolov2_separable_conv_model";var Le=o=>typeof o=="number";function so(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!Le(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(t=>t||{}).every(t=>Le(t.x)&&Le(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(Le)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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io(o,t,e,r){let{extractWeights:a,getRemainingWeights:i}=L(o),s=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=qo(a,s),u;if(t.withSeparableConvs){let[f,l,d,b,y,h,g,T,x]=r,E=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),B=p(l,d,"conv1"),V=p(d,b,"conv2"),U=p(b,y,"conv3"),O=p(y,h,"conv4"),ot=p(h,g,"conv5"),nt=T?p(g,T,"conv6"):void 0,at=x?p(T,x,"conv7"):void 0,Et=c(x||T||g,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}else{let[f,l,d,b,y,h,g,T,x]=r,E=m(f,l,"conv0"),B=m(l,d,"conv1"),V=m(d,b,"conv2"),U=m(b,y,"conv3"),O=m(y,h,"conv4"),ot=m(h,g,"conv5"),nt=m(g,T,"conv6"),at=m(T,x,"conv7"),Et=c(x,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:u,paramMappings:s}}function Zo(o,t){let e=k(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function i(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let s=Ot(e);return{extractConvParams:a,extractConvWithBatchNormParams:i,extractSeparableConvParams:s}}function co(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}=Zo(o,e),s;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:c>7?i("conv6"):void 0,conv7:c>8?i("conv7"):void 0,conv8:r("conv8")}}else s={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:s,paramMappings:e}}var q=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Se=class Se extends I{constructor(t){super("TinyYolov2"),so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=et(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=et(r,e.conv6),r=et(r,e.conv7),vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ut(vt(t,e.conv0,"valid",!1)):rt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?rt(r,e.conv6):r,r=e.conv7?rt(r,e.conv7):r,vt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await F(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new q(e),i=await F(t),s=await this.forwardInput(i,r),c=n.tidy(()=>n.unstack(s)[0].expandDims()),m={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(c,i.getReshapedInputDimensions(0),a);s.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),d=p.map(h=>this.config.classes[h.label]);return br(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new It(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return co(t,this.config)}extractParams(t){let e=this.config.filterSizes||Se.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return io(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:i}=e,s=Math.max(a,i),c=s/a,m=s/i,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let g=t.reshape([p,p,u,this.boxEncodingSize]),T=g.slice([0,0,0,0],[p,p,u,4]),x=g.slice([0,0,0,4],[p,p,u,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,x,E]}),b=[],y=await l.array(),h=await f.array();for(let g=0;gr){let B=(T+fe(h[g][T][x][0]))/p*c,V=(g+fe(h[g][T][x][1]))/p*m,U=Math.exp(h[g][T][x][2])*this.config.anchors[x].x/p*c,O=Math.exp(h[g][T][x][3])*this.config.anchors[x].y/p*m,ot=B-U/2,nt=V-O/2,at={row:g,col:T,anchor:x},{classScore:Et,label:mr}=this.withClassScores?await this.extractPredictedClass(d,at):{classScore:1,label:0};b.push({box:new Ct(ot,nt,ot+U,nt+O),score:E,classScore:E*Et,label:mr,...at})}}return f.dispose(),l.dispose(),d.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:i}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>s[r][a][i][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}};Se.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Xt=Se;var Jt=class extends Xt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:to,classes:["face"],...t?{anchors:ro,meanRgb:oo}:{anchors:eo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ao:no}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function cd(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Ae=class extends q{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Y=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function _t(o,t,e,r,a=({alignedRect:i})=>i){let i=o.map(m=>Yt(m)?a(m):m.detection),s=r||(t instanceof n.Tensor?await oe(t,i):await re(t,i)),c=await e(s);return s.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return _t([o],t,async i=>e(i[0]),r,a)}var mo=.4,po=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],uo=[117.001,114.697,97.404];var Zt=class extends Xt{constructor(){let t={withSeparableConvs:!0,iouThreshold:mo,classes:["face"],anchors:po,meanRgb:uo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Tt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new Gt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new jt,faceExpressionNet:new we,ageGenderNet:new Me},Ko=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Rd=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),$d=(o,t)=>P.tinyYolov2.locateFaces(o,t),Qo=o=>P.faceLandmark68Net.detectLandmarks(o),Od=o=>P.faceLandmark68TinyNet.detectLandmarks(o),Hd=o=>P.faceRecognitionNet.computeFaceDescriptor(o),zd=o=>P.faceExpressionNet.predictExpressions(o),Yd=o=>P.ageGenderNet.predictAgeAndGender(o),tn=o=>P.ssdMobilenetv1.load(o),Vd=o=>P.tinyFaceDetector.load(o),Gd=o=>P.tinyYolov2.load(o),jd=o=>P.faceLandmark68Net.load(o),Ud=o=>P.faceLandmark68TinyNet.load(o),Xd=o=>P.faceRecognitionNet.load(o),Jd=o=>P.faceExpressionNet.load(o),qd=o=>P.ageGenderNet.load(o),Zd=tn,Kd=Ko,Qd=Qo;var We=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Pt=class extends We{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Ft(this,this.input)}},wt=class extends We{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Dt(this,this.input)}},ct=class extends Pt{withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},mt=class extends wt{withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var ke=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Ft=class extends ke{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:i,gender:s,genderProbability:c}=e[a];return sr(ir(r,s,c),i)})}withFaceExpressions(){return new Pt(this,this.input)}},Dt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,i=>P.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new wt(this,this.input)}},pt=class extends Ft{withFaceExpressions(){return new ct(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},ut=class extends Dt{withFaceExpressions(){return new mt(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var Be=class extends Y{constructor(e,r){super();this.parentTask=e;this.input=r}},ft=class extends Be{async run(){let t=await this.parentTask;return(await _t(t,this.input,r=>Promise.all(r.map(a=>P.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}},lt=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let e=await 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allFacesTinyYolov2,yr as awaitMediaLoaded,Tr as bufferToImage,Hd as computeFaceDescriptor,Bt as createCanvas,be as createCanvasFromMedia,gl as createFaceDetectionNet,gf as createFaceRecognitionNet,Jo as createSsdMobilenetv1,Tb as createTinyFaceDetector,cd as createTinyYolov2,cr as detectAllFaces,Qo as detectFaceLandmarks,Od as detectFaceLandmarksTiny,Qd as detectLandmarks,qh as detectSingleFace,Sr as draw,_ as env,fo as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,St as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,wr as fetchJson,Ii as fetchNetWeights,st as fetchOrThrow,ki as fetchVideo,W as getContext2dOrThrow,kt as getMediaDimensions,_r as imageTensorToCanvas,Pr as imageToSquare,On as inverseSigmoid,dr as iou,Qe as isMediaElement,he as isMediaLoaded,Tf as isWithAge,Q as isWithFaceDetection,Lr as isWithFaceExpressions,Yt as 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Me=class extends I{constructor(t=new Ee(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:e}=this;if(!e)throw new Error(`${this._name} - load model before inference`);return n.tidy(()=>{let r=t instanceof tt?this.faceFeatureExtractor.forwardInput(t):t,a=n.avgPool(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),i=se(a,e.fc.age).as1D(),s=se(a,e.fc.gender);return{age:i,gender:s}})}forwardInput(t){return n.tidy(()=>{let{age:e,gender:r}=this.runNet(t);return{age:e,gender:n.softmax(r)}})}async forward(t){return this.forwardInput(await F(t))}async predictAgeAndGender(t){let e=await F(t),r=await this.forwardInput(e),a=n.unstack(r.age),i=n.unstack(r.gender),s=a.map((m,p)=>({ageTensor:m,genderTensor:i[p]})),c=await Promise.all(s.map(async({ageTensor:m,genderTensor:p})=>{let u=m.dataSync()[0],f=p.dataSync()[0],l=f>.5,d=l?"male":"female",b=l?f:1-f;return 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io(o,t,e,r){let{extractWeights:a,getRemainingWeights:i}=L(o),s=[],{extractConvParams:c,extractConvWithBatchNormParams:m,extractSeparableConvParams:p}=qo(a,s),u;if(t.withSeparableConvs){let[f,l,d,b,y,h,g,T,x]=r,E=t.isFirstLayerConv2d?c(f,l,3,"conv0"):p(f,l,"conv0"),B=p(l,d,"conv1"),V=p(d,b,"conv2"),U=p(b,y,"conv3"),O=p(y,h,"conv4"),ot=p(h,g,"conv5"),nt=T?p(g,T,"conv6"):void 0,at=x?p(T,x,"conv7"):void 0,Et=c(x||T||g,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}else{let[f,l,d,b,y,h,g,T,x]=r,E=m(f,l,"conv0"),B=m(l,d,"conv1"),V=m(d,b,"conv2"),U=m(b,y,"conv3"),O=m(y,h,"conv4"),ot=m(h,g,"conv5"),nt=m(g,T,"conv6"),at=m(T,x,"conv7"),Et=c(x,5*e,1,"conv8");u={conv0:E,conv1:B,conv2:V,conv3:U,conv4:O,conv5:ot,conv6:nt,conv7:at,conv8:Et}}if(i().length!==0)throw new Error(`weights remaing after extract: ${i().length}`);return{params:u,paramMappings:s}}function Zo(o,t){let e=k(o,t);function r(c){let m=e(`${c}/sub`,1),p=e(`${c}/truediv`,1);return{sub:m,truediv:p}}function a(c){let m=e(`${c}/filters`,4),p=e(`${c}/bias`,1);return{filters:m,bias:p}}function i(c){let m=a(`${c}/conv`),p=r(`${c}/bn`);return{conv:m,bn:p}}let s=Ot(e);return{extractConvParams:a,extractConvWithBatchNormParams:i,extractSeparableConvParams:s}}function co(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:a,extractSeparableConvParams:i}=Zo(o,e),s;if(t.withSeparableConvs){let c=t.filterSizes&&t.filterSizes.length||9;s={conv0:t.isFirstLayerConv2d?r("conv0"):i("conv0"),conv1:i("conv1"),conv2:i("conv2"),conv3:i("conv3"),conv4:i("conv4"),conv5:i("conv5"),conv6:c>7?i("conv6"):void 0,conv7:c>8?i("conv7"):void 0,conv8:r("conv8")}}else s={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:r("conv8")};return N(o,e),{params:s,paramMappings:e}}var q=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Se=class Se extends I{constructor(t){super("TinyYolov2"),so(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=et(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=et(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=et(r,e.conv6),r=et(r,e.conv7),vt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ut(vt(t,e.conv0,"valid",!1)):rt(t,e.conv0);return r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv1),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv2),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv3),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv4),r=n.maxPool(r,[2,2],[2,2],"same"),r=rt(r,e.conv5),r=n.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?rt(r,e.conv6):r,r=e.conv7?rt(r,e.conv7):r,vt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return n.tidy(()=>{let a=n.cast(t.toBatchTensor(e,!1),"float32");return a=this.config.meanRgb?J(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,r):this.runTinyYolov2(a,r)})}async forward(t,e){return this.forwardInput(await F(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:a}=new q(e),i=await F(t),s=await this.forwardInput(i,r),c=n.tidy(()=>n.unstack(s)[0].expandDims()),m={width:i.getInputWidth(0),height:i.getInputHeight(0)},p=await this.extractBoxes(c,i.getReshapedInputDimensions(0),a);s.dispose(),c.dispose();let u=p.map(h=>h.box),f=p.map(h=>h.score),l=p.map(h=>h.classScore),d=p.map(h=>this.config.classes[h.label]);return br(u.map(h=>h.rescale(r)),f,this.config.iouThreshold,!0).map(h=>new It(f[h],l[h],d[h],u[h],m))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return co(t,this.config)}extractParams(t){let e=this.config.filterSizes||Se.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return io(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:a,height:i}=e,s=Math.max(a,i),c=s/a,m=s/i,p=t.shape[1],u=this.config.anchors.length,[f,l,d]=n.tidy(()=>{let g=t.reshape([p,p,u,this.boxEncodingSize]),T=g.slice([0,0,0,0],[p,p,u,4]),x=g.slice([0,0,0,4],[p,p,u,1]),E=this.withClassScores?n.softmax(g.slice([0,0,0,5],[p,p,u,this.config.classes.length]),3):n.scalar(0);return[T,x,E]}),b=[],y=await l.array(),h=await f.array();for(let g=0;gr){let B=(T+fe(h[g][T][x][0]))/p*c,V=(g+fe(h[g][T][x][1]))/p*m,U=Math.exp(h[g][T][x][2])*this.config.anchors[x].x/p*c,O=Math.exp(h[g][T][x][3])*this.config.anchors[x].y/p*m,ot=B-U/2,nt=V-O/2,at={row:g,col:T,anchor:x},{classScore:Et,label:mr}=this.withClassScores?await this.extractPredictedClass(d,at):{classScore:1,label:0};b.push({box:new Ct(ot,nt,ot+U,nt+O),score:E,classScore:E*Et,label:mr,...at})}}return f.dispose(),l.dispose(),d.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:a,anchor:i}=e,s=await t.array();return Array(this.config.classes.length).fill(0).map((c,m)=>s[r][a][i][m]).map((c,m)=>({classScore:c,label:m})).reduce((c,m)=>c.classScore>m.classScore?c:m)}};Se.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Xt=Se;var Jt=class extends Xt{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:to,classes:["face"],...t?{anchors:ro,meanRgb:oo}:{anchors:eo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?ao:no}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function cd(o,t=!0){let e=new Jt(t);return e.extractWeights(o),e}var Ae=class extends q{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Y=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function _t(o,t,e,r,a=({alignedRect:i})=>i){let i=o.map(m=>Yt(m)?a(m):m.detection),s=r||(t instanceof n.Tensor?await oe(t,i):await re(t,i)),c=await e(s);return s.forEach(m=>m instanceof n.Tensor&&m.dispose()),c}async function qt(o,t,e,r,a){return _t([o],t,async i=>e(i[0]),r,a)}var mo=.4,po=[new v(1.603231,2.094468),new v(6.041143,7.080126),new v(2.882459,3.518061),new v(4.266906,5.178857),new v(9.041765,10.66308)],uo=[117.001,114.697,97.404];var Zt=class extends Xt{constructor(){let t={withSeparableConvs:!0,iouThreshold:mo,classes:["face"],anchors:po,meanRgb:uo,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(a=>new D(a.score,a.relativeBox,{width:a.imageWidth,height:a.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var P={ssdMobilenetv1:new Tt,tinyFaceDetector:new Zt,tinyYolov2:new Jt,faceLandmark68Net:new Gt,faceLandmark68TinyNet:new Ie,faceRecognitionNet:new jt,faceExpressionNet:new we,ageGenderNet:new Me},Ko=(o,t)=>P.ssdMobilenetv1.locateFaces(o,t),Rd=(o,t)=>P.tinyFaceDetector.locateFaces(o,t),$d=(o,t)=>P.tinyYolov2.locateFaces(o,t),Qo=o=>P.faceLandmark68Net.detectLandmarks(o),Od=o=>P.faceLandmark68TinyNet.detectLandmarks(o),Hd=o=>P.faceRecognitionNet.computeFaceDescriptor(o),zd=o=>P.faceExpressionNet.predictExpressions(o),Yd=o=>P.ageGenderNet.predictAgeAndGender(o),tn=o=>P.ssdMobilenetv1.load(o),Vd=o=>P.tinyFaceDetector.load(o),Gd=o=>P.tinyYolov2.load(o),jd=o=>P.faceLandmark68Net.load(o),Ud=o=>P.faceLandmark68TinyNet.load(o),Xd=o=>P.faceRecognitionNet.load(o),Jd=o=>P.faceExpressionNet.load(o),qd=o=>P.ageGenderNet.load(o),Zd=tn,Kd=Ko,Qd=Qo;var We=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Pt=class extends We{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.faceExpressionNet.predictExpressions(a))),this.extractedFaces);return t.map((r,a)=>tr(r,e[a]))}withAgeAndGender(){return new Ft(this,this.input)}},wt=class extends We{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceExpressionNet.predictExpressions(r),this.extractedFaces);return tr(t,e)}withAgeAndGender(){return new Dt(this,this.input)}},ct=class extends Pt{withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},mt=class extends wt{withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var ke=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.extractedFaces=a}},Ft=class extends ke{async run(){let t=await this.parentTask,e=await _t(t,this.input,async r=>Promise.all(r.map(a=>P.ageGenderNet.predictAgeAndGender(a))),this.extractedFaces);return t.map((r,a)=>{let{age:i,gender:s,genderProbability:c}=e[a];return sr(ir(r,s,c),i)})}withFaceExpressions(){return new Pt(this,this.input)}},Dt=class extends ke{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:a}=await qt(t,this.input,i=>P.ageGenderNet.predictAgeAndGender(i),this.extractedFaces);return sr(ir(t,r,a),e)}withFaceExpressions(){return new wt(this,this.input)}},pt=class extends Ft{withFaceExpressions(){return new ct(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},ut=class extends Dt{withFaceExpressions(){return new mt(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var Be=class extends Y{constructor(e,r){super();this.parentTask=e;this.input=r}},ft=class extends Be{async run(){let t=await this.parentTask;return(await _t(t,this.input,r=>Promise.all(r.map(a=>P.faceRecognitionNet.computeFaceDescriptor(a))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,a)=>ar(t[a],r))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}},lt=class extends Be{async run(){let t=await this.parentTask;if(!t)return;let e=await qt(t,this.input,r=>P.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return ar(t,e)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}};var Re=class extends Y{constructor(e,r,a){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?P.faceLandmark68TinyNet:P.faceLandmark68Net}},$e=class extends Re{async run(){let t=await this.parentTask,e=t.map(s=>s.detection),r=this.input instanceof n.Tensor?await oe(this.input,e):await re(this.input,e),a=await Promise.all(r.map(s=>this.landmarkNet.detectLandmarks(s)));return r.forEach(s=>s instanceof n.Tensor&&s.dispose()),t.filter((s,c)=>a[c]).map((s,c)=>ie(s,a[c]))}withFaceExpressions(){return new ct(this,this.input)}withAgeAndGender(){return new pt(this,this.input)}withFaceDescriptors(){return new ft(this,this.input)}},Oe=class extends Re{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof n.Tensor?await oe(this.input,[e]):await re(this.input,[e]),a=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(i=>i instanceof n.Tensor&&i.dispose()),ie(t,a)}withFaceExpressions(){return new mt(this,this.input)}withAgeAndGender(){return new ut(this,this.input)}withFaceDescriptor(){return new lt(this,this.input)}};var He=class extends Y{constructor(e,r=new z){super();this.input=e;this.options=r}},me=class extends He{async run(){let{input:t,options:e}=this,r;if(e instanceof Ae)r=P.tinyFaceDetector.locateFaces(t,e);else if(e instanceof z)r=P.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof q)r=P.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(a=>St({},a)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new $e(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Pt(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ft(this.runAndExtendWithFaceDetections(),this.input)}},ze=class extends He{async run(){let t=await new me(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?St({},e):void 0)})}withFaceLandmarks(t=!1){return new Oe(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new wt(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Dt(this.runAndExtendWithFaceDetection(),this.input)}};function qh(o,t=new z){return new ze(o,t)}function cr(o,t=new z){return new me(o,t)}async function en(o,t){return cr(o,new z(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function rb(o,t={}){return cr(o,new q(t)).withFaceLandmarks().withFaceDescriptors()}var ob=en;function fo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((a,i)=>a-r[i]).reduce((a,i)=>a+i*i,0))}var lo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let a=1,i=()=>`person ${a++}`;this._labeledDescriptors=r.map(s=>{if(s instanceof gt)return s;if(s instanceof Float32Array)return new gt(i(),[s]);if(s.descriptor&&s.descriptor instanceof Float32Array)return new gt(i(),[s.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>fo(r,t)).reduce((r,a)=>r+a,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new Kt(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>gt.fromJSON(r));return new o(e,t.distanceThreshold)}};function Tb(o){let t=new Zt;return t.extractWeights(o),t}function rn(o,t){let{width:e,height:r}=new A(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(a=>rn(a,{width:e,height:r}));if(Yt(o)){let a=o.detection.forSize(e,r),i=o.unshiftedLandmarks.forSize(a.box.width,a.box.height);return ie(St(o,a),i)}return Q(o)?St(o,o.detection.forSize(e,r)):o instanceof $||o instanceof D?o.forSize(e,r):o}var Lb=Ar;export{Me as AgeGenderNet,Ct as BoundingBox,C as Box,Y as ComposableTask,ft as ComputeAllFaceDescriptorsTask,Be as ComputeFaceDescriptorsTaskBase,lt as ComputeSingleFaceDescriptorTask,$e as DetectAllFaceLandmarksTask,me as DetectAllFacesTask,Re as DetectFaceLandmarksTaskBase,He as DetectFacesTaskBase,Oe as DetectSingleFaceLandmarksTask,ze as DetectSingleFaceTask,A as Dimensions,Nr as FACE_EXPRESSION_LABELS,D as FaceDetection,Qr as FaceDetectionNet,we as FaceExpressionNet,it as FaceExpressions,Gt as FaceLandmark68Net,Ie as FaceLandmark68TinyNet,zr as FaceLandmarkNet,$ as FaceLandmarks,xr as FaceLandmarks5,Lt as FaceLandmarks68,Kt as FaceMatch,lo as FaceMatcher,jt as FaceRecognitionNet,rr as Gender,Qt as LabeledBox,gt as LabeledFaceDescriptors,tt as NetInput,I as NeuralNetwork,It as ObjectDetection,v as Point,vr as PredictedBox,Nt as Rect,Tt as SsdMobilenetv1,z as SsdMobilenetv1Options,Zt as TinyFaceDetector,Ae as TinyFaceDetectorOptions,Jt as TinyYolov2,q as TinyYolov2Options,ob as allFaces,en as allFacesSsdMobilenetv1,rb as allFacesTinyYolov2,yr as awaitMediaLoaded,Tr as bufferToImage,Hd as computeFaceDescriptor,Bt as createCanvas,be as createCanvasFromMedia,gl as createFaceDetectionNet,gf as createFaceRecognitionNet,Jo as createSsdMobilenetv1,Tb as createTinyFaceDetector,cd as createTinyYolov2,cr as detectAllFaces,Qo as detectFaceLandmarks,Od as detectFaceLandmarksTiny,Qd as detectLandmarks,qh as detectSingleFace,Sr as draw,_ as env,fo as euclideanDistance,sr as extendWithAge,ar as extendWithFaceDescriptor,St as extendWithFaceDetection,tr as extendWithFaceExpressions,ie as extendWithFaceLandmarks,ir as extendWithGender,oe as extractFaceTensors,re as extractFaces,Fi as fetchImage,wr as fetchJson,Ii as fetchNetWeights,st as fetchOrThrow,ki as fetchVideo,W as getContext2dOrThrow,kt as getMediaDimensions,_r as imageTensorToCanvas,Pr as imageToSquare,On as inverseSigmoid,dr as iou,Qe as isMediaElement,he as isMediaLoaded,Tf as isWithAge,Q as isWithFaceDetection,Lr as isWithFaceExpressions,Yt as isWithFaceLandmarks,Ff as isWithGender,qd as loadAgeGenderModel,Zd as loadFaceDetectionModel,Jd as loadFaceExpressionModel,jd as loadFaceLandmarkModel,Ud as loadFaceLandmarkTinyModel,Xd as loadFaceRecognitionModel,tn as loadSsdMobilenetv1Model,Vd as loadTinyFaceDetectorModel,Gd as loadTinyYolov2Model,Dr as loadWeightMap,Kd as locateFaces,Yi as matchDimensions,hr as minBbox,P as nets,br as nonMaxSuppression,J as normalize,gr as padToSquare,Yd as predictAgeAndGender,zd as recognizeFaceExpressions,rn as resizeResults,At as resolveInput,Rn as shuffleArray,fe as sigmoid,Ko as ssdMobilenetv1,n as tf,Rd as tinyFaceDetector,$d as tinyYolov2,F as toNetInput,lr as utils,so as validateConfig,Lb as version}; diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index 8b8aad1..9981175 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -29,7 +29,7 @@ Expected: ${s}.`)}typeof expect!="undefined"&&expect().nothing()}function AL(e,t ${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(Hc,i)}var dB=L({sparseSegmentMean_:cB});function hB(e,t,n){let a=E(e,"data","sparseSegmentSum"),r=E(t,"indices","sparseSegmentSum","int32"),s=E(n,"segmentIds","sparseSegmentSum","int32");if(a.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(jc,i)}var mB=L({sparseSegmentSum_:hB});function fB(e,t,n,a,r,s,i,o){let l=E(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=E(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let 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className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:O(()=>yn(a.shape,this.initialAccumulatorValue).variable(!1))});let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;O(()=>{let i=X(s,pt(r));s.assign(i);let o=X(z(he(r,rn(X(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}},Gw=class extends Rr{static get className(){return"Adam"}constructor(e,t,n,a=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],O(()=>{this.accBeta1=xe(t).variable(),this.accBeta2=xe(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=pe(1,this.accBeta1),a=pe(1,this.accBeta2);t.forEach((r,s)=>{let 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this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof Kx))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Ef.className="Sequential";ne.registerClass(Ef);function pj(e){return new Er(e)}function cj(e){return new Ef(e)}function j2(e){return S2(e)}function dj(e,t){f0.registerCallbackConstructor(e,t)}var xn=class extends ne.Serializable{getConfig(){return{}}},q2=class extends xn{apply(e,t=1){return FG(e,t)}};q2.className="elu";ne.registerClass(q2);var K2=class extends xn{apply(e){return Qm(e)}};K2.className="selu";ne.registerClass(K2);var X2=class extends xn{apply(e){return Ke(e)}};X2.className="relu";ne.registerClass(X2);var Y2=class extends xn{apply(e){return O(()=>ds(6,Ke(e)))}};Y2.className="relu6";ne.registerClass(Y2);var Z2=class extends xn{apply(e){return e}};Z2.className="linear";ne.registerClass(Z2);var J2=class extends xn{apply(e){return ha(e)}};J2.className="sigmoid";ne.registerClass(J2);var Q2=class extends 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xn{apply(e,t=1){return O(()=>z(ha(z(e,t)),e))}};oC.className="swish";ne.registerClass(oC);function ms(e){return e.getClassName()}function fx(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function fs(e){if(e==null){let t={};return t.className="linear",t.config={},fx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},fx(t)}else return e instanceof xn?e:fx(e)}function k0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var lC=class extends ne.Serializable{},Nd=class extends lC{constructor(e){super(),k0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,kd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Nd.className="L1L2";ne.registerClass(Nd);function hj(e){return k0(e),new Nd({l1:e!=null?e.l1:null,l2:0})}function mj(e){return k0(e),new Nd({l2:e!=null?e.l2:null,l1:0})}var FI={l1l2:"L1L2"};function ft(e){return Qw(e)}function $I(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in FI?FI[e]:e,config:{}};return $I(t)}else return e instanceof lC?e:$I(e)}var I0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};I0.className="ReLU";ne.registerClass(I0);var S0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return ud(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};S0.className="LeakyReLU";ne.registerClass(S0);var N0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?dn(pe(n,cd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};E0.className="Softmax";ne.registerClass(E0);function Fl(e,t,n){if(typeof e=="number")return bi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function gj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=uC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=aw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var pC=class cC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Fl(n.kernelSize,t,"kernelSize"),this.strides=Fl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,va(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=fs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Fl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(tr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ms(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},_f=class dC extends pC{constructor(t,n){super(t,n),this.kernel=null,dC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=_s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};fC.className="SeparableConv";var $0=class extends fC{constructor(e){super(2,e)}};$0.className="SeparableConv2D";ne.registerClass($0);var D0=class gC extends _f{constructor(t){super(1,t),gC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};D0.className="Conv1D";ne.registerClass(D0);var R0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ih(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};R0.className="Cropping2D";ne.registerClass(R0);var M0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,SG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};M0.className="UpSampling2D";ne.registerClass(M0);function bj(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=_0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ts(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var O0=class extends pC{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=bj(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};O0.className="DepthwiseConv2D";ne.registerClass(O0);function bC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function yC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(ea(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=At(p,1)),[d,g,c]})}var Mr=class xC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Rf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Vx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=bC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=yC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=wd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Wx(n,[1,a]):n):this.cell.stateSize>1?[Wx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===xC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Mr.className="RNN";ne.registerClass(Mr);var Td=class extends We{},$f=class extends Td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};$f.className="SimpleRNNCell";ne.registerClass($f);var P0=class extends Mr{constructor(e){e.cell=new $f(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};P0.className="SimpleRNN";ne.registerClass(P0);var Df=class extends Td{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};L0.className="GRU";ne.registerClass(L0);var Cd=class extends Td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends $a{apply(i,o){let l=r.apply([s]),u=new yf().apply([s]),p=r.apply([s*2]);return bI(bI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};z0.className="LSTM";ne.registerClass(z0);var Rf=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ri(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Ux(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):w2(t(),n),o=()=>Id(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var yj=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends $a{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return t0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=yj(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Mf.className="ConvLSTM2DCell";ne.registerClass(Mf);var W0=class extends vC{constructor(e){let t=new Mf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};W0.className="ConvLSTM2D";ne.registerClass(W0);var Of=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0w2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Of.className="Dropout";ne.registerClass(Of);var B0=class extends Of{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};B0.className="SpatialDropout1D";ne.registerClass(B0);var V0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=m2(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Dense";ne.registerClass(V0);var U0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof Kx))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Ef.className="Sequential";ne.registerClass(Ef);function pj(e){return new Er(e)}function cj(e){return new Ef(e)}function j2(e){return S2(e)}function dj(e,t){f0.registerCallbackConstructor(e,t)}var xn=class extends ne.Serializable{getConfig(){return{}}},q2=class extends xn{apply(e,t=1){return FG(e,t)}};q2.className="elu";ne.registerClass(q2);var K2=class extends xn{apply(e){return Qm(e)}};K2.className="selu";ne.registerClass(K2);var X2=class extends xn{apply(e){return Ke(e)}};X2.className="relu";ne.registerClass(X2);var Y2=class extends xn{apply(e){return O(()=>ds(6,Ke(e)))}};Y2.className="relu6";ne.registerClass(Y2);var Z2=class extends xn{apply(e){return e}};Z2.className="linear";ne.registerClass(Z2);var J2=class extends xn{apply(e){return ha(e)}};J2.className="sigmoid";ne.registerClass(J2);var Q2=class extends 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xn{apply(e,t=1){return O(()=>z(ha(z(e,t)),e))}};oC.className="swish";ne.registerClass(oC);function ms(e){return e.getClassName()}function fx(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function fs(e){if(e==null){let t={};return t.className="linear",t.config={},fx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},fx(t)}else return e instanceof xn?e:fx(e)}function k0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var lC=class extends ne.Serializable{},Nd=class extends lC{constructor(e){super(),k0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,kd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Nd.className="L1L2";ne.registerClass(Nd);function hj(e){return k0(e),new Nd({l1:e!=null?e.l1:null,l2:0})}function mj(e){return k0(e),new Nd({l2:e!=null?e.l2:null,l1:0})}var FI={l1l2:"L1L2"};function ft(e){return Qw(e)}function $I(e,t={}){return vd(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in FI?FI[e]:e,config:{}};return $I(t)}else return e instanceof lC?e:$I(e)}var I0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};I0.className="ReLU";ne.registerClass(I0);var S0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return ud(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};S0.className="LeakyReLU";ne.registerClass(S0);var N0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?dn(pe(n,cd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};E0.className="Softmax";ne.registerClass(E0);function Fl(e,t,n){if(typeof e=="number")return bi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. 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V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vl.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function gj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=uC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=aw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var pC=class cC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",cC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Fl(n.kernelSize,t,"kernelSize"),this.strides=Fl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,va(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=fs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Fl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(tr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ms(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},_f=class dC extends pC{constructor(t,n){super(t,n),this.kernel=null,dC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=_s(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};fC.className="SeparableConv";var $0=class extends fC{constructor(e){super(2,e)}};$0.className="SeparableConv2D";ne.registerClass($0);var D0=class gC extends _f{constructor(t){super(1,t),gC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!e0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};D0.className="Conv1D";ne.registerClass(D0);var R0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ih(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ih(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ih(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ih(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};R0.className="Cropping2D";ne.registerClass(R0);var M0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,SG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Zn.resizeNearestNeighbor(n,[r,s]):Zn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};M0.className="UpSampling2D";ne.registerClass(M0);function bj(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=_0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ts(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var O0=class extends pC{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=bj(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};O0.className="DepthwiseConv2D";ne.registerClass(O0);function bC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function yC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ba(t,0),r!=null&&(r=ba(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(ea(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=At(p,1)),[d,g,c]})}var Mr=class xC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Rf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Vx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=bC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=yC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=wd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Wx(n,[1,a]):n):this.cell.stateSize>1?[Wx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===xC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Mr.className="RNN";ne.registerClass(Mr);var Td=class extends We{},$f=class extends Td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0ea(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0ea(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=or(z(e,s),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};$f.className="SimpleRNNCell";ne.registerClass($f);var P0=class extends Mr{constructor(e){e.cell=new $f(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};P0.className="SimpleRNN";ne.registerClass(P0);var Df=class extends Td{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0ea(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};L0.className="GRU";ne.registerClass(L0);var Cd=class extends Td{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=fs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=Ul([1,hs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ul([1,hs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends $a{apply(i,o){let l=r.apply([s]),u=new yf().apply([s]),p=r.apply([s*2]);return bI(bI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0ea(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0ea(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};z0.className="LSTM";ne.registerClass(z0);var Rf=class extends Td{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ri(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Ux(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):w2(t(),n),o=()=>Id(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var yj=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Xr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends $a{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return t0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0ea(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0ea(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=yj(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Mf.className="ConvLSTM2DCell";ne.registerClass(Mf);var W0=class extends vC{constructor(e){let t=new Mf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};W0.className="ConvLSTM2D";ne.registerClass(W0);var Of=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0w2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Of.className="Dropout";ne.registerClass(Of);var B0=class extends Of{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};B0.className="SpatialDropout1D";ne.registerClass(B0);var V0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=fs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=m2(this.activation.getClassName()),r;return a!=null?r=or(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ms(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Dense";ne.registerClass(V0);var U0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},c1=class extends IC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),Pf(e,t,n,a,r,"max")}};c1.className="MaxPooling2D";ne.registerClass(c1);var d1=class extends IC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),Pf(e,t,n,a,r,"avg")}};d1.className="AveragePooling2D";ne.registerClass(d1);var SC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),va(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},h1=class extends SC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),wC(e,t,n,a,r,"max")}};h1.className="MaxPooling3D";ne.registerClass(h1);var m1=class extends SC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),va(a),wC(e,t,n,a,r,"avg")}};m1.className="AveragePooling3D";ne.registerClass(m1);var NC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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i;return this.mergeMode==="concat"?i=t0([a,r]):this.mergeMode==="sum"?i=X(a,r):this.mergeMode==="ave"?i=z(.5,X(a,r)):this.mergeMode==="mul"?i=z(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ri(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ri(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ba(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};v1.className="Bidirectional";ne.registerClass(v1);var w1=class extends We{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=ir(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. 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r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n),o=k("includeBatchInIndex",e,t,n),{result:l,indexes:u}=a.maxPoolWithArgmax(k("x",e,t,n),[i[1],i[2]],[r[1],r[2]],s,o);return[l,u]}case"AvgPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.avgPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"MaxPool3D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("kernelSize",e,t,n);return[a.maxPool3d(k("x",e,t,n),[i[1],i[2],i[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=k("strides",e,t,n),s=k("pad",e,t,n),i=k("dilations",e,t,n),o=r[1],l=r[2],u=i[1],p=i[2];return[a.dilation2d(k("x",e,t,n),k("filter",e,t,n),[o,l],s,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},jq=(e,t,n,a=on)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),s=k("dtype",e,t,n),i=k("value",e,t,n);return[a.fill(r,i,s)]}case"LinSpace":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("num",e,t,n);return[a.linspace(r,s,i)]}case"Multinomial":{let 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function HI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Xn(c)[0]));a=a||[];let p=new Set(a.map(c=>Xn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Qs(c)||g5(c)||b5(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function u5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Xn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=p5(m,l);return c5(f,l),f}function p5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Th=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function c5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Th(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Th(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Th(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Th(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function d5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Qs(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new oE(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=HI(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=u5(this.graph,a),l=d5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Xn(c)[0]]),s=n.map(c=>Xn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new GI(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Xn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=UI(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>pn(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(Qs(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(Qs(l))continue;let u=LI(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return Qs(l)||s.has(l.name)}if(!(Qs(t)||i==null))for(let l of i){if(o(l))continue;let u=LI(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new GI(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>pn(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Xn(v)[0]]),o=a.map(v=>Xn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=HI(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Xn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!Qs(v)&&!pn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Sr(d.node.name,a)),r[d.node.name]==null){let h=UI(d.node,r,a,this._resourceManager);c||([c]=Sr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Sr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Xn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Xn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Xn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},y5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},x5="?tfjs-format=file",v5="model.json",F1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new y5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await pN(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=a,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new jI(zI.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=zI.Instance.transformGraph(e.modelInitializer);this.initializer=new jI(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function w5(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=I5(e));let a=new F1(e,t,n);return await a.load(),a}function k5(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[a,r]=e;if(!a)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in a))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in a))throw new Error("Model JSON is missing 'weightsManifest'");let s=jt.getWeightSpecs(a.weightsManifest),i=jt.getModelArtifactsForJSONSync(a,s,r);t=jt.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=jt.fromMemorySync(e);else throw new Error("Unknown model format");let n=new F1(t);return n.load(),n}function I5(e){return e.endsWith("/")||(e=e+"/"),`${e}${v5}${x5}`}var 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a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Hl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=uE(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function pE(e){return e===null?null:Hl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function cE(e,t){let n=new Map;om(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(w.isPromise(r)){let s=await r;n.set(a,s)}}return om(e,t,n)}function Hl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=FS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function _5(e){return e==null||A5(e)||Array.isArray(e)||typeof 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sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},B5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},V5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return xe(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),O(()=>{let a=dt(t),r=n.length,s=a.length;w.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let a=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=a.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,n),i=k("valueDType",e,t,n),o=new Yq(s,i);return a.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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implemented`)}},Qq=(e,t,n,a=on)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];case"BitwiseAnd":return[a.bitwiseAnd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},e5=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},t5=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},n5=(e,t,n,a=on)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a5=(e,t,n,a=on)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.sum(k("x",e,t,n),o,l)]}case"All":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.all(k("x",e,t,n),o,l)]}case"Any":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.any(k("x",e,t,n),o,l)]}case"ArgMax":{let o=k("axis",e,t,n);return[a.argMax(k("x",e,t,n),o)]}case"ArgMin":{let o=k("axis",e,t,n);return[a.argMin(k("x",e,t,n),o)]}case"Prod":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.prod(k("x",e,t,n),o,l)]}case"Cumprod":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumprod(k("x",e,t,n),o,l,u)]}case"Cumsum":{let o=k("axis",e,t,n),l=k("exclusive",e,t,n),u=k("reverse",e,t,n);return[a.cumsum(k("x",e,t,n),o,l,u)]}case"Bincount":let r=k("x",e,t,n),s=k("weights",e,t,n),i=k("size",e,t,n);return[a.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,n),l=k("weights",e,t,n),u=k("size",e,t,n),p=k("binaryOutput",e,t,n);return[a.denseBincount(o,l,u,p)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},r5=(e,t,n,a=on)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s5=(e,t,n,a=on)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i5=(e,t,n,a=on)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},o5=(e,t,n,a=on)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},l5=(e,t,n,a=on)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function UI(e,t,n,a,r=O){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Pq(i,o,l));case"basic_math":return r(()=>Lq(i,o,l));case"control":return Gq(i,o,l);case"convolution":return r(()=>Hq(i,o,l));case"creation":return r(()=>jq(i,o,l));case"dynamic":return qq(i,o,l);case"evaluation":return r(()=>Kq(i,o,l));case"image":return r(()=>Jq(i,o,l));case"graph":return r(()=>Xq(i,o,l));case"logical":return r(()=>Qq(i,o,l));case"matrices":return r(()=>e5(i,o,l));case"normalization":return r(()=>t5(i,o,l));case"ragged":return r(()=>n5(i,o,l));case"reduction":return r(()=>a5(i,o,l));case"slice_join":return r(()=>r5(i,o,l));case"sparse":return r(()=>s5(i,o,l));case"spectral":return r(()=>i5(i,o,l));case"string":return r(()=>o5(i,o,l));case"transformation":return r(()=>l5(i,o,l));case"hash_table":return Zq(i,o,l,a);case"custom":let u=zC(i.op);if(u&&u.customExecutor)return u.customExecutor(new Oq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var GI=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function HI(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Xn(c)[0]));a=a||[];let p=new Set(a.map(c=>Xn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((Qs(c)||g5(c)||b5(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function u5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Xn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=p5(m,l);return c5(f,l),f}function p5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Th=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function c5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Th(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Th(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Th(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Th(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function d5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>Qs(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new oE(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=HI(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=u5(this.graph,a),l=d5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Xn(c)[0]]),s=n.map(c=>Xn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new GI(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Xn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=UI(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>pn(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(Qs(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(Qs(l))continue;let u=LI(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return Qs(l)||s.has(l.name)}if(!(Qs(t)||i==null))for(let l of i){if(o(l))continue;let u=LI(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new GI(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>pn(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Xn(v)[0]]),o=a.map(v=>Xn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=HI(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Xn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!Qs(v)&&!pn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Sr(d.node.name,a)),r[d.node.name]==null){let h=UI(d.node,r,a,this._resourceManager);c||([c]=Sr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Sr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!pn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Xn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Xn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Xn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},y5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},x5="?tfjs-format=file",v5="model.json",F1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new y5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await pN(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=a,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new jI(zI.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=zI.Instance.transformGraph(e.modelInitializer);this.initializer=new jI(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function w5(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=I5(e));let a=new F1(e,t,n);return await a.load(),a}function k5(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[a,r]=e;if(!a)throw new Error("modelJSON must be the first element of the array");if(!r||!(r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in a))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in a))throw new Error("Model JSON is missing 'weightsManifest'");let s=jt.getWeightSpecs(a.weightsManifest),i=jt.getModelArtifactsForJSONSync(a,s,r);t=jt.fromMemorySync(i)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=jt.fromMemorySync(e);else throw new Error("Unknown model format");let n=new F1(t);return n.load(),n}function I5(e){return e.endsWith("/")||(e=e+"/"),`${e}${v5}${x5}`}var S5="4.22.0",lE={};_e(lE,{CSVDataset:()=>xE,Dataset:()=>ip,FileDataSource:()=>TE,TextLineDataset:()=>yE,URLDataSource:()=>CE,array:()=>q5,csv:()=>i8,func:()=>o8,generator:()=>l8,microphone:()=>p8,version_data:()=>c8,webcam:()=>u8,zip:()=>K5});var N5=xs(bm()),T5=xs(bm());function C5(e,t){return om(e,t)}function om(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Hl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=om(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function E5(e,t=pE){return uE(e,t)}function uE(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Hl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=uE(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function pE(e){return e===null?null:Hl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function cE(e,t){let n=new Map;om(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(w.isPromise(r)){let s=await r;n.set(a,s)}}return om(e,t,n)}function Hl(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=FS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function _5(e){return e==null||A5(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ce||w.isTypedArray(e)}function A5(e){return e===null||typeof e!="object"&&typeof e!="function"}function F5(e){return C5(e,$5)}function $5(e){return e instanceof Ce?{value:e.clone(),recurse:!1}:Hl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var dE=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},hE=class mE extends dE{constructor(){super(mE.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,n=new Array(t),a=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new W5(this,e,t)}columnMajorBatch(e,t=!0,n=pE){return this.rowMajorBatch(e,t).map(a=>E5(a,n))}concatenate(e,t){return new gE(fE([this,e]),t)}take(e){return e<0||e==null?this:new z5(this,e)}skip(e){return e<0||e==null?this:new L5(this,e)}prefetch(e){return new bE(this,e)}shuffle(e,t){return new j5(this,e,t)}serial(){return new P5(this)}},M5=class extends sn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:F5(e),done:!1}}},O5=class extends sn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},P5=class extends sn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},L5=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},W5=class extends sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},B5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},V5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},U5=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},qI=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},D1=class extends sn{constructor(){super(),this.outputQueue=new hE,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},G5=class extends D1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},gE=class extends sn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var H5=class extends sn{constructor(e,t=es.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await cE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case es.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case es.SHORTEST:return{value:null,done:!0};case es.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},bE=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new dE(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},j5=class extends bE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=T5.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},ip=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Kn(async()=>(await n.iterator()).columnMajorBatch(e,t,X5),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Kn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Kn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Kn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Kn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Kn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Kn(async()=>{let a=$1(async()=>({value:await t.iterator(),done:!1}));return D5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=N5.alea(t||w.now().toString());return Kn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Kn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ip.MAX_BUFFER_SIZE=1e4;function Kn(e,t=null){return new class extends ip{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function q5(e){return Kn(async()=>fE(e),e.length)}function K5(e){if(!Hl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await cE(e,a=>{if(a instanceof ip)return{value:a.iterator(),recurse:!1};if(Hl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return R5(n,es.SHORTEST)},t)}function X5(e){if(e===null)return null;let t=e[0];return _5(t)?{value:Y5(e),recurse:!1}:{value:null,recurse:!0}}function Y5(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?At(e):bn(e)}var yE=class extends ip{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Ch='"',Jp=Symbol("out"),KI=Symbol("field"),Eh=Symbol("quote"),bx=Symbol("quoteafterquote"),XI=Symbol("quoteinquote"),xE=class extends ip{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new yE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(n))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new vE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},J5=class wE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Ea([i,s,l,o],[1,4])}else this.cropBox=Ea([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new wE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=qo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Zn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},kE=class{},IE=class extends sn{split(e){return new Q5(this,e)}},Q5=class extends IE{constructor(e,t){super(),this.upstream=e,this.impl=new e8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},e8=class extends D1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},t8=class extends sn{decodeUTF8(){return new n8(this)}},n8=class extends IE{constructor(e){super(),this.upstream=e,this.impl=new a8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},a8=class extends D1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},SE=class extends t8{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(G().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function r8(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=s8(e));let s=await(n||w.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new SE(i,t)}else throw new Error(s.statusText)}var s8=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function NE(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var TE=class extends kE{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(NE(this.input)&&G().get("IS_NODE")){let e=Iv();this.input=e.readFileSync(this.input.slice(7))}return new SE(this.input,this.options)}},CE=class extends kE{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return NE(this.url)?new TE(this.url,this.fileOptions).iterator():r8(this.url,this.fileOptions)}};function i8(e,t={}){return new xE(new CE(e),t)}function o8(e){let t=$1(e);return Kn(async()=>t)}function l8(e){return Kn(async()=>{let t=await e();return $1(()=>t.next())})}async function u8(e,t){return J5.create(e,t)}async function p8(e){return Z5.create(e)}var c8="4.21.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var d8=mr.whereImpl,R1=class EE extends Fc{nextDataId(){return EE.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new ym(this,Ta())}write(t,n,a){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&T.warn(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Ch='"',Jp=Symbol("out"),KI=Symbol("field"),Eh=Symbol("quote"),bx=Symbol("quoteafterquote"),XI=Symbol("quoteinquote"),xE=class extends ip{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new yE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(n))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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XK={kernelName:eu,backendName:"cpu",kernelFunc:KK},YK=lt(Ei,e=>Math.asin(e)),ZK={kernelName:Ei,backendName:"cpu",kernelFunc:YK},JK=lt(_i,e=>Math.asinh(e)),QK={kernelName:_i,backendName:"cpu",kernelFunc:JK},eX=lt(Ai,e=>Math.atan(e)),tX={kernelName:Ai,backendName:"cpu",kernelFunc:eX},nX=Mt((e,t)=>Math.atan2(e,t)),aX=Zt($i,nX),rX={kernelName:$i,backendName:"cpu",kernelFunc:aX},sX=lt(Fi,e=>Math.atanh(e)),iX={kernelName:Fi,backendName:"cpu",kernelFunc:sX};function K1(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Oe(r.outShape,n),g=f.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;vq?q=ie:s==="avg"&&(K+=ie,Z++)}if(isNaN(q))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:q}}}return f}function k_(e,t,n,a,r=!1,s=!1){let i=Oe(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Oe(t,n,e);for(let g=0;gD&&(D=H,r?$=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+b:(S*a.inWidth+B)*a.inChannels+b:$=M*c+U)}}i.set($,g,y,N,b)}}return i}function I_(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,b=r.padInfo.left,y=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Oe(r.outShape,n),v=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],_=r.outShape[4];for(let F=0;Fye?ye=gt:s==="avg"&&(ke+=gt,Se++),isNaN(ye))break}if(isNaN(ye))break}if(isNaN(ye))break}let Le=ue+S;v[Le]=s==="avg"?ke/Math.max(Se,1):ye}}}}return x}function oX(e,t){let n=Oe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=M&&(M=ee,B=H*p*d+K*p+J)}}}n.set(B,f,b,I,F,g)}}}return n}function lX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Oe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var hX={kernelName:Rc,backendName:"cpu",kernelFunc:dX};function mX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Oe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Oe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,q,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var fX={kernelName:Dc,backendName:"cpu",kernelFunc:mX};function gX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ue*F,tt=mt;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ue;for(let tt=0;ttMath.cos(e)),VX={kernelName:Wi,backendName:"cpu",kernelFunc:BX},UX=lt(Bi,e=>Math.cosh(e)),GX={kernelName:Bi,backendName:"cpu",kernelFunc:UX};function HX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Oe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},uY={kernelName:Rl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Rl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},pY={kernelName:Dl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Dl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${C}.`)}else if(r.dtype==="int32"&&(C<0||C>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${C}.`);m===1?(v[0]=C*g,v[1]=C*g,v[2]=C*g):v[N]=C*g}let I=x*4;b[I+0]=Math.round(v[0]),b[I+1]=Math.round(v[1]),b[I+2]=Math.round(v[2]),b[I+3]=Math.round(v[3])}s.width=h,s.height=c;let y=new ImageData(b,h,c);return d.putImageData(y,0,0),r}var dY={kernelName:Nm,backendName:"cpu",kernelFunc:cY};function Ed(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=bs({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=cr({inputs:{x:r},backend:n});let l=o.shape.length,u=w.parseAxisParam(s,o.shape),p=T.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=T.getInnerMostAxes(d.length,l)),T.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=T.computeOutAndReduceShapes(c.shape,d),f=T.upcastType(c.dtype,"int32"),g=lm(n,h,f),b=w.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let v=0;v=0&&(c=Ed({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var fY={kernelName:Tm,backendName:"cpu",kernelFunc:mY};function gY(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(w.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var bY={kernelName:cu,backendName:"cpu",kernelFunc:gY},yY=T.ERF_P,xY=T.ERF_A1,vY=T.ERF_A2,wY=T.ERF_A3,kY=T.ERF_A4,IY=T.ERF_A5,SY=lt(qi,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+yY*n);return t*(1-((((IY*a+kY)*a+wY)*a+vY)*a+xY)*a*Math.exp(-n*n))}),NY={kernelName:qi,backendName:"cpu",kernelFunc:SY};function cm(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var TY={kernelName:hu,backendName:"cpu",kernelFunc:cm},CY=Mt((e,t)=>e/t),X1=Zt(Hi,CY),lv={kernelName:Hi,backendName:"cpu",kernelFunc:X1};function T_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",p),c=w.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=w.getTypedArrayFromDType(a.dtype,w.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=w.sizeFromShape(s.shape),h=T.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=UE(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var UY={kernelName:fu,backendName:"cpu",kernelFunc:VY};function GY(e){let{inputs:t,backend:n}=e,{input:a}=t,r=w.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=T_(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var HY={kernelName:Em,backendName:"cpu",kernelFunc:GY},jY=lt(to,e=>Number.isFinite(e)?1:0,"bool"),qY={kernelName:to,backendName:"cpu",kernelFunc:jY},KY=lt(no,e=>Math.abs(e)===1/0?1:0,"bool"),XY={kernelName:no,backendName:"cpu",kernelFunc:KY},YY=lt(ao,e=>Number.isNaN(e)?1:0,"bool"),ZY={kernelName:ao,backendName:"cpu",kernelFunc:YY};function JY(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=KE(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var QY={kernelName:vu,backendName:"cpu",kernelFunc:JY},e7=lt(io,e=>Math.log1p(e)),t7={kernelName:io,backendName:"cpu",kernelFunc:e7},n7=Mt((e,t)=>e&&t),a7=Zt(wu,n7,null,"bool"),r7={kernelName:wu,backendName:"cpu",kernelFunc:a7},s7=lt(ku,e=>e?0:1,"bool"),i7={kernelName:ku,backendName:"cpu",kernelFunc:s7},o7=Mt((e,t)=>e||t),l7=Zt(Iu,o7,null,"bool"),u7={kernelName:Iu,backendName:"cpu",kernelFunc:l7};function p7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=w.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let v=d[b];x+=v*v}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))d=cr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),m=K1(c,r.shape,r.dtype,h,p,"max");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var g7={kernelName:po,backendName:"cpu",kernelFunc:f7};function b7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ge(r,"maxPool3d");let p=T.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=I_(d,r.shape,r.dtype,w.computeStrides(r.shape),p,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var y7={kernelName:Nu,backendName:"cpu",kernelFunc:b7};function x7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ge([r,s],"maxPool3DGrad");let p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=n.bufferSync(s),c=oX(d,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,b=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterDepth,v=p.effectiveFilterHeight,I=p.effectiveFilterWidth,N=x-1-p.padInfo.front,C=I-1-p.padInfo.left,_=v-1-p.padInfo.top,F=Oe(s.shape,"float32"),D=n.bufferSync(r);for(let $=0;$=p.outDepth||Math.floor(ee)!==ee))for(let ae=0;ae=p.outHeight||Math.floor(te)!==te))for(let se=0;se=p.outWidth||Math.floor(ie)!==ie)continue;let ve=x*v*I-1-c.get($,ee,te,ie,S),ue=J*v*I+ae*I+se,ye=ve===ue?1:0;if(ye===0)continue;let ke=D.get($,ee,te,ie,S);Z+=ke*ye}}}F.set(Z,$,M,B,U,S)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var v7={kernelName:Bc,backendName:"cpu",kernelFunc:x7};function w7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ge([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=Oe(c.outShape,o.dtype,k_(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,b=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,I=v-1-c.padInfo.left,N=x-1-c.padInfo.top,C=Oe(o.shape,"float32"),_=n.data.get(r.dataId).values,F=Oe(r.shape,"float32",_);for(let D=0;D=c.outHeight||Math.floor(K)!==K))for(let Z=0;Z=c.outWidth||Math.floor(J)!==J)continue;let ee=x*v-1-m.get(D,K,J,$),ae=q*v+Z,te=ee===ae?1:0;if(te===0)continue;let se=F.get(D,K,J,$);H+=se*te}}C.set(H,D,S,M,$)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var k7={kernelName:Wc,backendName:"cpu",kernelFunc:w7};function I7(e,t,n,a,r){let s=w.computeStrides(t),i=K1(e,t,n,s,r,"max"),o=k_(e,t,n,r,!0,a);return[i.values,o.values]}var S7={kernelName:Vc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ge(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,p=T.computePool2DInfo(a.shape,r,s,[1,1],i),[d,c]=I7(u,a.shape,a.dtype,o,p),h=l.write(d,p.outShape,a.dtype),m=l.write(c,p.outShape,a.dtype);return[{dataId:h,shape:p.outShape,dtype:a.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function N7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=w.parseAxisParam(s,r.shape),l=T.computeOutAndReduceShapes(r.shape,o)[1],u=w.sizeFromShape(l),p=[],d=n.makeTensorInfo([],"float32",new Float32Array([u]));p.push(d);let c=bs({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});p.push(c);let h=X1({inputs:{a:c,b:d},backend:n});p.push(h);let m=Ed({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var T7={kernelName:co,backendName:"cpu",kernelFunc:N7};function C7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"min");let o=w.parseAxisParam(s,r.shape),l=o,u=T.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=T.getInnerMostAxes(l.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",l,p.shape.length);let[d,c]=T.computeOutAndReduceShapes(p.shape,l),h=w.sizeFromShape(c),m=w.makeZerosTypedArray(w.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let b=0;by[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=s.map((y,x)=>y[0]+r.shape[x]),p=i==="reflect"?0:1,d=n.data.get(r.dataId).values,c=r.shape.length,h=w.computeStrides(r.shape),m=w.sizeFromShape(o),f=o.length,g=w.computeStrides(o),b=w.getTypedArrayFromDType(r.dtype,m);for(let y=0;y=u[I]&&(x[I]=(u[I]-1)*2-x[I]+p);x=x.map((I,N)=>I-l[N]);let v=w.locToIndex(x,c,h);b[y]=d[v]}return{dataId:n.write(b,o,r.dtype),shape:o,dtype:r.dtype}}var A7={kernelName:fo,backendName:"cpu",kernelFunc:_7},F7=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),$7=Zt(go,F7),D7={kernelName:go,backendName:"cpu",kernelFunc:$7},R7=xs(bm());function E_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=w.parseAxisParam([o],r.shape),u=C_({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=T.expandShapeToKeepDim(u.shape,l),d=xt({inputs:{x:u},backend:n,attrs:{shape:p}}),c=j1({inputs:{a:r,b:d},backend:n}),h=LE({inputs:{x:c},backend:n}),m=Ed({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=X1({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var M7={kernelName:zo,backendName:"cpu",kernelFunc:E_};function O7(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;ge(r,"multinomial");let l=o?r:E_({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=w.makeZerosTypedArray(w.sizeFromShape(c),"int32");for(let m=0;m=0&&d[c]{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=cm({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=Kl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Z7={kernelName:Du,backendName:"cpu",kernelFunc:A_};function J7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ge(r,"pad");let o=s.map((b,y)=>b[0]+r.shape[y]+b[1]),l=s.map(b=>b[0]),u=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),d=r.shape.length,c=w.computeStrides(r.shape),h=w.sizeFromShape(o),m=o.length,f=w.computeStrides(o),g=w.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Oe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var hX={kernelName:Rc,backendName:"cpu",kernelFunc:dX};function 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=xi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var xX={kernelName:nu,backendName:"cpu",kernelFunc:yX};function vX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=P1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var wX={kernelName:au,backendName:"cpu",kernelFunc:vX};function kX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var IX={kernelName:Mc,backendName:"cpu",kernelFunc:kX},SX=lt(ws,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return cr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>yi({inputs:{input:v},backend:n})),g=l.map(v=>ql({inputs:{input:v},backend:n})),b=Kl({inputs:f,backend:n,attrs:{axis:s}}),y=Kl({inputs:g,backend:n,attrs:{axis:s}}),x=Yn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=L1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var _X={kernelName:su,backendName:"cpu",kernelFunc:Kl};function S_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ue*F,tt=mt;for(let nt=0;nt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ue=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ue;for(let tt=0;ttMath.cos(e)),VX={kernelName:Wi,backendName:"cpu",kernelFunc:BX},UX=lt(Bi,e=>Math.cosh(e)),GX={kernelName:Bi,backendName:"cpu",kernelFunc:UX};function HX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Oe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},uY={kernelName:Rl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Rl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},pY={kernelName:Dl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${Dl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function cY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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l=o?r:E_({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=w.makeZerosTypedArray(w.sizeFromShape(c),"int32");for(let m=0;m=0&&d[c]{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=cm({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=Kl({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var Z7={kernelName:Du,backendName:"cpu",kernelFunc:A_};function J7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ge(r,"pad");let o=s.map((b,y)=>b[0]+r.shape[y]+b[1]),l=s.map(b=>b[0]),u=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),d=r.shape.length,c=w.computeStrides(r.shape),h=w.sizeFromShape(o),m=o.length,f=w.computeStrides(o),g=w.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let b=0;bv+l[I]),x=w.locToIndex(y,m,f);g[x]=u[b]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var F_={kernelName:xo,backendName:"cpu",kernelFunc:J7},Q7=Mt((e,t)=>Math.pow(e,t)),eZ=Zt(vo,Q7),tZ={kernelName:vo,backendName:"cpu",kernelFunc:eZ};function nZ(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.data.get(b.dataId).values),u=r.map(b=>b.shape),p=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,[c,h,m]=n_(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var aZ={kernelName:Am,backendName:"cpu",kernelFunc:nZ};function rZ(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[u,p]=a_(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var sZ={kernelName:Fm,backendName:"cpu",kernelFunc:rZ};function iZ(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,c=o.map(g=>n.data.get(g.dataId).values),h=o.map(g=>g.shape),[m,f]=r_(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var oZ={kernelName:$m,backendName:"cpu",kernelFunc:iZ};function lZ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=B1(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var uZ={kernelName:Uc,backendName:"cpu",kernelFunc:lZ},pZ=lt(Io,e=>1/e),cZ={kernelName:Io,backendName:"cpu",kernelFunc:pZ};function dZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ge(r,"resizeBilinear");let l=w.computeStrides(r.shape),[u,p]=o,[d,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(w.sizeFromShape([d,u,p,m])),b=[s&&u>1?c-1:c,s&&p>1?h-1:h],y=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=0,v=b[0]/y[0],I=b[1]/y[1];for(let N=0;N1?u-1:u,i&&h>1?p-1:p],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],b=f[0]/g[0],y=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let I=0;I1?c-1:c,s&&p>1?h-1:h],y=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=b[0]/y[0],v=b[1]/y[1],I=0;for(let N=0;N1?p-1:p,i&&m>1?d-1:d],y=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=b[0]/y[0],v=b[1]/y[1],I=1/x,N=1/v,C=Math.ceil(I)*2+2,_=Math.ceil(N)*2+2;for(let F=0;F=h)continue;let te=D+ae*l[1],se=ae*x,ie=Math.min(p-1,i?Math.round(se):Math.floor(se));if($===ie)for(let ve=0;ve<_;ve++){let ue=ve+K;if(ue<0||ue>=m)continue;let ye=te+ue*l[2],ke=ue*v,Se=Math.min(d-1,i?Math.round(ke):Math.floor(ke));U===Se&&(J+=g[ye+Z])}}f[H+Z]=J}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var xZ={kernelName:Mu,backendName:"cpu",kernelFunc:yZ};function vZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ge(r,"reverse");let i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return cr({inputs:{x:r},backend:n});let l=new Wt(r.shape,r.dtype),u=n.bufferSync(r);for(let p=0;pc[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var wZ={kernelName:Eo,backendName:"cpu",kernelFunc:vZ},kZ={kernelName:Zu,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=w.getTypedArrayFromDType(a.dtype,w.sizeFromShape(a.shape)),[u,p,d,c]=a.shape,[h,m]=T.getImageCenter(i,p,d),f=255,g=Math.sin(r),b=Math.cos(r),y=o.data.get(a.dataId).values;for(let x=0;x=0&&M=0&&B{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2===0?t:t+1}),SZ={kernelName:_o,backendName:"cpu",kernelFunc:IZ};function NZ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=ni(h,m,i,d,u,l,o,p,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var TZ={kernelName:Pu,backendName:"cpu",kernelFunc:NZ};function CZ(e,t){let n=0,a=e.length,r=0;for(;n1||r.shape.length===1?1:w.sizeFromShape(r.shape.slice(1));for(let m=0;me>=0?MZ*e:RZ*(Math.exp(e)-1)),PZ={kernelName:Fo,backendName:"cpu",kernelFunc:OZ},LZ=lt(Ro,e=>e<0?-1:e>0?1:0),zZ={kernelName:Ro,backendName:"cpu",kernelFunc:LZ},WZ=lt($o,e=>Math.sin(e)),BZ={kernelName:$o,backendName:"cpu",kernelFunc:WZ},VZ=lt(Do,e=>Math.sinh(e)),UZ={kernelName:Do,backendName:"cpu",kernelFunc:VZ},GZ=11920928955078125e-23,eS=Math.log(GZ)+2,HZ=lt(Oo,e=>{let t=e>-eS,n=em.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ts(r,Yr):m=new rr(r,Yr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Ah(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ta().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ts(s,Yr):h=new rr(s,Yr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ta().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Oe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=pee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ta().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new see(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new UQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[vi(t.shape),...wi(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[vi(n),...wi(n)],i=new AA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=ic(s),l;r?l=new H9(o):l=new G9(o);let u=!0,p=[n!=null?n:Ah(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Nc.DENSE){let b=i!=null?i:Ah(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!Tc(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=U9(t,p,d),h=this.getAndSaveBinary(c,()=>B9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||V9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oee:lee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=Z_(a,l),n.texShape=d),s!=null){let c=ic(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=op(d[0],d[1])),l?h=new X9(c,g):h=new aS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=ca.PIXELS:x.usage=ca.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=hee(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Zw(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(J1(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=lA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ta().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ta().makeTensorFromDataId(u,n,a,l)}};lk.nextDataId=0;function hee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew lk,2);var fee={forceHalfFloat:$A},uk=` + `}},iee=mr.whereImpl,oee=1e-7,lee=1e-4,yx={};function uee(e){return e in yx||(yx[e]={}),yx[e]}var pee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),cee=600;function dee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*cee/1024/1024}var lk=class FA extends Fc{nextDataId(){return FA.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof Wh)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new Wh(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new Wh(a),this.binaryCache=uee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new HQ(this.gpgpu),this.numMBBeforeWarning=dee(),this.texData=new ym(this,Ta())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=ic(n),p=new aS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:ca.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:ca.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ts(o,Yr):c=new rr(o,Yr);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ts(r,Yr):m=new rr(r,Yr);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Ah(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ta().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ts(s,Yr):h=new rr(s,Yr);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ta().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Oe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Oe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=pee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ta().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new see(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new UQ(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[vi(t.shape),...wi(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[vi(n),...wi(n)],i=new AA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=ic(s),l;r?l=new H9(o):l=new G9(o);let u=!0,p=[n!=null?n:Ah(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Nc.DENSE){let b=i!=null?i:Ah(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!Tc(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=U9(t,p,d),h=this.getAndSaveBinary(c,()=>B9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||V9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oee:lee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=Z_(a,l),n.texShape=d),s!=null){let c=ic(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=op(d[0],d[1])),l?h=new X9(c,g):h=new aS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=ca.PIXELS:x.usage=ca.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=hee(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Zw(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(J1(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=lA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ta().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ta().makeTensorFromDataId(u,n,a,l)}};lk.nextDataId=0;function hee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew lk,2);var fee={forceHalfFloat:$A},uk=` if (isnan(a)) return a; if (isnan(b)) return b; `,ki=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` @@ -5006,5 +5006,5 @@ return a / b;`,jse=` } `}};function Xle(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],u=0,p=T.getAxesPermutation([u],o),d=r;p!=null&&(d=Sn({inputs:{x:r},backend:n,attrs:{perm:p}}),l.push(d),u=T.getInnerMostAxes(1,o)[0]);let c=T.segment_util.computeOutShape(d.shape,u,i),h=w.sizeFromShape([d.shape[u]]),m=ce({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=Mm(r.dtype),g=(v,I,N,C,_)=>{let F=v.shape[0],D=v.shape[1],$=T.segment_util.segOpComputeOptimalWindowSize(D,_),S={windowSize:$,inSize:D,batchSize:F,numSegments:_},M=new Kle(S,I),B=n.compileAndRun(M,[v,N],C);if(l.push(B),B.shape[1]===_)return B;let U=iF({backend:n,attrs:{start:0,stop:_,step:1,dtype:"float32"}}),H=oF({inputs:{x:U},backend:n,attrs:{reps:[D/$]}});return l.push(U),l.push(H),g(B,I,H,C,_)},b=g(m,"unsortedSegmentSum",s,f,i),y=ce({inputs:{x:b},backend:n,attrs:{shape:c}}),x=y;if(p!=null){l.push(y);let v=T.getUndoAxesPermutation(p);x=Sn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Yle={kernelName:Qc,backendName:"webgl",kernelFunc:Xle},Zle=[Ree,Oee,zee,Vee,Gee,qee,Xee,Zee,tte,ate,ite,ute,dte,gte,xte,wte,Ite,Cte,_te,Fte,Mte,Vte,Gte,Kte,Yte,nne,rne,lne,bee,cne,gne,vne,Tne,_ne,Fne,Dne,Mne,zne,Vne,Hne,qne,Xne,Zne,eae,nae,iae,lae,cae,mae,gae,vae,Sae,Eae,Fae,Rae,Mae,Pae,zae,Bae,Uae,Hae,Xae,Jae,tre,are,ire,ure,hre,bre,gee,xre,mne,kre,Nre,Ere,xee,$re,Ore,Lre,Vre,Hre,Xre,Jre,nse,ise,use,cse,fse,bse,xse,Ise,Nse,Cse,_se,Fse,Mse,zse,Use,Zse,kee,tie,rie,oie,pie,Jte,hie,fie,bie,vie,Sie,wee,Tie,Eie,Aie,$ie,Die,Qte,qse,Oie,Wie,Gie,See,Kie,Zie,toe,roe,loe,poe,hoe,goe,xoe,koe,Noe,Eoe,$oe,Moe,zoe,Voe,Wte,Xse,Hoe,qoe,Xoe,Zoe,Qoe,tle,ale,sle,ole,ple,dle,mle,gle,xle,wle,Ile,Nle,Kse,Fee,Ele,Fle,Dle,Ole,Wle,Ule,$ee,Hle,qle,Yle,mie];for(let e of Zle)ed(e);var Qe;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Qe||(Qe={}));var Ac;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Ac||(Ac={}));var lF;function Jle(e){lF=e.wasm.cwrap(ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Qle(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Ac[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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Please use 'channelsLast'.`);let $=a.makeOutput(h.outShape,"float32"),S=a.dataIdMap.get($.dataId).id;return $F(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,b,y,x,D,v,I,N,C,_,F,S),$}var Vpe={kernelName:Ui,backendName:"wasm",setupFunc:Wpe,kernelFunc:Bpe},DF;function Upe(e){DF=e.wasm.cwrap("Diag",null,["number","number","number","number"])}function Gpe(e){let{inputs:t,backend:n}=e,{x:a}=t,r=w.sizeFromShape(a.shape),s=n.makeOutput([...a.shape,...a.shape],a.dtype);return DF(n.dataIdMap.get(a.dataId).id,Qe[a.dtype],r,n.dataIdMap.get(s.dataId).id),s}var Hpe={kernelName:Lc,backendName:"wasm",setupFunc:Upe,kernelFunc:Gpe},RF;function jpe(e){RF=e.wasm.cwrap(Gi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;if(r.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return MF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var Zpe={kernelName:Rl,backendName:"wasm",setupFunc:Xpe,kernelFunc:Ype},OF;function Jpe(e){OF=e.wasm.cwrap(Dl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qpe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(r.shape,r.dtype);return OF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var ece={kernelName:Dl,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},tce=Xe(ji),PF;function nce(e){PF=e.wasm.cwrap(cu,null,["number","number","number"])}function ace(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=n.makeOutput(r.shape,"float32"),i=o=>n.dataIdMap.get(o.dataId).id;return PF(i(r),i(a),i(s)),s}var rce={kernelName:cu,backendName:"wasm",setupFunc:nce,kernelFunc:ace},sce=!1,ice=Ut(du,sce,"bool"),oce=Xe(qi),lce=Xe(Ki,"float32");function yv(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),zn({inputs:{x:r},backend:a,attrs:{shape:o}})}var uce={kernelName:hu,backendName:"wasm",kernelFunc:yv},pce=Xe(Xi,"float32");function LF(e){let{attrs:{shape:t,value:n},backend:a}=e,{attrs:{dtype:r}}=e;r=r||w.inferDtype(n);let s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var cce={kernelName:zc,backendName:"wasm",kernelFunc:LF},zF;function dce(e){zF=e.wasm.cwrap(mu,null,["number","number","number","number","number","number"])}function hce(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,u,p]=a.shape;return zF(s,o,l,u,p,i),r}var mce={kernelName:mu,backendName:"wasm",kernelFunc:hce,setupFunc:dce},fce=Xe(Yi),gce=!1,bce=Ut(Zi,gce),WF;function yce(e){WF=e.wasm.cwrap(Ji,null,["number","number","number","number","number","number","number"])}function xce(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:u}=n,p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return WF(p,d,c,h,m,r,g),f}var vce={kernelName:Ji,backendName:"wasm",setupFunc:yce,kernelFunc:xce},BF;function wce(e){BF=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return BF(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Ice={kernelName:oi,backendName:"wasm",setupFunc:wce,kernelFunc:kce},VF;function Sce(e){VF=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nce(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:p,dataFormat:d,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=T.computeConv2DInfo(r.shape,s.shape,l,p,u,c,!0),g=Ac[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=a.dataIdMap.get(r.dataId).id,y=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let te=a.dataIdMap.get(i.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${x})`);v=te.id}let I=f.filterHeight,N=f.filterWidth,C=f.padInfo.top,_=f.padInfo.right,F=f.padInfo.bottom,D=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,M=f.strideHeight,B=f.strideWidth,U=f.inChannels,H=f.padInfo.type==="SAME"?1:0,q=f.batchSize,K=f.inHeight,Z=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(J.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return VF(b,q,K,Z,y,I,N,v,C,_,F,D,H,$,S,M,B,U,x,g,ae,m||0,ee),J}var Tce={kernelName:li,backendName:"wasm",setupFunc:Sce,kernelFunc:Nce},UF;function Cce(e){UF=e.wasm.cwrap(gu,null,["number","number","number","number","number","number","array","number"])}function Ece(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Yw.prepareAndValidate(a,r),u=t.makeOutput(s,a.dtype);if(i===0)return u;let p=r.shape,d=p[p.length-1],c=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(u.dataId).id;return UF(c,Qe[a.dtype],h,i,d,o,m,f),u}var _ce={kernelName:gu,backendName:"wasm",setupFunc:Cce,kernelFunc:Ece},GF;function Ace(e){GF=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Fce(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],u=t.readSync(s.dataId),p=r.shape[l];for(let C=0;C=0,()=>`GatherV2: the index value ${_} is not in [0, ${p-1}]`)}let d=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=zn({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=w.sizeFromShape(s.shape),m=zn({inputs:{x:s},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),f=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(f,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=c.shape.length-1,y=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(m.dataId).id,v=t.dataIdMap.get(g.dataId).id,I=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),N=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer);return GF(y,Qe[r.dtype],I,b,x,d.batchSize,N,v),t.disposeData(c.dataId),t.disposeData(m.dataId),g.shape=d.outputShape,g}var $ce={kernelName:fu,backendName:"wasm",setupFunc:Ace,kernelFunc:Fce},Dce=!1,Rce=Ut(bu,Dce,"bool"),Mce=!1,Oce=Ut(Qi,Mce,"bool"),Pce=Xe(to,"bool"),Lce=Xe(no,"bool"),zce=Xe(ao,"bool"),HF;function Wce(e){HF=e.wasm.cwrap(ro,null,["number","number","number","number"])}function Bce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;HF(r,Qe[t.dtype],n,i)}return s}var Vce={kernelName:ro,backendName:"wasm",setupFunc:Wce,kernelFunc:Bce},Uce=!1,Gce=Ut(yu,Uce,"bool"),Hce=!1,jce=Ut(xu,Hce,"bool"),jF;function qce(e){jF=e.wasm.cwrap(vu,null,["number","number","number","number"])}function Kce(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return jF(n.dataIdMap.get(o.dataId).id,a,r,i),o}var Xce={kernelName:vu,backendName:"wasm",setupFunc:qce,kernelFunc:Kce},Yce=Xe(so),Zce=Xe(io),Jce=!1,Qce=Ut(wu,Jce,"bool"),ede=Xe(ku),tde=!1,nde=Ut(Iu,tde,"bool"),ade=!1,rde=Ut(jS,ade,"bool"),qF;function sde(e){qF=e.wasm.cwrap(oo,null,["number","number","number","number","number","number","number"])}function ide(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return qF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var ode={kernelName:oo,backendName:"wasm",setupFunc:sde,kernelFunc:ide},KF;function lde(e){KF=e.wasm.cwrap(Su,null,["number","number","number","number","number","number","number","number","number"])}function ude(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let d=n.makeOutput(r.shape,r.dtype);return KF(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,i.shape[3],o,l,u,p),d}var pde={kernelName:Su,backendName:"wasm",setupFunc:lde,kernelFunc:ude},XF;function cde(e){XF=e.wasm.cwrap(lo,null,["number","number","number","number"])}function dde(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:c}=Ds(i,r,t);if(c){let y=t.dataIdMap.get(u.dataId).id;l=u,o=y}let h=l.shape.length;T.assertAxesAreInnerMostDims("max",p,h);let[m,f]=T.computeOutAndReduceShapes(l.shape,p),g=w.sizeFromShape(f),b=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let y=t.dataIdMap.get(b.dataId).id;XF(o,Qe[i.dtype],g,y)}if(c&&t.disposeData(u.dataId),s){let y=T.expandShapeToKeepDim(b.shape,d);b.shape=y}return b}var hde={kernelName:lo,backendName:"wasm",setupFunc:cde,kernelFunc:dde},mde=!1,fde=Ut(uo,mde),YF;function gde(e){YF=e.wasm.cwrap(po,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,p=T.computePool2DInfo(r.shape,i,o,1,l,u),d=p.filterHeight,c=p.filterWidth,h=p.padInfo.top,m=p.padInfo.right,f=p.padInfo.bottom,g=p.padInfo.left,b=p.dilationHeight,y=p.dilationWidth,x=p.strideHeight,v=p.strideWidth,I=p.inChannels,N=p.outChannels;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${p.dataFormat}'. 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Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,[1,1],o),d=n.makeOutput(p.outShape,r.dtype),c=n.makeOutput(p.outShape,"int32");return e$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(d.dataId).id,n.dataIdMap.get(c.dataId).id,Qe[r.dtype],l,p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.effectiveFilterHeight,p.effectiveFilterWidth,p.padInfo.top,p.padInfo.left),[d,c]}var Ade={kernelName:Vc,backendName:"wasm",setupFunc:Ede,kernelFunc:_de},t$;function Fde(e){t$=e.wasm.cwrap(co,null,["number, number, number"])}function $de(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:p,axes:d,originalAxes:c,inputWasTransposed:h}=Ds(i,r,t),m=d;if(h){let 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ug=class extends fn{constructor(t=new lg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t,r=ya(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Bd(r,n.fc.age).as1D(),i=Bd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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d=n(l,`${p}/depthwise_conv`),c=r(l,u,1,`${p}/pointwise_conv`);return{depthwise_conv:d,pointwise_conv:c}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),u=s(32,64,"mobilenetv1/conv_1"),p=s(64,128,"mobilenetv1/conv_2"),d=s(128,128,"mobilenetv1/conv_3"),c=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),b=s(512,512,"mobilenetv1/conv_9"),y=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),I=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:u,conv_2:p,conv_3:d,conv_4:c,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:b,conv_10:y,conv_11:x,conv_12:v,conv_13:I}}function o(){let 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p=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),d=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function wD(e){let 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ige(e){let t=dt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function oge(e,t){let{sizes:n,centers:a}=ige(e),r=dt(De(t,[1,0])),s=he(z(dn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(dn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(At([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function SD(e,t,n){return O(()=>{let a=e.shape[0],r=oge(W(On(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ha(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function ol(e,t){return O(()=>{let n=e.shape[0],a=W(il(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(il(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function ND(e,t,n){return O(()=>{let 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ll=class extends fn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=kD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=ND(s.out,s.conv11,n.prediction_layer);return SD(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ma(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Ft(p[x],new xp(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return wD(t)}extractParams(t){return vD(t)}};function lge(e){let t=new ll;return t.extractWeights(e),t}function YCe(e){return lge(e)}var TD=class extends ll{};var CD=.4,ED=[new He(.738768,.874946),new He(2.42204,2.65704),new He(4.30971,7.04493),new He(10.246,4.59428),new He(12.6868,11.8741)],_D=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],AD=[117.001,114.697,97.404],FD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";var hg=e=>typeof e=="number";function DD(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hg(t.x)&&hg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Mp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Br(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Mp(n)})}function Vr(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=_s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Mp(n)})}function uge(e,t){let n=Tp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Cp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=uge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function pge(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Ep(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function MD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=pge(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var yr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mg=class mg extends fn{constructor(t){super("TinyYolov2"),DD(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Br(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Br(a,n.conv6),a=Br(a,n.conv7),il(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mp(il(t,n.conv0,"valid",!1)):Vr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Vr(a,n.conv6):a,a=n.conv7?Vr(a,n.conv7):a,il(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?br(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new yr(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return U$(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new yp(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return MD(t,this.config)}extractParams(t){let n=this.config.filterSizes||mg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+qf(g[b][y][x][0]))/u*o,N=(b+qf(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new bp(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};mg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Op=mg;var Pp=class extends Op{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:CD,classes:["face"],...t?{anchors:_D,meanRgb:AD}:{anchors:ED,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:FD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function BEe(e,t=!0){let n=new Pp(t);return n.extractWeights(e),n}var fg=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Oa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ul(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Fp(l)?r(l):l.detection),i=a||(t instanceof Ce?await Ld(t,s):await Pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Lp(e,t,n,a,r){return ul([e],t,async s=>n(s[0]),a,r)}var OD=.4,PD=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],LD=[117.001,114.697,97.404];var zp=class extends Op{constructor(){let t={withSeparableConvs:!0,iouThreshold:OD,classes:["face"],anchors:PD,meanRgb:LD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new ll,tinyFaceDetector:new zp,tinyYolov2:new Pp,faceLandmark68Net:new Dp,faceLandmark68TinyNet:new cg,faceRecognitionNet:new Rp,faceExpressionNet:new sg,ageGenderNet:new ug},cge=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),b_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),y_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),dge=e=>rt.faceLandmark68Net.detectLandmarks(e),x_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),v_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),w_e=e=>rt.faceExpressionNet.predictExpressions(e),k_e=e=>rt.ageGenderNet.predictAgeAndGender(e),hge=e=>rt.ssdMobilenetv1.load(e),I_e=e=>rt.tinyFaceDetector.load(e),S_e=e=>rt.tinyYolov2.load(e),N_e=e=>rt.faceLandmark68Net.load(e),T_e=e=>rt.faceLandmark68TinyNet.load(e),C_e=e=>rt.faceRecognitionNet.load(e),E_e=e=>rt.faceExpressionNet.load(e),__e=e=>rt.ageGenderNet.load(e),A_e=hge,F_e=cge,$_e=dge;var gg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},pl=class extends gg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Nk(a,n[r]))}withAgeAndGender(){return new dl(this,this.input)}},cl=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Nk(t,n)}withAgeAndGender(){return new hl(this,this.input)}},Ps=class extends pl{withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ls=class extends cl{withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var bg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},dl=class extends bg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Fk($k(a,i,o),s)})}withFaceExpressions(){return new pl(this,this.input)}},hl=class extends bg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Lp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Fk($k(t,a,r),n)}withFaceExpressions(){return new cl(this,this.input)}},zs=class extends dl{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ws=class extends hl{withFaceExpressions(){return new Ls(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var yg=class extends Oa{constructor(n,a){super();this.parentTask=n;this.input=a}},Bs=class extends yg{async run(){let t=await this.parentTask;return(await ul(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Ak(t[r],a))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}},Vs=class extends yg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Ak(t,n)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}};var xg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},vg=class extends xg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Ld(this.input,n):await Pd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Vd(i,r[o]))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},wg=class extends xg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Ld(this.input,[n]):await Pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Vd(t,r)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var kg=class extends Oa{constructor(n,a=new Ma){super();this.input=n;this.options=a}},Gd=class extends kg{async run(){let{input:t,options:n}=this,a;if(n instanceof fg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ma)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>wp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new vg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new dl(this.runAndExtendWithFaceDetections(),this.input)}},Ig=class extends kg{async run(){let t=await new Gd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?wp({},n):void 0)})}withFaceLandmarks(t=!1){return new wg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new cl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new hl(this.runAndExtendWithFaceDetection(),this.input)}};function _Ae(e,t=new Ma){return new Ig(e,t)}function Dk(e,t=new Ma){return new Gd(e,t)}async function mge(e,t){return Dk(e,new Ma(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function MAe(e,t={}){return Dk(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var OAe=mge;function zD(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var WD=class e{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof rl)return i;if(i instanceof Float32Array)return new rl(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new rl(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>zD(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Dd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>rl.fromJSON(a));return new e(n,t.distanceThreshold)}};function eFe(e){let t=new zp;return t.extractWeights(e),t}function fge(e,t){let{width:n,height:a}=new aa(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>fge(r,{width:n,height:a}));if(Fp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Vd(wp(e,r),s)}return zr(e)?wp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof Ft?e.forSize(n,a):e}var cFe=oD;export{ug as AgeGenderNet,bp as BoundingBox,mn as Box,Oa as ComposableTask,Bs as ComputeAllFaceDescriptorsTask,yg as ComputeFaceDescriptorsTaskBase,Vs as ComputeSingleFaceDescriptorTask,vg as DetectAllFaceLandmarksTask,Gd as DetectAllFacesTask,xg as DetectFaceLandmarksTaskBase,kg as DetectFacesTaskBase,wg as DetectSingleFaceLandmarksTask,Ig as DetectSingleFaceTask,aa as Dimensions,rD as FACE_EXPRESSION_LABELS,Ft as FaceDetection,TD as FaceDetectionNet,sg as FaceExpressionNet,Os as FaceExpressions,Dp as FaceLandmark68Net,cg as FaceLandmark68TinyNet,fD as FaceLandmarkNet,ka as FaceLandmarks,H$ as FaceLandmarks5,vp as FaceLandmarks68,Dd as FaceMatch,WD as FaceMatcher,Rp as FaceRecognitionNet,Ck as Gender,Rd as LabeledBox,rl as LabeledFaceDescriptors,Wr as NetInput,fn as NeuralNetwork,yp as ObjectDetection,He as Point,j$ as PredictedBox,xp as Rect,ll as SsdMobilenetv1,Ma as SsdMobilenetv1Options,zp as TinyFaceDetector,fg as TinyFaceDetectorOptions,Pp as TinyYolov2,yr as TinyYolov2Options,OAe as allFaces,mge as allFacesSsdMobilenetv1,MAe as allFacesTinyYolov2,q$ as awaitMediaLoaded,K$ as bufferToImage,v_e as computeFaceDescriptor,Np as createCanvas,Zf as createCanvasFromMedia,YCe as createFaceDetectionNet,Y2e as createFaceRecognitionNet,lge as createSsdMobilenetv1,eFe as createTinyFaceDetector,BEe as createTinyYolov2,Dk as detectAllFaces,dge as detectFaceLandmarks,x_e as detectFaceLandmarksTiny,$_e as detectLandmarks,_Ae as detectSingleFace,iD as draw,at as env,zD as euclideanDistance,Fk as extendWithAge,Ak as extendWithFaceDescriptor,wp as extendWithFaceDetection,Nk as extendWithFaceExpressions,Vd as extendWithFaceLandmarks,$k as extendWithGender,Ld as extractFaceTensors,Pd as extractFaces,sIe as fetchImage,Z$ as fetchJson,pIe as fetchNetWeights,Ms as fetchOrThrow,gIe as fetchVideo,ra as getContext2dOrThrow,Sp as getMediaDimensions,X$ as imageTensorToCanvas,Y$ as imageToSquare,v0e as inverseSigmoid,B$ as iou,Sk as isMediaElement,Yf as isMediaLoaded,eCe as isWithAge,zr as isWithFaceDetection,sD as 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t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>lloo({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let u=0;ul[p]<=n)}return s}function br(e,t){return O(()=>{let[n,a,r]=t,s=yn([...e.shape.slice(0,3),1],n,"float32"),i=yn([...e.shape.slice(0,3),1],a,"float32"),o=yn([...e.shape.slice(0,3),1],r,"float32"),l=et([s,i,o],3);return pe(e,l)})}function G$(e,t=!1){return O(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=c=>{let h=e.shape.slice();return h[i]=c,yn(h,0,"float32")},l=o(s),u=r-l.shape[i],d=[t&&u?o(u):null,e,l].filter(c=>!!c).map(c=>re(c,"float32"));return et(d,i)})}function y0e(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function qf(e){return 1/(1+Math.exp(-e))}function v0e(e){return Math.log(e/(1-e))}var xp=class extends 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c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return _n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function pD(e,t,n){return X($t(e,t.filters,n,"same"),t.bias)}function Tk(e,t,n=!0){let a=n?Ke(e):e;return a=Un(a,t.separable_conv0,[1,1]),a=Un(Ke(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=X(a,pD(e,t.expansion_conv,[2,2])),a}function Zfe(e,t){let n=Un(Ke(e),t.separable_conv0,[1,1]);return n=Un(Ke(n),t.separable_conv1,[1,1]),n=Un(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var lg=class extends fn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let a=re(t.toBatchTensor(112,!0),"float32"),s=br(a,[122.782,117.001,104.298]).div(255),i=Ke(pD(s,n.entry_flow.conv_in,[2,2]));return i=Tk(i,n.entry_flow.reduction_block_0,!1),i=Tk(i,n.entry_flow.reduction_block_1),gr(this._numMainBlocks,0,1).forEach(o=>{i=Zfe(i,n.middle_flow[`main_block_${o}`])}),i=Tk(i,n.exit_flow.reduction_block),i=Ke(Un(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return uD(t,this._numMainBlocks)}extractParams(t){return lD(t,this._numMainBlocks)}};function cD(e){let t=[],{extractWeights:n,getRemainingWeights:a}=An(e),r=eg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function dD(e){let t=[],n=sa(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return _n(e,t),{params:r,paramMappings:t}}var Ck=(n=>(n.FEMALE="female",n.MALE="male",n))(Ck||{});var ug=class extends fn{constructor(t=new lg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof Wr?this.faceFeatureExtractor.forwardInput(t):t,r=ya(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Bd(r,n.fc.age).as1D(),i=Bd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var ll=class extends fn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=kD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=ND(s.out,s.conv11,n.prediction_layer);return SD(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ma(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Ft(p[x],new xp(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return wD(t)}extractParams(t){return vD(t)}};function lge(e){let t=new ll;return t.extractWeights(e),t}function YCe(e){return lge(e)}var TD=class extends ll{};var CD=.4,ED=[new He(.738768,.874946),new He(2.42204,2.65704),new He(4.30971,7.04493),new He(10.246,4.59428),new He(12.6868,11.8741)],_D=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],AD=[117.001,114.697,97.404],FD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";var hg=e=>typeof e=="number";function DD(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!hg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>hg(t.x)&&hg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(hg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Mp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Br(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Mp(n)})}function Vr(e,t){return O(()=>{let n=xa(e,[[0,0],[1,1],[1,1],[0,0]]);return n=_s(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Mp(n)})}function uge(e,t){let n=Tp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Cp(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function RD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=uge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function pge(e,t){let n=sa(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Ep(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function MD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=pge(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var yr=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var mg=class mg extends fn{constructor(t){super("TinyYolov2"),DD(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Br(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Br(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Br(a,n.conv6),a=Br(a,n.conv7),il(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Mp(il(t,n.conv0,"valid",!1)):Vr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Vr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Vr(a,n.conv6):a,a=n.conv7?Vr(a,n.conv7):a,il(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?br(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new yr(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return U$(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new yp(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return MD(t,this.config)}extractParams(t){let n=this.config.filterSizes||mg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return RD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+qf(g[b][y][x][0]))/u*o,N=(b+qf(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new bp(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};mg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Op=mg;var Pp=class extends Op{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:CD,classes:["face"],...t?{anchors:_D,meanRgb:AD}:{anchors:ED,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:FD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function BEe(e,t=!0){let n=new Pp(t);return n.extractWeights(e),n}var fg=class extends yr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Oa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ul(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Fp(l)?r(l):l.detection),i=a||(t instanceof Ce?await Ld(t,s):await Pd(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Lp(e,t,n,a,r){return ul([e],t,async s=>n(s[0]),a,r)}var OD=.4,PD=[new He(1.603231,2.094468),new He(6.041143,7.080126),new He(2.882459,3.518061),new He(4.266906,5.178857),new He(9.041765,10.66308)],LD=[117.001,114.697,97.404];var zp=class extends Op{constructor(){let t={withSeparableConvs:!0,iouThreshold:OD,classes:["face"],anchors:PD,meanRgb:LD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Ft(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var rt={ssdMobilenetv1:new ll,tinyFaceDetector:new zp,tinyYolov2:new Pp,faceLandmark68Net:new Dp,faceLandmark68TinyNet:new cg,faceRecognitionNet:new Rp,faceExpressionNet:new sg,ageGenderNet:new ug},cge=(e,t)=>rt.ssdMobilenetv1.locateFaces(e,t),b_e=(e,t)=>rt.tinyFaceDetector.locateFaces(e,t),y_e=(e,t)=>rt.tinyYolov2.locateFaces(e,t),dge=e=>rt.faceLandmark68Net.detectLandmarks(e),x_e=e=>rt.faceLandmark68TinyNet.detectLandmarks(e),v_e=e=>rt.faceRecognitionNet.computeFaceDescriptor(e),w_e=e=>rt.faceExpressionNet.predictExpressions(e),k_e=e=>rt.ageGenderNet.predictAgeAndGender(e),hge=e=>rt.ssdMobilenetv1.load(e),I_e=e=>rt.tinyFaceDetector.load(e),S_e=e=>rt.tinyYolov2.load(e),N_e=e=>rt.faceLandmark68Net.load(e),T_e=e=>rt.faceLandmark68TinyNet.load(e),C_e=e=>rt.faceRecognitionNet.load(e),E_e=e=>rt.faceExpressionNet.load(e),__e=e=>rt.ageGenderNet.load(e),A_e=hge,F_e=cge,$_e=dge;var gg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},pl=class extends gg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Nk(a,n[r]))}withAgeAndGender(){return new dl(this,this.input)}},cl=class extends gg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Nk(t,n)}withAgeAndGender(){return new hl(this,this.input)}},Ps=class extends pl{withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ls=class extends cl{withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var bg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},dl=class extends bg{async run(){let t=await this.parentTask,n=await ul(t,this.input,async a=>Promise.all(a.map(r=>rt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Fk($k(a,i,o),s)})}withFaceExpressions(){return new pl(this,this.input)}},hl=class extends bg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Lp(t,this.input,s=>rt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Fk($k(t,a,r),n)}withFaceExpressions(){return new cl(this,this.input)}},zs=class extends dl{withFaceExpressions(){return new Ps(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},Ws=class extends hl{withFaceExpressions(){return new Ls(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var yg=class extends Oa{constructor(n,a){super();this.parentTask=n;this.input=a}},Bs=class extends yg{async run(){let t=await this.parentTask;return(await ul(t,this.input,a=>Promise.all(a.map(r=>rt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Ak(t[r],a))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}},Vs=class extends yg{async run(){let t=await this.parentTask;if(!t)return;let n=await Lp(t,this.input,a=>rt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Ak(t,n)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}};var xg=class extends Oa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?rt.faceLandmark68TinyNet:rt.faceLandmark68Net}},vg=class extends xg{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Ld(this.input,n):await Pd(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Vd(i,r[o]))}withFaceExpressions(){return new Ps(this,this.input)}withAgeAndGender(){return new zs(this,this.input)}withFaceDescriptors(){return new Bs(this,this.input)}},wg=class extends xg{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Ld(this.input,[n]):await Pd(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Vd(t,r)}withFaceExpressions(){return new Ls(this,this.input)}withAgeAndGender(){return new Ws(this,this.input)}withFaceDescriptor(){return new Vs(this,this.input)}};var kg=class extends Oa{constructor(n,a=new Ma){super();this.input=n;this.options=a}},Gd=class extends kg{async run(){let{input:t,options:n}=this,a;if(n instanceof fg)a=rt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ma)a=rt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof yr)a=rt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>wp({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new vg(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new dl(this.runAndExtendWithFaceDetections(),this.input)}},Ig=class extends kg{async run(){let t=await new Gd(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?wp({},n):void 0)})}withFaceLandmarks(t=!1){return new wg(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new cl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new hl(this.runAndExtendWithFaceDetection(),this.input)}};function _Ae(e,t=new Ma){return new Ig(e,t)}function Dk(e,t=new Ma){return new Gd(e,t)}async function mge(e,t){return Dk(e,new Ma(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function MAe(e,t={}){return Dk(e,new yr(t)).withFaceLandmarks().withFaceDescriptors()}var OAe=mge;function zD(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s*s,0))}var WD=class e{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof rl)return i;if(i instanceof Float32Array)return new rl(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new rl(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>zD(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Dd(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distancet.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>rl.fromJSON(a));return new e(n,t.distanceThreshold)}};function eFe(e){let t=new zp;return t.extractWeights(e),t}function fge(e,t){let{width:n,height:a}=new aa(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>fge(r,{width:n,height:a}));if(Fp(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return Vd(wp(e,r),s)}return zr(e)?wp(e,e.detection.forSize(n,a)):e instanceof ka||e instanceof Ft?e.forSize(n,a):e}var cFe=oD;export{ug as AgeGenderNet,bp as BoundingBox,mn as Box,Oa as ComposableTask,Bs as ComputeAllFaceDescriptorsTask,yg as ComputeFaceDescriptorsTaskBase,Vs as ComputeSingleFaceDescriptorTask,vg as DetectAllFaceLandmarksTask,Gd as DetectAllFacesTask,xg as DetectFaceLandmarksTaskBase,kg as DetectFacesTaskBase,wg as DetectSingleFaceLandmarksTask,Ig as DetectSingleFaceTask,aa as Dimensions,rD as FACE_EXPRESSION_LABELS,Ft as FaceDetection,TD as FaceDetectionNet,sg as FaceExpressionNet,Os as FaceExpressions,Dp as FaceLandmark68Net,cg as FaceLandmark68TinyNet,fD as FaceLandmarkNet,ka as FaceLandmarks,H$ as FaceLandmarks5,vp as FaceLandmarks68,Dd as FaceMatch,WD as FaceMatcher,Rp as FaceRecognitionNet,Ck as Gender,Rd as LabeledBox,rl as LabeledFaceDescriptors,Wr as NetInput,fn as NeuralNetwork,yp as ObjectDetection,He as Point,j$ as PredictedBox,xp as Rect,ll as SsdMobilenetv1,Ma as SsdMobilenetv1Options,zp as TinyFaceDetector,fg as TinyFaceDetectorOptions,Pp as TinyYolov2,yr as TinyYolov2Options,OAe as allFaces,mge as allFacesSsdMobilenetv1,MAe as allFacesTinyYolov2,q$ as awaitMediaLoaded,K$ as bufferToImage,v_e as computeFaceDescriptor,Np as createCanvas,Zf as createCanvasFromMedia,YCe as createFaceDetectionNet,Y2e as createFaceRecognitionNet,lge as createSsdMobilenetv1,eFe as createTinyFaceDetector,BEe as createTinyYolov2,Dk as detectAllFaces,dge as detectFaceLandmarks,x_e as detectFaceLandmarksTiny,$_e as detectLandmarks,_Ae as detectSingleFace,iD as draw,at as env,zD as euclideanDistance,Fk as extendWithAge,Ak as extendWithFaceDescriptor,wp as extendWithFaceDetection,Nk as extendWithFaceExpressions,Vd as extendWithFaceLandmarks,$k as extendWithGender,Ld as extractFaceTensors,Pd as extractFaces,sIe as fetchImage,Z$ as fetchJson,pIe as fetchNetWeights,Ms as fetchOrThrow,gIe as fetchVideo,ra as getContext2dOrThrow,Sp as getMediaDimensions,X$ as imageTensorToCanvas,Y$ as imageToSquare,v0e as inverseSigmoid,B$ as iou,Sk as isMediaElement,Yf as isMediaLoaded,eCe as isWithAge,zr as isWithFaceDetection,sD as isWithFaceExpressions,Fp as isWithFaceLandmarks,rCe as isWithGender,__e as loadAgeGenderModel,A_e as loadFaceDetectionModel,E_e as loadFaceExpressionModel,N_e as loadFaceLandmarkModel,T_e as loadFaceLandmarkTinyModel,C_e as loadFaceRecognitionModel,hge as loadSsdMobilenetv1Model,I_e as loadTinyFaceDetectorModel,S_e as loadTinyYolov2Model,Q$ as loadWeightMap,F_e as locateFaces,IIe as matchDimensions,V$ as minBbox,rt as nets,U$ as nonMaxSuppression,br as normalize,G$ as padToSquare,k_e as predictAgeAndGender,w_e as recognizeFaceExpressions,fge as resizeResults,kp as resolveInput,y0e as shuffleArray,qf as sigmoid,cge as ssdMobilenetv1,Pe as tf,b_e as tinyFaceDetector,y_e as tinyYolov2,vt as toNetInput,W$ as utils,DD as validateConfig,cFe as version}; //# sourceMappingURL=face-api.esm.js.map diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map index 1b2a327..a54c4f2 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["tfjs.esm.js", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/isNodejs.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/bufferToVideo.ts", "../src/dom/fetchVideo.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeightMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", 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R0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(R0||(R0={}));var F0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(F0||(F0={}));var O0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(O0||(O0={}));var NK={float32:F0,int32:$0,bool:R0,complex64:O0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return NK[r][t]}function lc(r){return ur(r,\"int32\")}function rx(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function nx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function M0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function kK(r,t){return t.some(e=>e.id===r.id)}function ih(r){let t=[];return eE(r,t,new Set),t}function eE(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!TK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),eE(s,t,e))}}function TK(r){return Array.isArray(r)||typeof r==\"object\"}function P0(r){return r.kernelName!=null}var ox=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ah=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ox}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Yg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Bo)&&typeof n.then==\"function\"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a=\"float32\",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Wp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype===\"complex64\"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=P0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(P0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Wp(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=P0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=v0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",o=o||this.backend;let s=t;n===\"string\"&&Vo(t[0])&&(s=t.map(u=>fu(u)));let i=o.write(s,e,n),a=new Lt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n===\"string\"){let u=this.state.tensorInfo.get(i),l=b0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||\"float32\";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Lt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof ml||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let n=t.size*Tp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=v0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Ep(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ih(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>\"gradients() received an empty list of xs.\"),n!=null&&n.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));_(s instanceof Lt,()=>\"The result y returned by f() must be a tensor.\");let i=K_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let a={};a[s.id]=n==null?_K(s.shape):n,j_(a,i,l=>this.tidy(l),EK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(_i(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{_(e.every(a=>a instanceof Lt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Lt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),_(_i(n.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),_(c.every(m=>m instanceof Lt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=ac(),n=await this.backend.time(t);return n.wallMs=ac()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ox;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ah.nextTensorId=0;ah.nextVariableId=0;function _K(r){let t=Yd(jt(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function L0(){let r=C0();if(r._tfengine==null){let t=new Zd(r);r._tfengine=new ah(t)}return T_(r._tfengine.ENV),J_(()=>r._tfengine),r._tfengine}var T=L0();function EK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>B0,isMobile:()=>$K,mockIsMobile:()=>DK});function AK(){return typeof navigator!=\"undefined\"&&navigator!=null}var z0;function DK(r){z0=r}function $K(r){if(z0!==void 0)return z0;if(r||AK()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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e=C(r,\"x\",\"bitwiseAnd\"),n=C(t,\"y\",\"bitwiseAnd\");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!==\"int32\"||n.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel($a,o)}var FE=k({bitwiseAnd_:Bj});function Vj(r,t){let e=C(r,\"s0\",\"broadcastArgs\",\"int32\"),n=C(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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s={x:C(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(os,s,i)}var im=k({cumsum_:s6});function i6(r,t,e,n=!1){let o=C(r,\"x\",\"denseBincount\"),s=C(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(jl,i,a)}var mh=k({denseBincount_:i6});function a6(r,t,e=\"NHWC\"){let n=C(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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vm=k({unsortedSegmentSum_:G5});function W5(r,t=0){let e=C(r,\"x\",\"unstack\",\"string_or_numeric\");_(t>=-e.shape.length&&t`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Ki,n,o)}var gr=k({unstack_:W5});function OA(r,t){return dh(r,t,\"right\")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>\"mask cannot be scalar\"),$e(a.slice(s,s+i),o.shape,\"mask's shape must match the first K dimensions of tensor's shape,\");let u=1;for(let h=s;ha).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype===\"complex64\"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:K5});function j5(r,t,e,n,o=!0){let s=C(r,\"v\",\"movingAverage\"),i=C(t,\"x\",\"movingAverage\"),a=C(e,\"decay\",\"movingAverage\");M0(s,i),_(sn(s.shape,i.shape),()=>\"Shape mismatch in v and x\");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>\"When using zeroDebias: true, step is required.\");let p=C(n,\"step\",\"movingAverage\");c=ut(c,at(u,qr(a,p)))}return K(s,c)}var X5=k({movingAverage_:j5});function Y5(r,t,e){Le(e);let n=C(r,\"indices\",\"scatterND\",\"int32\"),o=C(t,\"updates\",\"scatterND\");Im(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var Z5=k({scatterND_:Y5});function MA(r,t,e,n){if(r.dtype!==\"int32\")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has 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T.runKernel(Dp,p,m)}var Sm=k({conv2DBackpropFilter_:i8});function bc(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,So(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=we(r.shape,t.shape);return n.length>0&&(e=mt(e,n)),R(e,r.shape)}function Ic(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Or(r);if(t===\"elu\")return aa(r);if(t===\"relu6\")return cm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return Cu(r,n);if(t===\"sigmoid\")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t===\"linear\";function a8({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||\"linear\",Cc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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Got dilations '${s}'`);let[A,D,F,M]=E,V=bc(N,F,u),G=gy(D.shape,V,A,e,n,s,i),W=hy(D,V,A.shape,e,n,s,i);if(M!=null){let q=wc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((E,A,D)=>{let F=T.runKernel(Zi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):pn((E,A,D,F)=>{let M=T.runKernel(Zi,w,I);return F([A,E,M,D]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m,g)}var zA=k({fusedDepthwiseConv2d_:c8});function p8({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Cc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=K(V,o)),Ic(V,s,i,a)}let u=C(r,\"a\",\"fused matMul\"),l=C(t,\"b\",\"fused matMul\");[u,l]=Xt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,\"bias\",\"fused matMul\"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,j]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(j,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(Xi,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let j=T.runKernel(Xi,F,M);return H([G,W,j,q]),{value:R(j,w),gradFunc:D}})(I,N,E)}var BA=k({fusedMatMul_:p8});function m8(r){return xh(r,.54,.46)}var VA=k({hammingWindow_:m8});function f8(r){return xh(r,.5,.5)}var xy=k({hannWindow_:f8});function d8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o===\"bilinear\"||o===\"nearest\",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var WA=k({cropAndResize_:g8});function x8(r){let t=C(r,\"image\",\"flipLeftRight\",\"float32\");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var UA=k({flipLeftRight_:x8});function y8(r){let t=C(r,\"image\",\"grayscaleToRGB\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var HA=k({grayscaleToRGB_:y8});function b8(r){let t=C(r,\"image\",\"RGBToGrayscale\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,\"float32\"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu(\"ij,j->i\",s,i);break;case 3:a=wu(\"ijk,k->ij\",s,i);break;case 4:a=wu(\"ijkl,l->ijk\",s,i);break;case 5:a=wu(\"ijklm,m->ijkl\",s,i);break;case 6:a=wu(\"ijklmn,n->ijklm\",s,i);break;default:throw new Error(\"Not a valid tensor rank.\")}return a=je(a,-1),J(a,o)}var qA=k({rgbToGrayscale_:b8});function w8(r,t,e=0,n=.5){let o=C(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var KA=k({rotateWithOffset_:w8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>\"scores must be a 1D tensor\"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Iy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(m,\"int32\"),selectedScores:Oe(f)}}var QA=E8;function A8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(Qa,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var t2=k({nonMaxSuppressionPadded_:A8});async function D8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let 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u={image:i,transforms:a},l={interpolation:e,fillMode:n,fillValue:o,outputShape:s};return T.runKernel(cl,u,l)}var n2=k({transform_:M8});function P8(r,t,e){let n=C(r,\"a\",\"bandPart\");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t==\"number\"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,\"numLower\",\"bandPart\")):(_(t.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),a=Ie(yl(t,0),s,lo(t,s))),typeof e==\"number\"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,\"numUpper\",\"bandPart\")):(_(e.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),u=Ie(yl(e,0),i,lo(e,i)));let l=R(pa(0,s,1,\"int32\"),[-1,1]),c=pa(0,i,1,\"int32\"),p=at(l,c),m=Fr(Vn(p,a),cn(p,Ut(u))),f=ke([s,i],n.dtype);return R(Fe(gr(R(n,[-1,s,i])).map(d=>Ie(m,d,f))),o)}var o2=k({bandPart_:P8});function L8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let o=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=hr(r,r.shape[0],0).map(o=>Wn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o{let s=n[o];if(o>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let 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Je;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,\"losses\",\"computeWeightedLoss\"),o=null;t!=null&&(o=C(t,\"weights\",\"computeWeightedLoss\"));let s=o==null?n:$(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return Ne(s);{let i=n.size/o.size,a=ut(mt(s),mt(o));return i>1?ut(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ut(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ci(i,pt(0))),\"float32\");return ut(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Kr=k({computeWeightedLoss_:B8});function V8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"absoluteDifference\"),s=C(t,\"predictions\",\"absoluteDifference\"),i=null;e!=null&&(i=C(e,\"weights\",\"absoluteDifference\")),$e(o.shape,s.shape,\"Error in absoluteDifference: \");let a=_e(at(o,s));return Kr(a,i,n)}var l2=k({absoluteDifference_:V8});function G8(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"cosineDistance\"),i=C(t,\"predictions\",\"cosineDistance\"),a=null;n!=null&&(a=C(n,\"weights\",\"cosineDistance\")),$e(s.shape,i.shape,\"Error in cosineDistance: \");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Kr(l,a,o)}var u2=k({cosineDistance_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"hingeLoss\"),s=C(t,\"predictions\",\"hingeLoss\"),i=null;e!=null&&(i=C(e,\"weights\",\"hingeLoss\")),$e(o.shape,s.shape,\"Error in hingeLoss: \");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Kr(u,i,n)}var c2=k({hingeLoss_:W8});function U8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"huberLoss\"),i=C(t,\"predictions\",\"huberLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"huberLoss\")),$e(s.shape,i.shape,\"Error in huberLoss: \");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=K($(pt(.5),Wt(c)),$(u,p));return Kr(m,a,o)}var p2=k({huberLoss_:U8});function H8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"logLoss\"),i=C(t,\"predictions\",\"logLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"logLoss\")),$e(s.shape,i.shape,\"Error in logLoss: \");let u=pt(1),l=pt(n),c=Ut($(s,Nr(K(i,l)))),p=$(at(u,s),Nr(K(at(u,i),l))),m=at(c,p);return Kr(m,a,o)}var m2=k({logLoss_:H8});function q8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"meanSquaredError\"),s=C(t,\"predictions\",\"meanSquaredError\"),i=null;e!=null&&(i=C(e,\"weights\",\"meanSquaredError\")),$e(o.shape,s.shape,\"Error in meanSquaredError: \");let a=wm(o,s);return Kr(a,i,n)}var f2=k({meanSquaredError_:q8});function K8(r,t){let e=C(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),n=C(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");$e(e.shape,n.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return K(at(o,s),i)}function j8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=C(t,\"logits\",\"sigmoidCrossEntropy\"),a=null;if(e!=null&&(a=C(e,\"weights\",\"sigmoidCrossEntropy\")),$e(s.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=K($(s,at(c,l)),$(p,l))}let u=K8(s,i);return Kr(u,a,o)}var d2=k({sigmoidCrossEntropy_:j8});function X8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var x2=k({sparseReshape_:J8});function Q8(r,t,e){let n=C(r,\"data\",\"sparseSegmentMean\"),o=C(t,\"indices\",\"sparseSegmentMean\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,\"data\",\"sparseSegmentSum\"),o=C(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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className(){return\"Adadelta\"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=K($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ut(ge(K(l,this.epsilon)),ge(K(u,this.epsilon))),a),m=K($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=K($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Sc=class extends jr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=K(a,Wt(i));a.assign(u);let l=K($(ut(i,ge(K(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends jr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=K($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=K($(ut(d,K(ge(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(qr(this.beta1,this.iterations_+1)),this.accBeta2.assign(qr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var kc=class extends jr{static get className(){return\"Adamax\"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=ut(-this.learningRate,K($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=_e(l),h=kn(f,d);c.assign(m),p.assign(h);let g=K($(ut(o,n),ut(m,K(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(K(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Il=class extends jr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=K($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends jr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=K($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=K($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),ge(at(c,K(Wt(m),this.epsilon)))),d=K($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=K($(u,this.decay),$(Wt(a),1-this.decay)),m=K($(l,this.momentum),ut($(a,this.learningRate),ge(K(p,this.epsilon))));u.assign(p),l.assign(m);let f=at(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var mY=[vc,Sc,Nc,kc,Tc,_c,Il];function S2(){for(let r of mY)bN(r)}var 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used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof r))throw new _t(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=wn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className=\"Sequential\";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className=\"elu\";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className=\"selu\";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className=\"relu\";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className=\"relu6\";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className=\"linear\";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className=\"sigmoid\";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className=\"hardSigmoid\";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className=\"softplus\";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className=\"softsign\";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className=\"tanh\";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className=\"softmax\";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className=\"logSoftmax\";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className=\"gelu\";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className=\"gelu_new\";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className=\"mish\";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className=\"swish\";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function xi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},QN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:\"L1L2\"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ve(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className=\"ReLU\";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className=\"LeakyReLU\";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className=\"Softmax\";Q.registerClass(rf);function zu(r,t,e){if(typeof r==\"number\")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function ek(r,t){return B(()=>(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function wJ(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=xn()),Me(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Vt(r,[0,2,1])),o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=rm(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=yn(a,e)),a})}function XR(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Dx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=yn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",r.verifyArgs(e),this.rank=t,tr(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,\"filters\"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className=\"Conv2D\";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className=\"Conv3D\";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. 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Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className=\"Conv3DTranspose\";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className=\"SeparableConv\";var af=class extends Eb{constructor(t){super(2,t)}};af.className=\"SeparableConv2D\";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className=\"Conv1D\";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className=\"Cropping2D\";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className=\"UpSampling2D\";Q.registerClass(cf);function CJ(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className=\"DepthwiseConv2D\";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className=\"RNN\";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight(\"kernel\",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className=\"SimpleRNNCell\";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className=\"SimpleRNN\";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className=\"GRU\";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className=\"LSTM\";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className=\"ConvRNN2D\";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>tr(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>tr(u,\"strides\")),this.padding=s||\"valid\",hn(this.padding),this.dataFormat=i||\"channelsLast\",Me(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>tr(u,\"dilationRate\"))}build(t){var e;t=Gt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Xc.className=\"ConvLSTM2DCell\";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className=\"ConvLSTM2D\";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className=\"Dropout\";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className=\"SpatialDropout1D\";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className=\"Dense\";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className=\"Activation\";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className=\"RepeatVector\";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className=\"Reshape\";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className=\"Permute\";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className=\"Masking\";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,tr(this.outputDim,\"outputDim\"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=rn(n,\"int32\"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className=\"Embedding\";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className=\"Dot\";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className=\"GaussianNoise\";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className=\"GaussianDropout\";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),u=-1.6732632423543772*1.0507009873554805,l=cn(Gn(n),this.rate);l=rn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=K($(s,l),$(K(l,-1),u));return K($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Ff.className=\"AlphaDropout\";Q.registerClass(Ff);function zh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Cx(r,t,e,n,o,s);else if(r.rank===3)i=vx(r,t,e,n,o,s);else if(r.rank===4)i=Sx(r,t,e,n,o,s);else throw new _t(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function NJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance;return[zh(r,i,a,e,t,o),i,a]})}function kJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className=\"BatchNormalization\";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=xe(t.betaInitializer||\"zeros\"),this.gammaInitializer=xe(t.gammaInitializer||\"ones\"),this.betaRegularizer=ve(t.betaRegularizer),this.gammaRegularizer=ve(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Eo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=xn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>_J(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Pf.className=\"ZeroPadding2D\";Q.registerClass(Pf);function Mb(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=Ph(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ku(r,t,e,a):i=xu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=ek(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Lf.className=\"MaxPooling1D\";Q.registerClass(Lf);var zf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};zf.className=\"AveragePooling1D\";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Bf.className=\"MaxPooling2D\";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};Vf.className=\"AveragePooling2D\";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"max\")}};Gf.className=\"MaxPooling3D\";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"avg\")}};Wf.className=\"AveragePooling3D\";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Uf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Uf.className=\"GlobalAveragePooling1D\";Q.registerClass(Uf);var Hf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Hf.className=\"GlobalMaxPooling1D\";Q.registerClass(Hf);var Ob=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Ne(n,[1,2]):Ne(n,[2,3])})}};qf.className=\"GlobalAveragePooling2D\";Q.registerClass(qf);var Kf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Kf.className=\"GlobalMaxPooling2D\";Q.registerClass(Kf);var Pb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},jf=class extends Pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),nk((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};jf.className=\"TimeDistributed\";Q.registerClass(jf);function EJ(r){da(eR,\"BidirectionalMergeMode\",r)}var AJ=\"concat\",Xf=class extends Pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?AJ:t.mergeMode,EJ(this.mergeMode),t.weights)throw new _t(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode===\"concat\"?a=_m([o,s]):this.mergeMode===\"sum\"?a=K(o,s):this.mergeMode===\"ave\"?a=$(.5,K(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Xf.className=\"Bidirectional\";Q.registerClass(Xf);var Yf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=rn(t,\"float32\")),K($(t,this.scale),this.offset)))}};Yf.className=\"Rescaling\";Q.registerClass(Yf);var{resizeBilinear:DJ,cropAndResize:$J}=fn,Zf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N{let s=DJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Zf.className=\"CenterCrop\";Q.registerClass(Zf);function ZR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=rn(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=mh(a,n,e,i):u=mh(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var Jf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=rn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ZR(t,this.outputMode,this.numTokens,n)})}};Jf.className=\"CategoryEncoding\";Q.registerClass(Jf);var FJ=[\"bilinear\",\"nearest\"],JR=new Set(FJ),Qf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(JR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation=\"bilinear\";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation===\"bilinear\")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...JR]} are supported`)})}};Qf.className=\"Resizing\";Q.registerClass(Qf);var Bh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Bh.className=\"RandomSeed\";var Vh=class extends Et{constructor(t){super(t),this.randomGenerator=new Bh(t.seed)}getConfig(){let 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o=v(\"start\",r,t,e),s=v(\"stop\",r,t,e),i=v(\"step\",r,t,e);return[n.range(o,s,i,v(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=v(\"shape\",r,t,e),s=v(\"mean\",r,t,e),i=v(\"stdDev\",r,t,e),a=v(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,v(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(v(\"shape\",r,t,e),v(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ek(r,t,e){let n=v(\"boxes\",r,t,e),o=v(\"scores\",r,t,e),s=v(\"maxOutputSize\",r,t,e),i=v(\"iouThreshold\",r,t,e),a=v(\"scoreThreshold\",r,t,e),u=v(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var SF=async(r,t,e,n,o=ae)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Ek(r,t,e),p=await 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v(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(v(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(v(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=v(\"x\",r,t,e),a=v(\"data\",r,t,e),u=v(\"message\",r,t,e),l=v(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v(\"keyDType\",r,t,e),i=v(\"valueDType\",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=v(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case\"ResizeBilinear\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=v(\"image\",r,t,e),s=v(\"boxes\",r,t,e),i=v(\"boxInd\",r,t,e),a=v(\"cropSize\",r,t,e),u=v(\"method\",r,t,e),l=v(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=v(\"images\",r,t,e),s=v(\"transforms\",r,t,e),i=v(\"outputShape\",r,t,e),a=v(\"fillValue\",r,t,e),u=v(\"interpolation\",r,t,e),l=v(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case\"Equal\":return[n.equal(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Greater\":return[n.greater(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Less\":return[n.less(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(v(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(v(\"condition\",r,t,e),v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(v(\"a\",r,t,e),v(\"b\",r,t,e),v(\"transposeA\",r,t,e),v(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(v(\"equation\",r,t,e),...v(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(v(\"x\",r,t,e),v(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=v(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=v(\"numArgs\",r,t,e),l=v(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=v(\"args\",r,t,e);return[n.fused.matMul({a:v(\"a\",r,t,e),b:v(\"b\",r,t,e),transposeA:v(\"transposeA\",r,t,e),transposeB:v(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(v(\"a\",r,t,e),v(\"numLower\",r,t,e),v(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(v(\"x\",r,t,e),v(\"axis\",r,t,e),v(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(v(\"x\",r,t,e),v(\"radius\",r,t,e),v(\"bias\",r,t,e),v(\"alpha\",r,t,e),v(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(v(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v(\"paramsNestedSplits\",r,t,e),v(\"paramsDenseValues\",r,t,e),v(\"indices\",r,t,e),v(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v(\"starts\",r,t,e),v(\"limits\",r,t,e),v(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(v(\"shape\",r,t,e),v(\"values\",r,t,e),v(\"defaultValue\",r,t,e),v(\"rowPartitionTensors\",r,t,e),v(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case\"Max\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.max(v(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.mean(v(\"x\",r,t,e),a,u)]}case\"Min\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.min(v(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.sum(v(\"x\",r,t,e),a,u)]}case\"All\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.all(v(\"x\",r,t,e),a,u)]}case\"Any\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.any(v(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=v(\"axis\",r,t,e);return[n.argMax(v(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=v(\"axis\",r,t,e);return[n.argMin(v(\"x\",r,t,e),a)]}case\"Prod\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.prod(v(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumprod(v(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumsum(v(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=v(\"x\",r,t,e),s=v(\"weights\",r,t,e),i=v(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=v(\"x\",r,t,e),u=v(\"weights\",r,t,e),l=v(\"size\",r,t,e),c=v(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=v(\"n\",r,t,e),s=v(\"axis\",r,t,e),i=v(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=v(\"axis\",r,t,e),s=v(\"batchDims\",r,t,e),i=v(\"x\",r,t,e),a=v(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=v(\"dims\",r,t,e),s=[];for(let a=0;a{let o=v(\"axis\",r,t,e),s=v(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=v(\"axis\",r,t,e),s=v(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=v(\"reps\",r,t,e);return[n.tile(v(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=v(\"axis\",r,t,e),s=v(\"numOrSizeSplits\",r,t,e),i=v(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=v(\"sparseIndices\",r,t,e),s=v(\"outputShape\",r,t,e),i=v(\"sparseValues\",r,t,e),a=v(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v(\"indices\",r,t,e),v(\"values\",r,t,e),v(\"denseShape\",r,t,e),v(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v(\"inputIndices\",r,t,e),v(\"inputShape\",r,t,e),v(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case\"FFT\":return[n.fft(v(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(v(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(v(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(v(\"input\",r,t,e),v(\"pattern\",r,t,e),v(\"rewrite\",r,t,e),v(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v(\"data\",r,t,e),v(\"dataSplits\",r,t,e),v(\"separator\",r,t,e),v(\"nGramWidths\",r,t,e),v(\"leftPad\",r,t,e),v(\"rightPad\",r,t,e),v(\"padWidth\",r,t,e),v(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v(\"input\",r,t,e),v(\"delimiter\",r,t,e),v(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(v(\"input\",r,t,e),v(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case\"Cast\":return[n.cast(v(\"x\",r,t,e),v(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=v(\"axis\",r,t,e);return[n.expandDims(v(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=v(\"axis\",r,t,e);return[n.squeeze(v(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=v(\"blockShape\",r,t,e),s=v(\"paddings\",r,t,e);return[n.spaceToBatchND(v(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=v(\"blockShape\",r,t,e),s=v(\"crops\",r,t,e);return[n.batchToSpaceND(v(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=v(\"blockSize\",r,t,e),s=v(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(v(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(v(\"s0\",r,t,e),v(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>fF(i,a,u));case\"basic_math\":return o(()=>dF(i,a,u));case\"control\":return wF(i,a,u);case\"convolution\":return o(()=>CF(i,a,u));case\"creation\":return o(()=>vF(i,a,u));case\"dynamic\":return SF(i,a,u);case\"evaluation\":return o(()=>NF(i,a,u));case\"image\":return o(()=>_F(i,a,u));case\"graph\":return o(()=>kF(i,a,u));case\"logical\":return o(()=>EF(i,a,u));case\"matrices\":return o(()=>AF(i,a,u));case\"normalization\":return o(()=>DF(i,a,u));case\"ragged\":return o(()=>$F(i,a,u));case\"reduction\":return o(()=>RF(i,a,u));case\"slice_join\":return o(()=>FF(i,a,u));case\"sparse\":return o(()=>OF(i,a,u));case\"spectral\":return o(()=>MF(i,a,u));case\"string\":return o(()=>PF(i,a,u));case\"transformation\":return o(()=>LF(i,a,u));case\"hash_table\":return TF(i,a,u,n);case\"custom\":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I=\"\";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&v(\"isConstant\",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt=\"?tfjs-format=file\",gtt=\"model.json\",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error(\"Model artifacts missing streamWeights function\");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new r(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: 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extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:\"cpu\",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:\"cpu\",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var CM={kernelName:ql,backendName:\"cpu\",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:\"cpu\",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:\"cpu\",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:\"cpu\",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,\"NHWC\",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype===\"int32\"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var KM={kernelName:Qd,backendName:\"cpu\",kernelFunc:Uet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=md(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var XM={kernelName:Fp,backendName:\"cpu\",kernelFunc:Het};function qet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var YM={kernelName:La,backendName:\"cpu\",kernelFunc:qet};var Ket=S.ERF_P,jet=S.ERF_A1,Xet=S.ERF_A2,Yet=S.ERF_A3,Zet=S.ERF_A4,Jet=S.ERF_A5,Qet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ket*e);return t*(1-((((Jet*n+Zet)*n+Yet)*n+Xet)*n+jet)*n*Math.exp(-e*e))}),ZM={kernelName:us,backendName:\"cpu\",kernelFunc:Qet};function yd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var JM={kernelName:Mi,backendName:\"cpu\",kernelFunc:yd};var trt=Qt((r,t)=>r/t),tg=oe(as,trt),eg={kernelName:as,backendName:\"cpu\",kernelFunc:tg};function $w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=yw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var sP={kernelName:Pi,backendName:\"cpu\",kernelFunc:urt};function crt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var iP={kernelName:Mp,backendName:\"cpu\",kernelFunc:crt};var prt=At(gs,r=>Number.isFinite(r)?1:0,\"bool\"),aP={kernelName:gs,backendName:\"cpu\",kernelFunc:prt};var mrt=At(xs,r=>Math.abs(r)===1/0?1:0,\"bool\"),lP={kernelName:xs,backendName:\"cpu\",kernelFunc:mrt};var frt=At(ys,r=>Number.isNaN(r)?1:0,\"bool\"),uP={kernelName:ys,backendName:\"cpu\",kernelFunc:frt};function drt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var cP={kernelName:Ha,backendName:\"cpu\",kernelFunc:drt};var hrt=At(Is,r=>Math.log1p(r)),pP={kernelName:Is,backendName:\"cpu\",kernelFunc:hrt};var grt=Qt((r,t)=>r&&t),xrt=oe(qa,grt,null,\"bool\"),mP={kernelName:qa,backendName:\"cpu\",kernelFunc:xrt};var yrt=At(Ka,r=>r?0:1,\"bool\"),fP={kernelName:Ka,backendName:\"cpu\",kernelFunc:yrt};var brt=Qt((r,t)=>r||t),wrt=oe(ja,brt,null,\"bool\"),dP={kernelName:ja,backendName:\"cpu\",kernelFunc:wrt};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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kt=L();kt.registerFlag(\"HAS_WEBGL\",()=>kt.getNumber(\"WEBGL_VERSION\")>0);kt.registerFlag(\"WEBGL_VERSION\",()=>Vw(2)?2:Vw(1)?1:0);kt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);kt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>kt.get(\"WEBGL_VERSION\")===2);kt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);kt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);kt.registerFlag(\"WEBGL_PACK\",()=>kt.getBool(\"HAS_WEBGL\"));kt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CLIP\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CONV2DTRANSPOSE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>n1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>o1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=kt.getNumber(\"WEBGL_VERSION\");return r===0?0:s1(r)});kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>kt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!du.isMobile());kt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>i1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>kt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:kt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));kt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>a1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>l1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>kt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);kt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});kt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>du.isMobile()?1:-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});kt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);kt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);kt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);kt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>kt.getBool(\"IS_TEST\"));kt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);kt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);kt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);kt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Si(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function up(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Qnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Qnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function vd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function Sd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Gw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:LL}=S;function zL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Ww(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>tot(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=Ue(),u=not(a),l,c,p=iot(a);return t.isPacked?(l=eot(t.logicalShape,i,e.enableShapeUniforms),c=sot(a)):(l=rot(t.logicalShape,i,e.enableShapeUniforms),c=oot(a)),e.packedInputs&&(p+=cot),[p,u,c,o,l,s,e.userCode].join(`\n`)}function kd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Cot(r,t);case 1:return Sot(r,t);case 2:return kot(r,t);case 3:return _ot(r,t);case 4:return Aot(r,t);case 5:return Dot(r);case 6:return $ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Iot(r);case 1:return vot(r,t);case 2:return Not(r,t);case 3:return Tot(r,t);default:return Eot(r,t)}}function tot(r,t,e=!1,n){let o=\"\";e?o+=BL(r,n):o+=kd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Rot(r,t):o+=Fot(r,t)),o}function eot(r,t,e){switch(r.length){case 0:return VL();case 1:return pot(r,t,e);case 2:return bot(r,t,e);case 3:return fot(r,t,e);default:return hot(r,t,e)}}function rot(r,t,e){switch(r.length){case 0:return VL();case 1:return mot(r,t,e);case 2:return wot(r,t,e);case 3:return dot(r,t,e);case 4:return got(r,t,e);case 5:return xot(r,t);case 6:return yot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function not(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function oot(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function sot(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function iot(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${aot}\n ${lot}\n ${uot}\n `}var aot=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,lot=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,uot=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,cot=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function VL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function pot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function mot(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function fot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function dot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${up([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=Si([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function hot(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`\n`);let m=\"\";i<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(\", \");let f=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`\n return vec4(outputValue.xy, outputValue.xy);\n `;else if(h&&!x)i===1?f=`\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `:f=`\n return vec4(outputValue.x);\n `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f=\"return vec4(outputValue.x);\":a.indexOf(b)>-1?f=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":a.indexOf(w)>-1&&(f=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n vec4 ${o}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${n}(${m});\n ${f}\n }\n `}function Fot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o=\"get\"+n+\"AtOutCoords\",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`\n float ${o}() {\n return sampleTexture(${e}, resultUV);\n }\n `;let l=zt(u),c=LL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?m=\"\":u<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`\n`);let d=\"\";return u<2&&a>0?d=\"coords\":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(\", \"),`\n float ${o}() {\n ${l} coords = getOutputCoords();\n ${m}\n return get${n}(${d});\n }\n `}function zt(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ww(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(\", \")}function WL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=zL(o,i,t),u=HT(r.gl,a),l=r.createProgram(u);return L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},u1(r,t,l)))}function u1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function GL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function UL(r,t,e,n,o){t.program.enableShapeUniforms||(GL(t.inShapeInfos,e),GL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Ww(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function he(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Uw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${e.output} = result;\n }\n `}};var qw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Kw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var Pot={R:0,G:1,B:2,A:3},ug=class{constructor(t,e=!1,n=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let s=\"result\";e&&(s=\"floor(result * 255. + 0.5)\");let i=\"\";for(let a=0;ay1,createBufferFromOutputTexture:()=>I1,createFloat16MatrixTexture:()=>d1,createFloat16PackedMatrixTexture:()=>x1,createFloat32MatrixTexture:()=>f1,createIndexBuffer:()=>m1,createPackedMatrixTexture:()=>g1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>p1,createVertexShader:()=>c1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>v1,downloadFloat32MatrixFromBuffer:()=>C1,downloadMatrixFromPackedOutputTexture:()=>N1,downloadPackedMatrixFromBuffer:()=>S1,getInternalFormatForFloat16MatrixTexture:()=>Yw,getInternalFormatForFloat16PackedMatrixTexture:()=>Qw,getInternalFormatForFloat32MatrixTexture:()=>Xw,getInternalFormatForPackedMatrixTexture:()=>Jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Zw,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>w1});function c1(r){let t=Ue(),e=`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function C1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function v1(r,t,e,n){let[o,s]=lp(t,e),i=4,a=new Uint8Array(DL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function S1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array($L(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function N1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var pp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,BT(e,t)):this.gl=qn(e),t=this.gl,L().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),L().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=bd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=bd(this.gl,i);else if(L().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Kn(this.gl,o))this.colorBufferHalfFloatExtension=bd(this.gl,o);else if(L().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=p1(this.gl),this.indexBuffer=m1(this.gl),this.framebuffer=JT(this.gl),this.textureConfig=ig(this.gl,this.textureHalfFloatExtension)}get debug(){return L().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),w1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),b1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),x1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),g1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Bw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return S1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return C1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=I1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>N1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=c1(e));let n=qT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),KT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&ag(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),y1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ag(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?QT(this.gl,t,e):t1(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),e1(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ag(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bd(this.gl,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Lot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),lg(this.gl,t,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(lg(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):Bw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;lg(o,t,this.framebuffer),this.debug&&wd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function Lot(r){let t=0;for(;t`${r}.${e}`)}function rr(r,t){return t===1?[r]:T1(r,t)}function Pz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ad=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${zot(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Sd():vd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function zot(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?PL([\"r\",\"c\",\"d\"],\"inputShape\"):Si([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var oI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=zz(e,n),s=Bz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Lz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=zz(n,o),i=Bz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Lz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Bot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Lz(r,t,e,n,o){let s=Vot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Bot(e,s);return i*a}function Vot(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Jw(t);case Lr.PACKED_2X2_FLOAT16:return Qw(t);case Lr.UNPACKED_FLOAT32:return Xw(t);case Lr.UNPACKED_FLOAT16:return Yw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Zw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Got(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function zz(r,t){if(r===Jr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return Got(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Bz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},xr=\"if (isnan(x)) return x;\",Vz=\"return x;\",_1=\"return abs(x);\";var Gz=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Wz=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Uz=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",Hz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Kz=\"return x;\",jz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Xz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Yz=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Zz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Dn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var sI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr(\"rc\",e),o=zt(e),s=Pz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber(\"WEBGL_VERSION\"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber(\"WEBGL_VERSION\"));e=new pp(n),this.binaryCache=Kot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var $n=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jn=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=he(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=rr(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t3={kernelName:go,backendName:\"webgl\",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var e3={kernelName:Ap,backendName:\"webgl\",kernelFunc:Rn};var E1=\"return (a < 0.) ? b * a : a;\",A1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(A1,o.shape,i.shape):new $n(E1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var r3={kernelName:bs,backendName:\"webgl\",kernelFunc:Jot};var D1=\"return (a < 0.) ? b * a : a;\",$1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Qot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn($1,n.shape,o.shape):new $n(D1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var n3={kernelName:Os,backendName:\"webgl\",kernelFunc:Qot};var Po=\"if (isnan(x)) return x;\";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r===\"linear\")return t?Kz:Vz;if(r===\"relu\")return t?Xz:Wz;if(r===\"elu\")return t?jz:Gz;if(r===\"relu6\")return t?Yz:Uz;if(r===\"prelu\")return t?$1:D1;if(r===\"leakyrelu\")return t?A1:E1;if(r===\"sigmoid\")return t?Zz:Hz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Rd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?i3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var a3={kernelName:Gi,backendName:\"webgl\",kernelFunc:rt};var fg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var aI=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function est(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=est(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=T1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Ml(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>F1&&j===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=mg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Rd(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var p3={kernelName:Xi,backendName:\"webgl\",kernelFunc:nst};var m3=\"return abs(x);\";function ost(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=eI(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Dn(n.shape,m3):o=new zr(n.shape,m3),e.runWebGLProgram(o,[n],n.dtype)}var f3={kernelName:Ai,backendName:\"webgl\",kernelFunc:ost};var sst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,ist=It({opSnippet:sst}),d3={kernelName:Go,backendName:\"webgl\",kernelFunc:ist};var ast=xr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,lst=It({opSnippet:ast}),h3={kernelName:Wo,backendName:\"webgl\",kernelFunc:lst};var g3=\"return a + b;\",ust=ce({opSnippet:g3,packedOpSnippet:g3,supportsComplex:!0,cpuKernelImpl:qL}),x3={kernelName:no,backendName:\"webgl\",kernelFunc:ust};var cI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var pI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function mI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=mI({inputs:n.slice(0,u),backend:e}),c=mI({inputs:n.slice(u),backend:e});return mI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new pI(n[0].shape,s):new cI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var y3={kernelName:Uo,backendName:\"webgl\",kernelFunc:mI};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var b3={kernelName:Ea,backendName:\"webgl\",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var w3={kernelName:Aa,backendName:\"webgl\",kernelFunc:pst};var fI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var dI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=rr(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=rr(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=rr(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=rr(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=rr(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function I3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new fI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=I3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function C3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new dI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=C3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function hI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=I3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return C3(r,t,n)}function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var v3={kernelName:Di,backendName:\"webgl\",kernelFunc:mst};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var S3={kernelName:$i,backendName:\"webgl\",kernelFunc:fst};var dst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,hst=It({opSnippet:dst}),N3={kernelName:Ho,backendName:\"webgl\",kernelFunc:hst};var gst=xr+\"return log(x + sqrt(x * x + 1.0));\",xst=It({opSnippet:gst}),k3={kernelName:qo,backendName:\"webgl\",kernelFunc:xst};var yst=xr+`\n return atan(x);\n`,bst=It({opSnippet:yst}),T3={kernelName:Ko,backendName:\"webgl\",kernelFunc:bst};var wst=$d+`\n return atan(a, b);\n`,Ist=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xn+`\n return result;\n`,Cst=ce({opSnippet:wst,packedOpSnippet:Ist}),_3={kernelName:Xo,backendName:\"webgl\",kernelFunc:Cst};var vst=xr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sst=It({opSnippet:vst}),E3={kernelName:jo,backendName:\"webgl\",kernelFunc:Sst};var Ni=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},qu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let M=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${M} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var A3={kernelName:Yo,backendName:\"webgl\",kernelFunc:Nst};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var D3={kernelName:Ri,backendName:\"webgl\",kernelFunc:kst};var gI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},xI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new xI(m);return e.runWebGLProgram(f,[o],i.dtype)}var $3={kernelName:Hl,backendName:\"webgl\",kernelFunc:Tst};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;vi([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new gI(c);return e.runWebGLProgram(p,[o],i.dtype)}var R3={kernelName:Ul,backendName:\"webgl\",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var F3={kernelName:Zo,backendName:\"webgl\",kernelFunc:Est};var yI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var bI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var Ast=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new bI(n.shape,o.shape,s.shape,c,p,u):new yI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},O3={kernelName:ds,backendName:\"webgl\",kernelFunc:Ast};var wI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=Dst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${O1[a]} = start[${a}] + coords.${O1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},O1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Dst(r){if(r===1)return\"sourceLoc\";if(r<=6)return O1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var II=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=rr(\"coords\",this.rank),o=rr(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function $st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ki(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=Sz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new II(u):new wI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),$st(o,a,u,e)}var M3={kernelName:Ui,backendName:\"webgl\",kernelFunc:ki};var Rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ki({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},P3={kernelName:Fi,backendName:\"webgl\",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=tI(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var L3={kernelName:Da,backendName:\"webgl\",kernelFunc:Fst};var Ost=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Mst=`\n return float(int(a.r) & int(b.r));\n`;function Pst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),i=L().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=jL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Ost,n.shape,o.shape,!1):a=new $n(Mst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var z3={kernelName:$a,backendName:\"webgl\",kernelFunc:Pst};function Lst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var B3={kernelName:ql,backendName:\"webgl\",kernelFunc:Lst};var zst=\"return float(a != b);\",M1=ce({opSnippet:zst,cpuKernelImpl:hz,dtype:\"bool\"}),V3={kernelName:Za,backendName:\"webgl\",kernelFunc:M1};function 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i=e.texData.get(o.dataId).values,[a,u,l]=XL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return W3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=M1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var U3={kernelName:fo,backendName:\"webgl\",kernelFunc:P1};var H3=\"return ceil(x);\",Vst=It({opSnippet:H3,packedOpSnippet:H3,cpuKernelImpl:YL}),q3={kernelName:Jo,backendName:\"webgl\",kernelFunc:Vst};var CI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var vI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new vI(o.shape):a=new CI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var K3={kernelName:ho,backendName:\"webgl\",kernelFunc:Gst};var SI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function j3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Wst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new SI(n.shape),i=[j3(n,o.complexTensorInfos.real),j3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var X3={kernelName:Kl,backendName:\"webgl\",kernelFunc:Wst};var NI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${kI(a,l,g)}),\n vec2(${kI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${kI(a,l,d)}),\n vec2(${kI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function kI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var Y3={kernelName:Pp,backendName:\"webgl\",kernelFunc:hp};function Fd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Fd(f,t,e),g=Fd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=ZL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Ust(s,t,e),c=new NI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Ust(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function L1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Fd(u,s,e)}var Z3={kernelName:Oi,backendName:\"webgl\",kernelFunc:L1};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},_I=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function AI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function DI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=AI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=AI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>F1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function $I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=AI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=AI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new EI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Ml(a,!0):null,q=new Rd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let j=n.runWebGLProgram(q,H,\"float32\"),Y=rt({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});I.push(j);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=DI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new Md(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=$I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Od(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var J3={kernelName:Qo,backendName:\"webgl\",kernelFunc:Hst};var RI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},FI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},OI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},MI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new RI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var Q3={kernelName:Dp,backendName:\"webgl\",kernelFunc:qst};var PI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new PI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new FI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var tB={kernelName:ts,backendName:\"webgl\",kernelFunc:Kst};function jst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new _I(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var eB={kernelName:es,backendName:\"webgl\",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new OI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var rB={kernelName:Ra,backendName:\"webgl\",kernelFunc:Xst};function Yst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new MI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var nB={kernelName:Fa,backendName:\"webgl\",kernelFunc:Yst};var Zst=Po+`\n return cos(x);\n`,Jst=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Qst=It({opSnippet:Zst,packedOpSnippet:Jst}),oB={kernelName:rs,backendName:\"webgl\",kernelFunc:Qst};var tit=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,eit=It({opSnippet:tit}),sB={kernelName:ns,backendName:\"webgl\",kernelFunc:eit};var LI=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new LI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},iB={kernelName:Ma,backendName:\"webgl\",kernelFunc:rit};var gp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(gp||(gp={}));var dg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===gp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${aB(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${lB(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${lB(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${aB(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function aB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function lB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function zI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new dg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new dg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function nit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Prod,o,e,s,i,a)}var uB={kernelName:Oa,backendName:\"webgl\",kernelFunc:nit};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Sum,o,e,s,i,a)}var cB={kernelName:os,backendName:\"webgl\",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=tI(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var pB={kernelName:jl,backendName:\"webgl\",kernelFunc:sit};var BI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function iit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new BI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var mB={kernelName:Pa,backendName:\"webgl\",kernelFunc:iit};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Ld(p):m=new Pd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var fB={kernelName:ss,backendName:\"webgl\",kernelFunc:ait};var VI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},GI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new VI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var dB={kernelName:$p,backendName:\"webgl\",kernelFunc:lit};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new GI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var hB={kernelName:Rp,backendName:\"webgl\",kernelFunc:uit};var WI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function cit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new WI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var gB={kernelName:Xl,backendName:\"webgl\",kernelFunc:cit};var UI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new UI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var xB={kernelName:is,backendName:\"webgl\",kernelFunc:pit};function mit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var yB={kernelName:Fp,backendName:\"webgl\",kernelFunc:mit};var fit=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",dit=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,hit=It({opSnippet:fit,packedOpSnippet:dit}),bB={kernelName:ls,backendName:\"webgl\",kernelFunc:hit};var git=\"return (b >= 0.0) ? a : a * (b + 1.0);\",xit=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,yit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(xit,n.shape,o.shape):new $n(git,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},wB={kernelName:La,backendName:\"webgl\",kernelFunc:yit};var bit=`\n return vec4(equal(a, b));\n`,wit=\"return float(a == b);\",Iit=ce({opSnippet:wit,packedOpSnippet:bit,dtype:\"bool\",cpuKernelImpl:JL}),IB={kernelName:za,backendName:\"webgl\",kernelFunc:Iit};var Cit=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,vit=It({opSnippet:Cit}),CB={kernelName:us,backendName:\"webgl\",kernelFunc:vit};var Sit=Po+`\n return exp(x);\n`,Nit=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z1=It({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:QL,dtype:\"float32\"}),vB={kernelName:cs,backendName:\"webgl\",kernelFunc:z1};function HI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var SB={kernelName:Mi,backendName:\"webgl\",kernelFunc:HI};var NB=\"return exp(x) - 1.0;\",kit=It({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:tz}),kB={kernelName:ps,backendName:\"webgl\",kernelFunc:kit};var hg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function qI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new hg(\"real\",u,t),c=new hg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Tit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!1,e)}var TB={kernelName:Op,backendName:\"webgl\",kernelFunc:Tit};var KI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new KI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var _B={kernelName:Jl,backendName:\"webgl\",kernelFunc:Ll};var jI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var EB={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new jI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var AB=\"return floor(x);\",_it=It({opSnippet:AB,packedOpSnippet:AB,cpuKernelImpl:ez}),DB={kernelName:ms,backendName:\"webgl\",kernelFunc:_it};var Eit=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Ait=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,Dit=ce({opSnippet:Eit,packedOpSnippet:Ait,dtype:\"int32\"}),$B={kernelName:fs,backendName:\"webgl\",kernelFunc:Dit};var XI=class{constructor(t){this.variableNames=[\"A\"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var YI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var RB={kernelName:th,backendName:\"webgl\",kernelFunc:$it},zd,B1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function $it(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(zd==null||h!==B1)&&(B1=h,zd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:B1})),zd.canvas.width=u,zd.canvas.height=l,zd.drawImage(o,0,0,u,l),o=zd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new YI(p):new XI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function Rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(M,V)=>{if(V===\"NCHW\"&&M.shape.length===1&&M.shape[0]!==1){let G=rt({inputs:{x:M},backend:e,attrs:{shape:[M.shape[0],1,1]}});return b.push(G),G}return M};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(M),b.push(M)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=DI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Ml(f,!0):null,F=new Md(g,w,D,I,N),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",M)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=$I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Ml(f,!1):null,F=new Od(g,w,D,I,N),M=E();x=e.runWebGLProgram(F,M,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var FB={kernelName:Yi,backendName:\"webgl\",kernelFunc:Rit};function Fit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ml(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(M),d.push(M)}let A;x?A=new Ld(g,I,b,N,E):A=new Pd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(M=>e.disposeIntermediateTensorInfo(M)),F}var OB={kernelName:Zi,backendName:\"webgl\",kernelFunc:Fit};var ZI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Oit(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=rz(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new ZI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var MB={kernelName:Va,backendName:\"webgl\",kernelFunc:Oit};var JI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Mit(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function Mit(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=nz(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new JI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var PB={kernelName:Pi,backendName:\"webgl\",kernelFunc:V1};var Pit=\"return float(a > b);\",Lit=`\n return vec4(greaterThan(a, b));\n`,zit=ce({opSnippet:Pit,packedOpSnippet:Lit,cpuKernelImpl:oz,dtype:\"bool\"}),LB={kernelName:Ga,backendName:\"webgl\",kernelFunc:zit};var Bit=\"return float(a >= b);\",Vit=`\n return vec4(greaterThanEqual(a, b));\n`,Git=ce({opSnippet:Bit,packedOpSnippet:Vit,dtype:\"bool\",cpuKernelImpl:sz}),zB={kernelName:hs,backendName:\"webgl\",kernelFunc:Git};function Wit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!0,e)}var BB={kernelName:Mp,backendName:\"webgl\",kernelFunc:Wit};var Uit=\"return float(!isnan(x) && !isinf(x));\",Hit=It({opSnippet:Uit,dtype:\"bool\"}),VB={kernelName:gs,backendName:\"webgl\",kernelFunc:Hit};var qit=\"return float(isinf(x));\",Kit=It({opSnippet:qit,dtype:\"bool\"}),GB={kernelName:xs,backendName:\"webgl\",kernelFunc:Kit};var jit=\"return float(isnan(x));\",Xit=It({opSnippet:jit,dtype:\"bool\"}),WB={kernelName:ys,backendName:\"webgl\",kernelFunc:Xit};var Yit=\"return float(a < b);\",Zit=`\n return vec4(lessThan(a, b));\n`,Jit=ce({opSnippet:Yit,packedOpSnippet:Zit,cpuKernelImpl:iz,dtype:\"bool\"}),UB={kernelName:Wa,backendName:\"webgl\",kernelFunc:Jit};var Qit=\"return float(a <= b);\",tat=`\n return vec4(lessThanEqual(a, b));\n`,eat=ce({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:az,dtype:\"bool\"}),HB={kernelName:Ua,backendName:\"webgl\",kernelFunc:eat};function rat(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=lz(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var qB={kernelName:Ha,backendName:\"webgl\",kernelFunc:rat};var nat=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,oat=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,sat=It({opSnippet:nat,packedOpSnippet:oat,cpuKernelImpl:uz}),KB={kernelName:ws,backendName:\"webgl\",kernelFunc:sat};var iat=Po+`\n return log(1.0 + x);\n`,aat=It({opSnippet:iat}),jB={kernelName:Is,backendName:\"webgl\",kernelFunc:aat};var lat=\"return float(a >= 1.0 && b >= 1.0);\",uat=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,cat=ce({opSnippet:lat,packedOpSnippet:uat,dtype:\"bool\"}),XB={kernelName:qa,backendName:\"webgl\",kernelFunc:cat};var pat=\"return float(!(x >= 1.0));\",mat=It({opSnippet:pat}),YB={kernelName:Ka,backendName:\"webgl\",kernelFunc:mat};var fat=\"return float(a >= 1.0 || b >= 1.0);\",dat=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,hat=ce({opSnippet:fat,packedOpSnippet:dat,dtype:\"bool\"}),ZB={kernelName:ja,backendName:\"webgl\",kernelFunc:hat};var QI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var tC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var gat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new tC(o.shape,s,i,a,u):new QI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},JB={kernelName:Cs,backendName:\"webgl\",kernelFunc:gat};var eC=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var xat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new eC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},QB={kernelName:Xa,backendName:\"webgl\",kernelFunc:xat};function tV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var nV={kernelName:Ns,backendName:\"webgl\",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var oV={kernelName:Li,backendName:\"webgl\",kernelFunc:Cat};var rC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},nC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new nC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var sV={kernelName:tu,backendName:\"webgl\",kernelFunc:vat};function Sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;vi([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ni(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new rC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var iV={kernelName:Ql,backendName:\"webgl\",kernelFunc:Sat};function aV(r,t,e,n){let o=new Ni(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Ni(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var lV={kernelName:eu,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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uC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=rr(\"rc\",o),l=rr(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new uC(o.shape,s,i):new lC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},EV={kernelName:Rs,backendName:\"webgl\",kernelFunc:q1};var Kat=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,jat=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var DV={kernelName:Ms,backendName:\"webgl\",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=xz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var $V={kernelName:Lp,backendName:\"webgl\",kernelFunc:Zat};function Jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=yz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var RV={kernelName:zp,backendName:\"webgl\",kernelFunc:Jat};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=bz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var FV={kernelName:Bp,backendName:\"webgl\",kernelFunc:Qat};var K1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=wz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},OV={kernelName:ru,backendName:\"webgl\",kernelFunc:K1};var tlt=\"return 1.0 / x;\",elt=It({opSnippet:tlt}),MV={kernelName:Ps,backendName:\"webgl\",kernelFunc:elt};var rlt=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,nlt=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,olt=It({opSnippet:rlt,packedOpSnippet:nlt}),PV={kernelName:Ls,backendName:\"webgl\",kernelFunc:olt};var slt=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,ilt=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,alt=It({opSnippet:slt,packedOpSnippet:ilt}),LV={kernelName:Vs,backendName:\"webgl\",kernelFunc:alt};var cC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var pC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new pC(o.shape,u,l,s,i):new cC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var zV={kernelName:Bs,backendName:\"webgl\",kernelFunc:llt};var mC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function ult(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new mC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var BV={kernelName:rl,backendName:\"webgl\",kernelFunc:ult};var fC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var dC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dC(o.shape,u,l,s,i):new fC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var VV={kernelName:zs,backendName:\"webgl\",kernelFunc:clt};var hC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function plt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new hC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var GV={kernelName:el,backendName:\"webgl\",kernelFunc:plt};var gC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var xC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xC(o.shape,a):new gC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var WV={kernelName:Gs,backendName:\"webgl\",kernelFunc:mlt};var yC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var UV={kernelName:pl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new yC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var flt=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,dlt=It({opSnippet:flt}),HV={kernelName:Ws,backendName:\"webgl\",kernelFunc:dlt};var hlt=\"return inversesqrt(x);\",glt=It({opSnippet:hlt,cpuKernelImpl:Iz}),qV={kernelName:Us,backendName:\"webgl\",kernelFunc:glt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var bC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function xlt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new bC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var KV={kernelName:nl,backendName:\"webgl\",kernelFunc:xlt};var wC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function ylt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new wC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],\"int32\",u)}var jV={kernelName:sl,backendName:\"webgl\",kernelFunc:ylt};var IC=class{constructor(t,e,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function blt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new IC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var XV={kernelName:Wi,backendName:\"webgl\",kernelFunc:blt};var wlt=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Ilt=It({opSnippet:wlt}),YV={kernelName:Hs,backendName:\"webgl\",kernelFunc:Ilt};var Clt=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,vlt=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Slt=It({opSnippet:Clt,packedOpSnippet:vlt,cpuKernelImpl:vz}),ZV={kernelName:Xs,backendName:\"webgl\",kernelFunc:Slt};var Nlt=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,klt=It({opSnippet:Nlt}),JV={kernelName:js,backendName:\"webgl\",kernelFunc:klt};var Tlt=Po+`\n return sin(x);\n`,_lt=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Elt=It({opSnippet:Tlt,packedOpSnippet:_lt}),QV={kernelName:qs,backendName:\"webgl\",kernelFunc:Elt};var Alt=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Dlt=It({opSnippet:Alt}),tG={kernelName:Ks,backendName:\"webgl\",kernelFunc:Dlt};var $lt=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Rlt=It({opSnippet:$lt}),eG={kernelName:Ys,backendName:\"webgl\",kernelFunc:Rlt};var Flt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},rG={kernelName:Hi,backendName:\"webgl\",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var nG={kernelName:nu,backendName:\"webgl\",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=kz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var oG={kernelName:il,backendName:\"webgl\",kernelFunc:Mlt};function Plt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var sG={kernelName:ou,backendName:\"webgl\",kernelFunc:Plt};function Llt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var iG={kernelName:su,backendName:\"webgl\",kernelFunc:Llt};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype===\"string\"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=Cz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var aG={kernelName:al,backendName:\"webgl\",kernelFunc:zlt};function Blt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ki({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var lG={kernelName:qi,backendName:\"webgl\",kernelFunc:Blt};var uG=\"return sqrt(x);\",Vlt=It({opSnippet:uG,packedOpSnippet:uG,cpuKernelImpl:Tz}),cG={kernelName:Zs,backendName:\"webgl\",kernelFunc:Vlt};var Glt=\"return x * x;\",Wlt=It({opSnippet:Glt}),pG={kernelName:iu,backendName:\"webgl\",kernelFunc:Wlt};var mG=\"return (a - b) * (a - b);\",Ult=ce({opSnippet:mG,packedOpSnippet:mG}),fG={kernelName:ti,backendName:\"webgl\",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=_z(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var dG={kernelName:ec,backendName:\"webgl\",kernelFunc:Hlt};function qlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var hG={kernelName:xo,backendName:\"webgl\",kernelFunc:qlt};var CC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function Klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=ki({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=Ez(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new CC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var gG={kernelName:ll,backendName:\"webgl\",kernelFunc:Klt};function jlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Az(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],\"string\",d),e.makeTensorInfo(p.shape,\"int32\",h)]}var xG={kernelName:au,backendName:\"webgl\",kernelFunc:jlt};function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=Dz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",l),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var yG={kernelName:lu,backendName:\"webgl\",kernelFunc:Xlt};function Ylt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(o<=0)throw new Error(\"Number of buckets must be at least 1\");let i=e.readSync(s.dataId),a=$z(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var bG={kernelName:uu,backendName:\"webgl\",kernelFunc:Ylt};var Zlt=\"return tan(x);\",Jlt=It({opSnippet:Zlt}),wG={kernelName:ri,backendName:\"webgl\",kernelFunc:Jlt};var Qlt=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,tut=It({opSnippet:Qlt}),IG={kernelName:ni,backendName:\"webgl\",kernelFunc:tut};function eut(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Ku(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var CG={kernelName:ol,backendName:\"webgl\",kernelFunc:eut};var vC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Fz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new vC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var vG={kernelName:oo,backendName:\"webgl\",kernelFunc:j1};var SC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},NC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function xp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function SG(r){let t=1;for(;tu){let M=e.readSync(o.dataId),[V,G]=Oz(M,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Ll({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&xp(e,f);let x=SG(s),b=SG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(M,V,G)=>{let W=I(),q=new SC(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],Y=w;w=e.runWebGLProgram(q,W,\"int32\",j),xp(e,Y)};for(let M=1;M=1;G/=2)N(V,G,[h,b])}for(let M=b;M>x;M/=2){let V=I(),G=new NC([h,M/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),xp(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=ki({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),xp(e,E);let A=V1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});xp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),xp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),xp(e,F),[A,w]}var NG={kernelName:ul,backendName:\"webgl\",kernelFunc:nut};var kC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function out(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new kC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var kG={kernelName:cl,backendName:\"webgl\",kernelFunc:out};function sut(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;vi(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=Mz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var TG={kernelName:cu,backendName:\"webgl\",kernelFunc:sut};function iut(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var _G={kernelName:Ki,backendName:\"webgl\",kernelFunc:iut};var TC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new TC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=K1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),j=j1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(j),g(q,N,j,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var EG={kernelName:pu,backendName:\"webgl\",kernelFunc:aut};var lut=[p3,f3,d3,h3,x3,y3,b3,w3,v3,S3,N3,k3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,L3,z3,B3,U3,q3,K3,e3,X3,Z3,J3,Q3,tB,eB,rB,nB,oB,sB,iB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,bB,wB,IB,CB,vB,SB,kB,TB,_B,EB,DB,$B,RB,FB,OB,MB,PB,LB,zB,t3,BB,Y3,VB,GB,WB,r3,UB,HB,qB,KB,jB,XB,YB,ZB,JB,QB,eV,rV,nV,oV,sV,iV,lV,cV,pV,mV,fV,dV,bV,s3,wV,IV,CV,vV,V3,SV,TV,_V,EV,AV,n3,DV,$V,RV,FV,OV,G3,hV,MV,PV,LV,a3,zV,BV,VV,GV,WV,UV,HV,qV,KV,jV,XV,YV,ZV,JV,QV,tG,M3,yV,eG,rG,nG,oG,sG,iG,aG,lG,cG,pG,fG,dG,hG,gG,xG,yG,bG,xV,u3,wG,IG,CG,vG,NG,kG,c3,TG,_G,EG,NV];for(let r of lut)rc(r);var Nt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(Nt||(Nt={}));var ju;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ju||(ju={}));var AG;function uut(r){AG=r.wasm.cwrap(Xi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function cut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return AG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var DG={kernelName:Xi,backendName:\"wasm\",setupFunc:uut,kernelFunc:cut};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var $G=yt(Ai);var RG=yt(Go);var FG=yt(Wo);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var put=!0,OG=ee(no,put);var MG;function mut(r){MG=r.wasm.cwrap(Uo,null,[\"array\",\"number\",\"number\",\"number\"])}function fut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return MG(s,o.length,Nt[n.dtype],i),n}var PG={kernelName:Uo,backendName:\"wasm\",setupFunc:mut,kernelFunc:fut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var LG={kernelName:go,backendName:\"wasm\",kernelFunc:yp};var zG;function dut(r){zG=r.wasm.cwrap(so,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=gut(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var BG={kernelName:so,backendName:\"wasm\",kernelFunc:mo,setupFunc:dut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var iW={kernelName:Gi,backendName:\"wasm\",kernelFunc:mr};var aW;function Eut(r){aW=r.wasm.cwrap(Zo,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=ep(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Dut(u,c[0],m,s,i);else if(f===3)$ut(u,c[0],c[1],m,s,i);else if(f===4)Rut(u,c[0],c[1],c[2],m,s,i);else{let d=ep(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Dut(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(h.dataId),g}var cW={kernelName:Fi,backendName:\"wasm\",kernelFunc:Fut};var pW;function Out(r){pW=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function Mut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return pW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var mW={kernelName:Da,backendName:\"wasm\",setupFunc:Out,kernelFunc:Mut};var Put=!0,fW=ee($a,Put);function Lut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var dW={kernelName:ql,backendName:\"wasm\",kernelFunc:Lut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var hW={kernelName:fo,backendName:\"wasm\",kernelFunc:Fn};var gW=yt(Jo);var xW;function zut(r){xW=r.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\"])}function But(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return xW(a,s,i,l),u}var yW={kernelName:ho,backendName:\"wasm\",setupFunc:zut,kernelFunc:But};function X1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return 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x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var OW={kernelName:Oa,backendName:\"wasm\",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype===\"float32\"||o.dtype===\"int32\",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:\"wasm\",setupFunc:ect,kernelFunc:rct};var LW;function nct(r){LW=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function oct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return LW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var zW={kernelName:jl,backendName:\"wasm\",setupFunc:nct,kernelFunc:oct};var BW;function sct(r){BW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:\"wasm\",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return GW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,M,I,N,E,A,D,F,G),V}var WW={kernelName:ss,backendName:\"wasm\",setupFunc:act,kernelFunc:lct};var UW;function uct(r){UW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function cct(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return UW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var HW={kernelName:Xl,backendName:\"wasm\",setupFunc:uct,kernelFunc:cct};var qW;function pct(r){qW=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:\"wasm\",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return YW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var ZW={kernelName:Yl,backendName:\"wasm\",setupFunc:hct,kernelFunc:gct};var JW=yt(ls);var QW;function xct(r){QW=r.wasm.cwrap(La,null,[\"number\",\"number\",\"number\"])}function yct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return QW(i(o),i(n),i(s)),s}var tU={kernelName:La,backendName:\"wasm\",setupFunc:xct,kernelFunc:yct};var bct=!1,eU=ee(za,bct,\"bool\");var rU=yt(us);var nU=yt(cs,\"float32\");function EC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var oU={kernelName:Mi,backendName:\"wasm\",kernelFunc:EC};var sU=yt(ps,\"float32\");function Z1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var iU={kernelName:Jl,backendName:\"wasm\",kernelFunc:Z1};var aU;function wct(r){aU=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ict(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return aU(s,a,u,l,c,i),o}var lU={kernelName:Ba,backendName:\"wasm\",kernelFunc:Ict,setupFunc:wct};var uU=yt(ms);var Cct=!1,cU=ee(fs,Cct);var pU;function vct(r){pU=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return pU(c,p,m,f,d,o,g),h}var mU={kernelName:ds,backendName:\"wasm\",setupFunc:vct,kernelFunc:Sct};var fU;function Nct(r){fU=r.wasm.cwrap(Yi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function kct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return fU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var dU={kernelName:Yi,backendName:\"wasm\",setupFunc:Nct,kernelFunc:kct};var hU;function Tct(r){hU=r.wasm.cwrap(Zi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return hU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var gU={kernelName:Zi,backendName:\"wasm\",setupFunc:Tct,kernelFunc:_ct};var xU;function Ect(r){xU=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Act(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return xU(f,Nt[n.dtype],h,i,p,a,g,x),l}var yU={kernelName:Va,backendName:\"wasm\",setupFunc:Ect,kernelFunc:Act};var bU;function Dct(r){bU=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function $ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:\"wasm\",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,\"bool\");var Fct=!1,CU=ee(hs,Fct,\"bool\");var vU=yt(gs,\"bool\");var SU=yt(xs,\"bool\");var NU=yt(ys,\"bool\");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,[\"number\",\"number\",\"number\",\"number\"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:\"wasm\",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,\"bool\");var Lct=!1,EU=ee(Ua,Lct,\"bool\");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:\"wasm\",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,\"bool\");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,\"bool\");var Wct=!1,PU=ee(E_,Wct,\"bool\");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:\"wasm\",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:\"wasm\",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:\"wasm\",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return HU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var qU={kernelName:Ns,backendName:\"wasm\",setupFunc:Zct,kernelFunc:Jct};var KU;function Qct(r){KU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return KU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var jU={kernelName:Li,backendName:\"wasm\",setupFunc:Qct,kernelFunc:tpt};var XU;function ept(r){XU=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return XU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var YU={kernelName:tu,backendName:\"wasm\",setupFunc:ept,kernelFunc:rpt};var ZU;function npt(r){ZU=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function opt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return ZU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var JU={kernelName:Ql,backendName:\"wasm\",setupFunc:npt,kernelFunc:opt};var QU;function spt(r){QU=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ipt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,\"int32\");return QU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var t4={kernelName:eu,backendName:\"wasm\",setupFunc:spt,kernelFunc:ipt};var e4;function apt(r){e4=r.wasm.cwrap(ks,null,[\"number, number, number\"])}function lpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;e4(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var r4={kernelName:ks,backendName:\"wasm\",setupFunc:apt,kernelFunc:lpt};var n4;function upt(r){n4=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\"])}function cpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let 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wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n=\"tfjs-backend-wasm.wasm\";return r&&t?n=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(n=\"tfjs-backend-wasm-simd.wasm\"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),L().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=rq.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:\"\")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error(\"WASM backend not initialized.\");return i_}var Hmt=\"4.21.0\";var qmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq=\"4.21.0\",Kmt=\"4.21.0\",jmt=\"4.21.0\",Xmt=\"4.21.0\",Ymt=\"4.21.0\",Zmt={tfjs:sq,\"tfjs-core\":sq,\"tfjs-converter\":Kmt,\"tfjs-backend-cpu\":jmt,\"tfjs-backend-webgl\":Xmt,\"tfjs-backend-wasm\":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require, @typescript-eslint/no-require-imports\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n // eslint-disable-next-line no-undef\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: NodeJS.ErrnoException | null, buffer: string | Buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n const Canvas: (new () => HTMLCanvasElement) = (global as any)['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n const Video: (new () => HTMLVideoElement) = (global as any)['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n resolve(null);\n return;\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => (typeof buf === 'string' ? Buffer.from(buf) : buf.buffer))));\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'] as const;\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { Point } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n && (obj as any)['landmarks'] instanceof FaceLandmarks\n && (obj as any)['unshiftedLandmarks'] instanceof FaceLandmarks\n && (obj as any)['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh: FaceLandmarks) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians: number) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a: Point, b: Point) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever: Point, pivot: Point) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y);\n const opposite = pivot.y - lever.y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2);\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh.positions || mesh.positions.length !== 68) return angle;\n const pt = mesh.positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.14\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"build\": \"node build.js\",\n \"dev\": \"build --profile development\",\n \"typings\": \"build --profile typings\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^2.0.0\",\n \"@microsoft/api-extractor\": \"^7.47.7\",\n \"@tensorflow/tfjs\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.21.0\",\n \"@tensorflow/tfjs-converter\": \"^4.21.0\",\n \"@tensorflow/tfjs-core\": \"^4.21.0\",\n \"@tensorflow/tfjs-data\": \"^4.21.0\",\n \"@tensorflow/tfjs-layers\": \"^4.21.0\",\n \"@tensorflow/tfjs-node\": \"^4.21.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.21.0\",\n \"@types/node\": \"^22.5.4\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.1\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"ajv\": \"^8.17.1\",\n \"esbuild\": \"^0.23.1\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.30.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"node-fetch\": \"^3.3.2\",\n \"rimraf\": \"^6.0.1\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.7.0\",\n \"typedoc\": \"^0.26.7\",\n \"typescript\": \"5.5.4\"\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2ExtractBoxesResult, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results: TinyYolov2ExtractBoxesResult[] = [];\n const scoresData = await scoresTensor.array() as number[][][][];\n const boxesData = await boxesTensor.array() as number[][][][];\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], - "mappings": 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R0;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(R0||(R0={}));var F0;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(F0||(F0={}));var O0;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(O0||(O0={}));var NK={float32:F0,int32:$0,bool:R0,complex64:O0};function ur(r,t){if(r===\"string\"||t===\"string\"){if(r===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${t}`)}return NK[r][t]}function lc(r){return ur(r,\"int32\")}function rx(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function nx(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function M0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function kK(r,t){return t.some(e=>e.id===r.id)}function ih(r){let t=[];return eE(r,t,new Set),t}function eE(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!TK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),eE(s,t,e))}}function TK(r){return Array.isArray(r)||typeof r==\"object\"}function P0(r){return r.kernelName!=null}var ox=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ah=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ox}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Yg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Bo)&&typeof n.then==\"function\"){let o=++this.pendingBackendInitId,s=n.then(i=>o(othis.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;ethis.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a=\"float32\",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Wp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype===\"complex64\"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=P0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(P0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Wp(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=P0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=v0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>\"saveAllInputs is true, expected inputs to be an array.\"),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",o=o||this.backend;let s=t;n===\"string\"&&Vo(t[0])&&(s=t.map(u=>fu(u)));let i=o.write(s,e,n),a=new Lt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n===\"string\"){let u=this.state.tensorInfo.get(i),l=b0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||\"float32\";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Lt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(t,e){this.state.numTensors++,t.dtype===\"string\"&&this.state.numStringTensors++;let n=0;t.dtype!==\"complex64\"&&t.dtype!==\"string\"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof ml||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!==\"complex64\"&&t.dtype!==\"string\"){let n=t.size*Tp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=v0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Ep(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ih(t),n=new Set(e.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>\"gradients() received an empty list of xs.\"),n!=null&&n.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",t));_(s instanceof Lt,()=>\"The result y returned by f() must be a tensor.\");let i=K_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let a={};a[s.id]=n==null?_K(s.shape):n,j_(a,i,l=>this.tidy(l),EK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(_i(t),()=>\"The f passed in customGrad(f) must be a function.\"),(...e)=>{_(e.every(a=>a instanceof Lt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Lt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),_(_i(n.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),_(c.every(m=>m instanceof Lt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=ac(),n=await this.backend.time(t);return n.wallMs=ac()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ox;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ah.nextTensorId=0;ah.nextVariableId=0;function _K(r){let t=Yd(jt(r),\"float32\");return T.makeTensor(t,r,\"float32\")}function L0(){let r=C0();if(r._tfengine==null){let t=new Zd(r);r._tfengine=new ah(t)}return T_(r._tfengine.ENV),J_(()=>r._tfengine),r._tfengine}var T=L0();function EK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>B0,isMobile:()=>$K,mockIsMobile:()=>DK});function AK(){return typeof navigator!=\"undefined\"&&navigator!=null}var z0;function DK(r){z0=r}function $K(r){if(z0!==void 0)return z0;if(r||AK()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let t=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!t){let e=r;return 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e=C(r,\"x\",\"bitwiseAnd\"),n=C(t,\"y\",\"bitwiseAnd\");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!==\"int32\"||n.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel($a,o)}var FE=k({bitwiseAnd_:Bj});function Vj(r,t){let e=C(r,\"s0\",\"broadcastArgs\",\"int32\"),n=C(t,\"s1\",\"broadcastArgs\",\"int32\");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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s={x:C(r,\"x\",\"cumsum\")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(os,s,i)}var im=k({cumsum_:s6});function i6(r,t,e,n=!1){let o=C(r,\"x\",\"denseBincount\"),s=C(t,\"weights\",\"denseBincount\");_(o.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(jl,i,a)}var mh=k({denseBincount_:i6});function a6(r,t,e=\"NHWC\"){let n=C(r,\"x\",\"depthToSpace\",\"float32\"),o=e===\"NHWC\"?n.shape[1]:n.shape[2],s=e===\"NHWC\"?n.shape[2]:n.shape[3],i=e===\"NHWC\"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: 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vm=k({unsortedSegmentSum_:G5});function W5(r,t=0){let e=C(r,\"x\",\"unstack\",\"string_or_numeric\");_(t>=-e.shape.length&&t`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Ki,n,o)}var gr=k({unstack_:W5});function OA(r,t){return dh(r,t,\"right\")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s0,()=>\"mask cannot be scalar\"),$e(a.slice(s,s+i),o.shape,\"mask's shape must match the first K dimensions of tensor's shape,\");let u=1;for(let h=s;ha).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype===\"complex64\"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:K5});function j5(r,t,e,n,o=!0){let s=C(r,\"v\",\"movingAverage\"),i=C(t,\"x\",\"movingAverage\"),a=C(e,\"decay\",\"movingAverage\");M0(s,i),_(sn(s.shape,i.shape),()=>\"Shape mismatch in v and x\");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>\"When using zeroDebias: true, step is required.\");let p=C(n,\"step\",\"movingAverage\");c=ut(c,at(u,qr(a,p)))}return K(s,c)}var X5=k({movingAverage_:j5});function Y5(r,t,e){Le(e);let n=C(r,\"indices\",\"scatterND\",\"int32\"),o=C(t,\"updates\",\"scatterND\");Im(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var Z5=k({scatterND_:Y5});function MA(r,t,e,n){if(r.dtype!==\"int32\")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);if(r.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);let o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has 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T.runKernel(Dp,p,m)}var Sm=k({conv2DBackpropFilter_:i8});function bc(r,t,e){if(e==null||e===\"linear\")return r;if(e===\"relu\")return $(r,So(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=we(r.shape,t.shape);return n.length>0&&(e=mt(e,n)),R(e,r.shape)}function Ic(r,t,e,n){if(t===\"linear\")return r;if(t===\"relu\")return Or(r);if(t===\"elu\")return aa(r);if(t===\"relu6\")return cm(r);if(t===\"prelu\")return _u(r,e);if(t===\"leakyrelu\")return Cu(r,n);if(t===\"sigmoid\")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t===\"linear\";function a8({x:r,filter:t,strides:e,pad:n,dataFormat:o=\"NHWC\",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u=\"linear\",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||\"linear\",Cc(T.state.gradientDepth,u)===!1){_(o===\"NHWC\",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and 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Got dilations '${s}'`);let[A,D,F,M]=E,V=bc(N,F,u),G=gy(D.shape,V,A,e,n,s,i),W=hy(D,V,A.shape,e,n,s,i);if(M!=null){let q=wc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((E,A,D)=>{let F=T.runKernel(Zi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):pn((E,A,D,F)=>{let M=T.runKernel(Zi,w,I);return F([A,E,M,D]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m,g)}var zA=k({fusedDepthwiseConv2d_:c8});function p8({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s=\"linear\",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Cc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=K(V,o)),Ic(V,s,i,a)}let u=C(r,\"a\",\"fused matMul\"),l=C(t,\"b\",\"fused matMul\");[u,l]=Xt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,\"bias\",\"fused matMul\"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,\"prelu weights\",\"fused matMul\"));let D=(V,G)=>{let[W,q,H,j]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(j,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(Xi,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let j=T.runKernel(Xi,F,M);return H([G,W,j,q]),{value:R(j,w),gradFunc:D}})(I,N,E)}var BA=k({fusedMatMul_:p8});function m8(r){return xh(r,.54,.46)}var VA=k({hammingWindow_:m8});function f8(r){return xh(r,.5,.5)}var xy=k({hannWindow_:f8});function d8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o===\"bilinear\"||o===\"nearest\",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var WA=k({cropAndResize_:g8});function x8(r){let t=C(r,\"image\",\"flipLeftRight\",\"float32\");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var UA=k({flipLeftRight_:x8});function y8(r){let t=C(r,\"image\",\"grayscaleToRGB\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var HA=k({grayscaleToRGB_:y8});function b8(r){let t=C(r,\"image\",\"RGBToGrayscale\"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,\"float32\"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu(\"ij,j->i\",s,i);break;case 3:a=wu(\"ijk,k->ij\",s,i);break;case 4:a=wu(\"ijkl,l->ijk\",s,i);break;case 5:a=wu(\"ijklm,m->ijkl\",s,i);break;case 6:a=wu(\"ijklmn,n->ijklm\",s,i);break;default:throw new Error(\"Not a valid tensor rank.\")}return a=je(a,-1),J(a,o)}var qA=k({rgbToGrayscale_:b8});function w8(r,t,e=0,n=.5){let o=C(r,\"image\",\"rotateWithOffset\",\"float32\");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var KA=k({rotateWithOffset_:w8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>\"scores must be a 1D tensor\"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. 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i=C(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=C(t,\"scores\",\"nonMaxSuppressionAsync\"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Iy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(m,\"int32\"),selectedScores:Oe(f)}}var QA=E8;function A8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,\"boxes\",\"nonMaxSuppression\"),a=C(t,\"scores\",\"nonMaxSuppression\"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(Qa,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var t2=k({nonMaxSuppressionPadded_:A8});async function D8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let 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u={image:i,transforms:a},l={interpolation:e,fillMode:n,fillValue:o,outputShape:s};return T.runKernel(cl,u,l)}var n2=k({transform_:M8});function P8(r,t,e){let n=C(r,\"a\",\"bandPart\");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t==\"number\"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,\"numLower\",\"bandPart\")):(_(t.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),a=Ie(yl(t,0),s,lo(t,s))),typeof e==\"number\"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,\"numUpper\",\"bandPart\")):(_(e.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),u=Ie(yl(e,0),i,lo(e,i)));let l=R(pa(0,s,1,\"int32\"),[-1,1]),c=pa(0,i,1,\"int32\"),p=at(l,c),m=Fr(Vn(p,a),cn(p,Ut(u))),f=ke([s,i],n.dtype);return R(Fe(gr(R(n,[-1,s,i])).map(d=>Ie(m,d,f))),o)}var o2=k({bandPart_:P8});function L8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let o=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=hr(r,r.shape[0],0).map(o=>Wn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o{let s=n[o];if(o>0)for(let i=0;i=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let 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Je;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,\"losses\",\"computeWeightedLoss\"),o=null;t!=null&&(o=C(t,\"weights\",\"computeWeightedLoss\"));let s=o==null?n:$(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return Ne(s);{let i=n.size/o.size,a=ut(mt(s),mt(o));return i>1?ut(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ut(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ci(i,pt(0))),\"float32\");return ut(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Kr=k({computeWeightedLoss_:B8});function V8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"absoluteDifference\"),s=C(t,\"predictions\",\"absoluteDifference\"),i=null;e!=null&&(i=C(e,\"weights\",\"absoluteDifference\")),$e(o.shape,s.shape,\"Error in absoluteDifference: \");let a=_e(at(o,s));return Kr(a,i,n)}var l2=k({absoluteDifference_:V8});function G8(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"cosineDistance\"),i=C(t,\"predictions\",\"cosineDistance\"),a=null;n!=null&&(a=C(n,\"weights\",\"cosineDistance\")),$e(s.shape,i.shape,\"Error in cosineDistance: \");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Kr(l,a,o)}var u2=k({cosineDistance_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"hingeLoss\"),s=C(t,\"predictions\",\"hingeLoss\"),i=null;e!=null&&(i=C(e,\"weights\",\"hingeLoss\")),$e(o.shape,s.shape,\"Error in hingeLoss: \");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Kr(u,i,n)}var c2=k({hingeLoss_:W8});function U8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"huberLoss\"),i=C(t,\"predictions\",\"huberLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"huberLoss\")),$e(s.shape,i.shape,\"Error in huberLoss: \");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=K($(pt(.5),Wt(c)),$(u,p));return Kr(m,a,o)}var p2=k({huberLoss_:U8});function H8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"labels\",\"logLoss\"),i=C(t,\"predictions\",\"logLoss\"),a=null;e!=null&&(a=C(e,\"weights\",\"logLoss\")),$e(s.shape,i.shape,\"Error in logLoss: \");let u=pt(1),l=pt(n),c=Ut($(s,Nr(K(i,l)))),p=$(at(u,s),Nr(K(at(u,i),l))),m=at(c,p);return Kr(m,a,o)}var m2=k({logLoss_:H8});function q8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,\"labels\",\"meanSquaredError\"),s=C(t,\"predictions\",\"meanSquaredError\"),i=null;e!=null&&(i=C(e,\"weights\",\"meanSquaredError\")),$e(o.shape,s.shape,\"Error in meanSquaredError: \");let a=wm(o,s);return Kr(a,i,n)}var f2=k({meanSquaredError_:q8});function K8(r,t){let e=C(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),n=C(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");$e(e.shape,n.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return K(at(o,s),i)}function j8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=C(t,\"logits\",\"sigmoidCrossEntropy\"),a=null;if(e!=null&&(a=C(e,\"weights\",\"sigmoidCrossEntropy\")),$e(s.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=K($(s,at(c,l)),$(p,l))}let u=K8(s,i);return Kr(u,a,o)}var d2=k({sigmoidCrossEntropy_:j8});function X8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var x2=k({sparseReshape_:J8});function Q8(r,t,e){let n=C(r,\"data\",\"sparseSegmentMean\"),o=C(t,\"indices\",\"sparseSegmentMean\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,\"data\",\"sparseSegmentSum\"),o=C(t,\"indices\",\"sparseSegmentSum\",\"int32\"),s=C(e,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(n.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but 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className(){return\"Adadelta\"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=K($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ut(ge(K(l,this.epsilon)),ge(K(u,this.epsilon))),a),m=K($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=K($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Sc=class extends jr{static get className(){return\"Adagrad\"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=K(a,Wt(i));a.assign(u);let l=K($(ut(i,ge(K(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends jr{static get className(){return\"Adam\"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=K($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=K($(ut(d,K(ge(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(qr(this.beta1,this.iterations_+1)),this.accBeta2.assign(qr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var kc=class extends jr{static get className(){return\"Adamax\"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=ut(-this.learningRate,K($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=_e(l),h=kn(f,d);c.assign(m),p.assign(h);let g=K($(ut(o,n),ut(m,K(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(K(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Il=class extends jr{static get className(){return\"SGD\"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=K($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error(\"SGD 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends jr{static get className(){return\"RMSProp\"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=K($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=K($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),ge(at(c,K(Wt(m),this.epsilon)))),d=K($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=K($(u,this.decay),$(Wt(a),1-this.decay)),m=K($(l,this.momentum),ut($(a,this.learningRate),ge(K(p,this.epsilon))));u.assign(p),l.assign(m);let f=at(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var mY=[vc,Sc,Nc,kc,Tc,_c,Il];function S2(){for(let r of mY)bN(r)}var 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used.\");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className===\"Merge\")throw new z(\"Legacy serialization format not supported yet.\");s=e}else y.assert(e.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof r))throw new _t(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=wn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className=\"Sequential\";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className=\"elu\";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className=\"selu\";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className=\"relu\";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className=\"relu6\";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className=\"linear\";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className=\"sigmoid\";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className=\"hardSigmoid\";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className=\"softplus\";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className=\"softsign\";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className=\"tanh\";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className=\"softmax\";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className=\"logSoftmax\";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className=\"gelu\";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className=\"gelu_new\";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className=\"mish\";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className=\"swish\";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"activation\")}function xi(r){if(r==null){let t={};return t.className=\"linear\",t.config={},QN(t)}if(typeof r==\"string\"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className=\"L1L2\";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:\"L1L2\"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ve(r){if(r==null)return null;if(typeof r==\"string\"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className=\"ReLU\";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className=\"LeakyReLU\";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes==\"number\")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight(\"alpha\",e,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className=\"Softmax\";Q.registerClass(rf);function zu(r,t,e){if(typeof r==\"number\")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,1]):r))}function ek(r,t){return B(()=>(Me(t),t===\"channelsFirst\"?Vt(r,[0,2,3,4,1]):r))}function wJ(r,t,e,n=1,o=\"valid\",s,i=1){return B(()=>{if(s==null&&(s=xn()),Me(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s===\"channelsFirst\"&&(r=Vt(r,[0,2,1])),o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let a=rm(r,t,n,o===\"same\"?\"same\":\"valid\",\"NWC\",i);return e!=null&&(a=yn(a,e)),a})}function XR(r,t,e,n=[1,1],o=\"valid\",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:e,activation:a}),s===\"channelsFirst\"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o=\"valid\",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o===\"causal\")throw new _t(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return a=Dx(a,t,n,o===\"same\"?\"same\":\"valid\",\"NDHWC\",i),e!=null&&(a=yn(a,e)),s===\"channelsFirst\"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",r.verifyArgs(e),this.rank=t,tr(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,\"kernelSize\"),this.strides=zu(e.strides==null?1:e.strides,t,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co(\"kernelSize\"in t,\"required key 'kernelSize' not in config\"),typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,\"filters\"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat===\"channelsLast\"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className=\"Conv2D\";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className=\"Conv3D\";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z(\"Input should have rank 4; Received input shape: \"+JSON.stringify(t));let e=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[e]==null)throw new z(\"The channel dimension of the inputs should be defined. 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Found `None`.\");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",o,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat===\"channelsFirst\"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat===\"channelsFirst\"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className=\"Conv3DTranspose\";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(e.padding!=null&&e.padding!==\"same\"&&e.padding!==\"valid\")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className=\"SeparableConv\";var af=class extends Eb{constructor(t){super(2,t)}};af.className=\"SeparableConv2D\";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!=\"number\"&&!Fy(t.kernelSize,\"number\",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className=\"Conv1D\";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping==\"number\"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]==\"number\"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?\"channelsLast\":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat===\"channelsFirst\"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat===\"channelsLast\"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className=\"Cropping2D\";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?\"nearest\":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat===\"channelsFirst\"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation===\"nearest\"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className=\"UpSampling2D\";Q.registerClass(cf);function CJ(r,t,e=[1,1],n=\"valid\",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n===\"same\"?\"same\":\"valid\",\"NHWC\",s),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat===\"channelsFirst\"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className=\"DepthwiseConv2D\";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z(\"When inputs is an array, neither initialState or constants should be provided\");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),o!=null&&(o=J(J(o,\"bool\"),\"float32\"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className=\"RNN\";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight(\"kernel\",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className=\"SimpleRNNCell\";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className=\"SimpleRNN\";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.resetAfter)throw new z(\"GRUCell does not support reset_after parameter set to true.\");this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className=\"GRU\";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className=\"CustomInit\",e)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className=\"LSTM\";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols==\"function\")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z(\"ConvRNN2D cell does not support constants\");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e===\"channelsFirst\",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className=\"ConvRNN2D\";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,\"filters\"),this.kernelSize=zu(n,2,\"kernelSize\"),this.kernelSize.forEach(u=>tr(u,\"kernelSize\")),this.strides=zu(o||1,2,\"strides\"),this.strides.forEach(u=>tr(u,\"strides\")),this.padding=s||\"valid\",hn(this.padding),this.dataFormat=i||\"channelsLast\",Me(this.dataFormat),this.dilationRate=zu(a||1,2,\"dilationRate\"),this.dilationRate.forEach(u=>tr(u,\"dilationRate\"))}build(t){var e;t=Gt(t);let n=this.dataFormat===\"channelsFirst\"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight(\"kernel\",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className=\"CustomInit\",e)}else u=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,[\"units\"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Xc.className=\"ConvLSTM2DCell\";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className=\"ConvLSTM2D\";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className=\"Dropout\";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className=\"SpatialDropout1D\";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,\"units\"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className=\"Dense\";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to \"Flatten\" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className=\"Activation\";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className=\"RepeatVector\";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className=\"Reshape\";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(t.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className=\"Permute\";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className=\"Masking\";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,\"inputDim\"),this.outputDim=t.outputDim,tr(this.outputDim,\"outputDim\"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!==\"int32\"&&(n=rn(n,\"int32\"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className=\"Embedding\";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t(\"Dot layer does not support tensors of 4D or higher rank yet.\");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className=\"Dot\";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className=\"GaussianNoise\";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className=\"GaussianDropout\";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),u=-1.6732632423543772*1.0507009873554805,l=cn(Gn(n),this.rate);l=rn(l,\"float32\");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=K($(s,l),$(K(l,-1),u));return K($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Ff.className=\"AlphaDropout\";Q.registerClass(Ff);function zh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Cx(r,t,e,n,o,s);else if(r.rank===3)i=vx(r,t,e,n,o,s);else if(r.rank===4)i=Sx(r,t,e,n,o,s);else throw new _t(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function NJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance;return[zh(r,i,a,e,t,o),i,a]})}function kJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance,u=[];for(let d of 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className=\"BatchNormalization\";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=xe(t.betaInitializer||\"zeros\"),this.gammaInitializer=xe(t.gammaInitializer||\"ones\"),this.betaRegularizer=ve(t.betaRegularizer),this.gammaRegularizer=ve(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let s=0;s=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Eo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(e==null&&(e=xn()),e!==\"channelsLast\"&&e!==\"channelsFirst\")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat===\"channelsFirst\"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>_J(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Pf.className=\"ZeroPadding2D\";Q.registerClass(Pf);function Mb(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=Ph(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=ku(r,t,e,a):i=xu(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n=\"valid\"),o==null&&(o=xn()),s==null&&(s=\"max\"),r=ek(r,o);let i,a=n===\"same\"?\"same\":\"valid\";return s===\"max\"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o===\"channelsFirst\"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize==\"number\")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]==\"number\")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,\"poolSize\"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides==\"number\")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]==\"number\")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Lf.className=\"MaxPooling1D\";Q.registerClass(Lf);var zf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};zf.className=\"AveragePooling1D\";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"max\")}};Bf.className=\"MaxPooling2D\";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,\"avg\")}};Vf.className=\"AveragePooling2D\";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,\"poolSize\"),tr(this.strides,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat===\"channelsFirst\"?t[2]:t[1],n=this.dataFormat===\"channelsFirst\"?t[3]:t[2],o=this.dataFormat===\"channelsFirst\"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"max\")}};Gf.className=\"MaxPooling3D\";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,\"avg\")}};Wf.className=\"AveragePooling3D\";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Uf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Uf.className=\"GlobalAveragePooling1D\";Q.registerClass(Uf);var Hf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Hf.className=\"GlobalMaxPooling1D\";Q.registerClass(Hf);var Ob=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat===\"channelsLast\"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Ne(n,[1,2]):Ne(n,[2,3])})}};qf.className=\"GlobalAveragePooling2D\";Q.registerClass(qf);var Kf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat===\"channelsLast\"?Sr(n,[1,2]):Sr(n,[2,3])})}};Kf.className=\"GlobalMaxPooling2D\";Q.registerClass(Kf);var Pb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},jf=class extends Pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),nk((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};jf.className=\"TimeDistributed\";Q.registerClass(jf);function EJ(r){da(eR,\"BidirectionalMergeMode\",r)}var AJ=\"concat\",Xf=class extends Pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?AJ:t.mergeMode,EJ(this.mergeMode),t.weights)throw new _t(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t(\"Support for constants in Bidirectional layers is not implemented yet.\");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode===\"concat\"?a=_m([o,s]):this.mergeMode===\"sum\"?a=K(o,s):this.mergeMode===\"ave\"?a=$(.5,K(o,s)):this.mergeMode===\"mul\"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t(\"Deserialization of a Bidirectional layer with numConstants present is not supported yet.\");let o=e;return o.layer=n,new t(o)}};Xf.className=\"Bidirectional\";Q.registerClass(Xf);var Yf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!==\"float32\"&&(t=rn(t,\"float32\")),K($(t,this.scale),this.offset)))}};Yf.className=\"Rescaling\";Q.registerClass(Yf);var{resizeBilinear:DJ,cropAndResize:$J}=fn,Zf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N{let s=DJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Zf.className=\"CenterCrop\";Q.registerClass(Zf);function ZR(r,t,e,n){let o=St(r);if(o.dtype!==\"int32\"&&(o=rn(o,\"int32\")),t===\"int\")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t===\"oneHot\"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=[\"multiHot\",\"oneHot\"].includes(t),a=o,u;if(typeof n!=\"undefined\"&&t===\"count\"?u=mh(a,n,e,i):u=mh(a,[],e,i),t!==\"tfIdf\")return u;if(n)return $(u,n);throw new z(\"When outputMode is 'tfIdf', weights must be provided.\")}var Jf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode=\"multiHot\"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode===\"oneHot\"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!==\"int32\"&&(t=rn(t,\"int32\"));let n;if(typeof e.countWeights!=\"undefined\"){if(this.outputMode!==\"count\")throw new z(`countWeights is not used when outputMode !== count.\n Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ZR(t,this.outputMode,this.numTokens,n)})}};Jf.className=\"CategoryEncoding\";Q.registerClass(Jf);var FJ=[\"bilinear\",\"nearest\"],JR=new Set(FJ),Qf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(JR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation=\"bilinear\";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation===\"bilinear\")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation===\"nearest\")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...JR]} are supported`)})}};Qf.className=\"Resizing\";Q.registerClass(Qf);var Bh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Bh.className=\"RandomSeed\";var Vh=class extends Et{constructor(t){super(t),this.randomGenerator=new Bh(t.seed)}getConfig(){let 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o=v(\"start\",r,t,e),s=v(\"stop\",r,t,e),i=v(\"step\",r,t,e);return[n.range(o,s,i,v(\"dtype\",r,t,e))]}case\"TruncatedNormal\":{let o=v(\"shape\",r,t,e),s=v(\"mean\",r,t,e),i=v(\"stdDev\",r,t,e),a=v(\"seed\",r,t,e);return[n.truncatedNormal(o,s,i,v(\"dtype\",r,t,e),a)]}case\"Zeros\":return[n.zeros(v(\"shape\",r,t,e),v(\"dtype\",r,t,e))];case\"ZerosLike\":return[n.zerosLike(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ek(r,t,e){let n=v(\"boxes\",r,t,e),o=v(\"scores\",r,t,e),s=v(\"maxOutputSize\",r,t,e),i=v(\"iouThreshold\",r,t,e),a=v(\"scoreThreshold\",r,t,e),u=v(\"softNmsSigma\",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var SF=async(r,t,e,n,o=ae)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Ek(r,t,e),p=await 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v(\"x\",r,t,e).map(c=>n.tensor1d(c.shape));case\"Size\":return[n.scalar(v(\"x\",r,t,e).size,\"int32\")];case\"Rank\":return[n.scalar(v(\"x\",r,t,e).rank,\"int32\")];case\"NoOp\":return[n.scalar(1)];case\"Print\":let i=v(\"x\",r,t,e),a=v(\"data\",r,t,e),u=v(\"message\",r,t,e),l=v(\"summarize\",r,t,e);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),\"int32\")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v(\"keyDType\",r,t,e),i=v(\"valueDType\",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"values\",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let o=v(\"tableHandle\",r,t,e,n),s=v(\"keys\",r,t,e),i=v(\"defaultValue\",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let o=v(\"tableHandle\",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case\"ResizeBilinear\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case\"ResizeNearestNeighbor\":{let o=v(\"images\",r,t,e),s=v(\"size\",r,t,e),i=v(\"alignCorners\",r,t,e),a=v(\"halfPixelCenters\",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case\"CropAndResize\":{let o=v(\"image\",r,t,e),s=v(\"boxes\",r,t,e),i=v(\"boxInd\",r,t,e),a=v(\"cropSize\",r,t,e),u=v(\"method\",r,t,e),l=v(\"extrapolationValue\",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case\"ImageProjectiveTransformV3\":{let o=v(\"images\",r,t,e),s=v(\"transforms\",r,t,e),i=v(\"outputShape\",r,t,e),a=v(\"fillValue\",r,t,e),u=v(\"interpolation\",r,t,e),l=v(\"fillMode\",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case\"Equal\":return[n.equal(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"NotEqual\":return[n.notEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Greater\":return[n.greater(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"GreaterEqual\":return[n.greaterEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Less\":return[n.less(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LessEqual\":return[n.lessEqual(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalAnd\":return[n.logicalAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"LogicalNot\":return[n.logicalNot(v(\"a\",r,t,e))];case\"LogicalOr\":return[n.logicalOr(v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"Select\":case\"SelectV2\":return[n.where(v(\"condition\",r,t,e),v(\"a\",r,t,e),v(\"b\",r,t,e))];case\"BitwiseAnd\":return[n.bitwiseAnd(v(\"a\",r,t,e),v(\"b\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[n.matMul(v(\"a\",r,t,e),v(\"b\",r,t,e),v(\"transposeA\",r,t,e),v(\"transposeB\",r,t,e))];case\"Einsum\":return[n.einsum(v(\"equation\",r,t,e),...v(\"tensors\",r,t,e))];case\"Transpose\":return[n.transpose(v(\"x\",r,t,e),v(\"perm\",r,t,e))];case\"_FusedMatMul\":let[o,s]=v(\"fusedOps\",r,t,e),i=o===\"biasadd\",a=s===\"prelu\",u=v(\"numArgs\",r,t,e),l=v(\"leakyreluAlpha\",r,t,e);if(i){if(a&&u!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!a&&u!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,p]=v(\"args\",r,t,e);return[n.fused.matMul({a:v(\"a\",r,t,e),b:v(\"b\",r,t,e),transposeA:v(\"transposeA\",r,t,e),transposeB:v(\"transposeB\",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case\"MatrixBandPart\":return[n.linalg.bandPart(v(\"a\",r,t,e),v(\"numLower\",r,t,e),v(\"numUpper\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case\"EuclideanNorm\":return[n.euclideanNorm(v(\"x\",r,t,e),v(\"axis\",r,t,e),v(\"keepDims\",r,t,e))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"FusedBatchNormV3\":return[n.batchNorm(v(\"x\",r,t,e),v(\"mean\",r,t,e),v(\"variance\",r,t,e),v(\"offset\",r,t,e),v(\"scale\",r,t,e),v(\"epsilon\",r,t,e))];case\"LRN\":return[n.localResponseNormalization(v(\"x\",r,t,e),v(\"radius\",r,t,e),v(\"bias\",r,t,e),v(\"alpha\",r,t,e),v(\"beta\",r,t,e))];case\"Softmax\":return[n.softmax(v(\"x\",r,t,e))];case\"LogSoftmax\":return[n.logSoftmax(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v(\"paramsNestedSplits\",r,t,e),v(\"paramsDenseValues\",r,t,e),v(\"indices\",r,t,e),v(\"outputRaggedRank\",r,t,e));return o.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v(\"starts\",r,t,e),v(\"limits\",r,t,e),v(\"splits\",r,t,e));return[o,s]}case\"RaggedTensorToTensor\":return[n.raggedTensorToTensor(v(\"shape\",r,t,e),v(\"values\",r,t,e),v(\"defaultValue\",r,t,e),v(\"rowPartitionTensors\",r,t,e),v(\"rowPartitionTypes\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case\"Max\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.max(v(\"x\",r,t,e),a,u)]}case\"Mean\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.mean(v(\"x\",r,t,e),a,u)]}case\"Min\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.min(v(\"x\",r,t,e),a,u)]}case\"Sum\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.sum(v(\"x\",r,t,e),a,u)]}case\"All\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.all(v(\"x\",r,t,e),a,u)]}case\"Any\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.any(v(\"x\",r,t,e),a,u)]}case\"ArgMax\":{let a=v(\"axis\",r,t,e);return[n.argMax(v(\"x\",r,t,e),a)]}case\"ArgMin\":{let a=v(\"axis\",r,t,e);return[n.argMin(v(\"x\",r,t,e),a)]}case\"Prod\":{let a=v(\"axis\",r,t,e),u=v(\"keepDims\",r,t,e);return[n.prod(v(\"x\",r,t,e),a,u)]}case\"Cumprod\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumprod(v(\"x\",r,t,e),a,u,l)]}case\"Cumsum\":{let a=v(\"axis\",r,t,e),u=v(\"exclusive\",r,t,e),l=v(\"reverse\",r,t,e);return[n.cumsum(v(\"x\",r,t,e),a,u,l)]}case\"Bincount\":let o=v(\"x\",r,t,e),s=v(\"weights\",r,t,e),i=v(\"size\",r,t,e);return[n.bincount(o,s,i)];case\"DenseBincount\":{let a=v(\"x\",r,t,e),u=v(\"weights\",r,t,e),l=v(\"size\",r,t,e),c=v(\"binaryOutput\",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let o=v(\"n\",r,t,e),s=v(\"axis\",r,t,e),i=v(\"tensors\",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case\"Gather\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gather(o,n.cast(s,\"int32\"),0)]}case\"GatherV2\":{let o=v(\"axis\",r,t,e),s=v(\"batchDims\",r,t,e),i=v(\"x\",r,t,e),a=v(\"indices\",r,t,e);return[n.gather(i,n.cast(a,\"int32\"),o,s)]}case\"Reverse\":{let o=v(\"dims\",r,t,e),s=[];for(let a=0;a{let o=v(\"axis\",r,t,e),s=v(\"tensors\",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error(\"the input tensors shape does not match\");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case\"Unpack\":{let o=v(\"axis\",r,t,e),s=v(\"tensor\",r,t,e);return n.unstack(s,o)}case\"Tile\":{let o=v(\"reps\",r,t,e);return[n.tile(v(\"x\",r,t,e),o)]}case\"Split\":case\"SplitV\":{let o=v(\"axis\",r,t,e),s=v(\"numOrSizeSplits\",r,t,e),i=v(\"x\",r,t,e);return n.split(i,s,o)}case\"ScatterNd\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"shape\",r,t,e);return[n.scatterND(o,s,i)]}case\"GatherNd\":{let o=v(\"x\",r,t,e),s=v(\"indices\",r,t,e);return[n.gatherND(o,s)]}case\"SparseToDense\":{let o=v(\"sparseIndices\",r,t,e),s=v(\"outputShape\",r,t,e),i=v(\"sparseValues\",r,t,e),a=v(\"defaultValue\",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case\"TensorScatterUpdate\":{let o=v(\"indices\",r,t,e),s=v(\"values\",r,t,e),i=v(\"tensor\",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v(\"indices\",r,t,e),v(\"values\",r,t,e),v(\"denseShape\",r,t,e),v(\"defaultValue\",r,t,e));return[o,s,i,a]}case\"SparseReshape\":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v(\"inputIndices\",r,t,e),v(\"inputShape\",r,t,e),v(\"newShape\",r,t,e));return[o,s]}case\"SparseSegmentMean\":return[n.sparse.sparseSegmentMean(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];case\"SparseSegmentSum\":return[n.sparse.sparseSegmentSum(v(\"data\",r,t,e),v(\"indices\",r,t,e),v(\"segmentIds\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case\"FFT\":return[n.fft(v(\"x\",r,t,e))];case\"IFFT\":return[n.ifft(v(\"x\",r,t,e))];case\"RFFT\":return[n.rfft(v(\"x\",r,t,e))];case\"IRFFT\":return[n.irfft(v(\"x\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case\"StaticRegexReplace\":return[n.string.staticRegexReplace(v(\"input\",r,t,e),v(\"pattern\",r,t,e),v(\"rewrite\",r,t,e),v(\"replaceGlobal\",r,t,e))];case\"StringNGrams\":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v(\"data\",r,t,e),v(\"dataSplits\",r,t,e),v(\"separator\",r,t,e),v(\"nGramWidths\",r,t,e),v(\"leftPad\",r,t,e),v(\"rightPad\",r,t,e),v(\"padWidth\",r,t,e),v(\"preserveShortSequences\",r,t,e));return[o,s]}case\"StringSplit\":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v(\"input\",r,t,e),v(\"delimiter\",r,t,e),v(\"skipEmpty\",r,t,e));return[o,s,i]}case\"StringToHashBucketFast\":return[n.string.stringToHashBucketFast(v(\"input\",r,t,e),v(\"numBuckets\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case\"Cast\":return[n.cast(v(\"x\",r,t,e),v(\"dtype\",r,t,e))];case\"ExpandDims\":{let o=v(\"axis\",r,t,e);return[n.expandDims(v(\"x\",r,t,e),o)]}case\"Squeeze\":{let o=v(\"axis\",r,t,e);return[n.squeeze(v(\"x\",r,t,e),o)]}case\"Reshape\":return[n.reshape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"EnsureShape\":return[n.ensureShape(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"MirrorPad\":return[n.mirrorPad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"mode\",r,t,e))];case\"PadV2\":case\"Pad\":return[n.pad(v(\"x\",r,t,e),v(\"padding\",r,t,e),v(\"constantValue\",r,t,e))];case\"SpaceToBatchND\":{let o=v(\"blockShape\",r,t,e),s=v(\"paddings\",r,t,e);return[n.spaceToBatchND(v(\"x\",r,t,e),o,s)]}case\"BatchToSpaceND\":{let o=v(\"blockShape\",r,t,e),s=v(\"crops\",r,t,e);return[n.batchToSpaceND(v(\"x\",r,t,e),o,s)]}case\"DepthToSpace\":{let o=v(\"blockSize\",r,t,e),s=v(\"dataFormat\",r,t,e).toUpperCase();return[n.depthToSpace(v(\"x\",r,t,e),o,s)]}case\"BroadcastTo\":return[n.broadcastTo(v(\"x\",r,t,e),v(\"shape\",r,t,e))];case\"BroadcastArgs\":return[n.broadcastArgs(v(\"s0\",r,t,e),v(\"s1\",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case\"arithmetic\":return o(()=>fF(i,a,u));case\"basic_math\":return o(()=>dF(i,a,u));case\"control\":return wF(i,a,u);case\"convolution\":return o(()=>CF(i,a,u));case\"creation\":return o(()=>vF(i,a,u));case\"dynamic\":return SF(i,a,u);case\"evaluation\":return o(()=>NF(i,a,u));case\"image\":return o(()=>_F(i,a,u));case\"graph\":return o(()=>kF(i,a,u));case\"logical\":return o(()=>EF(i,a,u));case\"matrices\":return o(()=>AF(i,a,u));case\"normalization\":return o(()=>DF(i,a,u));case\"ragged\":return o(()=>$F(i,a,u));case\"reduction\":return o(()=>RF(i,a,u));case\"slice_join\":return o(()=>FF(i,a,u));case\"sparse\":return o(()=>OF(i,a,u));case\"spectral\":return o(()=>MF(i,a,u));case\"string\":return o(()=>PF(i,a,u));case\"transformation\":return o(()=>LF(i,a,u));case\"hash_table\":return TF(i,a,u,n);case\"custom\":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?\"\":`${e.frameName}-${e.iterationId}`).join(\"/\"):\"\"}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g==\"string\"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a==\"string\"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a==\"string\"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+\"--\"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I=\"\";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m=\"\";if(p.node.op===\"Enter\"&&v(\"isConstant\",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op===\"Merge\"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt=\"?tfjs-format=file\",gtt=\"model.json\",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version=\"n/a\",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error(\"Model artifacts missing streamWeights function\");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t==\"string\"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return 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er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return\"TODO: fill in upstream of chained summaries 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At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(t={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let e=new r(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(t,e={}){if(!L().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!t){if(t=document.createElement(\"video\"),!e.resizeWidth||!e.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: 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extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get(\"IS_BROWSER\"))this.decoder=new 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a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:\"cpu\",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:\"cpu\",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var CM={kernelName:ql,backendName:\"cpu\",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype===\"complex64\"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:\"cpu\",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],\"conv2d\");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat===\"channelsLast\",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:\"cpu\",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:\"cpu\",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],\"float32\"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,\"NHWC\",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:\"cpu\",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,\"NHWC\",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||\"2d\";if(c!==\"2d\")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype===\"float32\"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype===\"int32\"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var KM={kernelName:Qd,backendName:\"cpu\",kernelFunc:Uet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,\"sum\");let a;o.dtype===\"bool\"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:\"int32\"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims(\"sum\",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,\"int32\"),g=md(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var XM={kernelName:Fp,backendName:\"cpu\",kernelFunc:Het};function qet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],\"eluGrad\");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,\"float32\",s)}var YM={kernelName:La,backendName:\"cpu\",kernelFunc:qet};var Ket=S.ERF_P,jet=S.ERF_A1,Xet=S.ERF_A2,Yet=S.ERF_A3,Zet=S.ERF_A4,Jet=S.ERF_A5,Qet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ket*e);return t*(1-((((Jet*n+Zet)*n+Yet)*n+Xet)*n+jet)*n*Math.exp(-e*e))}),ZM={kernelName:us,backendName:\"cpu\",kernelFunc:Qet};function yd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var JM={kernelName:Mi,backendName:\"cpu\",kernelFunc:yd};var trt=Qt((r,t)=>r/t),tg=oe(as,trt),eg={kernelName:as,backendName:\"cpu\",kernelFunc:tg};function $w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType(\"float32\",c),m=y.getTypedArrayFromDType(\"float32\",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=yw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var sP={kernelName:Pi,backendName:\"cpu\",kernelFunc:urt};function crt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var iP={kernelName:Mp,backendName:\"cpu\",kernelFunc:crt};var prt=At(gs,r=>Number.isFinite(r)?1:0,\"bool\"),aP={kernelName:gs,backendName:\"cpu\",kernelFunc:prt};var mrt=At(xs,r=>Math.abs(r)===1/0?1:0,\"bool\"),lP={kernelName:xs,backendName:\"cpu\",kernelFunc:mrt};var frt=At(ys,r=>Number.isNaN(r)?1:0,\"bool\"),uP={kernelName:ys,backendName:\"cpu\",kernelFunc:frt};function drt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bw(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var cP={kernelName:Ha,backendName:\"cpu\",kernelFunc:drt};var hrt=At(Is,r=>Math.log1p(r)),pP={kernelName:Is,backendName:\"cpu\",kernelFunc:hrt};var grt=Qt((r,t)=>r&&t),xrt=oe(qa,grt,null,\"bool\"),mP={kernelName:qa,backendName:\"cpu\",kernelFunc:xrt};var yrt=At(Ka,r=>r?0:1,\"bool\"),fP={kernelName:Ka,backendName:\"cpu\",kernelFunc:yrt};var brt=Qt((r,t)=>r||t),wrt=oe(ja,brt,null,\"bool\"),dP={kernelName:ja,backendName:\"cpu\",kernelFunc:wrt};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,\"LRN\");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. 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kt=L();kt.registerFlag(\"HAS_WEBGL\",()=>kt.getNumber(\"WEBGL_VERSION\")>0);kt.registerFlag(\"WEBGL_VERSION\",()=>Vw(2)?2:Vw(1)?1:0);kt.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\",()=>!1);kt.registerFlag(\"WEBGL_BUFFER_SUPPORTED\",()=>kt.get(\"WEBGL_VERSION\")===2);kt.registerFlag(\"WEBGL_CPU_FORWARD\",()=>!0);kt.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\",()=>!1);kt.registerFlag(\"WEBGL_PACK\",()=>kt.getBool(\"HAS_WEBGL\"));kt.registerFlag(\"WEBGL_PACK_NORMALIZATION\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CLIP\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_REDUCE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_LAZILY_UNPACK\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_CONV_IM2COL\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_PACK_CONV2DTRANSPOSE\",()=>kt.getBool(\"WEBGL_PACK\"));kt.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\",()=>n1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\",()=>o1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",()=>{let r=kt.getNumber(\"WEBGL_VERSION\");return r===0?0:s1(r)});kt.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>kt.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!du.isMobile());kt.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>i1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>kt.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:kt.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));kt.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>a1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>l1(kt.getNumber(\"WEBGL_VERSION\")));kt.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>kt.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);kt.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});kt.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>du.isMobile()?1:-1,r=>{if(typeof r!=\"number\")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${r}.`);if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});kt.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);kt.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);kt.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);kt.registerFlag(\"WEBGL_EXP_CONV\",()=>!1);kt.registerFlag(\"SOFTWARE_WEBGL_ENABLED\",()=>kt.getBool(\"IS_TEST\"));kt.registerFlag(\"WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE\",()=>1/0);kt.registerFlag(\"WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE\",()=>!1);kt.registerFlag(\"WEBGL2_ISNAN_CUSTOM\",()=>!1);kt.registerFlag(\"ENGINE_COMPILE_ONLY\",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber(\"WEBGL_VERSION\")===2?(r=\"#version 300 es\",t=\"in\",e=\"out\",n=\"in\",o=\"texture\",s=\"outputColor\",i=\"out vec4 outputColor;\",a=L().getBool(\"WEBGL2_ISNAN_CUSTOM\")?`\n bool isnan_custom(float val) {\n uint floatToUint = floatBitsToUint(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan_custom(val.x),\n isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n }\n\n #define isnan(value) isnan_custom(value)\n `:\"\",u=\"\",l=`\n #define round(value) newRound(value)\n int newRound(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 newRound(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `):(r=\"\",t=\"attribute\",e=\"varying\",n=\"varying\",o=\"texture2D\",s=\"gl_FragColor\",i=\"\",a=`\n #define isnan(value) isnan_custom(value)\n bool isnan_custom(float val) {\n return (val > 0. || val < 1. || val == 0.) ? false : true;\n }\n bvec4 isnan_custom(vec4 val) {\n return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n }\n `,u=`\n uniform float INFINITY;\n\n bool isinf(float val) {\n return abs(val) == INFINITY;\n }\n bvec4 isinf(vec4 val) {\n return equal(abs(val), vec4(INFINITY));\n }\n `,l=`\n int round(float value) {\n return int(floor(value + 0.5));\n }\n\n ivec4 round(vec4 value) {\n return ivec4(floor(value + vec4(0.5)));\n }\n `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Si(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join(\"\")}function up(r,t,e=\"index\"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join(\"\")}function Qnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PL(r,t,e=\"index\"){let n=r.map((s,i)=>i),o=Qnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join(\"\")}function vd(r){let t=y.computeStrides(r).map(e=>e.toString());return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n }\n`}function Sd(){return`\n int getFlatIndex(ivec3 coords) {\n return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n }\n`}var Gw=`\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n`;var{getBroadcastDims:LL}=S;function zL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:\"\"};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Ww(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push(\"uniform int outShape;\");break;case 2:n.push(\"uniform ivec2 outShape;\"),n.push(\"uniform int outShapeStrides;\");break;case 3:n.push(\"uniform ivec3 outShape;\"),n.push(\"uniform ivec2 outShapeStrides;\");break;case 4:n.push(\"uniform ivec4 outShape;\"),n.push(\"uniform ivec3 outShapeStrides;\");break;default:break}n.push(\"uniform ivec2 outTexShape;\")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:\"\"};`)});let o=n.join(`\n`),s=r.map(f=>tot(f,t,e.packedInputs,e.enableShapeUniforms)).join(`\n`),i=t.texShape,a=Ue(),u=not(a),l,c,p=iot(a);return t.isPacked?(l=eot(t.logicalShape,i,e.enableShapeUniforms),c=sot(a)):(l=rot(t.logicalShape,i,e.enableShapeUniforms),c=oot(a)),e.packedInputs&&(p+=cot),[p,u,c,o,l,s,e.userCode].join(`\n`)}function kd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Cot(r,t);case 1:return Sot(r,t);case 2:return kot(r,t);case 3:return _ot(r,t);case 4:return Aot(r,t);case 5:return Dot(r);case 6:return $ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Iot(r);case 1:return vot(r,t);case 2:return Not(r,t);case 3:return Tot(r,t);default:return Eot(r,t)}}function tot(r,t,e=!1,n){let o=\"\";e?o+=BL(r,n):o+=kd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Rot(r,t):o+=Fot(r,t)),o}function eot(r,t,e){switch(r.length){case 0:return VL();case 1:return pot(r,t,e);case 2:return bot(r,t,e);case 3:return fot(r,t,e);default:return hot(r,t,e)}}function rot(r,t,e){switch(r.length){case 0:return VL();case 1:return mot(r,t,e);case 2:return wot(r,t,e);case 3:return dot(r,t,e);case 4:return got(r,t,e);case 5:return xot(r,t);case 6:return yot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function not(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function oot(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function sot(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function iot(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${aot}\n ${lot}\n ${uot}\n `}var aot=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n int texelIndex = index / 2;\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,lot=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n int texNumC, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,uot=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,cot=`\n float getChannel(vec4 frag, vec2 innerDims) {\n vec2 modCoord = mod(innerDims, 2.);\n return modCoord.x == 0. ?\n (modCoord.y == 0. ? frag.r : frag.g) :\n (modCoord.y == 0. ? frag.b : frag.a);\n }\n float getChannel(vec4 frag, int dim) {\n float modCoord = mod(float(dim), 2.);\n return modCoord == 0. ? frag.r : frag.g;\n }\n`;function VL(){return`\n int getOutputCoords() {\n return 0;\n }\n `}function pot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:n[1]===1?e?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n }\n `:`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}function mot(r,t,e){return t[0]===1?e?`\n int getOutputCoords() {\n return int(resultUV.x * float(outTexShape[1]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.x * ${t[1]}.0);\n }\n `:t[1]===1?e?`\n int getOutputCoords() {\n return int(resultUV.y * float(outTexShape[0]));\n }\n `:`\n int getOutputCoords() {\n return int(resultUV.y * ${t[0]}.0);\n }\n `:e?`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n return resTexRC.x * outTexShape[1] + resTexRC.y;\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n return resTexRC.x * ${t[1]} + resTexRC.y;\n }\n `}function fot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec3(b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${s};\n index -= b * ${s};\n\n int r = 2 * (index / ${o});\n int c = imod(index, ${o}) * 2;\n\n return ivec3(b, r, c);\n }\n `}function dot(r,t,e){if(e)return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(outTexShape[0], outTexShape[1]));\n int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n ${up([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n`;let n=Si([\"r\",\"c\",\"d\"],r);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = resTexRC.x * ${t[1]} + resTexRC.y;\n ${n}\n return ivec3(r, c, d);\n }\n `}function hot(r,t,e){if(e)return`\n ivec4 getOutputCoords() {\n ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(packedTexShape[0], packedTexShape[1]));\n int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n int texelsInBatchN = texelsInBatch * outShape[1];\n\n int b2 = index / texelsInBatchN;\n index -= b2 * texelsInBatchN;\n\n int b = index / texelsInBatch;\n index -= b * texelsInBatch;\n\n int r = 2 * (index / texelsInLogicalRow);\n int c = imod(index, texelsInLogicalRow) * 2;\n\n return ivec4(b2, b, r, c);\n }\n `;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a=\"\",u=\"b, r, c\";for(let l=2;l=1?c=\"coords = 0;\":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`\n`);let m=\"\";i<2&&s>0?m=\"coords\":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(\", \");let f=\"return outputValue;\",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`\n return vec4(outputValue.xy, outputValue.xy);\n `;else if(h&&!x)i===1?f=`\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n `:f=`\n return vec4(outputValue.x);\n `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f=\"return vec4(outputValue.x);\":a.indexOf(b)>-1?f=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":a.indexOf(w)>-1&&(f=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n vec4 ${o}() {\n ${u} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${n}(${m});\n ${f}\n }\n `}function Fot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o=\"get\"+n+\"AtOutCoords\",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`\n float ${o}() {\n return sampleTexture(${e}, resultUV);\n }\n `;let l=zt(u),c=LL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?m=\"\":u<2&&c.length>=1?m=\"coords = 0;\":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`\n`);let d=\"\";return u<2&&a>0?d=\"coords\":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(\", \"),`\n float ${o}() {\n ${l} coords = getOutputCoords();\n ${m}\n return get${n}(${d});\n }\n `}function zt(r){if(r<=1)return\"int\";if(r===2)return\"ivec2\";if(r===3)return\"ivec3\";if(r===4)return\"ivec4\";if(r===5)return\"ivec5\";if(r===6)return\"ivec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Ww(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.lengthr[e]).join(\", \")}function WL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=zL(o,i,t),u=HT(r.gl,a),l=r.createProgram(u);return L().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},u1(r,t,l)))}function u1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,\"NAN\",!1),L().getNumber(\"WEBGL_VERSION\")===1&&(u=r.getUniformLocation(e,\"INFINITY\",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,\"outShape\",c),a=r.getUniformLocation(e,\"outShapeStrides\",c),i=r.getUniformLocation(e,\"outTexShape\",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function GL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function UL(r,t,e,n,o){t.program.enableShapeUniforms||(GL(t.inShapeInfos,e),GL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Ww(r.packedInputs,i.shape,u),m=\"\",f=\"\",d=\"\";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+=\"_\"+n+\"_\"+o+`${L().getNumber(\"WEBGL_VERSION\")}`,s}function he(r){return L().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Uw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${e.output} = result;\n }\n `}};var qw=class{constructor(t){this.variableNames=[\"A\"],this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n float x = getAAtOutCoords();\n ${e.output} = encode_float(x);\n }\n `}};var Kw=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`\n ${Gw}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${e.output} = encode_float(x);\n }\n `}};var Pot={R:0,G:1,B:2,A:3},ug=class{constructor(t,e=!1,n=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let o=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let s=\"result\";e&&(s=\"floor(result * 255. + 0.5)\");let i=\"\";for(let a=0;ay1,createBufferFromOutputTexture:()=>I1,createFloat16MatrixTexture:()=>d1,createFloat16PackedMatrixTexture:()=>x1,createFloat32MatrixTexture:()=>f1,createIndexBuffer:()=>m1,createPackedMatrixTexture:()=>g1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>p1,createVertexShader:()=>c1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>v1,downloadFloat32MatrixFromBuffer:()=>C1,downloadMatrixFromPackedOutputTexture:()=>N1,downloadPackedMatrixFromBuffer:()=>S1,getInternalFormatForFloat16MatrixTexture:()=>Yw,getInternalFormatForFloat16PackedMatrixTexture:()=>Qw,getInternalFormatForFloat32MatrixTexture:()=>Xw,getInternalFormatForPackedMatrixTexture:()=>Jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Zw,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>w1});function c1(r){let t=Ue(),e=`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function C1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function v1(r,t,e,n){let[o,s]=lp(t,e),i=4,a=new Uint8Array(DL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function S1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array($L(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function N1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var pp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber(\"WEBGL_VERSION\");if(t!=null?(this.gl=t,BT(e,t)):this.gl=qn(e),t=this.gl,L().getNumber(\"WEBGL_VERSION\")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n=\"WEBGL_color_buffer_float\",o=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),L().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",i=\"OES_texture_half_float\";if(this.textureFloatExtension=bd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=bd(this.gl,i);else if(L().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Kn(this.gl,o))this.colorBufferHalfFloatExtension=bd(this.gl,o);else if(L().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=p1(this.gl),this.indexBuffer=m1(this.gl),this.framebuffer=JT(this.gl),this.textureConfig=ig(this.gl,this.textureHalfFloatExtension)}get debug(){return L().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),w1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),b1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),x1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),g1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Bw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return S1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return C1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=I1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool(\"WEBGL_FENCE_API_ENABLED\")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>N1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=c1(e));let n=qT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),KT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&ag(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),y1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ag(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?QT(this.gl,t,e):t1(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),e1(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&ag(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bd(this.gl,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(t,L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Lot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;\"setTimeoutCustom\"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),lg(this.gl,t,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(lg(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):Bw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;lg(o,t,this.framebuffer),this.debug&&wd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function Lot(r){let t=0;for(;t`${r}.${e}`)}function rr(r,t){return t===1?[r]:T1(r,t)}function Pz(r,t){if(r===1)return\"rc\";let e=\"\";for(let n=0;n ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let e=\"\";for(let n=this.rank-2;n= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n= ${n};\n bool rEdge = rp1 >= ${o};\n `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),\n cEdge ? 0. : getA(${e[1]}),\n rEdge ? 0. : getA(${e[2]}),\n rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ad=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n=\"\";for(let o=0;o<4;o++){let s=\"thisRC = rc;\";o%2===1&&(s+=\"thisRC.z += 1;\"),o>1&&(s+=\"thisRC.y += 1;\"),n+=`\n ${s}\n ${o>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${o}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${o>0?\"}\":\"\"}\n `}this.userCode=`\n ${zot(e,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Sd():vd(t)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":t[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":t[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}};function zot(r,t){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${t?PL([\"r\",\"c\",\"d\"],\"inputShape\"):Si([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var oI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=zz(e,n),s=Bz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Lz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=zz(n,o),i=Bz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Lz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Bot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Lz(r,t,e,n,o){let s=Vot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Bot(e,s);return i*a}function Vot(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Jw(t);case Lr.PACKED_2X2_FLOAT16:return Qw(t);case Lr.UNPACKED_FLOAT32:return Xw(t);case Lr.UNPACKED_FLOAT16:return Yw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Zw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Got(r){return L().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function zz(r,t){if(r===Jr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return Got(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Bz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${e}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},xr=\"if (isnan(x)) return x;\",Vz=\"return x;\",_1=\"return abs(x);\";var Gz=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Wz=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,Uz=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ia=\"return x;\",Hz=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var Kz=\"return x;\",jz=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,Xz=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Yz=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Zz=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Dn=class{constructor(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${e}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var sI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr(\"rc\",e),o=zt(e),s=Pz(e,n),i=n.slice(-2),a=e<=1?\"rc\":`vec2(${i.join(\",\")})`;this.userCode=`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${a}));\n }\n `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber(\"WEBGL_VERSION\"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber(\"WEBGL_VERSION\"));e=new pp(n),this.binaryCache=Kot(L().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||L().getBool(\"DEBUG\"))&&this.checkNumericalProblems(t),n===\"complex64\"&&t!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o===\"string\")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o===\"complex64\"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool(\"DEBUG\")&&!L().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&L().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let l=null,c;if(i!==\"complex64\"&&L().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let p;if(i===\"complex64\"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype===\"string\")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(\", \")}else a.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool(\"WEBGL_CPU_FORWARD\")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get(\"ENGINE_COMPILE_ONLY\")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let t=L().getBool(\"DEBUG\");L().set(\"DEBUG\",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set(\"DEBUG\",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||\"RGBA\";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t===\"float32\"||t===\"complex64\")return r;if(t===\"int32\"||t===\"bool\"){let e=t===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var $n=class{constructor(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${t}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xn=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jn=class{constructor(t,e,n,o=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=he(s);let i=\"\";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(i=`\n ${zt(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?i+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:i+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let u=rr(\"coords\",s);this.enableShapeUniforms?i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:i+=`\n bool nextRowOutOfBounds =\n (${u[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${u[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${t}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${i}\n\n setOutput(result);\n }\n `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t3={kernelName:go,backendName:\"webgl\",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,\"complex64\"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var e3={kernelName:Ap,backendName:\"webgl\",kernelFunc:Rn};var E1=\"return (a < 0.) ? b * a : a;\",A1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),a=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(A1,o.shape,i.shape):new $n(E1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],\"float32\");return e.disposeIntermediateTensorInfo(i),u}var r3={kernelName:bs,backendName:\"webgl\",kernelFunc:Jot};var D1=\"return (a < 0.) ? b * a : a;\",$1=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function Qot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn($1,n.shape,o.shape):new $n(D1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],\"float32\")}var n3={kernelName:Os,backendName:\"webgl\",kernelFunc:Qot};var Po=\"if (isnan(x)) return x;\";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype===\"complex64\"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(d):d,x=u.dtype===\"string\"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r===\"linear\")return t?Kz:Vz;if(r===\"relu\")return t?Xz:Wz;if(r===\"elu\")return t?jz:Gz;if(r===\"relu6\")return t?Yz:Uz;if(r===\"prelu\")return t?$1:D1;if(r===\"leakyrelu\")return t?A1:E1;if(r===\"sigmoid\")return t?Zz:Hz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Rd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?\"i * 2, rc.y\":\"rc.y, i * 2\",f=s?\"rc.z, i * 2\":\"i * 2, rc.z\",d=o?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";a&&(u?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${a}\n }`:l?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${a}\n }`:g=`vec4 activation(vec4 x) {\n ${a}\n }`,x=\"result = activation(result);\");let b=i?\"result += getBiasAtOutCoords();\":\"\";i&&this.variableNames.push(\"bias\"),u&&this.variableNames.push(\"preluActivationWeights\"),l&&this.variableNames.push(\"leakyreluAlpha\");let w=\"rc.x\",I=\"rc.x\";t[0]`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?i3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var a3={kernelName:Gi,backendName:\"webgl\",kernelFunc:rt};var fg=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l=\"sumValue += dot(values, ones);\";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c=\"\";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${a}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${a};\n if (${u===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${u===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${u===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}};var aI=class{constructor(t,e){this.variableNames=[\"x\"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=\"0.0\",u=\"\";e===\"prod\"?a=\"1.0\":e===\"min\"?(a=\"1.0 / 1e-20\",u=\"min\"):e===\"max\"&&(a=\"-1.0 / 1e-20\",u=\"max\");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), 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);\n\n ${m}\n } else if (${p===2}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${p===3}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function est(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=est(r.shape),s=r;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],n=new Array(t);for(let o=0;o6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=T1(\"rc\",this.rank),i=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u===\"leakyrelu\",H=u!=null?Ml(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>F1&&j===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=mg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Rd(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var p3={kernelName:Xi,backendName:\"webgl\",kernelFunc:nst};var m3=\"return abs(x);\";function ost(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!==\"complex64\"){let s=e.texData.get(n.dataId),i=eI(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?o=new Dn(n.shape,m3):o=new zr(n.shape,m3),e.runWebGLProgram(o,[n],n.dtype)}var f3={kernelName:Ai,backendName:\"webgl\",kernelFunc:ost};var sst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n`,ist=It({opSnippet:sst}),d3={kernelName:Go,backendName:\"webgl\",kernelFunc:ist};var ast=xr+`\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,lst=It({opSnippet:ast}),h3={kernelName:Wo,backendName:\"webgl\",kernelFunc:lst};var g3=\"return a + b;\",ust=ce({opSnippet:g3,packedOpSnippet:g3,supportsComplex:!0,cpuKernelImpl:qL}),x3={kernelName:no,backendName:\"webgl\",kernelFunc:ust};var cI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n float result = ${o};\n setOutput(result);\n }\n `}};var pI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${n.join(`\n `)}\n\n vec4 result = ${o};\n setOutput(result);\n }\n `}};function mI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(n.length/2),l=mI({inputs:n.slice(0,u),backend:e}),c=mI({inputs:n.slice(u),backend:e});return mI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool(\"WEBGL_PACK\")?new pI(n[0].shape,s):new cI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var y3={kernelName:Uo,backendName:\"webgl\",kernelFunc:mI};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"all\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"all\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var b3={kernelName:Ea,backendName:\"webgl\",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims(\"any\",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,\"any\",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var w3={kernelName:Aa,backendName:\"webgl\",kernelFunc:pst};var fI=class{constructor(t,e,n){this.variableNames=[\"A\"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[s,i];let a=e===\"max\"?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${o}; i++) {\n int inIdx = ${u};\n float candidate = getA(batch, inIdx);\n if (candidate ${a} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var dI=class{constructor(t,e,n,o){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push(\"bestIndicesA\");let a=this.outputShape,u=a.length,l=zt(u),c=rr(\"coords\",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`\n ${D} sourceLocR = ${D}(${c.join()}, 0);\n ++${c[u-1]};\n ${D} sourceLocG = ${D}(${c.join()}, 0);\n ++${c[u-2]};\n ${D} sourceLocA = ${D}(${c.join()}, 0);\n --${c[u-1]};\n ${D} sourceLocB = ${D}(${c.join()}, 0);\n --${c[u-2]};`}else m=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;let f=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,m),d=\".\"+f[m-1],h=f.map(D=>\"int \"+D),g=rr(\"sourceLocR\",m-1).concat(\"inIdx.r\"),x=rr(\"sourceLocG\",m-1).concat(\"inIdx.g\"),b=rr(\"sourceLocB\",m-1).concat(\"inIdx.b\"),w=rr(\"sourceLocA\",m-1).concat(\"inIdx.a\"),I=n===\"max\"?\"greaterThan\":\"lessThan\",N=o?\"\":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${x.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${w.join()})));`,E=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${x.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?\"\":`\n float getBestIndicesAChannel(${h.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${h.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${A}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${e};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${E};\n\n for (int i = 0; i < ${e}; i++) {\n inIdx = srcIdx;\n ${N}\n vec4 candidate = ${E};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}};function I3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new fI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=I3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function C3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new dI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,\"int32\");if(l.shape.length===t.shape.length){let c=C3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function hI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=I3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return C3(r,t,n)}function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"max\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var v3={kernelName:Di,backendName:\"webgl\",kernelFunc:mst};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=hI(e,u,i[0],\"min\");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var S3={kernelName:$i,backendName:\"webgl\",kernelFunc:fst};var dst=xr+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,hst=It({opSnippet:dst}),N3={kernelName:Ho,backendName:\"webgl\",kernelFunc:hst};var gst=xr+\"return log(x + sqrt(x * x + 1.0));\",xst=It({opSnippet:gst}),k3={kernelName:qo,backendName:\"webgl\",kernelFunc:xst};var yst=xr+`\n return atan(x);\n`,bst=It({opSnippet:yst}),T3={kernelName:Ko,backendName:\"webgl\",kernelFunc:bst};var wst=$d+`\n return atan(a, b);\n`,Ist=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xn+`\n return result;\n`,Cst=ce({opSnippet:wst,packedOpSnippet:Ist}),_3={kernelName:Xo,backendName:\"webgl\",kernelFunc:Cst};var vst=xr+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sst=It({opSnippet:vst}),E3={kernelName:jo,backendName:\"webgl\",kernelFunc:Sst};var Ni=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e===\"avg\",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),n){let D=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${D} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let w=\"max\",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(I=\"avgValue / max(count, 1.0)\");let N=Math.floor(i/4)*4,E=i%4,A=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${w}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${u});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${p};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${N}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${A}\n }\n\n int xC = xCCorner + ${N};\n if (${E===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${A}\n } else if (${E===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${A}\n }\n }\n setOutput(${I});\n }\n `}},qu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=[\"x\"],e===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e===\"avg\",I=\"0.0\";if(w||(I=\"-1.0 / 1e-20\"),n){let M=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${M} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let N=\"max\",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e===\"avg\"&&(E=\"avgValue / max(count, 1.0)\");let A=Math.floor(i/4)*4,D=i%4,F=`\n if (${w}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${N}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${a}, ${u}, ${l});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${I};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${t.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${I});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${p}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${A}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${F}\n }\n\n int xC = xCCorner + ${A};\n if (${D===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${F}\n } else if (${D===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${F}\n }\n }\n }\n setOutput(${E});\n }\n `}};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,\"avgPool\");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"avg\",!1);return e.runWebGLProgram(p,[o],\"float32\")}var A3={kernelName:Yo,backendName:\"webgl\",kernelFunc:Nst};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,\"avg\",!1);return e.runWebGLProgram(m,[o],\"float32\")}var D3={kernelName:Ri,backendName:\"webgl\",kernelFunc:kst};var gI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${p});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${u};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${a}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},xI=class{constructor(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${u}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Tst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new xI(m);return e.runWebGLProgram(f,[o],i.dtype)}var $3={kernelName:Hl,backendName:\"webgl\",kernelFunc:Tst};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;vi([o,s],\"avgPoolGrad\");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new gI(c);return e.runWebGLProgram(p,[o],i.dtype)}var R3={kernelName:Ul,backendName:\"webgl\",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var F3={kernelName:Zo,backendName:\"webgl\",kernelFunc:Est};var yI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"0.0\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"1.0\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${a};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${i}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var bI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a=\"vec4(0.0)\";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push(\"offset\"),a=\"getOffsetAtOutCoords()\");let u=\"vec4(1.0)\";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push(\"scale\"),u=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=`\n void main() {\n vec4 offset = ${a};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${i}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var Ast=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(i==null||o.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(a==null||o.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new bI(n.shape,o.shape,s.shape,c,p,u):new yI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},O3={kernelName:ds,backendName:\"webgl\",kernelFunc:Ast};var wI=class{constructor(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let n=Dst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${O1[a]} = start[${a}] + coords.${O1[a]};`);o=`\n ${e} sourceLoc;\n ${e} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${o}\n setOutput(getSource(${n}));\n }\n `}},O1=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Dst(r){if(r===1)return\"sourceLoc\";if(r<=6)return O1.slice(0,r).map(t=>\"sourceLoc.\"+t).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var II=class{constructor(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let e=zt(this.rank),n=rr(\"coords\",this.rank),o=rr(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`\n result.x = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.y = ${i};\n --${o[this.rank-1]};\n }\n `,u=this.rank===1?\"\":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${t[this.rank-2]}) {\n ++${o[this.rank-2]};\n result.z = ${i};\n if (++${n[this.rank-1]} < ${t[this.rank-1]}) {\n ++${o[this.rank-1]};\n result.w = ${i};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`\n`);this.userCode=`\n void main() {\n ${e} coords = getOutputCoords();\n ${e} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${a}\n ${u}\n setOutput(result);\n }\n `}};function $st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ki(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype===\"string\"){let p=e.texData.get(o.dataId),m=Sz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new II(u):new wI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),$st(o,a,u,e)}var M3={kernelName:Ui,backendName:\"webgl\",kernelFunc:ki};var Rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ki({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},P3={kernelName:Fi,backendName:\"webgl\",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=tI(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var L3={kernelName:Da,backendName:\"webgl\",kernelFunc:Fst};var Ost=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Mst=`\n return float(int(a.r) & int(b.r));\n`;function Pst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),i=L().getNumber(\"WEBGL_VERSION\");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=jL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Ost,n.shape,o.shape,!1):a=new $n(Mst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var z3={kernelName:$a,backendName:\"webgl\",kernelFunc:Pst};function Lst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],\"int32\",Int32Array.from(a))}var B3={kernelName:ql,backendName:\"webgl\",kernelFunc:Lst};var zst=\"return float(a != b);\",M1=ce({opSnippet:zst,cpuKernelImpl:hz,dtype:\"bool\"}),V3={kernelName:Za,backendName:\"webgl\",kernelFunc:M1};function 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i=e.texData.get(o.dataId).values,[a,u,l]=XL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s===\"int32\")return W3(o,e);if(s===\"bool\"){let i=e.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),u=M1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var U3={kernelName:fo,backendName:\"webgl\",kernelFunc:P1};var H3=\"return ceil(x);\",Vst=It({opSnippet:H3,packedOpSnippet:H3,cpuKernelImpl:YL}),q3={kernelName:Jo,backendName:\"webgl\",kernelFunc:Vst};var CI=class{constructor(t){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var vI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=t,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool(\"WEBGL_PACK_CLIP\")?a=new vI(o.shape):a=new CI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var K3={kernelName:ho,backendName:\"webgl\",kernelFunc:Gst};var SI=class{constructor(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n `}};function j3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Wst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new SI(n.shape),i=[j3(n,o.complexTensorInfos.real),j3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var X3={kernelName:Kl,backendName:\"webgl\",kernelFunc:Wst};var NI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h= ${u[h-1]}) {\n return getChannel(\n getT${h}(${kI(a,l,g)}),\n vec2(${kI(c,l,g)}));\n }`}let f=u.length,d=u[u.length-1];m+=`\n return getChannel(\n getT${f}(${kI(a,l,d)}),\n vec2(${kI(c,l,d)}));`,this.userCode=`\n float getValue(${a.map(h=>\"int \"+h)}) {\n ${m}\n }\n\n void main() {\n ${s} coords = getOutputCoords();\n vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n ${i[o-1]} = ${i[o-1]} + 1;\n if (${i[o-1]} < ${n[o-1]}) {\n result.g = getValue(${i});\n }\n\n ${i[o-2]} = ${i[o-2]} + 1;\n if (${i[o-2]} < ${n[o-2]}) {\n result.a = getValue(${i});\n }\n\n ${i[o-1]} = ${i[o-1]} - 1;\n if (${i[o-2]} < ${n[o-2]} &&\n ${i[o-1]} < ${n[o-1]}) {\n result.b = getValue(${i});\n }\n setOutput(result);\n }\n `}};function kI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var Y3={kernelName:Pp,backendName:\"webgl\",kernelFunc:hp};function Fd(r,t,e){let n=r[0].dtype;if(n===\"complex64\"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Fd(f,t,e),g=Fd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n===\"string\"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=ZL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>a){let f=[];for(let h=0;hd.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Ust(s,t,e),c=new NI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Ust(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function L1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Fd(u,s,e)}var Z3={kernelName:Oi,backendName:\"webgl\",kernelFunc:L1};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,w=g?3:1,I=\"\",N=\"\";n&&(o?I=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?I=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:I=`\n float activation(float x) {\n ${n}\n }\n `,N=\"result = activation(result);\");let E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${I}\n\n const ivec2 strides = ivec2(${u}, ${l});\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${w}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${p};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${E}\n ${N}\n setOutput(result);\n }\n `}},_I=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${i}, ${a});\n const ivec3 pads = ivec3(${e}, ${n}, ${o});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${p}; wF++) {\n int xF = xFCorner + wF * ${u};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*u};\n `,a===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,u===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,u>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${a}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+p}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+p}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${l}\n\n ${o.output} = result;\n }\n `}};function AI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function DI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat===\"channelsLast\",d=!1,h=!1,g,x=[];if(s!=null){let I=AI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=AI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>F1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function $I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f===\"channelsLast\",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=AI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=AI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new EI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],\"float32\",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a===\"leakyrelu\",W=a?Ml(a,!0):null,q=new Rd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));H.push(Z),I.push(Z)}let j=n.runWebGLProgram(q,H,\"float32\"),Y=rt({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});I.push(j);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))f=DI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let h=new Md(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],\"float32\",g)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))f=$I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Od(m);f=e.runWebGLProgram(h,[o,s],\"float32\")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var J3={kernelName:Qo,backendName:\"webgl\",kernelFunc:Hst};var RI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n ${i?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},FI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat===\"channelsLast\",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${p}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n\n if (${i}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},OI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yF = 0; yF < ${t.outDepth}; yF++) {\n int xF = wF + yF * ${e} - ${s};\n\n if (xF < 0 || xF >= ${t.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${i};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${a};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},MI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${u}, ${l}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${e}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${e} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${i}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${t.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new RI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}var Q3={kernelName:Dp,backendName:\"webgl\",kernelFunc:qst};var PI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n int wCPerm = ${n} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&p===\"channelsLast\"){let f=[[m.strideHeight,m.strideWidth]],d=new PI(m);return e.runWebGLProgram(d,[o,s],\"float32\",f)}else{let f=new FI(m);return e.runWebGLProgram(f,[o,s],\"float32\")}}var tB={kernelName:ts,backendName:\"webgl\",kernelFunc:Kst};function jst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new _I(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var eB={kernelName:es,backendName:\"webgl\",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new OI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var rB={kernelName:Ra,backendName:\"webgl\",kernelFunc:Xst};function Yst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new MI(l);return e.runWebGLProgram(c,[o,s],\"float32\")}var nB={kernelName:Fa,backendName:\"webgl\",kernelFunc:Yst};var Zst=Po+`\n return cos(x);\n`,Jst=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Qst=It({opSnippet:Zst,packedOpSnippet:Jst}),oB={kernelName:rs,backendName:\"webgl\",kernelFunc:Qst};var tit=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,eit=It({opSnippet:tit}),sB={kernelName:ns,backendName:\"webgl\",kernelFunc:eit};var LI=class{constructor(t,e,n,o,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o===\"bilinear\"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,\"(y2-y1) * height_ratio\",`y1*${d} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${w});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${i}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${I};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${N};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new LI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],\"float32\")},iB={kernelName:Ma,backendName:\"webgl\",kernelFunc:rit};var gp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(gp||(gp={}));var dg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,i=this.op===gp.Prod?\"1.0\":\"0.0\",a=n?i:`getX(${aB(s,\"coords\",this.op)})`,u=this.outputShape[this.outputShape.length-1],l=\"\",c=\"\";n?(l=o?`end != ${u-1}`:\"end != 0\",c=o?\"end + 1\":\"end - 1\"):(l=o?`end + pow2 < ${u}`:\"end >= pow2\",c=o?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${zt(s)} coords = getOutputCoords();\n int end = ${lB(s,\"coords\",this.op)};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${l}) {\n int idx = ${c};\n ${lB(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${aB(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function aB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function lB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function zI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new dg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new dg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function nit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Prod,o,e,s,i,a)}var uB={kernelName:Oa,backendName:\"webgl\",kernelFunc:nit};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Sum,o,e,s,i,a)}var cB={kernelName:os,backendName:\"webgl\",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=tI(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var pB={kernelName:jl,backendName:\"webgl\",kernelFunc:sit};var BI=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${e};\n int offset_h = imod(h, ${e});\n int in_w = w / ${e};\n int offset_w = imod(w, ${e});\n int offset_d = (offset_h * ${e} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function iit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=new BI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var mB={kernelName:Pa,backendName:\"webgl\",kernelFunc:iit};var Pd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l=\"\",c=\"\";n&&(o?l=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?l=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:l=`\n float activation(float x) {\n ${n}\n }\n `,c=\"result = activation(result);\");let p=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${l}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${u};\n int q = d2 - d1 * ${u};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${i}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${a}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${p}\n ${c}\n setOutput(result);\n }\n `}};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`\n xC = xCCorner + ${b*l};\n `,u===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,l===1&&b>0?f+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:f+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):f+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,l>1?f+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:f+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):w===1?f+=`\n xC${b+1} = xTexelC${b};\n `:f+=`\n xCOffset = xC + ${w};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(f+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Ld(p):m=new Pd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],\"float32\",f)}var fB={kernelName:ss,backendName:\"webgl\",kernelFunc:ait};var VI=class{constructor(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${i} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${t.batchSize}; b++) {\n for (int yR = 0; yR < ${t.outHeight}; yR++) {\n int xR = wR + yR * ${e} - ${o};\n\n if (xR < 0 || xR >= ${t.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${t.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${t.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},GI=class{constructor(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${a});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${e}; wR++) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${e} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${u}; dm++) {\n int d2 = d1 * ${u} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new VI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var dB={kernelName:$p,backendName:\"webgl\",kernelFunc:lit};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new GI(p);return e.runWebGLProgram(m,[o,s],\"float32\")}var hB={kernelName:Rp,backendName:\"webgl\",kernelFunc:uit};var WI=class{constructor(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function cit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new WI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var gB={kernelName:Xl,backendName:\"webgl\",kernelFunc:cit};var UI=class{constructor(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${i});\n const ivec2 pads = ivec2(${p}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${a}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${e}) {\n for (int w = 0; w < ${u}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,\"NHWC\",u),c,p=new UI(l);c=e.runWebGLProgram(p,[o,s],\"float32\");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var xB={kernelName:is,backendName:\"webgl\",kernelFunc:pit};function mit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var yB={kernelName:Fp,backendName:\"webgl\",kernelFunc:mit};var fit=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",dit=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,hit=It({opSnippet:fit,packedOpSnippet:dit}),bB={kernelName:ls,backendName:\"webgl\",kernelFunc:hit};var git=\"return (b >= 0.0) ? a : a * (b + 1.0);\",xit=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,yit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jn(xit,n.shape,o.shape):new $n(git,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},wB={kernelName:La,backendName:\"webgl\",kernelFunc:yit};var bit=`\n return vec4(equal(a, b));\n`,wit=\"return float(a == b);\",Iit=ce({opSnippet:wit,packedOpSnippet:bit,dtype:\"bool\",cpuKernelImpl:JL}),IB={kernelName:za,backendName:\"webgl\",kernelFunc:Iit};var Cit=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${S.ERF_P};\n float a1 = ${S.ERF_A1};\n float a2 = ${S.ERF_A2};\n float a3 = ${S.ERF_A3};\n float a4 = ${S.ERF_A4};\n float a5 = ${S.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,vit=It({opSnippet:Cit}),CB={kernelName:us,backendName:\"webgl\",kernelFunc:vit};var Sit=Po+`\n return exp(x);\n`,Nit=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,z1=It({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:QL,dtype:\"float32\"}),vB={kernelName:cs,backendName:\"webgl\",kernelFunc:z1};function HI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var SB={kernelName:Mi,backendName:\"webgl\",kernelFunc:HI};var NB=\"return exp(x) - 1.0;\",kit=It({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:tz}),kB={kernelName:ps,backendName:\"webgl\",kernelFunc:kit};var hg=class{constructor(t,e,n){this.variableNames=[\"real\",\"imag\"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:\"1.0\",a;if(t===\"real\")a=\"return real * expR - imag * expI;\";else if(t===\"imag\")a=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${t}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${a}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${o});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${o}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${i};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function qI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new hg(\"real\",u,t),c=new hg(\"imag\",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,\"float32\"),f=e.runWebGLProgram(c,p,\"float32\"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Tit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!1,e)}var TB={kernelName:Op,backendName:\"webgl\",kernelFunc:Tit};var KI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s===\"string\"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new KI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var _B={kernelName:Jl,backendName:\"webgl\",kernelFunc:Ll};var jI=class{constructor(t){this.variableNames=[\"Image\"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${e} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${e}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var EB={kernelName:Ba,backendName:\"webgl\",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new jI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var AB=\"return floor(x);\",_it=It({opSnippet:AB,packedOpSnippet:AB,cpuKernelImpl:ez}),DB={kernelName:ms,backendName:\"webgl\",kernelFunc:_it};var Eit=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,Ait=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,Dit=ce({opSnippet:Eit,packedOpSnippet:Ait,dtype:\"int32\"}),$B={kernelName:fs,backendName:\"webgl\",kernelFunc:Dit};var XI=class{constructor(t){this.variableNames=[\"A\"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);\n\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var YI=class{constructor(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${o}.0, ${n}.0);\n vec4 values = ${e.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${e.output} = result;\n }\n `}};var RB={kernelName:th,backendName:\"webgl\",kernelFunc:$it},zd,B1=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function $it(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!=\"undefined\"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!=\"undefined\"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(zd==null||h!==B1)&&(B1=h,zd=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:B1})),zd.canvas.width=u,zd.canvas.height=l,zd.drawImage(o,0,0,u,l),o=zd.canvas}let m=e.makeTensorInfo(c,\"int32\");e.texData.get(m.dataId).usage=Jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool(\"WEBGL_PACK\")?new YI(p):new XI(p),d=e.runWebGLProgram(f,[m],\"int32\");return e.disposeData(m.dataId),d}function Rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f===\"leakyrelu\",E=()=>{let D=[o,s],F=(M,V)=>{if(V===\"NCHW\"&&M.shape.length===1&&M.shape[0]!==1){let G=rt({inputs:{x:M},backend:e,attrs:{shape:[M.shape[0],1,1]}});return b.push(G),G}return M};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(d,\"float32\"));D.push(M),b.push(M)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=DI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h===\"channelsLast\"&&L().getBool(\"WEBGL_EXP_CONV\")){let D=f?Ml(f,!0):null,F=new Md(g,w,D,I,N),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,\"float32\",M)}else if(L().getBool(\"WEBGL_CONV_IM2COL\"))x=$I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Ml(f,!1):null,F=new Od(g,w,D,I,N),M=E();x=e.runWebGLProgram(F,M,\"float32\")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var FB={kernelName:Yi,backendName:\"webgl\",kernelFunc:Rit};function Fit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ml(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m===\"leakyrelu\";if(I&&w.push(i),N&&w.push(a),E){let M=e.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));w.push(M),d.push(M)}let A;x?A=new Ld(g,I,b,N,E):A=new Pd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,\"float32\",D);return d.forEach(M=>e.disposeIntermediateTensorInfo(M)),F}var OB={kernelName:Zi,backendName:\"webgl\",kernelFunc:Fit};var ZI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;let s=zt(n.length),i=`\n int index;`;for(let a=0;a= ${this.paramsShape[a]};\n flattenIndex += index * ${this.strides[a]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${i}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function Oit(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype===\"string\"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=rz(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new ZI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var MB={kernelName:Va,backendName:\"webgl\",kernelFunc:Oit};var JI=class{constructor(t,e){this.variableNames=[\"A\",\"indices\"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Mit(t,2);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${o}));\n }\n `}};function Mit(r,t){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let o=0;o=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype===\"string\"){let b=e.bufferSync(f),w=e.bufferSync(m),I=nz(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new JI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var PB={kernelName:Pi,backendName:\"webgl\",kernelFunc:V1};var Pit=\"return float(a > b);\",Lit=`\n return vec4(greaterThan(a, b));\n`,zit=ce({opSnippet:Pit,packedOpSnippet:Lit,cpuKernelImpl:oz,dtype:\"bool\"}),LB={kernelName:Ga,backendName:\"webgl\",kernelFunc:zit};var Bit=\"return float(a >= b);\",Vit=`\n return vec4(greaterThanEqual(a, b));\n`,Git=ce({opSnippet:Bit,packedOpSnippet:Vit,dtype:\"bool\",cpuKernelImpl:sz}),zB={kernelName:hs,backendName:\"webgl\",kernelFunc:Git};function Wit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!0,e)}var BB={kernelName:Mp,backendName:\"webgl\",kernelFunc:Wit};var Uit=\"return float(!isnan(x) && !isinf(x));\",Hit=It({opSnippet:Uit,dtype:\"bool\"}),VB={kernelName:gs,backendName:\"webgl\",kernelFunc:Hit};var qit=\"return float(isinf(x));\",Kit=It({opSnippet:qit,dtype:\"bool\"}),GB={kernelName:xs,backendName:\"webgl\",kernelFunc:Kit};var jit=\"return float(isnan(x));\",Xit=It({opSnippet:jit,dtype:\"bool\"}),WB={kernelName:ys,backendName:\"webgl\",kernelFunc:Xit};var Yit=\"return float(a < b);\",Zit=`\n return vec4(lessThan(a, b));\n`,Jit=ce({opSnippet:Yit,packedOpSnippet:Zit,cpuKernelImpl:iz,dtype:\"bool\"}),UB={kernelName:Wa,backendName:\"webgl\",kernelFunc:Jit};var Qit=\"return float(a <= b);\",tat=`\n return vec4(lessThanEqual(a, b));\n`,eat=ce({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:az,dtype:\"bool\"}),HB={kernelName:Ua,backendName:\"webgl\",kernelFunc:eat};function rat(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=lz(n,o,s);return t.makeTensorInfo([i.length],\"float32\",i)}var qB={kernelName:Ha,backendName:\"webgl\",kernelFunc:rat};var nat=Po+`\n return x < 0.0 ? 0./0. : log(x);\n`,oat=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,sat=It({opSnippet:nat,packedOpSnippet:oat,cpuKernelImpl:uz}),KB={kernelName:ws,backendName:\"webgl\",kernelFunc:sat};var iat=Po+`\n return log(1.0 + x);\n`,aat=It({opSnippet:iat}),jB={kernelName:Is,backendName:\"webgl\",kernelFunc:aat};var lat=\"return float(a >= 1.0 && b >= 1.0);\",uat=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,cat=ce({opSnippet:lat,packedOpSnippet:uat,dtype:\"bool\"}),XB={kernelName:qa,backendName:\"webgl\",kernelFunc:cat};var pat=\"return float(!(x >= 1.0));\",mat=It({opSnippet:pat}),YB={kernelName:Ka,backendName:\"webgl\",kernelFunc:mat};var fat=\"return float(a >= 1.0 || b >= 1.0);\",dat=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,hat=ce({opSnippet:fat,packedOpSnippet:dat,dtype:\"bool\"}),ZB={kernelName:ja,backendName:\"webgl\",kernelFunc:hat};var QI=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${i}; j <= ${i}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${a}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${u};\n setOutput(val);\n }\n `}};var tC=class{constructor(t,e,n,o,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${i};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${i}; j <= ${i}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${u};\n setOutput(result);\n }\n `}};var gat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool(\"WEBGL_PACK_NORMALIZATION\")?new tC(o.shape,s,i,a,u):new QI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},JB={kernelName:Cs,backendName:\"webgl\",kernelFunc:gat};var eC=class{constructor(t,e,n,o,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${e})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${e} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${o}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${o})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var xat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new eC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},QB={kernelName:Xa,backendName:\"webgl\",kernelFunc:xat};function tV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,\"max\",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,\"max\",!1);return e.runWebGLProgram(p,[o],o.dtype)}var nV={kernelName:Ns,backendName:\"webgl\",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,\"max\",!1);return e.runWebGLProgram(m,[o],o.dtype)}var oV={kernelName:Li,backendName:\"webgl\",kernelFunc:Cat};var rC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${u});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${o}) {\n float dyR = float(dyRCorner + wR) / ${e}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${i}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${i} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},nC=class{constructor(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${m}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${u};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${e}.0;\n\n if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${i}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${a}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,\"max\",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new nC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var sV={kernelName:tu,backendName:\"webgl\",kernelFunc:vat};function Sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;vi([s,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ni(m,\"max\",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new rC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var iV={kernelName:Ql,backendName:\"webgl\",kernelFunc:Sat};function aV(r,t,e,n){let o=new Ni(e,\"max\",!1),s=n.runWebGLProgram(o,[r],\"float32\");o=new Ni(e,\"max\",!0,!0,t);let i=n.runWebGLProgram(o,[r],\"float32\");return[s,i]}var lV={kernelName:eu,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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uC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(\",\"),a=e.map((h,g)=>h[0]+t[g]).join(\",\"),u=rr(\"rc\",o),l=rr(\"source\",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?\"source\":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;\n if(${c}) {\n `,o===1?\"\":`}\n rc = outputLoc;\n ${u[o-2]} += 1;\n if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?\"\":` ${u[o-1]} += 1;\n if(${c}) {`],f=o===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\";for(let h=0,g=o===1?2:4;h{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new uC(o.shape,s,i):new lC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},EV={kernelName:Rs,backendName:\"webgl\",kernelFunc:q1};var Kat=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,jat=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var DV={kernelName:Ms,backendName:\"webgl\",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=xz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],\"int32\",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var $V={kernelName:Lp,backendName:\"webgl\",kernelFunc:Zat};function Jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=yz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],\"int32\",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var RV={kernelName:zp,backendName:\"webgl\",kernelFunc:Jat};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=bz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var FV={kernelName:Bp,backendName:\"webgl\",kernelFunc:Qat};var K1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=wz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},OV={kernelName:ru,backendName:\"webgl\",kernelFunc:K1};var tlt=\"return 1.0 / x;\",elt=It({opSnippet:tlt}),MV={kernelName:Ps,backendName:\"webgl\",kernelFunc:elt};var rlt=xr+`\n return (x < 0.0) ? 0.0 : x;\n`,nlt=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,olt=It({opSnippet:rlt,packedOpSnippet:nlt}),PV={kernelName:Ls,backendName:\"webgl\",kernelFunc:olt};var slt=xr+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,ilt=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,alt=It({opSnippet:slt,packedOpSnippet:ilt}),LV={kernelName:Vs,backendName:\"webgl\",kernelFunc:alt};var cC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var pC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new pC(o.shape,u,l,s,i):new cC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],\"float32\")}var zV={kernelName:Bs,backendName:\"webgl\",kernelFunc:llt};var mC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function ult(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new mC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var BV={kernelName:rl,backendName:\"webgl\",kernelFunc:ult};var fC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":f=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/p[0]},\n ${c[1]/p[1]});\n const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var dC=class{constructor(t,e,n,o,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?\"0.5\":\"0.0\",f;s?f=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":f=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/p[0]},\n ${c[1]/p[1]},\n ${c[1]/p[1]});\n const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,\n ${u}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dC(o.shape,u,l,s,i):new fC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var VV={kernelName:zs,backendName:\"webgl\",kernelFunc:clt};var hC=class{constructor(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${p});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${i}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${a}) {\n continue;\n }\n\n float sourceFracRow =\n float(${u[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${u[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${o}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function plt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new hC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var GV={kernelName:el,backendName:\"webgl\",kernelFunc:plt};var gC=class{constructor(t,e){this.variableNames=[\"x\"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${t[0]} - coord - 1));\n }\n `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(\",\"),i=zt(n);this.userCode=`\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var xC=class{constructor(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr(\"rc\",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${t[0]} - rc - 1),\n ${t[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),\n ${t[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${a} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${u(o.slice())};\n if(${s}){\n result.g = ${l(o.slice())};\n }\n if(${i}) {\n result.b = ${c(o.slice())};\n if(${s}) {\n result.a = ${p(o.slice())};\n }\n }\n setOutput(result);\n }\n `;function u(d){return m(d)}function l(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",m(d)}function c(d){return d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function p(d){return d[n-1]=\"(\"+d[n-1]+\" + 1)\",d[n-2]=\"(\"+d[n-2]+\" + 1)\",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xC(o.shape,a):new gC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var WV={kernelName:Gs,backendName:\"webgl\",kernelFunc:mlt};var yC=class{constructor(t,e){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=t[1],o=t[2];this.outputShape=t;let s=\"\";typeof e==\"number\"?s=`float outputValue = ${e.toFixed(2)};`:s=`\n vec3 fill = vec3(${e.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var UV={kernelName:pl,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new yC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var flt=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,dlt=It({opSnippet:flt}),HV={kernelName:Ws,backendName:\"webgl\",kernelFunc:dlt};var hlt=\"return inversesqrt(x);\",glt=It({opSnippet:hlt,cpuKernelImpl:Iz}),qV={kernelName:Us,backendName:\"webgl\",kernelFunc:glt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${t}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${e}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${d};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var bC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p=\"\";n===1?p=\"i\":n===2&&(p=\"i, j\");let m=`getIndices(${p})`,f=\"\";o===1?f=\"i\":o===2&&(f=\"i, coords[1]\");let d=`getUpdates(${f})`,h=\"\";u&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=e>1?\"strides[j]\":\"strides\",b=e>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${l} strides = ${l}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${t}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${e}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${e}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${d};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function xlt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],\"float32\",new Float32Array([0])),g;L().getBool(\"WEBGL_PACK\")?g=new bC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var KV={kernelName:nl,backendName:\"webgl\",kernelFunc:xlt};var wC=class{constructor(t,e,n,o){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[t,n];let s=\"while (left < right) {\",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber(\"WEBGL_VERSION\")===2?s:i,u=o===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${a}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${u} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function ylt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new wC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],\"int32\",u)}var jV={kernelName:sl,backendName:\"webgl\",kernelFunc:ylt};var IC=class{constructor(t,e,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s=\"resRC\",o=\"resRC\";else{let a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],u=[],l=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function blt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new IC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var XV={kernelName:Wi,backendName:\"webgl\",kernelFunc:blt};var wlt=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${S.SELU_SCALEALPHA};\n float scale = ${S.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Ilt=It({opSnippet:wlt}),YV={kernelName:Hs,backendName:\"webgl\",kernelFunc:Ilt};var Clt=Po+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,vlt=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Slt=It({opSnippet:Clt,packedOpSnippet:vlt,cpuKernelImpl:vz}),ZV={kernelName:Xs,backendName:\"webgl\",kernelFunc:Slt};var Nlt=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,klt=It({opSnippet:Nlt}),JV={kernelName:js,backendName:\"webgl\",kernelFunc:klt};var Tlt=Po+`\n return sin(x);\n`,_lt=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xn}\n return result;\n`,Elt=It({opSnippet:Tlt,packedOpSnippet:_lt}),QV={kernelName:qs,backendName:\"webgl\",kernelFunc:Elt};var Alt=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Dlt=It({opSnippet:Alt}),tG={kernelName:Ks,backendName:\"webgl\",kernelFunc:Dlt};var $lt=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Rlt=It({opSnippet:$lt}),eG={kernelName:Ys,backendName:\"webgl\",kernelFunc:Rlt};var Flt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;xe.disposeIntermediateTensorInfo(x)),g},rG={kernelName:Hi,backendName:\"webgl\",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],\"bool\",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var nG={kernelName:nu,backendName:\"webgl\",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=kz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var oG={kernelName:il,backendName:\"webgl\",kernelFunc:Mlt};function Plt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var sG={kernelName:ou,backendName:\"webgl\",kernelFunc:Plt};function Llt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var iG={kernelName:su,backendName:\"webgl\",kernelFunc:Llt};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype===\"string\"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=Cz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var aG={kernelName:al,backendName:\"webgl\",kernelFunc:zlt};function Blt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ki({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var lG={kernelName:qi,backendName:\"webgl\",kernelFunc:Blt};var uG=\"return sqrt(x);\",Vlt=It({opSnippet:uG,packedOpSnippet:uG,cpuKernelImpl:Tz}),cG={kernelName:Zs,backendName:\"webgl\",kernelFunc:Vlt};var Glt=\"return x * x;\",Wlt=It({opSnippet:Glt}),pG={kernelName:iu,backendName:\"webgl\",kernelFunc:Wlt};var mG=\"return (a - b) * (a - b);\",Ult=ce({opSnippet:mG,packedOpSnippet:mG}),fG={kernelName:ti,backendName:\"webgl\",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=_z(i,\"string\",n);return e.makeTensorInfo(o.shape,\"string\",a)}var dG={kernelName:ec,backendName:\"webgl\",kernelFunc:Hlt};function qlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var hG={kernelName:xo,backendName:\"webgl\",kernelFunc:qlt};var CC=class{constructor(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a=\"\";if(o===1)a=\"coords * strides + begin\";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${t});\n ${s} strides = ${s}(${e});\n\n void main() {\n ${i} coords = getOutputCoords();\n setOutput(getX(${a}));\n }\n `}};function Klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=ki({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=Ez(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new CC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var gG={kernelName:ll,backendName:\"webgl\",kernelFunc:Klt};function jlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Az(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],\"string\",d),e.makeTensorInfo(p.shape,\"int32\",h)]}var xG={kernelName:au,backendName:\"webgl\",kernelFunc:jlt};function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=Dz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],\"int32\",l),e.makeTensorInfo([m],\"string\",c),e.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var yG={kernelName:lu,backendName:\"webgl\",kernelFunc:Xlt};function Ylt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(o<=0)throw new Error(\"Number of buckets must be at least 1\");let i=e.readSync(s.dataId),a=$z(i,o);return e.makeTensorInfo(s.shape,\"int32\",a)}var bG={kernelName:uu,backendName:\"webgl\",kernelFunc:Ylt};var Zlt=\"return tan(x);\",Jlt=It({opSnippet:Zlt}),wG={kernelName:ri,backendName:\"webgl\",kernelFunc:Jlt};var Qlt=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,tut=It({opSnippet:Qlt}),IG={kernelName:ni,backendName:\"webgl\",kernelFunc:tut};function eut(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Ku(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var CG={kernelName:ol,backendName:\"webgl\",kernelFunc:eut};var vC=class{constructor(t,e){this.variableNames=[\"A\"];let n=new Array(t.length);for(let i=0;i5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],n=[];for(let o=0;o5){let u=e.readSync(o.dataId),l=o.dtype===\"string\"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Fz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new vC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var vG={kernelName:oo,backendName:\"webgl\",kernelFunc:j1};var SC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},NC=class{constructor(t){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=t,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function xp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function SG(r){let t=1;for(;tu){let M=e.readSync(o.dataId),[V,G]=Oz(M,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,\"int32\",[])];if(c===1)return[o,Ll({attrs:{shape:l,dtype:\"int32\",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&xp(e,f);let x=SG(s),b=SG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(M,V,G)=>{let W=I(),q=new SC(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],Y=w;w=e.runWebGLProgram(q,W,\"int32\",j),xp(e,Y)};for(let M=1;M=1;G/=2)N(V,G,[h,b])}for(let M=b;M>x;M/=2){let V=I(),G=new NC([h,M/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,\"int32\",q),xp(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=ki({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),xp(e,E);let A=V1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});xp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),xp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),xp(e,F),[A,w]}var NG={kernelName:ul,backendName:\"webgl\",kernelFunc:nut};var kC=class{constructor(t,e,n,o,s,i){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=i;let a=n===\"nearest\"?1:2,u;switch(o){case\"constant\":u=1;break;case\"reflect\":u=2;break;case\"wrap\":u=3;break;case\"nearest\":u=4;break;default:u=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${u} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${u} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${e}));\n float mapY = mapCoord(inY, float(${t}));\n\n if (${a} == 1) {\n int coordY = int(round(mapY));\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function out(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new kC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],\"float32\")}var kG={kernelName:cl,backendName:\"webgl\",kernelFunc:out};function sut(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;vi(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=Mz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],\"int32\",l)]}var TG={kernelName:cu,backendName:\"webgl\",kernelFunc:sut};function iut(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;he.disposeIntermediateTensorInfo(h)),d}var _G={kernelName:Ki,backendName:\"webgl\",kernelFunc:iut};var TC=class{constructor(t,e){this.variableNames=[\"x\",\"segmentIds\"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u=\"0.0\",l=\"sumValue\",c=Math.floor(n/4)*4,p=n%4,m=`\n sumValue += dot(values, segFilter);\n `,f=\"\";s%n>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let d=\"\";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${u};\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${d}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${i})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${i})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${p===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${l});\n }\n `}};function aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new TC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=K1({backend:e,attrs:{start:0,stop:D,step:1,dtype:\"float32\"}}),j=j1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(j),g(q,N,j,A,D)},x=g(d,\"unsortedSegmentSum\",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var EG={kernelName:pu,backendName:\"webgl\",kernelFunc:aut};var lut=[p3,f3,d3,h3,x3,y3,b3,w3,v3,S3,N3,k3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,L3,z3,B3,U3,q3,K3,e3,X3,Z3,J3,Q3,tB,eB,rB,nB,oB,sB,iB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,bB,wB,IB,CB,vB,SB,kB,TB,_B,EB,DB,$B,RB,FB,OB,MB,PB,LB,zB,t3,BB,Y3,VB,GB,WB,r3,UB,HB,qB,KB,jB,XB,YB,ZB,JB,QB,eV,rV,nV,oV,sV,iV,lV,cV,pV,mV,fV,dV,bV,s3,wV,IV,CV,vV,V3,SV,TV,_V,EV,AV,n3,DV,$V,RV,FV,OV,G3,hV,MV,PV,LV,a3,zV,BV,VV,GV,WV,UV,HV,qV,KV,jV,XV,YV,ZV,JV,QV,tG,M3,yV,eG,rG,nG,oG,sG,iG,aG,lG,cG,pG,fG,dG,hG,gG,xG,yG,bG,xV,u3,wG,IG,CG,vG,NG,kG,c3,TG,_G,EG,NV];for(let r of lut)rc(r);var Nt;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(Nt||(Nt={}));var ju;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(ju||(ju={}));var AG;function uut(r){AG=r.wasm.cwrap(Xi,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function cut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return AG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var DG={kernelName:Xi,backendName:\"wasm\",setupFunc:uut,kernelFunc:cut};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,[\"number\",\"number\",\"number\"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:\"wasm\",setupFunc:n,kernelFunc:o}}var $G=yt(Ai);var RG=yt(Go);var FG=yt(Wo);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:\"wasm\",setupFunc:o,kernelFunc:s}}var put=!0,OG=ee(no,put);var MG;function mut(r){MG=r.wasm.cwrap(Uo,null,[\"array\",\"number\",\"number\",\"number\"])}function fut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return MG(s,o.length,Nt[n.dtype],i),n}var PG={kernelName:Uo,backendName:\"wasm\",setupFunc:mut,kernelFunc:fut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype===\"string\")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var LG={kernelName:go,backendName:\"wasm\",kernelFunc:yp};var zG;function dut(r){zG=r.wasm.cwrap(so,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=gut(t.x.shape,n.perm),i=!0;for(let d=0;d=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var BG={kernelName:so,backendName:\"wasm\",kernelFunc:mo,setupFunc:dut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var iW={kernelName:Gi,backendName:\"wasm\",kernelFunc:mr};var aW;function Eut(r){aW=r.wasm.cwrap(Zo,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and 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t.dtype===\"string\"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype===\"string\"){let d=ep(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Dut(u,c[0],m,s,i);else if(f===3)$ut(u,c[0],c[1],m,s,i);else if(f===4)Rut(u,c[0],c[1],c[2],m,s,i);else{let d=ep(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Dut(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;lx*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(h.dataId),g}var cW={kernelName:Fi,backendName:\"wasm\",kernelFunc:Fut};var pW;function Out(r){pW=r.wasm.cwrap(Da,null,[\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\"])}function Mut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return pW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var mW={kernelName:Da,backendName:\"wasm\",setupFunc:Out,kernelFunc:Mut};var Put=!0,fW=ee($a,Put);function Lut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],\"int32\",void 0,new Int32Array(a))}var dW={kernelName:ql,backendName:\"wasm\",kernelFunc:Lut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var hW={kernelName:fo,backendName:\"wasm\",kernelFunc:Fn};var gW=yt(Jo);var xW;function zut(r){xW=r.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\"])}function But(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return xW(a,s,i,l),u}var yW={kernelName:ho,backendName:\"wasm\",setupFunc:zut,kernelFunc:But};function X1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype===\"string\"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return 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x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var OW={kernelName:Oa,backendName:\"wasm\",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype===\"float32\"||o.dtype===\"int32\",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:\"wasm\",setupFunc:ect,kernelFunc:rct};var LW;function nct(r){LW=r.wasm.cwrap(\"DenseBincount\",null,[\"number\",\"array\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"boolean\",\"number\"])}function oct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return LW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var zW={kernelName:jl,backendName:\"wasm\",setupFunc:nct,kernelFunc:oct};var BW;function sct(r){BW=r.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i===\"NHWC\"?o.shape[1]:o.shape[2],l=i===\"NHWC\"?o.shape[2]:o.shape[3],c=i===\"NHWC\"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i===\"NHWC\"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,\"float32\"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i===\"NHWC\"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:\"wasm\",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,\"float32\"),G=n.dataIdMap.get(V.dataId).id;return GW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,M,I,N,E,A,D,F,G),V}var WW={kernelName:ss,backendName:\"wasm\",setupFunc:act,kernelFunc:lct};var UW;function uct(r){UW=r.wasm.cwrap(\"Diag\",null,[\"number\",\"number\",\"number\",\"number\"])}function cct(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return UW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var HW={kernelName:Xl,backendName:\"wasm\",setupFunc:uct,kernelFunc:cct};var qW;function pct(r){qW=r.wasm.cwrap(is,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:\"wasm\",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,\"NHWC\",l),p=e.makeOutput(o.shape,o.dtype);return YW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var ZW={kernelName:Yl,backendName:\"wasm\",setupFunc:hct,kernelFunc:gct};var JW=yt(ls);var QW;function xct(r){QW=r.wasm.cwrap(La,null,[\"number\",\"number\",\"number\"])}function yct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,\"float32\"),i=a=>e.dataIdMap.get(a.dataId).id;return QW(i(o),i(n),i(s)),s}var tU={kernelName:La,backendName:\"wasm\",setupFunc:xct,kernelFunc:yct};var bct=!1,eU=ee(za,bct,\"bool\");var rU=yt(us);var nU=yt(cs,\"float32\");function EC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var oU={kernelName:Mi,backendName:\"wasm\",kernelFunc:EC};var sU=yt(ps,\"float32\");function Z1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var iU={kernelName:Jl,backendName:\"wasm\",kernelFunc:Z1};var aU;function wct(r){aU=r.wasm.cwrap(Ba,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ict(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return aU(s,a,u,l,c,i),o}var lU={kernelName:Ba,backendName:\"wasm\",kernelFunc:Ict,setupFunc:wct};var uU=yt(ms);var Cct=!1,cU=ee(fs,Cct);var pU;function vct(r){pU=r.wasm.cwrap(ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return pU(c,p,m,f,d,o,g),h}var mU={kernelName:ds,backendName:\"wasm\",setupFunc:vct,kernelFunc:Sct};var fU;function Nct(r){fU=r.wasm.cwrap(Yi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function kct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return fU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var dU={kernelName:Yi,backendName:\"wasm\",setupFunc:Nct,kernelFunc:kct};var hU;function Tct(r){hU=r.wasm.cwrap(Zi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type===\"SAME\"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,\"float32\"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return hU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var gU={kernelName:Zi,backendName:\"wasm\",setupFunc:Tct,kernelFunc:_ct};var xU;function Ect(r){xU=r.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Act(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return xU(f,Nt[n.dtype],h,i,p,a,g,x),l}var yU={kernelName:Va,backendName:\"wasm\",setupFunc:Ect,kernelFunc:Act};var bU;function Dct(r){bU=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function $ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:\"wasm\",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,\"bool\");var Fct=!1,CU=ee(hs,Fct,\"bool\");var vU=yt(gs,\"bool\");var SU=yt(xs,\"bool\");var NU=yt(ys,\"bool\");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,[\"number\",\"number\",\"number\",\"number\"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,\"float32\");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:\"wasm\",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,\"bool\");var Lct=!1,EU=ee(Ua,Lct,\"bool\");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],\"float32\");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:\"wasm\",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,\"bool\");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,\"bool\");var Wct=!1,PU=ee(E_,Wct,\"bool\");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:\"wasm\",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!==\"float32\"||s.dtype!==\"float32\"||i.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:\"wasm\",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,[\"number\",\"number\",\"number\",\"number\"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:\"wasm\",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,\"float32\"),D=n.dataIdMap.get(A.dataId).id;return HU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var qU={kernelName:Ns,backendName:\"wasm\",setupFunc:Zct,kernelFunc:Jct};var KU;function Qct(r){KU=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return KU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var jU={kernelName:Li,backendName:\"wasm\",setupFunc:Qct,kernelFunc:tpt};var XU;function ept(r){XU=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return XU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var YU={kernelName:tu,backendName:\"wasm\",setupFunc:ept,kernelFunc:rpt};var ZU;function npt(r){ZU=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function opt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return ZU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var JU={kernelName:Ql,backendName:\"wasm\",setupFunc:npt,kernelFunc:opt};var QU;function spt(r){QU=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ipt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,\"int32\");return QU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var t4={kernelName:eu,backendName:\"wasm\",setupFunc:spt,kernelFunc:ipt};var e4;function apt(r){e4=r.wasm.cwrap(ks,null,[\"number, number, number\"])}function lpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"mean\",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!==\"float32\"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:\"float32\"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,\"float32\");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;e4(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!==\"float32\"&&t.disposeData(b.dataId),w}var r4={kernelName:ks,backendName:\"wasm\",setupFunc:apt,kernelFunc:lpt};var n4;function upt(r){n4=r.wasm.cwrap(Ts,null,[\"number\",\"number\",\"number\",\"number\"])}function cpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let 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wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n=\"tfjs-backend-wasm.wasm\";return r&&t?n=\"tfjs-backend-wasm-threaded-simd.wasm\":r&&(n=\"tfjs-backend-wasm-simd.wasm\"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),L().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(\".worker.js\")){let l=rq.wasmWorkerContents.replace(/\\n/g,\"\\\\n\"),c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return a.endsWith(\".wasm\")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:\"\")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error(\"WASM backend not initialized.\");return i_}var Hmt=\"4.22.0\";var qmt=2;Xp(\"wasm\",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq=\"4.22.0\",Kmt=\"4.22.0\",jmt=\"4.22.0\",Xmt=\"4.22.0\",Ymt=\"4.22.0\",Zmt={tfjs:sq,\"tfjs-core\":sq,\"tfjs-converter\":Kmt,\"tfjs-backend-cpu\":jmt,\"tfjs-backend-webgl\":Xmt,\"tfjs-backend-wasm\":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike};\n", "export * from './drawContour';\nexport * from './drawDetections';\nexport * from './drawFaceExpressions';\nexport * from './DrawBox';\nexport * from './DrawFaceLandmarks';\nexport * from './DrawTextField';\n", "import { Point } from '../classes/index';\n\nexport function drawContour(\n ctx: CanvasRenderingContext2D,\n points: Point[],\n isClosed = false,\n) {\n ctx.beginPath();\n\n points.slice(1).forEach(({ x, y }, prevIdx) => {\n const from = points[prevIdx];\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(x, y);\n });\n\n if (isClosed) {\n const from = points[points.length - 1];\n const to = points[0];\n if (!from || !to) {\n return;\n }\n\n ctx.moveTo(from.x, from.y);\n ctx.lineTo(to.x, to.y);\n }\n\n ctx.stroke();\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Point } from '../classes/index';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n return tensor instanceof tf.Tensor && tensor.shape.length === dim;\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n return isTensor(tensor, 1);\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n return isTensor(tensor, 2);\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n return isTensor(tensor, 3);\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n return isTensor(tensor, 4);\n}\n\nexport function isFloat(num: number) {\n return num % 1 !== 0;\n}\n\nexport function isEven(num: number) {\n return num % 2 === 0;\n}\n\nexport function round(num: number, prec = 2) {\n const f = 10 ** prec;\n return Math.floor(num * f) / f;\n}\n\nexport function isDimensions(obj: any): boolean {\n return obj && obj.width && obj.height;\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n const scale = inputSize / Math.max(height, width);\n return new Dimensions(Math.round(width * scale), Math.round(height * scale));\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n .div(new Point(pts.length, pts.length));\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n return Array(num).fill(0).map((_, i) => start + (i * step));\n}\n\nexport function isValidNumber(num: any) {\n return !!num && (num !== Infinity) && (num !== -Infinity) && !Number.isNaN(num) || num === 0;\n}\n\nexport function isValidProbablitiy(num: any) {\n return isValidNumber(num) && num >= 0 && num <= 1.0;\n}\n", "import { isValidNumber } from '../utils/index';\n\nexport interface IDimensions {\n width: number\n height: number\n}\n\nexport class Dimensions implements IDimensions {\n private _width: number;\n\n private _height: number;\n\n constructor(width: number, height: number) {\n if (!isValidNumber(width) || !isValidNumber(height)) {\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`);\n }\n\n this._width = width;\n this._height = height;\n }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public reverse(): Dimensions {\n return new Dimensions(1 / this.width, 1 / this.height);\n }\n}\n", "export interface IPoint {\n x: number\n y: number\n}\n\nexport class Point implements IPoint {\n private _x: number;\n\n private _y: number;\n\n constructor(x: number, y: number) {\n this._x = x;\n this._y = y;\n }\n\n get x(): number { return this._x; }\n\n get y(): number { return this._y; }\n\n public add(pt: IPoint): Point {\n return new Point(this.x + pt.x, this.y + pt.y);\n }\n\n public sub(pt: IPoint): Point {\n return new Point(this.x - pt.x, this.y - pt.y);\n }\n\n public mul(pt: IPoint): Point {\n return new Point(this.x * pt.x, this.y * pt.y);\n }\n\n public div(pt: IPoint): Point {\n return new Point(this.x / pt.x, this.y / pt.y);\n }\n\n public abs(): Point {\n return new Point(Math.abs(this.x), Math.abs(this.y));\n }\n\n public magnitude(): number {\n return Math.sqrt((this.x ** 2) + (this.y ** 2));\n }\n\n public floor(): Point {\n return new Point(Math.floor(this.x), Math.floor(this.y));\n }\n}\n", "import { isDimensions, isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box implements IBoundingBox, IRect {\n public static isRect(rect: any): boolean {\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\n }\n\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions = false) {\n if (!Box.isRect(box)) {\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`);\n }\n\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`);\n }\n }\n\n private _x: number;\n\n private _y: number;\n\n private _width: number;\n\n private _height: number;\n\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions = true) {\n const box = (_box || {}) as any;\n\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\n\n if (!isRect && !isBbox) {\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`);\n }\n\n const [x, y, width, height] = isRect\n ? [box.x, box.y, box.width, box.height]\n : [box.left, box.top, box.right - box.left, box.bottom - box.top];\n\n Box.assertIsValidBox({\n x, y, width, height,\n }, 'Box.constructor', allowNegativeDimensions);\n\n this._x = x;\n this._y = y;\n this._width = width;\n this._height = height;\n }\n\n public get x(): number { return this._x; }\n\n public get y(): number { return this._y; }\n\n public get width(): number { return this._width; }\n\n public get height(): number { return this._height; }\n\n public get left(): number { return this.x; }\n\n public get top(): number { return this.y; }\n\n public get right(): number { return this.x + this.width; }\n\n public get bottom(): number { return this.y + this.height; }\n\n public get area(): number { return this.width * this.height; }\n\n public get topLeft(): Point { return new Point(this.left, this.top); }\n\n public get topRight(): Point { return new Point(this.right, this.top); }\n\n public get bottomLeft(): Point { return new Point(this.left, this.bottom); }\n\n public get bottomRight(): Point { return new Point(this.right, this.bottom); }\n\n public round(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.round(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public floor(): Box {\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n .map((val) => Math.floor(val));\n return new Box({\n x, y, width, height,\n });\n }\n\n public toSquare(): Box {\n let {\n x, y, width, height,\n } = this;\n const diff = Math.abs(width - height);\n if (width < height) {\n x -= (diff / 2);\n width += diff;\n }\n if (height < width) {\n y -= (diff / 2);\n height += diff;\n }\n\n return new Box({ x, y, width, height });\n }\n\n public rescale(s: IDimensions | number): Box {\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number;\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number;\n return new Box({\n x: this.x * scaleX,\n y: this.y * scaleY,\n width: this.width * scaleX,\n height: this.height * scaleY,\n });\n }\n\n public pad(padX: number, padY: number): Box {\n const [x, y, width, height] = [\n this.x - (padX / 2),\n this.y - (padY / 2),\n this.width + padX,\n this.height + padY,\n ];\n return new Box({ x, y, width, height });\n }\n\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box {\n const { x, y, right, bottom } = this;\n const clippedX = Math.max(x, 0);\n const clippedY = Math.max(y, 0);\n\n const newWidth = right - clippedX;\n const newHeight = bottom - clippedY;\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX);\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY);\n\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\n }\n\n public shift(sx: number, sy: number): Box {\n const { width, height } = this;\n const x = this.x + sx;\n const y = this.y + sy;\n\n return new Box({ x, y, width, height });\n }\n\n public padAtBorders(imageHeight: number, imageWidth: number) {\n const w = this.width + 1;\n const h = this.height + 1;\n\n const dx = 1;\n const dy = 1;\n let edx = w;\n let edy = h;\n\n let x = this.left;\n let y = this.top;\n let ex = this.right;\n let ey = this.bottom;\n\n if (ex > imageWidth) {\n edx = -ex + imageWidth + w;\n ex = imageWidth;\n }\n if (ey > imageHeight) {\n edy = -ey + imageHeight + h;\n ey = imageHeight;\n }\n if (x < 1) {\n edy = 2 - x;\n x = 1;\n }\n if (y < 1) {\n edy = 2 - y;\n y = 1;\n }\n\n return { dy, edy, dx, edx, y, ey, x, ex, w, h };\n }\n\n public calibrate(region: Box) {\n return new Box({\n left: this.left + (region.left * this.width),\n top: this.top + (region.top * this.height),\n right: this.right + (region.right * this.width),\n bottom: this.bottom + (region.bottom * this.height),\n }).toSquare().round();\n }\n}\n", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n left: number\n top: number\n right: number\n bottom: number\n}\n\nexport class BoundingBox extends Box implements IBoundingBox {\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions = false) {\n super({ left, top, right, bottom }, allowNegativeDimensions);\n }\n}\n", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n private _score: number;\n\n private _classScore: number;\n\n private _className: string;\n\n private _box: Rect;\n\n private _imageDims: Dimensions;\n\n constructor(\n score: number,\n classScore: number,\n className: string,\n relativeBox: IRect,\n imageDims: IDimensions,\n ) {\n this._imageDims = new Dimensions(imageDims.width, imageDims.height);\n this._score = score;\n this._classScore = classScore;\n this._className = className;\n this._box = new Box(relativeBox).rescale(this._imageDims);\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n\n public get className(): string { return this._className; }\n\n public get box(): Box { return this._box; }\n\n public get imageDims(): Dimensions { return this._imageDims; }\n\n public get imageWidth(): number { return this.imageDims.width; }\n\n public get imageHeight(): number { return this.imageDims.height; }\n\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()); }\n\n public forSize(width: number, height: number): ObjectDetection {\n return new ObjectDetection(\n this.score,\n this.classScore,\n this.className,\n this.relativeBox,\n { width, height },\n );\n }\n}\n", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n score: number\n box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n constructor(\n score: number,\n relativeBox: Rect,\n imageDims: IDimensions,\n ) {\n super(score, score, '', relativeBox, imageDims);\n }\n\n public override forSize(width: number, height: number): FaceDetection {\n const { score, relativeBox, imageDims } = super.forSize(width, height);\n return new FaceDetection(score, relativeBox, imageDims);\n }\n}\n", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU = true) {\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\n const interSection = width * height;\n\n return isIOU\n ? interSection / (box1.area + box2.area - interSection)\n : interSection / Math.min(box1.area, box2.area);\n}\n", "import { BoundingBox, IPoint } from '../classes/index';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n const xs = pts.map((pt) => pt.x);\n const ys = pts.map((pt) => pt.y);\n const minX = xs.reduce((min, x) => (x < min ? x : min), Infinity);\n const minY = ys.reduce((min, y) => (y < min ? y : min), Infinity);\n const maxX = xs.reduce((max, x) => (max < x ? x : max), 0);\n const maxY = ys.reduce((max, y) => (max < y ? y : max), 0);\n\n return new BoundingBox(minX, minY, maxX, maxY);\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n boxes: Box[],\n scores: number[],\n iouThreshold: number,\n isIOU = true,\n): number[] {\n let indicesSortedByScore = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .sort((c1, c2) => c1.score - c2.score)\n .map((c) => c.boxIndex);\n\n const pick: number[] = [];\n\n while (indicesSortedByScore.length > 0) {\n const curr = indicesSortedByScore.pop() as number;\n pick.push(curr);\n\n const indices = indicesSortedByScore;\n\n const outputs: number[] = [];\n for (let i = 0; i < indices.length; i++) {\n const idx = indices[i];\n\n const currBox = boxes[curr];\n const idxBox = boxes[idx];\n\n outputs.push(iou(currBox, idxBox, isIOU));\n }\n\n indicesSortedByScore = indicesSortedByScore.filter(\n (_, j) => outputs[j] <= iouThreshold,\n );\n }\n\n return pick;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n return tf.tidy(() => {\n const [r, g, b] = meanRgb;\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\n\n return tf.sub(x, avg_rgb);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(imgTensor: tf.Tensor4D, isCenterImage = false): tf.Tensor4D {\n return tf.tidy(() => {\n const [height, width] = imgTensor.shape.slice(1);\n if (height === width) return imgTensor;\n const dimDiff = Math.abs(height - width);\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\n const paddingAxis = height > width ? 2 : 1;\n const createPaddingTensor = (paddingAmountLocal: number): tf.Tensor => {\n const paddingTensorShape = imgTensor.shape.slice();\n paddingTensorShape[paddingAxis] = paddingAmountLocal;\n return tf.fill(paddingTensorShape, 0, 'float32');\n };\n const paddingTensorAppend = createPaddingTensor(paddingAmount);\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number);\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount ? createPaddingTensor(remainingPaddingAmount) : null;\n const tensorsToStack = [paddingTensorPrepend, imgTensor, paddingTensorAppend]\n .filter((t) => !!t)\n .map((t) => tf.cast(t as tf.Tensor4D, 'float32')) as tf.Tensor4D[];\n return tf.concat(tensorsToStack, paddingAxis);\n });\n}\n", "export function shuffleArray(inputArray: any[]) {\n const array = inputArray.slice();\n for (let i = array.length - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n const x = array[i];\n array[i] = array[j];\n array[j] = x;\n }\n return array;\n}\n", "export * from './iou';\nexport * from './minBbox';\nexport * from './nonMaxSuppression';\nexport * from './normalize';\nexport * from './padToSquare';\nexport * from './shuffleArray';\n\nexport function sigmoid(x: number) {\n return 1 / (1 + Math.exp(-x));\n}\n\nexport function inverseSigmoid(x: number) {\n return Math.log(x / (1 - x));\n}\n", "import { Box } from './Box';\n\nexport interface IRect {\n x: number\n y: number\n width: number\n height: number\n}\n\nexport class Rect extends Box implements IRect {\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions = false) {\n super({ x, y, width, height }, allowNegativeDimensions);\n }\n}\n", "import { minBbox } from '../ops/index';\nimport { getCenterPoint } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5;\nconst relY = 0.43;\nconst relScale = 0.45;\n\nexport interface IFaceLandmarks {\n positions: Point[]\n shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n protected _shift: Point;\n\n protected _positions: Point[];\n\n protected _imgDims: Dimensions;\n\n constructor(\n relativeFaceLandmarkPositions: Point[],\n imgDims: IDimensions,\n shift: Point = new Point(0, 0),\n ) {\n const { width, height } = imgDims;\n this._imgDims = new Dimensions(width, height);\n this._shift = shift;\n this._positions = relativeFaceLandmarkPositions.map(\n (pt) => pt.mul(new Point(width, height)).add(shift),\n );\n }\n\n public get shift(): Point { return new Point(this._shift.x, this._shift.y); }\n\n public get imageWidth(): number { return this._imgDims.width; }\n\n public get imageHeight(): number { return this._imgDims.height; }\n\n public get positions(): Point[] { return this._positions; }\n\n public get relativePositions(): Point[] {\n return this._positions.map(\n (pt) => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight)),\n );\n }\n\n public forSize(width: number, height: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n { width, height },\n );\n }\n\n public shiftBy(x: number, y: number): T {\n return new (this.constructor as any)(\n this.relativePositions,\n this._imgDims,\n new Point(x, y),\n );\n }\n\n public shiftByPoint(pt: Point): T {\n return this.shiftBy(pt.x, pt.y);\n }\n\n /**\n * Aligns the face landmarks after face detection from the relative positions of the faces\n * bounding box, or it's current shift. This function should be used to align the face images\n * after face detection has been performed, before they are passed to the face recognition net.\n * This will make the computed face descriptor more accurate.\n *\n * @param detection (optional) The bounding box of the face or the face detection result. If\n * no argument was passed the position of the face landmarks are assumed to be relative to\n * it's current shift.\n * @returns The bounding box of the aligned face.\n */\n public align(\n detection?: FaceDetection | IRect | IBoundingBox | null,\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { },\n ): Box {\n if (detection) {\n const box = detection instanceof FaceDetection\n ? detection.box.floor()\n : new Box(detection);\n\n return this.shiftBy(box.x, box.y).align(null, options);\n }\n\n const { useDlibAlignment, minBoxPadding } = { useDlibAlignment: false, minBoxPadding: 0.2, ...options };\n\n if (useDlibAlignment) {\n return this.alignDlib();\n }\n\n return this.alignMinBbox(minBoxPadding);\n }\n\n private alignDlib(): Box {\n const centers = this.getRefPointsForAlignment();\n\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers;\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude();\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\n\n const size = Math.floor(eyeToMouthDist / relScale);\n\n const refPoint = getCenterPoint(centers);\n // TODO: pad in case rectangle is out of image bounds\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\n\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\n }\n\n private alignMinBbox(padding: number): Box {\n const box = minBbox(this.positions);\n return box.pad(box.width * padding, box.height * padding);\n }\n\n protected getRefPointsForAlignment(): Point[] {\n throw new Error('getRefPointsForAlignment not implemented by base class');\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n protected override getRefPointsForAlignment(): Point[] {\n const pts = this.positions;\n return [\n pts[0],\n pts[1],\n getCenterPoint([pts[3], pts[4]]),\n ];\n }\n}\n", "import { getCenterPoint } from '../utils/index';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n public getJawOutline(): Point[] {\n return this.positions.slice(0, 17);\n }\n\n public getLeftEyeBrow(): Point[] {\n return this.positions.slice(17, 22);\n }\n\n public getRightEyeBrow(): Point[] {\n return this.positions.slice(22, 27);\n }\n\n public getNose(): Point[] {\n return this.positions.slice(27, 36);\n }\n\n public getLeftEye(): Point[] {\n return this.positions.slice(36, 42);\n }\n\n public getRightEye(): Point[] {\n return this.positions.slice(42, 48);\n }\n\n public getMouth(): Point[] {\n return this.positions.slice(48, 68);\n }\n\n protected override getRefPointsForAlignment(): Point[] {\n return [\n this.getLeftEye(),\n this.getRightEye(),\n this.getMouth(),\n ].map(getCenterPoint);\n }\n}\n", "import { round } from '../utils/index';\n\nexport interface IFaceMatch {\n label: string\n distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n private _label: string;\n private _distance: number;\n\n constructor(label: string, distance: number) {\n this._label = label;\n this._distance = distance;\n }\n\n public get label(): string { return this._label; }\n\n public get distance(): number { return this._distance; }\n\n public toString(withDistance = true): string {\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`;\n }\n}\n", "import { isValidNumber } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box {\n public static assertIsValidLabeledBox(box: any, callee: string) {\n Box.assertIsValidBox(box, callee);\n if (!isValidNumber(box.label)) {\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`);\n }\n }\n\n private _label: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number) {\n super(box);\n this._label = label;\n }\n\n public get label(): number { return this._label; }\n}\n", "export class LabeledFaceDescriptors {\n private _label: string;\n\n private _descriptors: Float32Array[];\n\n constructor(label: string, descriptors: Float32Array[]) {\n if (!(typeof label === 'string')) {\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\n }\n\n if (!Array.isArray(descriptors) || descriptors.some((desc) => !(desc instanceof Float32Array))) {\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\n }\n\n this._label = label;\n this._descriptors = descriptors;\n }\n\n public get label(): string { return this._label; }\n\n public get descriptors(): Float32Array[] { return this._descriptors; }\n\n public toJSON(): any {\n return {\n label: this.label,\n descriptors: this.descriptors.map((d) => Array.from(d)),\n };\n }\n\n public static fromJSON(json: any): LabeledFaceDescriptors {\n const descriptors = json.descriptors.map((d: any) => new Float32Array(d));\n return new LabeledFaceDescriptors(json.label, descriptors);\n }\n}\n", "import { isValidProbablitiy } from '../utils/index';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n public static assertIsValidPredictedBox(box: any, callee: string) {\n LabeledBox.assertIsValidLabeledBox(box, callee);\n\n if (\n !isValidProbablitiy(box.score)\n || !isValidProbablitiy(box.classScore)\n ) {\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`);\n }\n }\n\n private _score: number;\n\n private _classScore: number;\n\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n super(box, label);\n this._score = score;\n this._classScore = classScore;\n }\n\n public get score(): number { return this._score; }\n\n public get classScore(): number { return this._classScore; }\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection = TSource & {\n detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n return obj.detection instanceof FaceDetection;\n}\n\nexport function extendWithFaceDetection(sourceObj: TSource, detection: FaceDetection): WithFaceDetection {\n const extension = { detection };\n return { ...sourceObj, ...extension };\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n const fetch = window.fetch;\n if (!fetch) throw new Error('fetch - missing fetch implementation for browser environment');\n\n const readFile = () => {\n throw new Error('readFile - filesystem not available for browser environment');\n };\n\n return {\n Canvas: HTMLCanvasElement,\n CanvasRenderingContext2D,\n Image: HTMLImageElement,\n ImageData,\n Video: HTMLVideoElement,\n createCanvasElement: () => document.createElement('canvas'),\n createImageElement: () => document.createElement('img'),\n createVideoElement: () => document.createElement('video'),\n fetch,\n readFile,\n };\n}\n", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof process !== 'undefined'\n && process.versions != null\n && process.versions.node != null;\n}\n", "import { FileSystem } from './types';\nimport { isNodejs } from './isNodejs';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n if (!fs && isNodejs()) {\n try {\n // eslint-disable-next-line global-require, @typescript-eslint/no-require-imports\n fs = require('fs');\n } catch (err) {\n requireFsError = (err as any).toString();\n }\n }\n\n const readFile = fs\n // eslint-disable-next-line no-undef\n ? (filePath: string) => new Promise((resolve, reject) => { fs.readFile(filePath, (err: NodeJS.ErrnoException | null, buffer: string | Buffer) => (err ? reject(err) : resolve(buffer))); })\n : () => { throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`); };\n return { readFile };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n const Canvas: (new () => HTMLCanvasElement) = (global as any)['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n const Video: (new () => HTMLVideoElement) = (global as any)['Video'] || global.HTMLVideoElement;\n\n const createCanvasElement = () => {\n if (Canvas) return new Canvas();\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\n };\n\n const createImageElement = () => {\n if (Image) return new Image();\n throw new Error('createImageElement - missing Image implementation for nodejs environment');\n };\n\n const createVideoElement = () => {\n if (Video) return new Video();\n throw new Error('createVideoElement - missing Video implementation for nodejs environment');\n };\n\n const fetch = global.fetch;\n // if (!fetch) throw new Error('fetch - missing fetch implementation for nodejs environment');\n\n const fileSystem = createFileSystem();\n\n return {\n Canvas: Canvas || class {},\n CanvasRenderingContext2D: global.CanvasRenderingContext2D || class {},\n Image: Image || class {},\n ImageData: global.ImageData || class {},\n Video: global.HTMLVideoElement || class {},\n createCanvasElement,\n createImageElement,\n createVideoElement,\n fetch,\n ...fileSystem,\n };\n}\n", "export function isBrowser(): boolean {\n return typeof window === 'object'\n && typeof document !== 'undefined'\n && typeof HTMLImageElement !== 'undefined'\n && typeof HTMLCanvasElement !== 'undefined'\n && typeof HTMLVideoElement !== 'undefined'\n && typeof ImageData !== 'undefined'\n && typeof CanvasRenderingContext2D !== 'undefined';\n}\n", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null;\n\nfunction getEnv(): Environment {\n if (!environment) {\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\n }\n return environment;\n}\n\nfunction setEnv(env: Environment) {\n environment = env;\n}\n\nfunction initialize() {\n // check for isBrowser() first to prevent electron renderer process\n // to be initialized with wrong environment due to isNodejs() returning true\n if (isBrowser()) return setEnv(createBrowserEnv());\n if (isNodejs()) return setEnv(createNodejsEnv());\n return null;\n}\n\nfunction monkeyPatch(env: Partial) {\n if (!environment) {\n initialize();\n }\n\n if (!environment) {\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\n }\n\n const { Canvas = environment.Canvas, Image = environment.Image } = env;\n environment.Canvas = Canvas;\n environment.Image = Image;\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());\n environment.createImageElement = env.createImageElement || (() => new Image());\n\n environment.ImageData = env.ImageData || environment.ImageData;\n environment.Video = env.Video || environment.Video;\n environment.fetch = env.fetch || environment.fetch;\n environment.readFile = env.readFile || environment.readFile;\n}\n\nexport const env = {\n getEnv,\n setEnv,\n initialize,\n createBrowserEnv,\n createFileSystem,\n createNodejsEnv,\n monkeyPatch,\n isBrowser,\n isNodejs,\n};\n\ninitialize();\n\nexport * from './types';\n", "import { env } from '../env/index';\n\nexport function resolveInput(arg: string | any) {\n if (!env.isNodejs() && typeof arg === 'string') {\n return document.getElementById(arg);\n }\n return arg;\n}\n", "import { env } from '../env/index';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n const { Canvas, CanvasRenderingContext2D } = env.getEnv();\n if (canvasArg instanceof CanvasRenderingContext2D) return canvasArg;\n const canvas = resolveInput(canvasArg);\n if (!(canvas instanceof Canvas)) throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n const ctx = canvas.getContext('2d', { willReadFrequently: true });\n if (!ctx) throw new Error('resolveContext2d - canvas 2d context is null');\n return ctx;\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IDimensions, IPoint } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\n// eslint-disable-next-line no-shadow\nexport enum AnchorPosition {\n // eslint-disable-next-line no-unused-vars\n TOP_LEFT = 'TOP_LEFT',\n // eslint-disable-next-line no-unused-vars\n TOP_RIGHT = 'TOP_RIGHT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_LEFT = 'BOTTOM_LEFT',\n // eslint-disable-next-line no-unused-vars\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n anchorPosition?: AnchorPosition\n backgroundColor?: string\n fontColor?: string\n fontSize?: number\n fontStyle?: string\n padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n public anchorPosition: AnchorPosition;\n\n public backgroundColor: string;\n\n public fontColor: string;\n\n public fontSize: number;\n\n public fontStyle: string;\n\n public padding: number;\n\n constructor(options: IDrawTextFieldOptions = {}) {\n const {\n anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = options;\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\n this.fontSize = fontSize || 14;\n this.fontStyle = fontStyle || 'Georgia';\n this.padding = padding || 4;\n }\n}\n\nexport class DrawTextField {\n public text: string[];\n\n public anchor : IPoint;\n\n public options: DrawTextFieldOptions;\n\n constructor(\n text: string | string[] | DrawTextField,\n anchor: IPoint,\n options: IDrawTextFieldOptions = {},\n ) {\n // eslint-disable-next-line no-nested-ternary\n this.text = typeof text === 'string'\n ? [text]\n : (text instanceof DrawTextField ? text.text : text);\n this.anchor = anchor;\n this.options = new DrawTextFieldOptions(options);\n }\n\n measureWidth(ctx: CanvasRenderingContext2D): number {\n const { padding } = this.options;\n return this.text.map((l) => ctx.measureText(l).width).reduce((w0, w1) => (w0 < w1 ? w1 : w0), 0) + (2 * padding);\n }\n\n measureHeight(): number {\n const { fontSize, padding } = this.options;\n return this.text.length * fontSize + (2 * padding);\n }\n\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n const { anchorPosition } = this.options;\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\n\n const textFieldWidth = this.measureWidth(ctx);\n const textFieldHeight = this.measureHeight();\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\n\n // adjust anchor if text box exceeds canvas borders\n if (canvasDims) {\n const { width, height } = canvasDims;\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0);\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0);\n return { x: newX, y: newY };\n }\n return { x, y };\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const canvas = resolveInput(canvasArg);\n const ctx = getContext2dOrThrow(canvas);\n\n const {\n backgroundColor, fontColor, fontSize, fontStyle, padding,\n } = this.options;\n\n ctx.font = `${fontSize}px ${fontStyle}`;\n const maxTextWidth = this.measureWidth(ctx);\n const textHeight = this.measureHeight();\n\n ctx.fillStyle = backgroundColor;\n const upperLeft = this.getUpperLeft(ctx, canvas);\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\n\n ctx.fillStyle = fontColor;\n this.text.forEach((textLine, i) => {\n const x = padding + upperLeft.x;\n const y = padding + upperLeft.y + ((i + 1) * fontSize);\n ctx.fillText(textLine, x, y);\n });\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { Box, IBoundingBox, IRect } from '../classes/index';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n boxColor?: string\n lineWidth?: number\n drawLabelOptions?: IDrawTextFieldOptions\n label?: string\n}\n\nexport class DrawBoxOptions {\n public boxColor: string;\n\n public lineWidth: number;\n\n public drawLabelOptions: DrawTextFieldOptions;\n\n public label?: string;\n\n constructor(options: IDrawBoxOptions = {}) {\n const {\n boxColor, lineWidth, label, drawLabelOptions,\n } = options;\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\n this.lineWidth = lineWidth || 2;\n this.label = label;\n\n const defaultDrawLabelOptions = {\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\n backgroundColor: this.boxColor,\n };\n this.drawLabelOptions = new DrawTextFieldOptions({ ...defaultDrawLabelOptions, ...drawLabelOptions });\n }\n}\n\nexport class DrawBox {\n public box: Box;\n\n public options: DrawBoxOptions;\n\n constructor(\n box: IBoundingBox | IRect,\n options: IDrawBoxOptions = {},\n ) {\n this.box = new Box(box);\n this.options = new DrawBoxOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const { boxColor, lineWidth } = this.options;\n\n const {\n x, y, width, height,\n } = this.box;\n ctx.strokeStyle = boxColor;\n ctx.lineWidth = lineWidth;\n ctx.strokeRect(x, y, width, height);\n\n const { label } = this.options;\n if (label) {\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg);\n }\n }\n}\n", "import { Box, IBoundingBox, IRect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils/index';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n canvasArg: string | HTMLCanvasElement,\n detections: TDrawDetectionsInput | Array,\n) {\n const detectionsArray = Array.isArray(detections) ? detections : [detections];\n\n detectionsArray.forEach((det) => {\n // eslint-disable-next-line no-nested-ternary\n const score = det instanceof FaceDetection\n ? det.score\n : (isWithFaceDetection(det) ? det.detection.score : undefined);\n\n // eslint-disable-next-line no-nested-ternary\n const box = det instanceof FaceDetection\n ? det.box\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\n\n const label = score ? `${round(score)}` : undefined;\n new DrawBox(box, { label }).draw(canvasArg);\n });\n}\n", "import { env } from '../env/index';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n const { Image, Video } = env.getEnv();\n\n return (media instanceof Image && media.complete)\n || (media instanceof Video && media.readyState >= 3);\n}\n", "import { env } from '../env/index';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n // eslint-disable-next-line consistent-return\n return new Promise((resolve, reject) => {\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n resolve(null);\n return;\n }\n\n function onError(e: Event) {\n if (!e.currentTarget) return;\n // eslint-disable-next-line no-use-before-define\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n reject(e);\n }\n\n function onLoad(e: Event) {\n if (!e.currentTarget) return;\n e.currentTarget.removeEventListener('load', onLoad);\n e.currentTarget.removeEventListener('error', onError);\n resolve(e);\n }\n\n media.addEventListener('load', onLoad);\n media.addEventListener('error', onError);\n });\n}\n", "import { env } from '../env/index';\n\nexport function bufferToImage(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToImage - expected buf to be of type: Blob'));\n const reader = new FileReader();\n reader.onload = () => {\n if (typeof reader.result !== 'string') reject(new Error('bufferToImage - expected reader.result to be a string, in onload'));\n const img = env.getEnv().createImageElement();\n img.onload = () => resolve(img);\n img.onerror = reject;\n img.src = reader.result as string;\n };\n reader.onerror = reject;\n reader.readAsDataURL(buf);\n });\n}\n", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n const { Image, Video } = env.getEnv();\n\n if (input instanceof Image) {\n return new Dimensions(input.naturalWidth, input.naturalHeight);\n }\n if (input instanceof Video) {\n return new Dimensions(input.videoWidth, input.videoHeight);\n }\n return new Dimensions(input.width, input.height);\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n const { createCanvasElement } = env.getEnv();\n const canvas = createCanvasElement();\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n const { ImageData } = env.getEnv();\n\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n throw new Error('createCanvasFromMedia - media has not finished loading yet');\n }\n\n const { width, height } = dims || getMediaDimensions(media);\n const canvas = createCanvas({ width, height });\n\n if (media instanceof ImageData) {\n getContext2dOrThrow(canvas).putImageData(media, 0, 0);\n } else {\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\n }\n return canvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { env } from '../env/index';\nimport { isTensor4D } from '../utils/index';\n\nexport async function imageTensorToCanvas(\n imgTensor: tf.Tensor,\n canvas?: HTMLCanvasElement,\n): Promise {\n const targetCanvas = canvas || env.getEnv().createCanvasElement();\n\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());\n await tf['browser'].toPixels(imgTensor3D, targetCanvas);\n\n imgTensor3D.dispose();\n\n return targetCanvas;\n}\n", "import { env } from '../env/index';\n\nexport function isMediaElement(input: any) {\n const { Image, Canvas, Video } = env.getEnv();\n\n return input instanceof Image\n || input instanceof Canvas\n || input instanceof Video;\n}\n", "import { env } from '../env/index';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage = false) {\n const { Image, Canvas } = env.getEnv();\n\n if (!(input instanceof Image || input instanceof Canvas)) {\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\n }\n\n if (inputSize <= 0) return createCanvas({ width: 1, height: 1 });\n const dims = getMediaDimensions(input);\n const scale = inputSize / Math.max(dims.height, dims.width);\n const width = scale * dims.width;\n const height = scale * dims.height;\n\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize });\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\n\n const offset = Math.abs(width - height) / 2;\n const dx = centerImage && width < height ? offset : 0;\n const dy = centerImage && height < width ? offset : 0;\n if (inputCanvas.width > 0 && inputCanvas.height > 0) getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\n\n return targetCanvas;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env/index';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils/index';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n private _imageTensors: Array = [];\n\n private _canvases: HTMLCanvasElement[] = [];\n\n private _batchSize: number;\n\n private _treatAsBatchInput = false;\n\n private _inputDimensions: number[][] = [];\n\n private _inputSize = 0;\n\n constructor(inputs: Array, treatAsBatchInput = false) {\n if (!Array.isArray(inputs)) {\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`);\n }\n\n this._treatAsBatchInput = treatAsBatchInput;\n this._batchSize = inputs.length;\n\n inputs.forEach((input, idx) => {\n if (isTensor3D(input)) {\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = input.shape;\n return;\n }\n\n if (isTensor4D(input)) {\n const batchSize = (input as any).shape[0];\n if (batchSize !== 1) {\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n\n this._imageTensors[idx] = input;\n this._inputDimensions[idx] = (input as any).shape.slice(1);\n return;\n }\n\n // @ts-ignore\n const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas as HTMLCanvasElement;\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\n });\n }\n\n public get imageTensors(): Array {\n return this._imageTensors;\n }\n\n public get canvases(): HTMLCanvasElement[] {\n return this._canvases;\n }\n\n public get isBatchInput(): boolean {\n return this.batchSize > 1 || this._treatAsBatchInput;\n }\n\n public get batchSize(): number {\n return this._batchSize;\n }\n\n public get inputDimensions(): number[][] {\n return this._inputDimensions;\n }\n\n public get inputSize(): number | undefined {\n return this._inputSize;\n }\n\n public get reshapedInputDimensions(): Dimensions[] {\n return range(this.batchSize, 0, 1).map(\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx),\n );\n }\n\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\n return this.canvases[batchIdx] || this.imageTensors[batchIdx];\n }\n\n public getInputDimensions(batchIdx: number): number[] {\n return this._inputDimensions[batchIdx];\n }\n\n public getInputHeight(batchIdx: number): number {\n return this._inputDimensions[batchIdx][0];\n }\n\n public getInputWidth(batchIdx: number): number {\n return this._inputDimensions[batchIdx][1];\n }\n\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\n if (typeof this.inputSize !== 'number') {\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\n }\n\n const width = this.getInputWidth(batchIdx);\n const height = this.getInputHeight(batchIdx);\n return computeReshapedDimensions({ width, height }, this.inputSize);\n }\n\n /**\n * Create a batch tensor from all input canvases and tensors\n * with size [batchSize, inputSize, inputSize, 3].\n *\n * @param inputSize Height and width of the tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The batch tensor.\n */\n public toBatchTensor(inputSize: number, isCenterInputs = true): tf.Tensor4D {\n this._inputSize = inputSize;\n\n return tf.tidy(() => {\n const inputTensors = range(this.batchSize, 0, 1).map((batchIdx) => {\n const input = this.getInput(batchIdx);\n\n if (input instanceof tf.Tensor) {\n let imgTensor = isTensor4D(input) ? input : tf.expandDims(input);\n imgTensor = padToSquare(imgTensor as tf.Tensor4D, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf['image'].resizeBilinear(imgTensor as tf.Tensor4D, [inputSize, inputSize], false, false);\n }\n\n return imgTensor.as3D(inputSize, inputSize, 3);\n }\n\n if (input instanceof env.getEnv().Canvas) {\n return tf['browser'].fromPixels(imageToSquare(input, inputSize, isCenterInputs));\n }\n\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`);\n });\n\n const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);\n // const batchTensor = tf.stack(inputTensors.map((t) => tf.cast(t, 'float32'))) as tf.Tensor4D;\n\n return batchTensor;\n });\n }\n}\n", "import { isTensor3D, isTensor4D } from '../utils/index';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise {\n if (inputs instanceof NetInput) return inputs;\n const inputArgArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArgArray.length) throw new Error('toNetInput - empty array passed as input');\n const getIdxHint = (idx: number) => (Array.isArray(inputs) ? ` at input index ${idx}:` : '');\n const inputArray = inputArgArray.map(resolveInput);\n inputArray.forEach((input, i) => {\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n if (typeof inputArgArray[i] === 'string') throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`);\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);\n }\n if (isTensor4D(input)) {\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n const batchSize = input.shape[0];\n if (batchSize !== 1) throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);\n }\n });\n // wait for all media elements being loaded\n await Promise.all(inputArray.map((input) => isMediaElement(input) && awaitMediaLoaded(input)));\n return new NetInput(inputArray, Array.isArray(inputs));\n}\n", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env/index';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(input: TNetInput, detections: Array): Promise {\n const { Canvas } = env.getEnv();\n let canvas = input as HTMLCanvasElement;\n if (!(input instanceof Canvas)) {\n const netInput = await toNetInput(input);\n if (netInput.batchSize > 1) throw new Error('extractFaces - batchSize > 1 not supported');\n const tensorOrCanvas = netInput.getInput(0);\n canvas = tensorOrCanvas instanceof Canvas ? tensorOrCanvas : await imageTensorToCanvas(tensorOrCanvas);\n }\n const ctx = getContext2dOrThrow(canvas);\n const boxes = detections\n .map((det) => (det instanceof FaceDetection ? det.forSize(canvas.width, canvas.height).box.floor() : det))\n .map((box) => box.clipAtImageBorders(canvas.width, canvas.height));\n return boxes.map(({ x, y, width, height }) => {\n const faceImg = createCanvas({ width, height });\n if (width > 0 && height > 0) getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x, y, width, height), 0, 0);\n return faceImg;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils/index';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array): Promise {\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\n }\n\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n throw new Error('extractFaceTensors - batchSize > 1 not supported');\n }\n\n return tf.tidy(() => {\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0);\n const boxes = detections.map((det) => (det instanceof FaceDetection ? det.forSize(imgWidth, imgHeight).box : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n const faceTensors = boxes\n .filter((box) => box.width > 0 && box.height > 0)\n .map(({ x, y, width, height }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n return faceTensors;\n });\n}\n", "import { env } from '../env/index';\n\nexport async function fetchOrThrow(\n url: string,\n // eslint-disable-next-line no-undef\n init?: RequestInit,\n): Promise {\n const { fetch } = env.getEnv();\n const res = await fetch(url, init);\n if (!(res.status < 400)) {\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);\n }\n return res;\n}\n", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('image/')) {\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToImage(blob);\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson(uri: string): Promise {\n return (await fetchOrThrow(uri)).json();\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise {\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());\n}\n", "import { env } from '../env/index';\n\nexport function bufferToVideo(buf: Blob): Promise {\n return new Promise((resolve, reject) => {\n if (!(buf instanceof Blob)) reject(new Error('bufferToVideo - expected buf to be of type: Blob'));\n\n const video = env.getEnv().createVideoElement();\n video.oncanplay = () => resolve(video);\n video.onerror = reject;\n video.playsInline = true;\n video.muted = true;\n video.src = URL.createObjectURL(buf);\n video.play();\n });\n}\n", "import { bufferToVideo } from './bufferToVideo';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchVideo(uri: string): Promise {\n const res = await fetchOrThrow(uri);\n const blob = await (res).blob();\n\n if (!blob.type.startsWith('video/')) {\n throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${blob.type}, for url: ${res.url}`);\n }\n return bufferToVideo(blob);\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`;\n\n if (!uri) {\n return {\n modelBaseUri: '',\n manifestUri: defaultManifestFilename,\n };\n }\n\n if (uri === '/') {\n return {\n modelBaseUri: '/',\n manifestUri: `/${defaultManifestFilename}`,\n };\n }\n // eslint-disable-next-line no-nested-ternary\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n uri = uri.replace(protocol, '');\n\n const parts = uri.split('/').filter((s) => s);\n\n const manifestFile = uri.endsWith('.json')\n ? parts[parts.length - 1]\n : defaultManifestFilename;\n\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri;\n\n return {\n modelBaseUri,\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n uri: string | undefined,\n defaultModelName: string,\n): Promise {\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);\n // @ts-ignore\n const manifest = await fetchJson(manifestUri);\n // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n return tf['io'].loadWeights(manifest, modelBaseUri);\n}\n", "import { IDimensions } from '../classes/index';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions = false) {\n const { width, height } = useMediaDimensions\n ? getMediaDimensions(reference)\n : reference;\n input.width = width;\n input.height = height;\n return { width, height };\n}\n", "import * as tf from '../dist/tfjs.esm';\n\nimport { ParamMapping } from './common/index';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom/index';\nimport { env } from './env/index';\n\nexport abstract class NeuralNetwork {\n constructor(name: string) {\n this._name = name;\n }\n\n protected _params: TNetParams | undefined = undefined;\n\n protected _paramMappings: ParamMapping[] = [];\n\n public _name: any;\n\n public get params(): TNetParams | undefined { return this._params; }\n\n public get paramMappings(): ParamMapping[] { return this._paramMappings; }\n\n public get isLoaded(): boolean { return !!this.params; }\n\n public getParamFromPath(paramPath: string): tf.Tensor {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n return obj[objProp];\n }\n\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n const { obj, objProp } = this.traversePropertyPath(paramPath);\n obj[objProp].dispose();\n obj[objProp] = tensor;\n }\n\n public getParamList() {\n return this._paramMappings.map(({ paramPath }) => ({\n path: paramPath,\n tensor: this.getParamFromPath(paramPath),\n }));\n }\n\n public getTrainableParams() {\n return this.getParamList().filter((param) => param.tensor instanceof tf.Variable);\n }\n\n public getFrozenParams() {\n return this.getParamList().filter((param) => !(param.tensor instanceof tf.Variable));\n }\n\n public variable() {\n this.getFrozenParams().forEach(({ path, tensor }) => {\n this.reassignParamFromPath(path, tensor.variable());\n });\n }\n\n public freeze() {\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n const tensor = tf.tensor(variable.dataSync());\n variable.dispose();\n this.reassignParamFromPath(path, tensor);\n });\n }\n\n public dispose(throwOnRedispose = true) {\n this.getParamList().forEach((param) => {\n if (throwOnRedispose && param.tensor.isDisposed) {\n throw new Error(`param tensor has already been disposed for path ${param.path}`);\n }\n param.tensor.dispose();\n });\n this._params = undefined;\n }\n\n public serializeParams(): Float32Array {\n return new Float32Array(\n this.getParamList()\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n .reduce((flat, arr) => flat.concat(arr)),\n );\n }\n\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise {\n if (weightsOrUrl instanceof Float32Array) {\n this.extractWeights(weightsOrUrl);\n return;\n }\n await this.loadFromUri(weightsOrUrl);\n }\n\n public async loadFromUri(uri: string | undefined) {\n if (uri && typeof uri !== 'string') {\n throw new Error(`${this._name}.loadFromUri - expected model uri`);\n }\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName());\n this.loadFromWeightMap(weightMap);\n }\n\n public async loadFromDisk(filePath: string | undefined) {\n if (filePath && typeof filePath !== 'string') {\n throw new Error(`${this._name}.loadFromDisk - expected model file path`);\n }\n const { readFile } = env.getEnv();\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName());\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => (typeof buf === 'string' ? Buffer.from(buf) : buf.buffer))));\n // @ts-ignore async-vs-sync mismatch\n const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk);\n const manifest = JSON.parse((await readFile(manifestUri)).toString());\n const weightMap = await loadWeights(manifest, modelBaseUri);\n this.loadFromWeightMap(weightMap);\n }\n\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { paramMappings, params } = this.extractParamsFromWeightMap(weightMap);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n public extractWeights(weights: Float32Array) {\n const { paramMappings, params } = this.extractParams(weights);\n this._paramMappings = paramMappings;\n this._params = params;\n }\n\n private traversePropertyPath(paramPath: string) {\n if (!this.params) {\n throw new Error('traversePropertyPath - model has no loaded params');\n }\n\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n // eslint-disable-next-line no-prototype-builtins\n if (!res.nextObj.hasOwnProperty(objProp)) {\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);\n }\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };\n }, { nextObj: this.params });\n\n const { obj, objProp } = result;\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);\n }\n\n return { obj, objProp };\n }\n\n protected abstract getDefaultModelName(): string\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n\n // eslint-disable-next-line no-unused-vars\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n x: tf.Tensor4D,\n params: SeparableConvParams,\n stride: [number, number],\n): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\n out = tf.add(out, params.bias);\n return out;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, SeparableConvParams } from '../common/index';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock3Params,\n isFirstLayer = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n });\n}\n\nexport function denseBlock4(\n x: tf.Tensor4D,\n denseBlockParams: DenseBlock4Params,\n isFirstLayer = false,\n isScaleDown = true,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out1 = tf.relu(\n isFirstLayer\n ? tf.add(\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n denseBlockParams.conv0.bias,\n )\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1]),\n ) as tf.Tensor4D;\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\n\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D;\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\n\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D;\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\n\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D;\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n x: tf.Tensor4D,\n params: ConvParams,\n padding: 'valid' | 'same' = 'same',\n withRelu = false,\n): tf.Tensor4D {\n return tf.tidy(() => {\n const out = tf.add(\n tf.conv2d(x, params.filters, [1, 1], padding),\n params.bias,\n ) as tf.Tensor4D;\n\n return withRelu ? tf.relu(out) : out;\n });\n}\n", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n Object.keys(weightMap).forEach((path) => {\n if (!paramMappings.some((pm) => pm.originalPath === path)) {\n weightMap[path].dispose();\n }\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams => {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\nexport function extractFCParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): FCParams => {\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\n const fc_bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return {\n weights: fc_weights,\n bias: fc_bias,\n };\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\n// eslint-disable-next-line no-unused-vars\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n originalPath?: string\n paramPath: string\n}\n\nexport type ConvParams = {\n filters: tf.Tensor4D\n bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n weights: tf.Tensor2D\n bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n // eslint-disable-next-line no-useless-constructor\n constructor(\n // eslint-disable-next-line no-unused-vars\n public depthwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public pointwise_filter: tf.Tensor4D,\n // eslint-disable-next-line no-unused-vars\n public bias: tf.Tensor1D,\n // eslint-disable-next-line no-empty-function\n ) {}\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n extractWeights: ExtractWeightsFunction,\n paramMappings: ParamMapping[],\n) {\n return (channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams => {\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/depthwise_filter` },\n { paramPath: `${mappedPrefix}/pointwise_filter` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n\nexport function loadSeparableConvParamsFactory(\n // eslint-disable-next-line no-unused-vars\n extractWeightEntry: (originalPath: string, paramRank: number) => T,\n) {\n return (prefix: string): SeparableConvParams => {\n const depthwise_filter = extractWeightEntry(`${prefix}/depthwise_filter`, 4);\n const pointwise_filter = extractWeightEntry(`${prefix}/pointwise_filter`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return new SeparableConvParams(\n depthwise_filter,\n pointwise_filter,\n bias,\n );\n };\n}\n", "import { isTensor } from '../utils/index';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n return (originalPath: string, paramRank: number, mappedPath?: string) => {\n const tensor = weightMap[originalPath];\n\n if (!isTensor(tensor, paramRank)) {\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);\n }\n\n paramMappings.push(\n { originalPath, paramPath: mappedPath || originalPath },\n );\n\n return tensor;\n };\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n let remainingWeights = weights;\n\n function extractWeights(numWeights: number): Float32Array {\n const ret = remainingWeights.slice(0, numWeights);\n remainingWeights = remainingWeights.slice(numWeights);\n return ret;\n }\n\n function getRemainingWeights(): Float32Array {\n return remainingWeights;\n }\n\n return {\n extractWeights,\n getRemainingWeights,\n };\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`);\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`);\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer = false): DenseBlock4Params {\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock4Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2');\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: {\n dense0, dense1, dense2, dense3,\n },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from './types';\n\n// eslint-disable-next-line no-unused-vars\nexport function loadConvParamsFactory(extractWeightEntry: (originalPath: string, paramRank: number) => T) {\n return (prefix: string): ConvParams => {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n\n return { filters, bias };\n };\n}\n", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractDenseBlock3Params(prefix: string, isFirstLayer = false): DenseBlock3Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n\n return { conv0, conv1, conv2 };\n }\n\n function extractDenseBlock4Params(prefix: string, isFirstLayer = false): DenseBlock4Params {\n const conv0 = isFirstLayer\n ? extractConvParams(`${prefix}/conv0`)\n : extractSeparableConvParams(`${prefix}/conv0`);\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`);\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`);\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`);\n\n return {\n conv0, conv1, conv2, conv3,\n };\n }\n\n return {\n extractDenseBlock3Params,\n extractDenseBlock4Params,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock4Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock4Params('dense0', true),\n dense1: extractDenseBlock4Params('dense1'),\n dense2: extractDenseBlock4Params('dense2'),\n dense3: extractDenseBlock4Params('dense3'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('FaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock4(normalized, params.dense0, true);\n out = denseBlock4(out, params.dense1);\n out = denseBlock4(out, params.dense2);\n out = denseBlock4(out, params.dense3);\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n x: tf.Tensor2D,\n params: FCParams,\n): tf.Tensor2D {\n return tf.tidy(() => tf.add(\n tf.matMul(x, params.weights),\n params.bias,\n ));\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const fc = extractFCParams(channelsIn, channelsOut, 'fc');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: extractFcParams('fc'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n const featureExtractorMap: tf.NamedTensorMap = {};\n const classifierMap: tf.NamedTensorMap = {};\n\n Object.keys(weightMap).forEach((key) => {\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\n map[key] = weightMap[key];\n });\n\n return { featureExtractorMap, classifierMap };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends NeuralNetwork {\n protected _faceFeatureExtractor: IFaceFeatureExtractor;\n\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor) {\n super(_name);\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): IFaceFeatureExtractor {\n return this._faceFeatureExtractor;\n }\n\n protected abstract override getDefaultModelName(): string\n\n protected abstract getClassifierChannelsIn(): number\n\n protected abstract getClassifierChannelsOut(): number\n\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\n });\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const cIn = this.getClassifierChannelsIn();\n const cOut = this.getClassifierChannelsOut();\n const classifierWeightSize = (cOut * cIn) + cOut;\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'] as const;\n\nexport class FaceExpressions {\n public neutral = 0;\n public happy = 0;\n public sad = 0;\n public angry = 0;\n public fearful = 0;\n public disgusted = 0;\n public surprised = 0;\n\n constructor(probabilities: number[] | Float32Array) {\n if (probabilities.length !== 7) {\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`);\n }\n\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n this[expression] = probabilities[idx];\n });\n }\n\n asSortedArray() {\n return FACE_EXPRESSION_LABELS\n .map((expression) => ({ expression, probability: this[expression] as number }))\n .sort((e0, e1) => e1.probability - e0.probability);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceExpressionNet', faceFeatureExtractor);\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n return tf.tidy(() => tf.softmax(this.runNet(input)));\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictExpressions(input: TNetInput) {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {\n const data = t.dataSync();\n t.dispose();\n return data;\n }));\n out.dispose();\n\n const predictionsByBatch = probabilitesByBatch\n .map((probabilites) => new FaceExpressions(probabilites as Float32Array));\n\n return netInput.isBatchInput\n ? predictionsByBatch\n : predictionsByBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_expression_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n\n protected getClassifierChannelsOut(): number {\n return 7;\n }\n}\n", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions = TSource & { expressions: FaceExpressions }\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n return obj.expressions instanceof FaceExpressions;\n}\n\nexport function extendWithFaceExpressions(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions {\n const extension = { expressions };\n return { ...sourceObj, ...extension };\n}\n", "import { IPoint, Point } from '../classes/index';\nimport { FaceExpressions } from '../faceExpressionNet/index';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils/index';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array, minConfidence = 0.1, textFieldAnchor?: IPoint) {\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\n\n faceExpressionsArray.forEach((e) => {\n // eslint-disable-next-line no-nested-ternary\n const expr = e instanceof FaceExpressions\n ? e\n : (isWithFaceExpressions(e) ? e.expressions : undefined);\n if (!expr) {\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\n }\n\n const sorted = expr.asSortedArray();\n const resultsToDisplay = sorted.filter((exprLocal) => exprLocal.probability > minConfidence);\n\n const anchor = isWithFaceDetection(e)\n ? e.detection.box.bottomLeft\n : (textFieldAnchor || new Point(0, 0));\n\n const drawTextField = new DrawTextField(\n resultsToDisplay.map((exprLocal) => `${exprLocal.expression} (${round(exprLocal.probability)})`),\n anchor,\n );\n drawTextField.draw(canvasArg);\n });\n}\n", "import { Point } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n landmarks: TFaceLandmarks;\n unshiftedLandmarks: TFaceLandmarks;\n alignedRect: FaceDetection;\n angle: {\n roll: number | undefined;\n pitch: number | undefined;\n yaw: number | undefined;\n };\n};\n\nexport function isWithFaceLandmarks(\n obj: any,\n): obj is WithFaceLandmarks, FaceLandmarks> {\n return (\n isWithFaceDetection(obj)\n && (obj as any)['landmarks'] instanceof FaceLandmarks\n && (obj as any)['unshiftedLandmarks'] instanceof FaceLandmarks\n && (obj as any)['alignedRect'] instanceof FaceDetection\n );\n}\n\nfunction calculateFaceAngle(mesh: FaceLandmarks) {\n // Helper to convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (radians: number) => (radians * 180) / Math.PI;\n const calcLengthBetweenTwoPoints = (a: Point, b: Point) => Math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2);\n\n const angle = {\n roll: undefined,\n pitch: undefined,\n yaw: undefined,\n };\n\n const calcYaw = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Calc x-distance from left side of the face (\"ear\") to facial midpoint (\"nose\")\n const leftToMidpoint = Math.floor(leftPoint.x - midPoint.x);\n // Calc x-distance from facial midpoint (\"nose\") to the right side of the face (\"ear\")\n const rightToMidpoint = Math.floor(midPoint.x - rightPoint.x);\n // Difference in distances coincidentally approximates to angles\n return leftToMidpoint - rightToMidpoint;\n };\n\n const calcRoll = (lever: Point, pivot: Point) => {\n // When rolling, the head seems to pivot from the nose/lips/chin area.\n // So, we'll choose any two points from the facial midline, where the first point should be the pivot, and the other \"lever\"\n // Plan/Execution: get the hypotenuse & opposite sides of a 90deg triangle ==> Calculate angle in radians\n const hypotenuse = Math.hypot(pivot.x - lever.x, pivot.y - lever.y);\n const opposite = pivot.y - lever.y;\n const angleInRadians = Math.asin(opposite / hypotenuse);\n const angleInDegrees = degrees(angleInRadians);\n const normalizeAngle = Math.floor(90 - angleInDegrees);\n // If lever more to the left of the pivot, then we're tilting left\n // \"-\" is negative direction. \"+\", or absence of a sign is positive direction\n const tiltDirection = pivot.x - lever.x < 0 ? -1 : 1;\n const result = normalizeAngle * tiltDirection;\n return result;\n };\n\n const calcPitch = (leftPoint: Point, midPoint: Point, rightPoint: Point) => {\n // Theory: While pitching, the nose is the most salient point --> That's what we'll use to make a trianle.\n // The \"base\" is between point that don't move when we pitch our head (i.e. an imaginary line running ear to ear through the nose).\n // Executuin: Get the opposite & adjacent lengths of the triangle from the ear's perspective. Use it to get angle.\n\n const base = calcLengthBetweenTwoPoints(leftPoint, rightPoint);\n // adjecent is base/2 technically.\n const baseCoords = new Point((leftPoint.x + rightPoint.x) / 2, (leftPoint.y + rightPoint.y) / 2);\n const midToBaseLength = calcLengthBetweenTwoPoints(midPoint, baseCoords);\n const angleInRadians = Math.atan(midToBaseLength / base);\n const angleInDegrees = Math.floor(degrees(angleInRadians));\n // Account for directionality.\n // pitch forwards (_i.e. tilting your head forwards) is positive (or no sign); backward is negative.\n const direction = baseCoords.y - midPoint.y < 0 ? -1 : 1;\n const result = angleInDegrees * direction;\n return result;\n };\n\n if (!mesh || !mesh.positions || mesh.positions.length !== 68) return angle;\n const pt = mesh.positions;\n angle.roll = calcRoll(pt[27], pt[66]);\n angle.pitch = calcPitch(pt[14], pt[30], pt[2]);\n angle.yaw = calcYaw(pt[14], pt[33], pt[2]);\n return angle;\n}\n\nexport function extendWithFaceLandmarks, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(\n sourceObj: TSource,\n unshiftedLandmarks: TFaceLandmarks,\n): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(\n sourceObj.detection.score,\n rect.rescale(imageDims.reverse()),\n imageDims,\n );\n const angle = calculateFaceAngle(unshiftedLandmarks);\n const extension = { landmarks, unshiftedLandmarks, alignedRect, angle };\n return { ...sourceObj, ...extension };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { IPoint } from '../classes/index';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n drawLines?: boolean\n drawPoints?: boolean\n lineWidth?: number\n pointSize?: number\n lineColor?: string\n pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n public drawLines: boolean;\n\n public drawPoints: boolean;\n\n public lineWidth: number;\n\n public pointSize: number;\n\n public lineColor: string;\n\n public pointColor: string;\n\n constructor(options: IDrawFaceLandmarksOptions = {}) {\n const {\n drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor,\n } = options;\n this.drawLines = drawLines;\n this.drawPoints = drawPoints;\n this.lineWidth = lineWidth || 1;\n this.pointSize = pointSize || 2;\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\n }\n}\n\nexport class DrawFaceLandmarks {\n public faceLandmarks: FaceLandmarks;\n\n public options: DrawFaceLandmarksOptions;\n\n constructor(\n faceLandmarks: FaceLandmarks,\n options: IDrawFaceLandmarksOptions = {},\n ) {\n this.faceLandmarks = faceLandmarks;\n this.options = new DrawFaceLandmarksOptions(options);\n }\n\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n const ctx = getContext2dOrThrow(canvasArg);\n\n const {\n drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor,\n } = this.options;\n\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n ctx.strokeStyle = lineColor;\n ctx.lineWidth = lineWidth;\n drawContour(ctx, this.faceLandmarks.getJawOutline());\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\n drawContour(ctx, this.faceLandmarks.getNose());\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\n drawContour(ctx, this.faceLandmarks.getRightEye(), true);\n drawContour(ctx, this.faceLandmarks.getMouth(), true);\n }\n\n if (drawPoints) {\n ctx.strokeStyle = pointColor;\n ctx.fillStyle = pointColor;\n\n const drawPoint = (pt: IPoint) => {\n ctx.beginPath();\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\n ctx.fill();\n };\n this.faceLandmarks.positions.forEach(drawPoint);\n }\n }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks>\n\nexport function drawFaceLandmarks(\n canvasArg: string | HTMLCanvasElement,\n faceLandmarks: DrawFaceLandmarksInput | Array,\n) {\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\n faceLandmarksArray.forEach((f) => {\n // eslint-disable-next-line no-nested-ternary\n const landmarks = f instanceof FaceLandmarks\n ? f\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\n if (!landmarks) {\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks> or array thereof');\n }\n\n new DrawFaceLandmarks(landmarks).draw(canvasArg);\n });\n}\n", "{\n \"name\": \"@vladmandic/face-api\",\n \"version\": \"1.7.14\",\n \"description\": \"FaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS\",\n \"sideEffects\": false,\n \"main\": \"dist/face-api.node.js\",\n \"module\": \"dist/face-api.esm.js\",\n \"browser\": \"dist/face-api.esm.js\",\n \"types\": \"types/face-api.d.ts\",\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/face-api/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/face-api/demo/webcam.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/face-api.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/node.js\",\n \"build\": \"node build.js\",\n \"dev\": \"build --profile development\",\n \"typings\": \"build --profile typings\",\n \"lint\": \"eslint src/ demo/\",\n \"test\": \"node --trace-warnings test/test-node.js\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"face-api\",\n \"faceapi\",\n \"face-detection\",\n \"age-gender\",\n \"emotion-detection\",\n \"face-recognition\",\n \"face\",\n \"face-description\",\n \"tensorflow\",\n \"tensorflowjs\",\n \"tfjs\"\n ],\n \"devDependencies\": {\n \"@canvas/image\": \"^2.0.0\",\n \"@microsoft/api-extractor\": \"^7.49.2\",\n \"@tensorflow/tfjs\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.22.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.22.0\",\n \"@tensorflow/tfjs-converter\": \"^4.22.0\",\n \"@tensorflow/tfjs-core\": \"^4.22.0\",\n \"@tensorflow/tfjs-data\": \"^4.22.0\",\n \"@tensorflow/tfjs-layers\": \"^4.22.0\",\n \"@tensorflow/tfjs-node\": \"^4.22.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.22.0\",\n \"@types/node\": \"^22.13.1\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.2\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"ajv\": \"^8.17.1\",\n \"esbuild\": \"^0.24.2\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-import\": \"^2.30.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"node-fetch\": \"^3.3.2\",\n \"rimraf\": \"^6.0.1\",\n \"seedrandom\": \"^3.0.5\",\n \"tslib\": \"^2.8.1\",\n \"typedoc\": \"^0.27.6\",\n \"typescript\": \"5.7.3\"\n }\n}\n", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common/index';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { entry_flow, middle_flow, exit_flow },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common/index';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils/index';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n const extractConvParams = loadConvParamsFactory(extractWeightEntry);\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`);\n\n return { separable_conv0, separable_conv1, expansion_conv };\n }\n\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`);\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`);\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow: Record<`main_block_${number}`, MainBlockParams> = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`);\n });\n\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\n\n const exit_flow = {\n reduction_block: exit_flow_reduction_block,\n separable_conv: exit_flow_separable_conv,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common/index';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { range } from '../utils/index';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput = true): tf.Tensor4D {\n let out = isActivateInput ? tf.relu(x) : x;\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = tf.maxPool(out, [3, 3], [2, 2], 'same');\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\n return out;\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\n out = tf.add(out, x);\n return out;\n}\n\nexport class TinyXception extends NeuralNetwork {\n private _numMainBlocks: number;\n\n constructor(numMainBlocks: number) {\n super('TinyXception');\n this._numMainBlocks = numMainBlocks;\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n if (!params) {\n throw new Error('TinyXception - load model before inference');\n }\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\n out = reductionBlock(out, params.entry_flow.reduction_block_1);\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`]);\n });\n out = reductionBlock(out, params.exit_flow.reduction_block);\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'tiny_xception_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this._numMainBlocks);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights, this._numMainBlocks);\n }\n}\n", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\n\n const age = extractFCParams(512, 1, 'fc/age');\n const gender = extractFCParams(512, 2, 'fc/gender');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { fc: { age, gender } },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common/index';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractFcParams(prefix: string): FCParams {\n const weights = extractWeightEntry(`${prefix}/weights`, 2);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${this._name} - load model before inference`);\n }\n\n return tf.tidy(() => {\n const bottleneckFeatures = input instanceof NetInput\n ? this.faceFeatureExtractor.forwardInput(input)\n : input;\n\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D();\n const gender = fullyConnectedLayer(pooled, params.fc.gender);\n return { age, gender };\n });\n }\n\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n return tf.tidy(() => {\n const { age, gender } = this.runNet(input);\n return { age, gender: tf.softmax(gender) };\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async predictAgeAndGender(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput);\n\n const ages = tf.unstack(out.age);\n const genders = tf.unstack(out.gender);\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n ageTensor,\n genderTensor: genders[i],\n }));\n\n const predictionsByBatch = await Promise.all(\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n const age = (ageTensor.dataSync())[0];\n const probMale = (genderTensor.dataSync())[0];\n const isMale = probMale > 0.5;\n const gender = isMale ? Gender.MALE : Gender.FEMALE;\n const genderProbability = isMale ? probMale : (1 - probMale);\n\n ageTensor.dispose();\n genderTensor.dispose();\n return { age, gender, genderProbability };\n }),\n );\n out.age.dispose();\n out.gender.dispose();\n\n return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction;\n }\n\n protected getDefaultModelName(): string {\n return 'age_gender_model';\n }\n\n public override dispose(throwOnRedispose = true) {\n this.faceFeatureExtractor.dispose(throwOnRedispose);\n super.dispose(throwOnRedispose);\n }\n\n public loadClassifierParams(weights: Float32Array) {\n const { params, paramMappings } = this.extractClassifierParams(weights);\n this._params = params;\n this._paramMappings = paramMappings;\n }\n\n public extractClassifierParams(weights: Float32Array) {\n return extractParams(weights);\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);\n\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\n\n return extractParamsFromWeightMap(classifierMap);\n }\n\n protected extractParams(weights: Float32Array) {\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\n\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\n const classifierWeights = weights.slice(weights.length - classifierWeightSize);\n\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\n return this.extractClassifierParams(classifierWeights);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { IDimensions, Point } from '../classes/index';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils/index';\n\nexport abstract class FaceLandmark68NetBase<\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n extends FaceProcessor {\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n const inputDimensions = originalDimensions.map(({ width, height }) => {\n const scale = inputSize / Math.max(height, width);\n return {\n width: width * scale,\n height: height * scale,\n };\n });\n\n const batchSize = inputDimensions.length;\n\n return tf.tidy(() => {\n const createInterleavedTensor = (fillX: number, fillY: number) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();\n\n // eslint-disable-next-line no-unused-vars\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n const { width, height } = inputDimensions[batchIdx];\n return cond(width, height) ? Math.abs(width - height) / 2 : 0;\n };\n\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h);\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w);\n\n const landmarkTensors = output\n .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n getPaddingX(batchIdx),\n getPaddingY(batchIdx),\n ))))\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(\n inputDimensions[batchIdx].width,\n inputDimensions[batchIdx].height,\n ))));\n\n return landmarkTensors as tf.Tensor2D;\n });\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n return tf.tidy(() => {\n const out = this.runNet(input);\n return this.postProcess(\n out,\n input.inputSize as number,\n input.inputDimensions.map(([height, width]) => ({ height, width })),\n );\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async detectLandmarks(input: TNetInput): Promise {\n const netInput = await toNetInput(input);\n const landmarkTensors = tf.tidy(\n () => tf.unstack(this.forwardInput(netInput)),\n );\n\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\n async (landmarkTensor, batchIdx) => {\n const landmarksArray = Array.from(landmarkTensor.dataSync());\n const xCoords = landmarksArray.filter((_, i) => isEven(i));\n const yCoords = landmarksArray.filter((_, i) => !isEven(i));\n\n return new FaceLandmarks68(\n Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n {\n height: netInput.getInputHeight(batchIdx),\n width: netInput.getInputWidth(batchIdx),\n },\n );\n },\n ));\n\n landmarkTensors.forEach((t) => t.dispose());\n\n return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n }\n\n protected getClassifierChannelsOut(): number {\n return 136;\n }\n}\n", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n super('FaceLandmark68Net', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 256;\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common/index';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeightMapTiny(\n weightMap: tf.NamedTensorMap,\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractDenseBlock3Params,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const params = {\n dense0: extractDenseBlock3Params('dense0', true),\n dense1: extractDenseBlock3Params('dense1'),\n dense2: extractDenseBlock3Params('dense2'),\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import { extractWeightsFactory, ParamMapping } from '../common/index';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractDenseBlock3Params,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1');\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2');\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n paramMappings,\n params: { dense0, dense1, dense2 },\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeightMapTiny } from './extractParamsFromWeightMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork implements IFaceFeatureExtractor {\n constructor() {\n super('TinyFaceFeatureExtractor');\n }\n\n public forwardInput(input: NetInput): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyFaceFeatureExtractor - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = denseBlock3(normalized, params.dense0, true);\n out = denseBlock3(out, params.dense1);\n out = denseBlock3(out, params.dense2);\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\n\n return out;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n protected getDefaultModelName(): string {\n return 'face_feature_extractor_tiny_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMapTiny(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParamsTiny(weights);\n }\n}\n", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase {\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n super('FaceLandmark68TinyNet', faceFeatureExtractor);\n }\n\n protected getDefaultModelName(): string {\n return 'face_landmark_68_tiny_model';\n }\n\n protected getClassifierChannelsIn(): number {\n return 128;\n }\n}\n", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\nexport class FaceLandmarkNet extends FaceLandmark68Net {}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n return tf.add(tf.mul(x, params.weights), params.biases);\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\nfunction convLayer(\n x: tf.Tensor4D,\n params: ConvLayerParams,\n strides: [number, number],\n withRelu: boolean,\n padding: 'valid' | 'same' = 'same',\n): tf.Tensor4D {\n const { filters, bias } = params.conv;\n\n let out = tf.conv2d(x, filters, strides, padding);\n out = tf.add(out, bias);\n out = scale(out, params.scale);\n return withRelu ? tf.relu(out) : out;\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], true);\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [1, 1], false);\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n return convLayer(x, params, [2, 2], true, 'valid');\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common/index';\nimport { isFloat } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n const weights = extractWeights(numFilterValues);\n const depth = weights.length / (numFilters * filterSize * filterSize);\n\n if (isFloat(depth)) {\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`);\n }\n\n return tf.tidy(\n () => tf.transpose(\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n [2, 3, 1, 0],\n ),\n );\n }\n\n function extractConvParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvParams {\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize);\n const bias = tf.tensor1d(extractWeights(numFilters));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/bias` },\n );\n\n return { filters, bias };\n }\n\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n const weights = tf.tensor1d(extractWeights(numWeights));\n const biases = tf.tensor1d(extractWeights(numWeights));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/weights` },\n { paramPath: `${mappedPrefix}/biases` },\n );\n\n return {\n weights,\n biases,\n };\n }\n\n function extractConvLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n ): ConvLayerParams {\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);\n\n return { conv, scale };\n }\n\n function extractResidualLayerParams(\n numFilterValues: number,\n numFilters: number,\n filterSize: number,\n mappedPrefix: string,\n isDown = false,\n ): ResidualLayerParams {\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);\n\n return { conv1, conv2 };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\n\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\n\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\n\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\n\n const fc = tf.tidy(\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]),\n );\n paramMappings.push({ paramPath: 'fc' });\n\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor2D } from '../utils/index';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n const weights = extractWeightEntry(`${prefix}/scale/weights`, 1);\n const biases = extractWeightEntry(`${prefix}/scale/biases`, 1);\n\n return { weights, biases };\n }\n\n function extractConvLayerParams(prefix: string): ConvLayerParams {\n const filters = extractWeightEntry(`${prefix}/conv/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/conv/bias`, 1);\n const scale = extractScaleLayerParams(prefix);\n\n return { conv: { filters, bias }, scale };\n }\n\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n return {\n conv1: extractConvLayerParams(`${prefix}/conv1`),\n conv2: extractConvLayerParams(`${prefix}/conv2`),\n };\n }\n\n return {\n extractConvLayerParams,\n extractResidualLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvLayerParams,\n extractResidualLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n const conv32_down = extractConvLayerParams('conv32_down');\n const conv32_1 = extractResidualLayerParams('conv32_1');\n const conv32_2 = extractResidualLayerParams('conv32_2');\n const conv32_3 = extractResidualLayerParams('conv32_3');\n\n const conv64_down = extractResidualLayerParams('conv64_down');\n const conv64_1 = extractResidualLayerParams('conv64_1');\n const conv64_2 = extractResidualLayerParams('conv64_2');\n const conv64_3 = extractResidualLayerParams('conv64_3');\n\n const conv128_down = extractResidualLayerParams('conv128_down');\n const conv128_1 = extractResidualLayerParams('conv128_1');\n const conv128_2 = extractResidualLayerParams('conv128_2');\n\n const conv256_down = extractResidualLayerParams('conv256_down');\n const conv256_1 = extractResidualLayerParams('conv256_1');\n const conv256_2 = extractResidualLayerParams('conv256_2');\n const conv256_down_out = extractResidualLayerParams('conv256_down_out');\n\n const { fc } = weightMap;\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\n\n if (!isTensor2D(fc)) {\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);\n }\n\n const params = {\n conv32_down,\n conv32_1,\n conv32_2,\n conv32_3,\n conv64_down,\n conv64_1,\n conv64_2,\n conv64_3,\n conv128_down,\n conv128_1,\n conv128_2,\n conv256_down,\n conv256_1,\n conv256_2,\n conv256_down_out,\n fc,\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = conv(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n out = tf.add(out, x);\n out = tf.relu(out);\n return out;\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n let out = convDown(x, params.conv1);\n out = convNoRelu(out, params.conv2);\n\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D;\n const zeros = tf.zeros(pooled.shape);\n const isPad = pooled.shape[3] !== out.shape[3];\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\n\n if (isAdjustShape) {\n const padShapeX = [...out.shape] as [number, number, number, number];\n padShapeX[1] = 1;\n const zerosW = tf.zeros(padShapeX);\n out = tf.concat([out, zerosW], 1);\n\n const padShapeY = [...out.shape] as [number, number, number, number];\n padShapeY[2] = 1;\n const zerosH = tf.zeros(padShapeY);\n out = tf.concat([out, zerosH], 2);\n }\n\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\n out = tf.add(pooled, out) as tf.Tensor4D;\n\n out = tf.relu(out);\n return out;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops/index';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\nexport class FaceRecognitionNet extends NeuralNetwork {\n constructor() {\n super('FaceRecognitionNet');\n }\n\n public forwardInput(input: NetInput): tf.Tensor2D {\n const { params } = this;\n\n if (!params) {\n throw new Error('FaceRecognitionNet - load model before inference');\n }\n\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n const meanRgb = [122.782, 117.001, 104.298];\n const normalized = normalize(batchTensor, meanRgb).div(255) as tf.Tensor4D;\n\n let out = convDown(normalized, params.conv32_down);\n out = tf.maxPool(out, 3, 2, 'valid');\n\n out = residual(out, params.conv32_1);\n out = residual(out, params.conv32_2);\n out = residual(out, params.conv32_3);\n\n out = residualDown(out, params.conv64_down);\n out = residual(out, params.conv64_1);\n out = residual(out, params.conv64_2);\n out = residual(out, params.conv64_3);\n\n out = residualDown(out, params.conv128_down);\n out = residual(out, params.conv128_1);\n out = residual(out, params.conv128_2);\n\n out = residualDown(out, params.conv256_down);\n out = residual(out, params.conv256_1);\n out = residual(out, params.conv256_2);\n out = residualDown(out, params.conv256_down_out);\n\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D;\n const fullyConnected = tf.matMul(globalAvg, params.fc);\n\n return fullyConnected as tf.Tensor2D;\n });\n }\n\n public async forward(input: TNetInput): Promise {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async computeFaceDescriptor(input: TNetInput): Promise {\n // @ts-ignore\n if (input?.shape?.some((dim) => dim <= 0)) return new Float32Array(128);\n const netInput = await toNetInput(input);\n const faceDescriptorTensors = tf.tidy(() => tf.unstack(this.forwardInput(netInput)));\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map((t) => t.data())) as Float32Array[];\n faceDescriptorTensors.forEach((t) => t.dispose());\n return netInput.isBatchInput ? faceDescriptorsForBatch : faceDescriptorsForBatch[0];\n }\n\n protected getDefaultModelName(): string {\n return 'face_recognition_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n const net = new FaceRecognitionNet();\n net.extractWeights(weights);\n return net;\n}\n", "export type WithFaceDescriptor = TSource & {\n descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n TSource\n>(\n sourceObj: TSource,\n descriptor: Float32Array,\n): WithFaceDescriptor {\n const extension = { descriptor };\n return { ...sourceObj, ...extension };\n}\n", "export type WithAge = TSource & {\n age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n return typeof obj.age === 'number';\n}\n\nexport function extendWithAge<\n TSource\n>(\n sourceObj: TSource,\n age: number,\n): WithAge {\n const extension = { age };\n return { ...sourceObj, ...extension };\n}\n", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils/index';\n\nexport type WithGender = TSource & {\n gender: Gender\n genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n return (obj.gender === Gender.MALE || obj.gender === Gender.FEMALE)\n && isValidProbablitiy(obj.genderProbability);\n}\n\nexport function extendWithGender<\n TSource\n>(\n sourceObj: TSource,\n gender: Gender,\n genderProbability: number,\n): WithGender {\n const extension = { gender, genderProbability };\n return { ...sourceObj, ...extension };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common/index';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\n { paramPath: `${mappedPrefix}/batch_norm_variance` },\n );\n\n return {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n };\n }\n\n function extractConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n isPointwiseConv?: boolean,\n ): ConvParams {\n const filters = tf.tensor4d(\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n [filterSize, filterSize, channelsIn, channelsOut],\n );\n const bias = tf.tensor1d(extractWeights(channelsOut));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/filters` },\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` },\n );\n\n return { filters, bias };\n }\n\n function extractPointwiseConvParams(\n channelsIn: number,\n channelsOut: number,\n filterSize: number,\n mappedPrefix: string,\n ): PointwiseConvParams {\n const {\n filters,\n bias,\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true);\n\n return {\n filters,\n batch_norm_offset: bias,\n };\n }\n\n function extractConvPairParams(\n channelsIn: number,\n channelsOut: number,\n mappedPrefix: string,\n ): MobileNetV1.ConvPairParams {\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`);\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`);\n\n return { depthwise_conv, pointwise_conv };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n conv_8,\n conv_9,\n conv_10,\n conv_11,\n conv_12,\n conv_13,\n };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\n\n const box_predictor_0 = {\n box_encoding_predictor: box_encoding_0_predictor,\n class_predictor: class_predictor_0,\n };\n const box_predictor_1 = {\n box_encoding_predictor: box_encoding_1_predictor,\n class_predictor: class_predictor_1,\n };\n const box_predictor_2 = {\n box_encoding_predictor: box_encoding_2_predictor,\n class_predictor: class_predictor_2,\n };\n const box_predictor_3 = {\n box_encoding_predictor: box_encoding_3_predictor,\n class_predictor: class_predictor_3,\n };\n const box_predictor_4 = {\n box_encoding_predictor: box_encoding_4_predictor,\n class_predictor: class_predictor_4,\n };\n const box_predictor_5 = {\n box_encoding_predictor: box_encoding_5_predictor,\n class_predictor: class_predictor_5,\n };\n return {\n conv_0,\n conv_1,\n conv_2,\n conv_3,\n conv_4,\n conv_5,\n conv_6,\n conv_7,\n box_predictor_0,\n box_predictor_1,\n box_predictor_2,\n box_predictor_3,\n box_predictor_4,\n box_predictor_5,\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(extractWeights, paramMappings);\n const mobilenetv1 = extractMobilenetV1Params();\n const prediction_layer = extractPredictionLayerParams();\n const extra_dim = tf.tensor3d(\n extractWeights(5118 * 4),\n [1, 5118, 4],\n );\n const output_layer = {\n extra_dim,\n };\n paramMappings.push({ paramPath: 'output_layer/extra_dim' });\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n\n return {\n params: {\n mobilenetv1,\n prediction_layer,\n output_layer,\n },\n paramMappings,\n };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common/index';\nimport { isTensor3D } from '../utils/index';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n const filters = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`);\n const batch_norm_offset = extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`);\n return { filters, batch_norm_offset };\n }\n\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n const mappedPrefix = `mobilenetv1/conv_${idx}`;\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`;\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`;\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`;\n\n const filters = extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`);\n const batch_norm_scale = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`);\n const batch_norm_offset = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`);\n const batch_norm_mean = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`);\n const batch_norm_variance = extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`);\n\n return {\n depthwise_conv: {\n filters,\n batch_norm_scale,\n batch_norm_offset,\n batch_norm_mean,\n batch_norm_variance,\n },\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv),\n };\n }\n\n function extractMobilenetV1Params(): MobileNetV1.Params {\n return {\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n conv_1: extractConvPairParams(1),\n conv_2: extractConvPairParams(2),\n conv_3: extractConvPairParams(3),\n conv_4: extractConvPairParams(4),\n conv_5: extractConvPairParams(5),\n conv_6: extractConvPairParams(6),\n conv_7: extractConvPairParams(7),\n conv_8: extractConvPairParams(8),\n conv_9: extractConvPairParams(9),\n conv_10: extractConvPairParams(10),\n conv_11: extractConvPairParams(11),\n conv_12: extractConvPairParams(12),\n conv_13: extractConvPairParams(13),\n };\n }\n\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/weights`, 4, `${mappedPrefix}/filters`);\n const bias = extractWeightEntry(`${prefix}/biases`, 1, `${mappedPrefix}/bias`);\n return { filters, bias };\n }\n\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n const box_encoding_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`,\n );\n const class_predictor = extractConvParams(\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n `prediction_layer/box_predictor_${idx}/class_predictor`,\n );\n return { box_encoding_predictor, class_predictor };\n }\n\n function extractPredictionLayerParams(): PredictionLayerParams {\n return {\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n box_predictor_0: extractBoxPredictorParams(0),\n box_predictor_1: extractBoxPredictorParams(1),\n box_predictor_2: extractBoxPredictorParams(2),\n box_predictor_3: extractBoxPredictorParams(3),\n box_predictor_4: extractBoxPredictorParams(4),\n box_predictor_5: extractBoxPredictorParams(5),\n };\n }\n\n return {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n): { params: NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n const {\n extractMobilenetV1Params,\n extractPredictionLayerParams,\n } = extractorsFactory(weightMap, paramMappings);\n const extra_dim = weightMap['Output/extra_dim'];\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\n if (!isTensor3D(extra_dim)) {\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);\n }\n\n const params = {\n mobilenetv1: extractMobilenetV1Params(),\n prediction_layer: extractPredictionLayerParams(),\n output_layer: {\n extra_dim,\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.conv2d(x, params.filters, strides, 'same');\n out = tf.add(out, params.batch_norm_offset);\n return tf.clipByValue(out, 0, 6);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513;\n\nfunction depthwiseConvLayer(x: tf.Tensor4D, params: MobileNetV1.DepthwiseConvParams, strides: [number, number]) {\n return tf.tidy(() => {\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\n out = tf.batchNorm(\n out,\n params.batch_norm_mean,\n params.batch_norm_variance,\n params.batch_norm_offset,\n params.batch_norm_scale,\n epsilon,\n );\n return tf.clipByValue(out, 0, 6);\n });\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n return [2, 4, 6, 12].some((idx) => idx === layerIdx) ? [2, 2] : [1, 1];\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n return tf.tidy(() => {\n let conv11;\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\n\n const convPairParams = [\n params.conv_1,\n params.conv_2,\n params.conv_3,\n params.conv_4,\n params.conv_5,\n params.conv_6,\n params.conv_7,\n params.conv_8,\n params.conv_9,\n params.conv_10,\n params.conv_11,\n params.conv_12,\n params.conv_13,\n ];\n\n convPairParams.forEach((param, i) => {\n const layerIdx = i + 1;\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\n if (layerIdx === 11) conv11 = out;\n });\n\n if (conv11 === null) {\n throw new Error('mobileNetV1 - output of conv layer 11 is null');\n }\n\n return {\n out,\n conv11: conv11 as any,\n };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n const boxesData = boxes.arraySync();\n const yminI = Math.min(boxesData[i][0], boxesData[i][2]);\n const xminI = Math.min(boxesData[i][1], boxesData[i][3]);\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\n const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n if (areaI <= 0 || areaJ <= 0) return 0.0;\n const intersectionYmin = Math.max(yminI, yminJ);\n const intersectionXmin = Math.max(xminI, xminJ);\n const intersectionYmax = Math.min(ymaxI, ymaxJ);\n const intersectionXmax = Math.min(xmaxI, xmaxJ);\n const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);\n return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\nexport function nonMaxSuppression(\n boxes: tf.Tensor2D,\n scores: number[],\n maxOutputSize: number,\n iouThreshold: number,\n scoreThreshold: number,\n): number[] {\n const numBoxes = boxes.shape[0];\n const outputSize = Math.min(maxOutputSize, numBoxes);\n\n const candidates = scores\n .map((score, boxIndex) => ({ score, boxIndex }))\n .filter((c) => c.score > scoreThreshold)\n .sort((c1, c2) => c2.score - c1.score);\n\n const suppressFunc = (x: number) => (x <= iouThreshold ? 1 : 0);\n const selected: number[] = [];\n\n candidates.forEach((c) => {\n if (selected.length >= outputSize) return;\n const originalScore = c.score;\n for (let j = selected.length - 1; j >= 0; --j) {\n const iou = IOU(boxes, c.boxIndex, selected[j]);\n if (iou === 0.0) continue;\n c.score *= suppressFunc(iou);\n if (c.score <= scoreThreshold) break;\n }\n if (originalScore === c.score) {\n selected.push(c.boxIndex);\n }\n });\n return selected;\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { OutputLayerParams } from './types';\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n const vec = tf.unstack(tf.transpose(x, [1, 0]));\n\n const sizes = [\n tf.sub(vec[2], vec[0]),\n tf.sub(vec[3], vec[1]),\n ];\n const centers = [\n tf.add(vec[0], tf.div(sizes[0], 2)),\n tf.add(vec[1], tf.div(sizes[1], 2)),\n ];\n return { sizes, centers };\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);\n\n const vec = tf.unstack(tf.transpose(x1, [1, 0]));\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);\n const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);\n const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);\n\n return tf.transpose(\n tf.stack([\n tf.sub(add0_out, div0_out),\n tf.sub(add1_out, div1_out),\n tf.add(add0_out, div0_out),\n tf.add(add1_out, div1_out),\n ]),\n [1, 0],\n );\n}\n\nexport function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {\n return tf.tidy(() => {\n const batchSize = boxPredictions.shape[0];\n\n let boxes = decodeBoxesLayer(\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,\n );\n boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\n\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;\n\n scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);\n\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];\n\n return { boxes: boxesByBatch, scores: scoresByBatch };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { convLayer } from '../common/index';\nimport { BoxPredictionParams } from './types';\n\nexport function boxPredictionLayer(\n x: tf.Tensor4D,\n params: BoxPredictionParams,\n) {\n return tf.tidy(() => {\n const batchSize = x.shape[0];\n const boxPredictionEncoding = tf.reshape(\n convLayer(x, params.box_encoding_predictor),\n [batchSize, -1, 1, 4],\n );\n const classPrediction = tf.reshape(\n convLayer(x, params.class_predictor),\n [batchSize, -1, 3],\n );\n return { boxPredictionEncoding, classPrediction };\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n x: tf.Tensor4D,\n conv11: tf.Tensor4D,\n params: PredictionLayerParams,\n) {\n return tf.tidy(() => {\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\n\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\n\n const boxPredictions = tf.concat([\n boxPrediction0.boxPredictionEncoding,\n boxPrediction1.boxPredictionEncoding,\n boxPrediction2.boxPredictionEncoding,\n boxPrediction3.boxPredictionEncoding,\n boxPrediction4.boxPredictionEncoding,\n boxPrediction5.boxPredictionEncoding,\n ], 1) as tf.Tensor4D;\n\n const classPredictions = tf.concat([\n boxPrediction0.classPrediction,\n boxPrediction1.classPrediction,\n boxPrediction2.classPrediction,\n boxPrediction3.classPrediction,\n boxPrediction4.classPrediction,\n boxPrediction5.classPrediction,\n ], 1) as tf.Tensor4D;\n\n return {\n boxPredictions,\n classPredictions,\n };\n });\n}\n", "export interface ISsdMobilenetv1Options {\n minConfidence?: number\n maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n protected _name = 'SsdMobilenetv1Options';\n\n private _minConfidence: number;\n\n private _maxResults: number;\n\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n this._minConfidence = minConfidence || 0.5;\n this._maxResults = maxResults || 100;\n\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);\n }\n\n if (typeof this._maxResults !== 'number') {\n throw new Error(`${this._name} - expected maxResults to be a number`);\n }\n }\n\n get minConfidence(): number { return this._minConfidence; }\n\n get maxResults(): number { return this._maxResults; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { Rect } from '../classes/index';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\nexport class SsdMobilenetv1 extends NeuralNetwork {\n constructor() {\n super('SsdMobilenetv1');\n }\n\n public forwardInput(input: NetInput) {\n const { params } = this;\n if (!params) throw new Error('SsdMobilenetv1 - load model before inference');\n return tf.tidy(() => {\n const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n const x = tf.sub(tf.div(batchTensor, 127.5), 1) as tf.Tensor4D; // input is normalized -1..1\n const features = mobileNetV1(x, params.mobilenetv1);\n const { boxPredictions, classPredictions } = predictionLayer(features.out, features.conv11, params.prediction_layer);\n return outputLayer(boxPredictions, classPredictions, params.output_layer);\n });\n }\n\n public async forward(input: TNetInput) {\n return this.forwardInput(await toNetInput(input));\n }\n\n public async locateFaces(input: TNetInput, options: ISsdMobilenetv1Options = {}): Promise {\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options);\n const netInput = await toNetInput(input);\n const { boxes: _boxes, scores: _scores } = this.forwardInput(netInput);\n const boxes = _boxes[0];\n const scores = _scores[0];\n for (let i = 1; i < _boxes.length; i++) {\n _boxes[i].dispose();\n _scores[i].dispose();\n }\n const scoresData = Array.from(scores.dataSync());\n const iouThreshold = 0.5;\n const indices = nonMaxSuppression(boxes, scoresData as number[], maxResults, iouThreshold, minConfidence);\n const reshapedDims = netInput.getReshapedInputDimensions(0);\n const inputSize = netInput.inputSize as number;\n const padX = inputSize / reshapedDims.width;\n const padY = inputSize / reshapedDims.height;\n const boxesData = boxes.arraySync();\n const results = indices\n .map((idx) => {\n const [top, bottom] = [\n Math.max(0, boxesData[idx][0]),\n Math.min(1.0, boxesData[idx][2]),\n ].map((val) => val * padY);\n const [left, right] = [\n Math.max(0, boxesData[idx][1]),\n Math.min(1.0, boxesData[idx][3]),\n ].map((val) => val * padX);\n return new FaceDetection(\n scoresData[idx] as number,\n new Rect(left, top, right - left, bottom - top),\n { height: netInput.getInputHeight(0), width: netInput.getInputWidth(0) },\n );\n });\n boxes.dispose();\n scores.dispose();\n return results;\n }\n\n protected getDefaultModelName(): string {\n return 'ssd_mobilenetv1_model';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap);\n }\n\n protected extractParams(weights: Float32Array) {\n return extractParams(weights);\n }\n}\n", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n const net = new SsdMobilenetv1();\n net.extractWeights(weights);\n return net;\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n return createSsdMobilenetv1(weights);\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(0.738768, 0.874946),\n new Point(2.42204, 2.65704),\n new Point(4.30971, 7.04493),\n new Point(10.246, 4.59428),\n new Point(12.6868, 11.8741),\n];\n\nexport const BOX_ANCHORS_SEPARABLE = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404];\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\n", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n withSeparableConvs: boolean\n iouThreshold: number\n anchors: Point[]\n classes: string[]\n meanRgb?: [number, number, number]\n withClassScores?: boolean,\n filterSizes?: number[]\n isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number';\n\nexport function validateConfig(config: any) {\n if (!config) {\n throw new Error(`invalid config: ${config}`);\n }\n\n if (typeof config.withSeparableConvs !== 'boolean') {\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);\n }\n\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);\n }\n\n if (\n !Array.isArray(config.classes)\n || !config.classes.length\n || !config.classes.every((c: any) => typeof c === 'string')\n ) {\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);\n }\n\n if (\n !Array.isArray(config.anchors)\n || !config.anchors.length\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n ) {\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`);\n }\n\n if (config.meanRgb && (\n !Array.isArray(config.meanRgb)\n || config.meanRgb.length !== 3\n || !config.meanRgb.every(isNumber)\n )) {\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n return tf.tidy(() => {\n const min = tf.mul(x, tf.scalar(0.10000000149011612));\n return tf.add(tf.relu(tf.sub(x, min)), min);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\n out = tf.sub(out, params.bn.sub);\n out = tf.mul(out, params.bn.truediv);\n out = tf.add(out, params.conv.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\n return tf.tidy(() => {\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D;\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\n out = tf.add(out, params.bias);\n return leaky(out);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { extractConvParamsFactory } from '../common/index';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\n\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n const sub = tf.tensor1d(extractWeights(size));\n const truediv = tf.tensor1d(extractWeights(size));\n\n paramMappings.push(\n { paramPath: `${mappedPrefix}/sub` },\n { paramPath: `${mappedPrefix}/truediv` },\n );\n return { sub, truediv };\n }\n\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);\n return { conv, bn };\n }\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\n\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParams(\n weights: Float32Array,\n config: TinyYolov2Config,\n boxEncodingSize: number,\n filterSizes: number[],\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const paramMappings: ParamMapping[] = [];\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(extractWeights, paramMappings);\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = config.isFirstLayerConv2d\n ? extractConvParams(s0, s1, 3, 'conv0')\n : extractSeparableConvParams(s0, s1, 'conv0');\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1');\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2');\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3');\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4');\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5');\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n } else {\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\n params = {\n conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8,\n };\n }\n if (getRemainingWeights().length !== 0) {\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);\n }\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { ConvParams } from '../common/index';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\n\n function extractBatchNormParams(prefix: string): BatchNorm {\n const sub = extractWeightEntry(`${prefix}/sub`, 1);\n const truediv = extractWeightEntry(`${prefix}/truediv`, 1);\n return { sub, truediv };\n }\n\n function extractConvParams(prefix: string): ConvParams {\n const filters = extractWeightEntry(`${prefix}/filters`, 4);\n const bias = extractWeightEntry(`${prefix}/bias`, 1);\n return { filters, bias };\n }\n\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n const conv = extractConvParams(`${prefix}/conv`);\n const bn = extractBatchNormParams(`${prefix}/bn`);\n return { conv, bn };\n }\n\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\n return {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n config: TinyYolov2Config,\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractConvWithBatchNormParams,\n extractSeparableConvParams,\n } = extractorsFactory(weightMap, paramMappings);\n\n let params: TinyYolov2NetParams;\n\n if (config.withSeparableConvs) {\n // eslint-disable-next-line no-mixed-operators\n const numFilters = (config.filterSizes && config.filterSizes.length || 9);\n params = {\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n conv1: extractSeparableConvParams('conv1'),\n conv2: extractSeparableConvParams('conv2'),\n conv3: extractSeparableConvParams('conv3'),\n conv4: extractSeparableConvParams('conv4'),\n conv5: extractSeparableConvParams('conv5'),\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n conv8: extractConvParams('conv8'),\n };\n } else {\n params = {\n conv0: extractConvWithBatchNormParams('conv0'),\n conv1: extractConvWithBatchNormParams('conv1'),\n conv2: extractConvWithBatchNormParams('conv2'),\n conv3: extractConvWithBatchNormParams('conv3'),\n conv4: extractConvWithBatchNormParams('conv4'),\n conv5: extractConvWithBatchNormParams('conv5'),\n conv6: extractConvWithBatchNormParams('conv6'),\n conv7: extractConvWithBatchNormParams('conv7'),\n conv8: extractConvParams('conv8'),\n };\n }\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n return { params, paramMappings };\n}\n", "export interface ITinyYolov2Options {\n inputSize?: number\n scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n protected _name = 'TinyYolov2Options';\n\n private _inputSize: number;\n\n private _scoreThreshold: number;\n\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n this._inputSize = inputSize || 416;\n this._scoreThreshold = scoreThreshold || 0.5;\n\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);\n }\n\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`);\n }\n }\n\n get inputSize(): number { return this._inputSize; }\n\n get scoreThreshold(): number { return this._scoreThreshold; }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common/index';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom/index';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops/index';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2ExtractBoxesResult, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork {\n public static DEFAULT_FILTER_SIZES = [3, 16, 32, 64, 128, 256, 512, 1024, 1024];\n\n private _config: TinyYolov2Config;\n\n constructor(config: TinyYolov2Config) {\n super('TinyYolov2');\n validateConfig(config);\n this._config = config;\n }\n\n public get config(): TinyYolov2Config {\n return this._config;\n }\n\n public get withClassScores(): boolean {\n return this.config.withClassScores || this.config.classes.length > 1;\n }\n\n public get boxEncodingSize(): number {\n return 5 + (this.withClassScores ? this.config.classes.length : 0);\n }\n\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n let out = convWithBatchNorm(x, params.conv0);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = convWithBatchNorm(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = convWithBatchNorm(out, params.conv6);\n out = convWithBatchNorm(out, params.conv7);\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n let out = this.config.isFirstLayerConv2d\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv1);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv2);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv3);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv4);\n out = tf.maxPool(out, [2, 2], [2, 2], 'same');\n out = depthwiseSeparableConv(out, params.conv5);\n out = tf.maxPool(out, [2, 2], [1, 1], 'same');\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\n return convLayer(out, params.conv8, 'valid', false);\n }\n\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n const { params } = this;\n\n if (!params) {\n throw new Error('TinyYolov2 - load model before inference');\n }\n\n return tf.tidy(() => {\n let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n batchTensor = this.config.meanRgb\n ? normalize(batchTensor, this.config.meanRgb)\n : batchTensor;\n batchTensor = batchTensor.div(255) as tf.Tensor4D;\n return this.config.withSeparableConvs\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams);\n });\n }\n\n public async forward(input: TNetInput, inputSize: number): Promise {\n return this.forwardInput(await toNetInput(input), inputSize);\n }\n\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise {\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams);\n const netInput = await toNetInput(input);\n const out = await this.forwardInput(netInput, inputSize);\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D;\n const inputDimensions = {\n width: netInput.getInputWidth(0),\n height: netInput.getInputHeight(0),\n };\n\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\n out.dispose();\n out0.dispose();\n\n const boxes = results.map((res) => res.box);\n const scores = results.map((res) => res.score);\n const classScores = results.map((res) => res.classScore);\n const classNames = results.map((res) => this.config.classes[res.label]);\n\n const indices = nonMaxSuppression(\n boxes.map((box) => box.rescale(inputSize)),\n scores,\n this.config.iouThreshold,\n true,\n );\n\n const detections = indices.map((idx) => new ObjectDetection(\n scores[idx],\n classScores[idx],\n classNames[idx],\n boxes[idx],\n inputDimensions,\n ));\n return detections;\n }\n\n protected getDefaultModelName(): string {\n return '';\n }\n\n protected extractParamsFromWeightMap(weightMap: tf.NamedTensorMap) {\n return extractParamsFromWeightMap(weightMap, this.config);\n }\n\n protected extractParams(weights: Float32Array) {\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\n\n const numFilters = filterSizes ? filterSizes.length : undefined;\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);\n }\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\n }\n\n protected async extractBoxes(\n outputTensor: tf.Tensor4D,\n inputBlobDimensions: Dimensions,\n scoreThreshold?: number,\n ) {\n const { width, height } = inputBlobDimensions;\n const inputSize = Math.max(width, height);\n const correctionFactorX = inputSize / width;\n const correctionFactorY = inputSize / height;\n\n const numCells = outputTensor.shape[1];\n const numBoxes = this.config.anchors.length;\n\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize]);\n\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\n const classScores = this.withClassScores\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n : tf.scalar(0);\n return [boxes, scores, classScores];\n });\n\n const results: TinyYolov2ExtractBoxesResult[] = [];\n const scoresData = await scoresTensor.array() as number[][][][];\n const boxesData = await boxesTensor.array() as number[][][][];\n for (let row = 0; row < numCells; row++) {\n for (let col = 0; col < numCells; col++) {\n for (let anchor = 0; anchor < numBoxes; anchor++) {\n const score = sigmoid(scoresData[row][col][anchor][0]);\n if (!scoreThreshold || score > scoreThreshold) {\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\n const widthLocal = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\n const heightLocal = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\n const x = (ctX - (widthLocal / 2));\n const y = (ctY - (heightLocal / 2));\n const pos = { row, col, anchor };\n const { classScore, label } = this.withClassScores\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n : { classScore: 1, label: 0 };\n results.push({\n box: new BoundingBox(x, y, x + widthLocal, y + heightLocal),\n score,\n classScore: score * classScore,\n label,\n ...pos,\n });\n }\n }\n }\n }\n\n boxesTensor.dispose();\n scoresTensor.dispose();\n classScoresTensor.dispose();\n return results;\n }\n\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\n const { row, col, anchor } = pos;\n const classesData = await classesTensor.array();\n return Array(this.config.classes.length).fill(0)\n .map((_, i) => classesData[row][col][anchor][i])\n .map((classScore, label) => ({\n classScore,\n label,\n }))\n .reduce((max, curr) => (max.classScore > curr.classScore ? max : curr));\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n BOX_ANCHORS,\n BOX_ANCHORS_SEPARABLE,\n DEFAULT_MODEL_NAME,\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n IOU_THRESHOLD,\n MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n constructor(withSeparableConvs = true) {\n const config = {\n withSeparableConvs,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n ...(withSeparableConvs\n ? {\n anchors: BOX_ANCHORS_SEPARABLE,\n meanRgb: MEAN_RGB_SEPARABLE,\n }\n : {\n anchors: BOX_ANCHORS,\n withClassScores: true,\n }),\n };\n\n super(config);\n }\n\n public get withSeparableConvs(): boolean {\n return this.config.withSeparableConvs;\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\nexport * from './config';\nexport * from './types';\nexport { TinyYolov2 };\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs = true) {\n const net = new TinyYolov2(withSeparableConvs);\n net.extractWeights(weights);\n return net;\n}\n", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\n\nexport type ITinyFaceDetectorOptions = ITinyYolov2Options\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n protected override _name = 'TinyFaceDetectorOptions';\n}\n", "export class ComposableTask {\n // eslint-disable-next-line no-unused-vars\n public async then(onfulfilled: (value: T) => T | PromiseLike): Promise {\n return onfulfilled(await this.run());\n }\n\n public async run(): Promise {\n throw new Error('ComposableTask - run is not implemented');\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults, TResult>(\n parentResults: TSource[],\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResults: (faces: Array) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment: (parentResult: WithFaceLandmarks) => FaceDetection = ({ alignedRect }) => alignedRect,\n) {\n const faceBoxes = parentResults.map((parentResult) => (isWithFaceLandmarks(parentResult)\n ? getRectForAlignment(parentResult)\n : parentResult.detection));\n const faces: Array = extractedFaces || (\n input instanceof tf.Tensor\n ? await extractFaceTensors(input, faceBoxes)\n : await extractFaces(input, faceBoxes)\n );\n const results = await computeResults(faces);\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return results;\n}\n\nexport async function extractSingleFaceAndComputeResult, TResult>(\n parentResult: TSource,\n input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise,\n extractedFaces?: Array | null,\n // eslint-disable-next-line no-unused-vars\n getRectForAlignment?: (parentResultLocal: WithFaceLandmarks) => FaceDetection,\n) {\n return extractAllFacesAndComputeResults(\n [parentResult],\n input,\n async (faces) => computeResult(faces[0]),\n extractedFaces,\n getRectForAlignment,\n );\n}\n", "import { Point } from '../classes/index';\n\nexport const IOU_THRESHOLD = 0.4;\n\nexport const BOX_ANCHORS = [\n new Point(1.603231, 2.094468),\n new Point(6.041143, 7.080126),\n new Point(2.882459, 3.518061),\n new Point(4.266906, 5.178857),\n new Point(9.041765, 10.66308),\n];\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404];\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FaceDetection, Point } from '../classes/index';\nimport { ParamMapping } from '../common/index';\nimport { TNetInput } from '../dom/index';\nimport { ITinyYolov2Options } from '../tinyYolov2/index';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n constructor() {\n const config = {\n withSeparableConvs: true,\n iouThreshold: IOU_THRESHOLD,\n classes: ['face'],\n anchors: BOX_ANCHORS,\n meanRgb: MEAN_RGB,\n isFirstLayerConv2d: true,\n filterSizes: [3, 16, 32, 64, 128, 256, 512],\n };\n\n super(config);\n }\n\n public get anchors(): Point[] {\n return this.config.anchors;\n }\n\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise {\n const objectDetections = await this.detect(input, forwardParams);\n return objectDetections.map((det) => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }));\n }\n\n protected override getDefaultModelName(): string {\n return 'tiny_face_detector_model';\n }\n\n protected override extractParamsFromWeightMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n return super.extractParamsFromWeightMap(weightMap);\n }\n}\n", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2/index';\n\nexport const nets = {\n ssdMobilenetv1: new SsdMobilenetv1(),\n tinyFaceDetector: new TinyFaceDetector(),\n tinyYolov2: new TinyYolov2(),\n faceLandmark68Net: new FaceLandmark68Net(),\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n faceRecognitionNet: new FaceRecognitionNet(),\n faceExpressionNet: new FaceExpressionNet(),\n ageGenderNet: new AgeGenderNet(),\n};\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise => nets.ssdMobilenetv1.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise => nets.tinyFaceDetector.locateFaces(input, options);\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise => nets.tinyYolov2.locateFaces(input, options);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise => nets.faceLandmark68Net.detectLandmarks(input);\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise => nets.faceLandmark68TinyNet.detectLandmarks(input);\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise => nets.faceRecognitionNet.computeFaceDescriptor(input);\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise => nets.faceExpressionNet.predictExpressions(input);\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise => nets.ageGenderNet.predictAgeAndGender(input);\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url);\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url);\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url);\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url);\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url);\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url);\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url);\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url);\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model;\nexport const locateFaces = ssdMobilenetv1;\nexport const detectLandmarks = detectFaceLandmarks;\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { TNetInput } from '../dom/index';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllFaceExpressionsTask> extends PredictFaceExpressionsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n\n const faceExpressionsByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(\n faces.map((face) => nets.faceExpressionNet.predictExpressions(face) as Promise),\n ),\n this.extractedFaces,\n );\n\n return parentResults.map(\n (parentResult, i) => extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]),\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsTask> extends PredictFaceExpressionsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n\n const faceExpressions = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceExpressionNet.predictExpressions(face) as Promise,\n this.extractedFaces,\n );\n\n return extendWithFaceExpressions(parentResult, faceExpressions);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(this, this.input);\n }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask>> extends PredictAllFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask>> extends PredictSingleFaceExpressionsTask {\n override withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom/index';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected extractedFaces?: Array,\n ) {\n super();\n }\n}\n\nexport class PredictAllAgeAndGenderTask> extends PredictAgeAndGenderTaskBase>[], TSource[]> {\n public override async run(): Promise>[]> {\n const parentResults = await this.parentTask;\n const ageAndGenderByFace = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n async (faces) => Promise.all(faces.map((face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise)),\n this.extractedFaces,\n );\n return parentResults.map((parentResult, i) => {\n const { age, gender, genderProbability } = ageAndGenderByFace[i];\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n });\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderTask> extends PredictAgeAndGenderTaskBase> | undefined, TSource | undefined> {\n public override async run(): Promise> | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.ageGenderNet.predictAgeAndGender(face) as Promise,\n this.extractedFaces,\n );\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(this, this.input);\n }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask>> extends PredictAllAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask>> extends PredictSingleAgeAndGenderTask {\n override withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n ) {\n super();\n }\n}\n\nexport class ComputeAllFaceDescriptorsTask>> extends ComputeFaceDescriptorsTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const descriptors = await extractAllFacesAndComputeResults(\n parentResults,\n this.input,\n (faces) => Promise.all(faces.map((face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise)),\n null,\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor(parentResults[i], descriptor));\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n\nexport class ComputeSingleFaceDescriptorTask>> extends ComputeFaceDescriptorsTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) return undefined;\n const descriptor = await extractSingleFaceAndComputeResult(\n parentResult,\n this.input,\n (face) => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise,\n null,\n // eslint-disable-next-line no-shadow, @typescript-eslint/no-shadow\n (parentResult) => parentResult.landmarks.align(null, { useDlibAlignment: true }),\n );\n return extendWithFaceDescriptor(parentResult, descriptor);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport * as tf from '../../dist/tfjs.esm';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom/index';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase extends ComposableTask {\n constructor(\n // eslint-disable-next-line no-unused-vars\n protected parentTask: ComposableTask | Promise,\n // eslint-disable-next-line no-unused-vars\n protected input: TNetInput,\n // eslint-disable-next-line no-unused-vars\n protected useTinyLandmarkNet: boolean,\n ) {\n super();\n }\n\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n return this.useTinyLandmarkNet\n ? nets.faceLandmark68TinyNet\n : nets.faceLandmark68Net;\n }\n}\n\nexport class DetectAllFaceLandmarksTask> extends DetectFaceLandmarksTaskBase[], TSource[]> {\n public override async run(): Promise[]> {\n const parentResults = await this.parentTask;\n const detections = parentResults.map((res) => res.detection);\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, detections)\n : await extractFaces(this.input, detections);\n const faceLandmarksByFace = await Promise.all(faces.map((face) => this.landmarkNet.detectLandmarks(face))) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n const result = parentResults\n .filter((_parentResult, i) => faceLandmarksByFace[i])\n .map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\n return result;\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptors() {\n return new ComputeAllFaceDescriptorsTask(this, this.input);\n }\n}\n\nexport class DetectSingleFaceLandmarksTask> extends DetectFaceLandmarksTaskBase | undefined, TSource | undefined> {\n public override async run(): Promise | undefined> {\n const parentResult = await this.parentTask;\n if (!parentResult) {\n return undefined;\n }\n const { detection } = parentResult;\n const faces: Array = this.input instanceof tf.Tensor\n ? await extractFaceTensors(this.input, [detection])\n : await extractFaces(this.input, [detection]);\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68;\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return extendWithFaceLandmarks(parentResult, landmarks);\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\n }\n\n withFaceDescriptor() {\n return new ComputeSingleFaceDescriptorTask(this, this.input);\n }\n}\n", "/* eslint-disable max-classes-per-file */\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom/index';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2/index';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase extends ComposableTask {\n // eslint-disable-next-line no-unused-vars\n constructor(protected input: TNetInput, protected options: FaceDetectionOptions = new SsdMobilenetv1Options()) {\n super();\n }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const { input, options } = this;\n let result;\n if (options instanceof TinyFaceDetectorOptions) result = nets.tinyFaceDetector.locateFaces(input, options);\n else if (options instanceof SsdMobilenetv1Options) result = nets.ssdMobilenetv1.locateFaces(input, options);\n else if (options instanceof TinyYolov2Options) result = nets.tinyYolov2.locateFaces(input, options);\n else throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options');\n return result;\n }\n\n private runAndExtendWithFaceDetections(): Promise[]> {\n return new Promise[]>((resolve, reject) => {\n this.run()\n .then((detections) => resolve(detections.map((detection) => extendWithFaceDetection({}, detection))))\n .catch((err) => reject(err));\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectAllFaceLandmarksTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictAllFaceExpressionsTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictAllAgeAndGenderTask(\n this.runAndExtendWithFaceDetections(),\n this.input,\n );\n }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase {\n public override async run(): Promise {\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n let faceDetectionWithHighestScore = faceDetections[0];\n faceDetections.forEach((faceDetection) => {\n if (faceDetection.score > faceDetectionWithHighestScore.score) faceDetectionWithHighestScore = faceDetection;\n });\n return faceDetectionWithHighestScore;\n }\n\n private runAndExtendWithFaceDetection(): Promise | undefined> {\n // eslint-disable-next-line no-async-promise-executor\n return new Promise | undefined>(async (resolve) => {\n const detection = await this.run();\n resolve(detection ? extendWithFaceDetection<{}>({}, detection) : undefined);\n });\n }\n\n withFaceLandmarks(useTinyLandmarkNet = false) {\n return new DetectSingleFaceLandmarksTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n useTinyLandmarkNet,\n );\n }\n\n withFaceExpressions() {\n return new PredictSingleFaceExpressionsTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n\n withAgeAndGender() {\n return new PredictSingleAgeAndGenderTask(\n this.runAndExtendWithFaceDetection(),\n this.input,\n );\n }\n}\n", "import { TNetInput } from '../dom/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectSingleFaceTask {\n return new DetectSingleFaceTask(input, options);\n}\n\nexport function detectAllFaces(input: TNetInput, options: FaceDetectionOptions = new SsdMobilenetv1Options()): DetectAllFacesTask {\n return new DetectAllFacesTask(input, options);\n}\n", "import { TNetInput } from '../dom/index';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories/index';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/index';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2/index';\nimport { detectAllFaces } from './detectFaces';\n\nexport async function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise>>[]> {\n return detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport async function allFacesTinyYolov2(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise>>[]> {\n return detectAllFaces(input, new TinyYolov2Options(forwardParams))\n .withFaceLandmarks()\n .withFaceDescriptors();\n}\n\nexport const allFaces = allFacesSsdMobilenetv1;\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n if (arr1.length !== arr2.length) throw new Error('euclideanDistance: arr1.length !== arr2.length');\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff * diff), 0),\n );\n}\n", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories/index';\n\nexport class FaceMatcher {\n private _labeledDescriptors: LabeledFaceDescriptors[];\n private _distanceThreshold: number;\n\n constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>, distanceThreshold = 0.6) {\n this._distanceThreshold = distanceThreshold;\n const inputArray = Array.isArray(inputs) ? inputs : [inputs];\n if (!inputArray.length) throw new Error('FaceRecognizer.constructor - expected atleast one input');\n let count = 1;\n const createUniqueLabel = () => `person ${count++}`;\n this._labeledDescriptors = inputArray.map((desc) => {\n if (desc instanceof LabeledFaceDescriptors) return desc;\n if (desc instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\n if (desc.descriptor && desc.descriptor instanceof Float32Array) return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\n throw new Error('FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>');\n });\n }\n\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors; }\n\n public get distanceThreshold(): number { return this._distanceThreshold; }\n\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n return descriptors\n .map((d) => euclideanDistance(d, queryDescriptor))\n .reduce((d1, d2) => d1 + d2, 0) / (descriptors.length || 1);\n }\n\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n return this.labeledDescriptors\n .map(({ descriptors, label }) => new FaceMatch(label, this.computeMeanDistance(queryDescriptor, descriptors)))\n .reduce((best, curr) => (best.distance < curr.distance ? best : curr));\n }\n\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n const bestMatch = this.matchDescriptor(queryDescriptor);\n return (bestMatch.distance < this._distanceThreshold) ? bestMatch : new FaceMatch('unknown', bestMatch.distance);\n }\n\n public toJSON(): any {\n return {\n distanceThreshold: this._distanceThreshold,\n labeledDescriptors: this._labeledDescriptors.map((ld) => ld.toJSON()),\n };\n }\n\n public static fromJSON(json: any): FaceMatcher {\n const labeledDescriptors = json.labeledDescriptors.map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n }\n}\n", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n const net = new TinyFaceDetector();\n net.extractWeights(weights);\n return net;\n}\n", "import { Dimensions, IDimensions } from './classes/index';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults(results: T, dimensions: IDimensions): T {\n const { width, height } = new Dimensions(dimensions.width, dimensions.height);\n\n if (width <= 0 || height <= 0) {\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`);\n }\n\n if (Array.isArray(results)) {\n // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n return (results as Array).map((obj) => resizeResults(obj, { width, height } as IDimensions)) as any as T;\n }\n\n if (isWithFaceLandmarks(results)) {\n const resizedDetection = results.detection.forSize(width, height);\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\n }\n\n if (isWithFaceDetection(results)) {\n return extendWithFaceDetection(results, results.detection.forSize(width, height));\n }\n\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n return (results as any).forSize(width, height);\n }\n\n return results;\n}\n", "import * as tf from '../dist/tfjs.esm';\nimport * as draw from './draw/index';\nimport * as utils from './utils/index';\nimport * as pkg from '../package.json';\n\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index';\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nexport const version = pkg.version as string;\n\n// set webgl defaults\n// if (browser) tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n"], + "mappings": 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compiled before being used.");return this.model.evaluate(t,n,a)}async evaluateDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,n)}predict(t,n={}){return this.model==null&&this.build(),this.model.predict(t,n)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,n,a={}){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fit(t,n,a)}async fitDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof nv))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Vf.className="Sequential";ne.registerClass(Vf);function wj(e){return new Ar(e)}function kj(e){return new Vf(e)}function mC(e){return j2(e)}function Ij(e,t){I0.registerCallbackConstructor(e,t)}var xn=class extends ne.Serializable{getConfig(){return{}}},fC=class extends xn{apply(e,t=1){return VG(e,t)}};fC.className="elu";ne.registerClass(fC);var gC=class extends xn{apply(e){return mf(e)}};gC.className="selu";ne.registerClass(gC);var bC=class extends xn{apply(e){return Ke(e)}};bC.className="relu";ne.registerClass(bC);var yC=class extends xn{apply(e){return O(()=>gs(6,Ke(e)))}};yC.className="relu6";ne.registerClass(yC);var xC=class extends xn{apply(e){return e}};xC.className="linear";ne.registerClass(xC);var vC=class extends xn{apply(e){return ma(e)}};vC.className="sigmoid";ne.registerClass(vC);var wC=class extends xn{apply(e){return GG(e)}};wC.className="hardSigmoid";ne.registerClass(wC);var kC=class extends xn{apply(e){return jo(e)}};kC.className="softplus";ne.registerClass(kC);var IC=class extends xn{apply(e){return UG(e)}};IC.className="softsign";ne.registerClass(IC);var SC=class extends xn{apply(e){return fs(e)}};SC.className="tanh";ne.registerClass(SC);var _0=class extends xn{apply(e,t=-1){return qa(e,t)}};_0.className="softmax";ne.registerClass(_0);var NC=class extends xn{apply(e,t=-1){return of(e,t)}};NC.className="logSoftmax";ne.registerClass(NC);var TC=class extends xn{apply(e){return O(()=>O(()=>{let t=Math.sqrt(2),n=z(.5,X(1,af(he(e,t))));return z(e,n)}))}};TC.className="gelu";ne.registerClass(TC);var CC=class extends xn{apply(e){return O(()=>z(.5,z(e,X(1,fs(z(rn(he(2,Math.PI)),X(e,z(.044715,cr(e,3)))))))))}};CC.className="gelu_new";ne.registerClass(CC);var EC=class extends xn{apply(e){return O(()=>z(e,fs(jo(e))))}};EC.className="mish";ne.registerClass(EC);var _C=class extends xn{apply(e,t=1){return O(()=>z(ma(z(e,t)),e))}};_C.className="swish";ne.registerClass(_C);function ys(e){return e.getClassName()}function Ix(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function xs(e){if(e==null){let t={};return t.className="linear",t.config={},Ix(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ix(t)}else return e instanceof xn?e:Ix(e)}function A0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var AC=class extends ne.Serializable{},Fd=class extends AC{constructor(e){super(),A0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Ed(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function Sj(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function Nj(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof AC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a(Rt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function FC(e,t){return O(()=>(Rt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function Tj(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${n.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Qm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ka(o,n)),o})}function tS(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function Cj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var $C=class DC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=xs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ys(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Uf=class RC extends $C{constructor(t,n){super(t,n),this.kernel=null,RC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=O2(this.activation.getClassName());if(s!=null&&this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=Tj(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=Cj(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Gf=class MC extends Uf{constructor(t){super(2,t),MC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gf.className="Conv2D";ne.registerClass(Gf);var Hf=class OC extends Uf{constructor(t){super(3,t),OC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hf.className="Conv3D";ne.registerClass(Hf);var L0=class extends Gf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=rr(o,d,u,this.padding),m=rr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ka(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=rr(t[a],o,s,this.padding),t[r]=rr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L0.className="Conv2DTranspose";ne.registerClass(L0);var z0=class extends Hf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=dw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var PC=class extends Uf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ds(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};PC.className="SeparableConv";var W0=class extends PC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class LC extends Uf{constructor(t){super(1,t),LC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,MG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function Ej(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=As(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends $C{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=Ej(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function zC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function WC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class BC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Kf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Yx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=zC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=WC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Kx(n,[1,a]):n):this.cell.stateSize>1?[Kx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===BC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},jf=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jf.className="SimpleRNNCell";ne.registerClass(jf);var H0=class extends Pr{constructor(e){e.cell=new jf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};j0.className="GRU";ne.registerClass(j0);var Dd=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ra{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};q0.className="LSTM";ne.registerClass(q0);var Kf=class extends $d{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ii(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Zx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):U2(t(),n),o=()=>_d(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _j=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return u0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_j(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xf.className="ConvLSTM2DCell";ne.registerClass(Xf);var K0=class extends VC{constructor(e){let t=new Xf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Yf=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0U2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Yf.className="Dropout";ne.registerClass(Yf);var X0=class extends Yf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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compiled before being used.");return this.model.evaluate(t,n,a)}async evaluateDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,n)}predict(t,n={}){return this.model==null&&this.build(),this.model.predict(t,n)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,n,a={}){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fit(t,n,a)}async fitDataset(t,n){if(!this.built)throw new sr("The model needs to be compiled before being used.");return this.model.fitDataset(t,n)}async trainOnBatch(t,n){return this.model.trainOnBatch(t,n)}static fromConfig(t,n,a={},r=!1){let s,i={};if(n instanceof Array){if(n[0].className==null||n[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");s=n}else w.assert(n.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=n.layers,delete n.layers,i=n;let o=new t(i);if(!(o instanceof nv))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let l of s){let u=Ba(l,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(t){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let n of this.layers){let a={};a.className=n.getClassName(),a.config=n.getConfig(),t.push(a)}return{name:this.name,layers:t}}};Vf.className="Sequential";ne.registerClass(Vf);function wj(e){return new Ar(e)}function kj(e){return new Vf(e)}function mC(e){return j2(e)}function Ij(e,t){I0.registerCallbackConstructor(e,t)}var xn=class extends ne.Serializable{getConfig(){return{}}},fC=class extends xn{apply(e,t=1){return VG(e,t)}};fC.className="elu";ne.registerClass(fC);var gC=class extends xn{apply(e){return mf(e)}};gC.className="selu";ne.registerClass(gC);var bC=class extends xn{apply(e){return Ke(e)}};bC.className="relu";ne.registerClass(bC);var yC=class extends xn{apply(e){return O(()=>gs(6,Ke(e)))}};yC.className="relu6";ne.registerClass(yC);var xC=class extends xn{apply(e){return e}};xC.className="linear";ne.registerClass(xC);var vC=class extends xn{apply(e){return ma(e)}};vC.className="sigmoid";ne.registerClass(vC);var wC=class extends xn{apply(e){return GG(e)}};wC.className="hardSigmoid";ne.registerClass(wC);var kC=class extends xn{apply(e){return jo(e)}};kC.className="softplus";ne.registerClass(kC);var IC=class extends xn{apply(e){return UG(e)}};IC.className="softsign";ne.registerClass(IC);var SC=class extends xn{apply(e){return fs(e)}};SC.className="tanh";ne.registerClass(SC);var _0=class extends xn{apply(e,t=-1){return qa(e,t)}};_0.className="softmax";ne.registerClass(_0);var NC=class extends xn{apply(e,t=-1){return of(e,t)}};NC.className="logSoftmax";ne.registerClass(NC);var TC=class extends xn{apply(e){return O(()=>O(()=>{let t=Math.sqrt(2),n=z(.5,X(1,af(he(e,t))));return z(e,n)}))}};TC.className="gelu";ne.registerClass(TC);var CC=class extends xn{apply(e){return O(()=>z(.5,z(e,X(1,fs(z(rn(he(2,Math.PI)),X(e,z(.044715,cr(e,3)))))))))}};CC.className="gelu_new";ne.registerClass(CC);var EC=class extends xn{apply(e){return O(()=>z(e,fs(jo(e))))}};EC.className="mish";ne.registerClass(EC);var _C=class extends xn{apply(e,t=1){return O(()=>z(ma(z(e,t)),e))}};_C.className="swish";ne.registerClass(_C);function ys(e){return e.getClassName()}function Ix(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"activation")}function xs(e){if(e==null){let t={};return t.className="linear",t.config={},Ix(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ix(t)}else return e instanceof xn?e:Ix(e)}function A0(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var AC=class extends ne.Serializable{},Fd=class extends AC{constructor(e){super(),A0(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return O(()=>{let t=It([1]);return this.hasL1&&(t=X(t,fe(z(this.l1,Lt(e))))),this.hasL2&&(t=X(t,fe(z(this.l2,Ed(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Fd.className="L1L2";ne.registerClass(Fd);function Sj(e){return A0(e),new Fd({l1:e!=null?e.l1:null,l2:0})}function Nj(e){return A0(e),new Fd({l2:e!=null?e.l2:null,l1:0})}var QI={l1l2:"L1L2"};function ft(e){return o0(e)}function eS(e,t={}){return Td(e,ne.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in QI?QI[e]:e,config:{}};return eS(t)}else return e instanceof AC?e:eS(e)}var F0=class extends We{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Te(e);let n=Ke(e);return this.maxValue!=null&&(n=an(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};F0.className="ReLU";ne.registerClass(F0);var $0=class extends We{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Te(e);return fd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$0.className="LeakyReLU";ne.registerClass($0);var D0=class extends We{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=St(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=Yt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=Je(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a{let n=Te(e),a=t.mask;if(a!=null){let r=z(pe(Pn(n.shape),re(a,n.dtype)),xe(-1e9));n=X(n,r)}return this.axis instanceof Array?this.axis.length>1?mn(pe(n,bd(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};O0.className="Softmax";ne.registerClass(O0);function Gl(e,t,n){if(typeof e=="number")return xi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a(Rt(t),t==="channelsFirst"?De(e,[0,2,3,1]):e))}function FC(e,t){return O(()=>(Rt(t),t==="channelsFirst"?De(e,[0,2,3,4,1]):e))}function Tj(e,t,n,a=1,r="valid",s,i=1){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${n.shape.length} instead`);if(s==="channelsFirst"&&(e=De(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Qm(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ka(o,n)),o})}function tS(e,t,n,a=[1,1],r="valid",s,i,o=null){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=P0(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=tu.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=De(l,[0,3,1,2])),l})}function Cj(e,t,n,a=[1,1,1],r="valid",s,i){return O(()=>{if(s==null&&(s=Ga()),Rt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=FC(e,s);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cw(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ka(o,n)),s==="channelsFirst"&&(o=De(o,[0,4,1,2,3])),o})}var $C=class DC extends We{constructor(t,n){if(super(n),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DC.verifyArgs(n),this.rank=t,tn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(n.kernelSize,t,"kernelSize"),this.strides=Gl(n.strides==null?1:n.strides,t,"strides"),this.padding=n.padding==null?"valid":n.padding,wa(this.padding),this.dataFormat=n.dataFormat==null?"channelsLast":n.dataFormat,Rt(this.dataFormat),this.activation=xs(n.activation),this.useBias=n.useBias==null?!0:n.useBias,this.biasInitializer=St(n.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(n.biasConstraint),this.biasRegularizer=Nt(n.biasRegularizer),this.activityRegularizer=Nt(n.activityRegularizer),this.dilationRate=Gl(n.dilationRate==null?1:n.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(ar("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ys(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Xt(this.biasConstraint)},n=super.getConfig();return Object.assign(t,n),t}},Uf=class RC extends $C{constructor(t,n){super(t,n),this.kernel=null,RC.verifyArgs(n),this.filters=n.filters,tn(this.filters,"filters"),this.kernelInitializer=St(n.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(n.kernelConstraint),this.kernelRegularizer=Nt(n.kernelRegularizer)}build(t){t=Je(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${t[n]}`);let a=t[n],r=this.kernelSize.concat([a,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[n]:a}}],this.built=!0}call(t,n){return O(()=>{t=Te(t);let a,r=this.bias==null?null:this.bias.read(),s=O2(this.activation.getClassName());if(s!=null&&this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)a=Tj(t,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)a=tS(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)a=Cj(t,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(a=this.activation.apply(a))}return a})}computeOutputShape(t){t=Je(t);let n=[],a=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Gf=class MC extends Uf{constructor(t){super(2,t),MC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Gf.className="Conv2D";ne.registerClass(Gf);var Hf=class OC extends Uf{constructor(t){super(3,t),OC.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hf.className="Conv3D";ne.registerClass(Hf);var L0=class extends Gf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],u=this.kernelSize[0],p=this.kernelSize[1],d=this.strides[0],c=this.strides[1],h=rr(o,d,u,this.padding),m=rr(l,c,p,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,1]));let g=ef(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=De(g,[0,3,1,2])),this.bias!=null&&(g=Ka(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=rr(t[a],o,s,this.padding),t[r]=rr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};L0.className="Conv2DTranspose";ne.registerClass(L0);var z0=class extends Hf{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==5)throw new V("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Te(e);if(n.shape.length!==5)throw new V(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],u=a[s],p=a[i],d=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],b=rr(l,m,d,this.padding),y=rr(u,f,c,this.padding),x=rr(p,g,h,this.padding),v=[r,b,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=De(n,[0,2,3,4,1]));let I=dw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(I=De(I,[0,4,1,2,3])),this.bias!==null&&(I=Ka(I,this.bias.read(),this.dataFormat)),this.activation!==null&&(I=this.activation.apply(I)),I})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],p=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[a]=rr(t[a],u,i,this.padding),t[r]=rr(t[r],p,o,this.padding),t[s]=rr(t[s],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};z0.className="Conv3DTranspose";ne.registerClass(z0);var PC=class extends Uf{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length{e=Te(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=De(e,[0,2,3,1])),n=Ds(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=De(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseConstraint),e.pointwiseConstraint=Xt(this.pointwiseConstraint),e}};PC.className="SeparableConv";var W0=class extends PC{constructor(e){super(2,e)}};W0.className="SeparableConv2D";ne.registerClass(W0);var B0=class LC extends Uf{constructor(t){super(1,t),LC.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!l0(t.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};B0.className="Conv1D";ne.registerClass(B0);var V0=class extends We{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return O(()=>{if(e=Te(e),this.dataFormat==="channelsLast"){let n=Ph(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ph(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ph(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ph(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};V0.className="Cropping2D";ne.registerClass(V0);var U0=class extends We{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,MG(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Te(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=De(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s]);return De(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Qn.resizeNearestNeighbor(n,[r,s]):Qn.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};U0.className="UpSampling2D";ne.registerClass(U0);function Ej(e,t,n=[1,1],a="valid",r,s){return O(()=>{r==null&&(r=Ga()),Rt(r);let i=P0(e,r);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=As(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=De(i,[0,3,1,2])),i})}var G0=class extends $C{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Te(e);let n=Ej(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ka(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Va(t,this.kernelSize[0],this.padding,this.strides[0]),s=Va(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Xt(this.depthwiseRegularizer),e}};G0.className="DepthwiseConv2D";ne.registerClass(G0);function zC(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function WC(e,t,n,a=!1,r,s,i=!1,o=!1){return O(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ua(2,l));if(t=De(t,u),s!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=re(re(r,"bool"),"float32"),r.rank===l-1&&(r=Gt(r,-1)),r=De(r,u)),a&&(t=ya(t,0),r!=null&&(r=ya(r,0)));let p=[],d,c=n,h=t.shape[0],m=dt(t),f;r!=null&&(f=dt(r));for(let b=0;be(y,c));if(r==null)d=x[0],c=x[1];else{let v=O(()=>{let I=f[b],N=pe(na(I),I),C=X(z(x[0],I),z(c[0],N)),_=c.map((F,D)=>X(z(x[1][D],I),z(F,N)));return{output:C,newStates:_}});d=v.output,c=v.newStates}o&&p.push(d)}let g;return o&&(g=Ft(p,1)),[d,g,c]})}var Pr=class BC extends We{constructor(t){super(t);let n;if(t.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?n=new Kf({cells:t.cell}):n=t.cell,n.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=n,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ua(0,t).map(n=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Yx(t)&&(t=t[0]),t=t;let n=this.cell.stateSize;Array.isArray(n)||(n=[n]);let a=n[0],r;if(this.returnSequences?r=[t[0],t[1],a]:r=[t[0],a],this.returnState){let s=[];for(let i of n)s.push([t[0],i]);return[r].concat(s)}else return r}computeMask(t,n){return O(()=>{Array.isArray(n)&&(n=n[0]);let a=this.returnSequences?n:null;if(this.returnState){let r=this.states.map(s=>null);return[a].concat(r)}else return a})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,n=[];for(let a=0;ai.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(t,n=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let a=this.inputSpec[0].shape[0];if(a==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_=[It([a,this.cell.stateSize])];else if(t==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>It([a,r])):this.states_[0]=It([a,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);n===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;rHt(r.clone()))})}apply(t,n){let a=n==null?null:n.initialState,r=n==null?null:n.constants;n==null&&(n={});let s=zC(t,a,r,this.numConstants);t=s.inputs,a=s.initialState,r=s.constants;let i=[],o=[];if(a!=null){n.initialState=a,i=i.concat(a),this.stateSpec=[];for(let l of a)this.stateSpec.push(new zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(n.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Ha){let l=[t].concat(i),u=this.inputSpec.concat(o),p=this.inputSpec;this.inputSpec=u;let d=super.apply(l,n);return this.inputSpec=p,d}else return super.apply(t,n)}call(t,n){return O(()=>{let a=n==null?null:n.mask,r=n==null?null:n.training,s=n==null?null:n.initialState;t=Te(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new V(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},l=WC((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},t,s,this.goBackwards,a,null,this.unroll,this.returnSequences),u=l[0],p=l[1],d=l[2];this.stateful&&this.resetStates(d,r);let c=this.returnSequences?p:u;return this.returnState?[c].concat(d):c})}getInitialState(t){return O(()=>{let n=It(t.shape);return n=fe(n,[1,2]),n=Cd(n),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(a=>a>1?Kx(n,[1,a]):n):this.cell.stateSize>1?[Kx(n,[1,this.cell.stateSize])]:[n]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),n={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(n.numConstants=this.numConstants);let a=this.cell.getConfig();return this.getClassName()===BC.className&&(n.cell={className:this.cell.getClassName(),config:a}),Object.assign(Object.assign(Object.assign({},a),t),n)}static fromConfig(t,n,a={}){let r=n.cell,s=Ba(r,a);return new t(Object.assign(n,{cell:s}))}};Pr.className="RNN";ne.registerClass(Pr);var $d=class extends We{},jf=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0na(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0na(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=ur(z(e,s),this.kernel.read()):r=ur(e,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),i!=null&&(n=z(n,i));let o=X(r,ur(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),recurrentConstraint:Xt(this.recurrentConstraint),biasConstraint:Xt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};jf.className="SimpleRNNCell";ne.registerClass(jf);var H0=class extends Pr{constructor(e){e.cell=new jf(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};H0.className="SimpleRNN";ne.registerClass(H0);var qf=class extends $d{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0na(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};j0.className="GRU";ne.registerClass(j0);var Dd=class extends $d{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=nu([1,bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nu([1,bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ra{apply(i,o){let l=r.apply([s]),u=new $f().apply([s]),p=r.apply([s*2]);return zI(zI(l,u),p)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0na(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0na(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,p;0{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};q0.className="LSTM";ne.registerClass(q0);var Kf=class extends $d{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return O(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i{ii(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ba(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Zx(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;ss!=null?s(t(),n):U2(t(),n),o=()=>_d(i,t,a);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _j=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return O(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=It(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. 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Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Ra{apply(p,d){let c=l.apply([u]),h=Pn([u]),m=l.apply([u*2]);return u0([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0na(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:z(J[ee],Z),u=l(a,o,0),p=l(a,o,1),d=l(a,o,2),c=l(a,o,3);0na(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),b=l(r,h,3),y=3,[x,v,I,N]=Ln(this.kernel.read(),i,y),[C,_,F,D]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),p=this.inputConv(p,v,_,this.padding),d=this.inputConv(d,I,F,this.padding),c=this.inputConv(c,N,D,this.padding);let[$,S,M,B]=Ln(this.recurrentKernel.read(),i,y);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),g=this.recurrentConv(g,M),b=this.recurrentConv(b,B);let U=this.recurrentActivation.apply(X(u,m)),H=this.recurrentActivation.apply(X(p,f)),q=X(z(H,s),z(U,this.activation.apply(X(d,g)))),K=z(this.recurrentActivation.apply(X(c,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=_j(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ka(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xf.className="ConvLSTM2DCell";ne.registerClass(Xf);var K0=class extends VC{constructor(e){let t=new Xf(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};K0.className="ConvLSTM2D";ne.registerClass(K0);var Yf=class extends We{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a{this.invokeCallHook(e,t);let n=Te(e);if(0U2(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Yf.className="Dropout";ne.registerClass(Yf);var X0=class extends Yf{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};X0.className="SpatialDropout1D";ne.registerClass(X0);var Y0=class extends We{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,tn(this.units,"units"),this.activation=xs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Te(e),a=O2(this.activation.getClassName()),r;return a!=null?r=ur(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=ur(n,this.kernel.read()),this.bias!=null&&(r=Ka(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ys(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Xt(this.kernelConstraint),biasConstraint:Xt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y0.className="Dense";ne.registerClass(Y0);var Z0=class extends We{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},x1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"max")}};x1.className="MaxPooling2D";ne.registerClass(x1);var v1=class extends HC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),Zf(e,t,n,a,r,"avg")}};v1.className="AveragePooling2D";ne.registerClass(v1);var jC=class extends We{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];tn(this.poolSize,"poolSize"),tn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),wa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Va(t,this.poolSize[0],this.padding,this.strides[0]),n=Va(n,this.poolSize[1],this.padding,this.strides[1]),a=Va(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Te(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},w1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"max")}};w1.className="MaxPooling3D";ne.registerClass(w1);var k1=class extends jC{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),wa(a),UC(e,t,n,a,r,"avg")}};k1.className="AveragePooling3D";ne.registerClass(k1);var qC=class extends We{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Ba(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};E1.className="Bidirectional";ne.registerClass(E1);var _1=class extends We{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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e=Je(e),e==null?[this.numTokens]:this.outputMode==="oneHot"&&e[e.length-1]!==1?(e.push(this.numTokens),e):(e[e.length-1]=this.numTokens,e)}call(e,t){return O(()=>{e=Te(e),e.dtype!=="int32"&&(e=lr(e,"int32"));let n;if(typeof t.countWeights!="undefined"){if(this.outputMode!=="count")throw new V(`countWeights is not used when outputMode !== count. 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t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function hS(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((ti(c)||C5(c)||E5(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=w5(m,l);return k5(f,l),f}function w5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function k5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function I5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ti(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new _E(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=v5(this.graph,a),l=I5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>dn(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(ti(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(ti(l))continue;let u=iS(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return ti(l)||s.has(l.name)}if(!(ti(t)||i==null))for(let l of i){if(o(l))continue;let u=iS(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new dS(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>dn(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Zn(v)[0]]),o=a.map(v=>Zn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=hS(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Zn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!ti(v)&&!dn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Tr(d.node.name,a)),r[d.node.name]==null){let h=cS(d.node,r,a,this._resourceManager);c||([c]=Tr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Tr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},_5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},A5="?tfjs-format=file",F5="model.json",z1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new _5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>t.getWeightStream==null?this.loadSync(t):this.loadStreaming(t)):this.loadSync(e)}loadSync(e){let t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(e.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let t=await $N(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=a,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new mS(oS.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=oS.Instance.transformGraph(e.modelInitializer);this.initializer=new mS(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Ee(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function $5(e,t={},n=jt){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=R5(e));let a=new z1(e,t,n);return await a.load(),a}function D5(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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r=k("logits",e,t,n),s=k("numSamples",e,t,n),i=k("seed",e,t,n);return[a.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,n),s=k("depth",e,t,n),i=k("onValue",e,t,n),o=k("offValue",e,t,n),l=k("dtype",e,t,n);return[a.oneHot(r,s,i,o,l)]}case"Ones":return[a.ones(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[a.onesLike(k("x",e,t,n))];case"RandomStandardNormal":return[a.randomStandardNormal(k("shape",e,t,n),k("dtype",e,t,n),k("seed",e,t,n))];case"RandomUniform":return[a.randomUniform(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("dtype",e,t,n))];case"RandomUniformInt":return[a.randomUniformInt(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("seed",e,t,n))];case"Range":{let r=k("start",e,t,n),s=k("stop",e,t,n),i=k("step",e,t,n);return[a.range(r,s,i,k("dtype",e,t,n))]}case"TruncatedNormal":{let 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performance."),console.log(l);for(let p=0;pe.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return xe(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),O(()=>{let a=dt(t),r=n.length,s=a.length;w.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let a=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=a.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,n),i=k("valueDType",e,t,n),o=new o5(s,i);return a.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,n,a),s=k("keys",e,t,n),i=k("values",e,t,n);return[await a.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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r=k("images",e,t,n),s=k("transforms",e,t,n),i=k("outputShape",e,t,n),o=k("fillValue",e,t,n),l=k("interpolation",e,t,n),u=k("fillMode",e,t,n);return[a.image.transform(r,s,l.toLowerCase(),u.toLowerCase(),o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p5=(e,t,n,a=on)=>{switch(e.op){case"Equal":return[a.equal(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[a.notEqual(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[a.greater(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[a.greaterEqual(k("a",e,t,n),k("b",e,t,n))];case"Less":return[a.less(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[a.lessEqual(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[a.logicalAnd(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[a.logicalNot(k("a",e,t,n))];case"LogicalOr":return[a.logicalOr(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[a.where(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];case"BitwiseAnd":return[a.bitwiseAnd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},c5=(e,t,n,a=on)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[a.matMul(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Einsum":return[a.einsum(k("equation",e,t,n),...k("tensors",e,t,n))];case"Transpose":return[a.transpose(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,s]=k("fusedOps",e,t,n),i=r==="biasadd",o=s==="prelu",l=k("numArgs",e,t,n),u=k("leakyreluAlpha",e,t,n);if(i){if(o&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,d]=k("args",e,t,n);return[a.fused.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:p,activation:s,preluActivationWeights:d,leakyreluAlpha:u})];case"MatrixBandPart":return[a.linalg.bandPart(k("a",e,t,n),k("numLower",e,t,n),k("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d5=(e,t,n,a=on)=>{switch(e.op){case"EuclideanNorm":return[a.euclideanNorm(k("x",e,t,n),k("axis",e,t,n),k("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[a.batchNorm(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[a.localResponseNormalization(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[a.softmax(k("x",e,t,n))];case"LogSoftmax":return[a.logSoftmax(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},h5=(e,t,n,a=on)=>{switch(e.op){case"RaggedGather":{let{outputNestedSplits:r,outputDenseValues:s}=a.raggedGather(k("paramsNestedSplits",e,t,n),k("paramsDenseValues",e,t,n),k("indices",e,t,n),k("outputRaggedRank",e,t,n));return r.concat(s)}case"RaggedRange":{let{rtNestedSplits:r,rtDenseValues:s}=a.raggedRange(k("starts",e,t,n),k("limits",e,t,n),k("splits",e,t,n));return[r,s]}case"RaggedTensorToTensor":return[a.raggedTensorToTensor(k("shape",e,t,n),k("values",e,t,n),k("defaultValue",e,t,n),k("rowPartitionTensors",e,t,n),k("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},m5=(e,t,n,a=on)=>{switch(e.op){case"Max":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.max(k("x",e,t,n),o,l)]}case"Mean":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.mean(k("x",e,t,n),o,l)]}case"Min":{let o=k("axis",e,t,n),l=k("keepDims",e,t,n);return[a.min(k("x",e,t,n),o,l)]}case"Sum":{let 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implemented`)}},f5=(e,t,n,a=on)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),s=k("axis",e,t,n),i=k("tensors",e,t,n);return i=i.slice(0,r),[a.concat(i,s)]}case"Gather":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gather(r,a.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),s=k("batchDims",e,t,n),i=k("x",e,t,n),o=k("indices",e,t,n);return[a.gather(i,a.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,n),s=[];for(let o=0;o{let r=k("axis",e,t,n),s=k("tensors",e,t,n),i=s[0].shape,o=a.squeeze(s[0]).shape,l=s.map(u=>{let p=w.arraysEqual(u.shape,i);if(!p&&!w.arraysEqual(a.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return p?u:a.reshape(u,i)});return[a.stack(l,r)]});case"Unpack":{let r=k("axis",e,t,n),s=k("tensor",e,t,n);return a.unstack(s,r)}case"Tile":{let r=k("reps",e,t,n);return[a.tile(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),s=k("numOrSizeSplits",e,t,n),i=k("x",e,t,n);return a.split(i,s,r)}case"ScatterNd":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("shape",e,t,n);return[a.scatterND(r,s,i)]}case"GatherNd":{let r=k("x",e,t,n),s=k("indices",e,t,n);return[a.gatherND(r,s)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),s=k("outputShape",e,t,n),i=k("sparseValues",e,t,n),o=k("defaultValue",e,t,n);return[a.sparseToDense(r,i,s,i.dtype===o.dtype?o:a.cast(o,i.dtype))]}case"TensorScatterUpdate":{let r=k("indices",e,t,n),s=k("values",e,t,n),i=k("tensor",e,t,n);return[a.tensorScatterUpdate(i,r,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},g5=(e,t,n,a=on)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:i,reverseIndexMap:o}=a.sparse.sparseFillEmptyRows(k("indices",e,t,n),k("values",e,t,n),k("denseShape",e,t,n),k("defaultValue",e,t,n));return[r,s,i,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=a.sparse.sparseReshape(k("inputIndices",e,t,n),k("inputShape",e,t,n),k("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[a.sparse.sparseSegmentMean(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];case"SparseSegmentSum":return[a.sparse.sparseSegmentSum(k("data",e,t,n),k("indices",e,t,n),k("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},b5=(e,t,n,a=on)=>{switch(e.op){case"FFT":return[a.fft(k("x",e,t,n))];case"IFFT":return[a.ifft(k("x",e,t,n))];case"RFFT":return[a.rfft(k("x",e,t,n))];case"IRFFT":return[a.irfft(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},y5=(e,t,n,a=on)=>{switch(e.op){case"StaticRegexReplace":return[a.string.staticRegexReplace(k("input",e,t,n),k("pattern",e,t,n),k("rewrite",e,t,n),k("replaceGlobal",e,t,n))];case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=a.string.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:i}=a.string.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[r,s,i]}case"StringToHashBucketFast":return[a.string.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},x5=(e,t,n,a=on)=>{switch(e.op){case"Cast":return[a.cast(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[a.expandDims(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[a.squeeze(k("x",e,t,n),r)]}case"Reshape":return[a.reshape(k("x",e,t,n),k("shape",e,t,n))];case"EnsureShape":return[a.ensureShape(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[a.mirrorPad(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[a.pad(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),s=k("paddings",e,t,n);return[a.spaceToBatchND(k("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),s=k("crops",e,t,n);return[a.batchToSpaceND(k("x",e,t,n),r,s)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),s=k("dataFormat",e,t,n).toUpperCase();return[a.depthToSpace(k("x",e,t,n),r,s)]}case"BroadcastTo":return[a.broadcastTo(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[a.broadcastArgs(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cS(e,t,n,a,r=O){let s=((i,o,l)=>{switch(i.category){case"arithmetic":return r(()=>Kq(i,o,l));case"basic_math":return r(()=>Xq(i,o,l));case"control":return t5(i,o,l);case"convolution":return r(()=>n5(i,o,l));case"creation":return r(()=>a5(i,o,l));case"dynamic":return r5(i,o,l);case"evaluation":return r(()=>s5(i,o,l));case"image":return r(()=>u5(i,o,l));case"graph":return r(()=>i5(i,o,l));case"logical":return r(()=>p5(i,o,l));case"matrices":return r(()=>c5(i,o,l));case"normalization":return r(()=>d5(i,o,l));case"ragged":return r(()=>h5(i,o,l));case"reduction":return r(()=>m5(i,o,l));case"slice_join":return r(()=>f5(i,o,l));case"sparse":return r(()=>g5(i,o,l));case"spectral":return r(()=>b5(i,o,l));case"string":return r(()=>y5(i,o,l));case"transformation":return r(()=>x5(i,o,l));case"hash_table":return l5(i,o,l,a);case"custom":let u=oE(i.op);if(u&&u.customExecutor)return u.customExecutor(new qq(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var dS=class{constructor(e={},t={},n={},a={},r){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.parseNodeNameCache=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function hS(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,u=new Set(Object.keys(e).map(c=>Zn(c)[0]));a=a||[];let p=new Set(a.map(c=>Zn(c.name)[0])),d=[...t];for(;d.length>0;){let c=d.pop();if((ti(c)||C5(c)||E5(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&!u.has(c.name)&&!p.has(c.name)){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function v5(e,t){let{usedNodes:n,inputs:a}=t,r=Object.keys(a).map(g=>Zn(g)[0]).map(g=>e.nodes[g]),s=e.initNodes||[],i=g=>n.has(typeof g=="string"?g:g.name);function o(g){return[...new Map(g.map(b=>[b.name,b])).values()]}let l=o([...r,...e.weights,...s]).filter(i),u=o([...l,...Object.values(e.nodes)]).filter(i),p=new Map(u.map(g=>[g.name,g])),d={};for(let g of u){d[g.name]=d[g.name]||0;for(let b of g.children)i(b)||(d[b.name]=Number.POSITIVE_INFINITY),d[b.name]=(d[b.name]||0)+1}let c=Object.entries(d).filter(([,g])=>g===0).map(([g])=>g),h=[...c];for(;c.length>0;){let g=c.pop(),b=p.get(g);for(let y of b.children.filter(i))--d[y.name]===0&&(h.push(y.name),c.push(y.name))}let m=h.map(g=>p.get(g)),f=w5(m,l);return k5(f,l),f}function w5(e,t){let n=new Map(e.map(s=>[s.name,s])),a=t.map(s=>s.name),r=new Set(a);for(;a.length>0;){let s=a.pop(),i=n.get(s);for(let o of i.children)!n.has(o.name)||r.has(o.name)||(r.add(o.name),a.push(o.name))}return e.filter(s=>r.has(s.name))}var Wh=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function k5(e,t){let n=new Map(e.map((o,l)=>[o.name,l])),a=new Set(t.map(o=>o.name)),r=o=>a.has(typeof o=="string"?o:o.name),s=new Set(e.map(o=>o.name)),i=o=>s.has(typeof o=="string"?o:o.name);for(let o of e){for(let l of o.children.filter(i)){if(!n.has(l.name))throw new Wh(`Child ${l.name} of node ${o.name} is unreachable.`);if(n.get(o.name)>n.get(l.name))throw new Wh(`Node ${o.name} is scheduled to run after its child ${l.name}.`)}if(!r(o))for(let l of o.inputs){if(!n.has(l.name))throw new Wh(`Input ${l.name} of node ${o.name} is unreachable.`);if(n.get(l.name)>n.get(o.name))throw new Wh(`Node ${o.name} is scheduled to run before its input ${l.name}.`)}}}function I5(e){let t=new Map(e.map((o,l)=>[o.name,l])),n=Number.MAX_SAFE_INTEGER,a=e.map((o,l)=>ti(o)?n:l),r=o=>{let l=a[t.get(o.name)];return l==null?-1:l},s=e.map((o,l)=>o.children.map(r).reduce((u,p)=>Math.max(u,p),a[l])),i=new Map;for(let o=0;ot[a].map(r=>r.id));this._weightIds=[].concat(...n),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let n=t.signatureKey||t.name;return t.defaultOutput?`${n}:${t.defaultOutput}`:n})}get functions(){return Object.keys(this._functions).reduce((t,n)=>(t[n]=this._functions[n].signature,t),{})}constructor(t,n){this.graph=t,this.parent=n,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(a=>{this._functionExecutorMap[a]=new _E(t.functions[a],this)})}getCompilationKey(t,n){let a=t.map(s=>s.name).sort(),r=n.map(s=>s.name).sort();return a.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(t,n){let a=hS(t,n,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=a;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(r.length>0){let u=n.map(d=>d.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${p}]. Missing the following inputs: [${r}]`)}let o=v5(this.graph,a),l=I5(o);return{orderedNodes:o,nodeLiveUntilMap:l}}cloneAndKeepTensor(t){if(t==null)return null;let n=t.clone();return Ht(n),n}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([n,a])=>[n,this.cloneTensorList(a)]))}execute(t,n){this.disposeIntermediateTensors(),t=this.mapInputs(t);let a=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n);let r=a.map(c=>this.graph.nodes[Zn(c)[0]]),s=n.map(c=>Zn(c)[0]),i=new Set(s),o=s.map(c=>this.graph.nodes[c]);o.length===0&&(o=this._outputs);let l=this.getCompilationKey(r,o),u=this.compiledMap.get(l);u==null&&(u=this.compile(t,o),this.compiledMap.set(l,u));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let p={},d={};return O(()=>{let c=new dS(this.weightMap,p,d,this.functionExecutorMap,this.parseNodeNameCache),h=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(b=>{let[y,x]=Zn(b,c),v=[];v[x]=t[b],h[y]=v,this.keepIntermediateTensors&&(this.clonedTensorsMap[y]=this.cloneTensorList(v))});let m=this.getFrozenTensorIds(h),{orderedNodes:f,nodeLiveUntilMap:g}=u;for(let b of f){if(h[b.name])continue;let y=cS(b,h,c,this._resourceManager);if(w.isPromise(y))throw new Error(`The execution of the op '${b.op}' returned a promise. Please use model.executeAsync() instead.`);h[b.name]=y,this.keepIntermediateTensors&&(this.clonedTensorsMap[b.name]=this.cloneTensorList(y)),this.checkTensorForDisposalWithNodeLiveUntilInfo(b,h,c,m,i,g.get(b.name))}return this.parent==null&&c.dispose(m),n.map(b=>dn(b,h,c))})}getFrozenTensorIds(t){let n=[].concat.apply([],Object.keys(t).map(a=>t[a]).map(a=>a.map(r=>r.id)));return new Set(n)}checkTensorForDisposal(t,n,a,r,s,i,o){if(!(ti(n)||i.has(t))){for(let l of a[t])l!=null&&(o[l.id]=(o[l.id]||0)+n.children.length);for(let l of n.inputs){if(ti(l))continue;let u=iS(l.name,a,r);if(u!=null)for(let p of u){if(!p||p.kept||s.has(p.id))continue;let d=o[p.id];d===1?(p.dispose(),delete o[p.id]):d!=null&&o[p.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,n,a,r,s,i){function o(l){return ti(l)||s.has(l.name)}if(!(ti(t)||i==null))for(let l of i){if(o(l))continue;let u=iS(l.name,n,a);for(let p of u)!p||p.kept||r.has(p.id)||p.dispose()}}async executeAsync(t,n){return this._executeAsync(t,n)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let n of t)n&&!n.isDisposed&&n.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,n,a=!1,r={},s={}){this.disposeIntermediateTensors(),a||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=G().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){this.keepIntermediateTensors=!1,console.warn(c.message)}let i=new dS(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let o=await this.executeWithControlFlow(t,i,n,a),l=n.map(c=>dn(c,o,i)),u=l.map(c=>c.id),p=Object.keys(t).map(c=>t[c].id),d=new Set([...u,...p,...this.weightIds]);return Object.values(o).forEach(c=>{c.forEach(h=>{h&&!h.isDisposed&&!d.has(h.id)&&h.dispose()})}),this.parent==null&&i.dispose(d),l}async executeFunctionAsync(t,n,a){let r=t.reduce((s,i,o)=>(s[this.inputs[o].name]=i,s),{});return this._executeAsync(r,this.outputNodes,!0,n,a)}async executeWithControlFlow(t,n,a,r){let s=Object.keys(t),i=s.map(v=>this.graph.nodes[Zn(v)[0]]),o=a.map(v=>Zn(v)[0]),l=new Set(o),u=o.map(v=>this.graph.nodes[v]);u.length===0&&(u=this._outputs);let{usedNodes:p,missingInputs:d,dynamicNode:c,syncInputs:h}=hS(t,u,this.weightMap,this._initNodes),m=[...i,...this.graph.weights,...this._initNodes||[]].map(v=>({node:v,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(v=>{let[I,N]=Zn(v),C=[];C[N]=t[v],f[I]=C});let g={},b=this.getFrozenTensorIds(f),y={};for(;m.length>0;){let v=this.processStack(i,m,n,f,y,b,l,g,p);await Promise.all(v)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let x=u.filter(v=>!ti(v)&&!dn(v.name,f,n)).map(v=>v.name);if(x.length>0){let v="";throw c!=null&&(v=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${x}] from the provided inputs [${s}]. Consider providing the following inputs: [${d}]. ${v}`)}return f}processStack(t,n,a,r,s,i,o,l,u){let p=[];for(;n.length>0;){let d=n.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,r,a)&&([c]=Tr(d.node.name,a)),r[d.node.name]==null){let h=cS(d.node,r,a,this._resourceManager);c||([c]=Tr(d.node.name,a));let m=a.currentContext;w.isPromise(h)?p.push(h.then(f=>(r[c]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(f)),a.currentContext=m,this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u),f))):(r[c]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(h)),this.checkTensorForDisposal(c,d.node,r,a,i,o,l),this.processChildNodes(d.node,n,a,r,s,u))}else this.processChildNodes(d.node,n,a,r,s,u)}return p}processChildNodes(t,n,a,r,s,i){t.children.forEach(o=>{let[l]=Tr(o.name,a);s[l]||!i.has(o.name)||(o.op==="Merge"?o.inputNames.some(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})):o.inputNames.every(u=>!!dn(u,r,a))&&(s[l]=!0,n.push({contexts:a.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(n=>n.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(n=>{let a=t[n],[r]=Zn(n),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,o=i.length===a.shape.length&&a.shape.every((l,u)=>i[u]===-1||i[u]===l);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${a.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(a.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(t){var n,a;let r={};for(let s in t){let i=(a=(n=this._signature)===null||n===void 0?void 0:n.inputs)===null||a===void 0?void 0:a[s];i!=null?r[i.name]=t[s]:r[s]=t[s]}return r}checkInputs(t){let n=Object.keys(t).filter(a=>{let[r]=Zn(a);return this.graph.nodes[r]==null});if(n.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${n}] that are not part of graph`)}mapOutputs(t){return t.map(n=>{var a,r;let s=(r=(a=this._signature)===null||a===void 0?void 0:a.outputs)===null||r===void 0?void 0:r[n];return s!=null?s.name:n},{})}checkOutputs(t){t.forEach(n=>{let[a]=Zn(n);if(!this.graph.nodes[a])throw new Error(`The output '${n}' is not found in the graph`)})}},_5=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},A5="?tfjs-format=file",F5="model.json",z1=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=jt){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new _5}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let n=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}if(this.signature=a,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new mS(oS.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=oS.Instance.transformGraph(e.modelInitializer);this.initializer=new mS(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof Ce?[e]:e,n={};return t.forEach((a,r)=>n[this.structuredOutputKeys[r]]=a),n}return e}predict(e,t){let n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){let n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof Ce)&&!Array.isArray(e)){let r=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(r!=null)for(let s in r){let i=r[s];i.resourceId!=null&&(e[s]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((r,s)=>{var i,o,l;let u=(l=(o=(i=this.signature)===null||i===void 0?void 0:i.inputs)===null||o===void 0?void 0:o[s])===null||l===void 0?void 0:l.resourceId;return u!=null?r[s]=this.resourceIdToCapturedInput[u]:r[s]=e[a++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,n=Object.keys(t);for(let a=0;a1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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M5="4.22.0",AE={};_e(AE,{CSVDataset:()=>BE,Dataset:()=>xp,FileDataSource:()=>KE,TextLineDataset:()=>WE,URLDataSource:()=>XE,array:()=>r8,csv:()=>b8,func:()=>y8,generator:()=>x8,microphone:()=>w8,version_data:()=>k8,webcam:()=>v8,zip:()=>s8});var O5=Is(Fm()),P5=Is(Fm());function L5(e,t){return wm(e,t)}function wm(e,t,n=new Map,a=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(ru(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=wm(o,t,n,a);s[i]=l}return a.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function z5(e,t=$E){return FE(e,t)}function FE(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(ru(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(u=>u[i]),l=FE(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function $E(e){return e===null?null:ru(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function DE(e,t){let n=new Map;wm(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(w.isPromise(r)){let s=await r;n.set(a,s)}}return wm(e,t,n)}function ru(e){let t=!1;if(G().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=QS();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ce)&&!(e instanceof Promise)&&!t)}function W5(e){return e==null||B5(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ce||w.isTypedArray(e)}function B5(e){return e===null||typeof e!="object"&&typeof e!="function"}function V5(e){return L5(e,U5)}function U5(e){return e instanceof Ce?{value:e.clone(),recurse:!1}:ru(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var RE=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},ME=class OE extends RE{constructor(){super(OE.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,n=new Array(t),a=this.length();for(let r=0;rt===!0)}rowMajorBatch(e,t=!0){return new Z5(this,e,t)}columnMajorBatch(e,t=!0,n=$E){return this.rowMajorBatch(e,t).map(a=>z5(a,n))}concatenate(e,t){return new LE(PE([this,e]),t)}take(e){return e<0||e==null?this:new Y5(this,e)}skip(e){return e<0||e==null?this:new X5(this,e)}prefetch(e){return new zE(this,e)}shuffle(e,t){return new a8(this,e,t)}serial(){return new K5(this)}},j5=class extends sn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:V5(e),done:!1}}},q5=class extends sn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},K5=class extends sn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},X5=class extends sn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Z5=class extends sn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},J5=class extends sn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},Q5=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},e8=class extends sn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},fS=class extends sn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Wa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Wa.getTensorsInContainer(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},B1=class extends sn{constructor(){super(),this.outputQueue=new ME,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},t8=class extends B1{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Wa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Wa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Wa.isTensorInList(r,a)||r.dispose();return!0}},LE=class extends sn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var n8=class extends sn{constructor(e,t=rs.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof sn?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await DE(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},zE=class extends sn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new RE(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},a8=class extends zE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=P5.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},xp=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is ${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Yn(async()=>(await n.iterator()).columnMajorBatch(e,t,i8),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Yn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Yn(async()=>(await t.iterator()).filter(a=>O(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Yn(async()=>(await t.iterator()).map(n=>O(()=>e(n))),this.size)}mapAsync(e){let t=this;return Yn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Yn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Yn(async()=>{let a=W1(async()=>({value:await t.iterator(),done:!1}));return G5(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=O5.alea(t||w.now().toString());return Yn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Yn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};xp.MAX_BUFFER_SIZE=1e4;function Yn(e,t=null){return new class extends xp{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function r8(e){return Yn(async()=>PE(e),e.length)}function s8(e){if(!ru(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n{let n=await DE(e,a=>{if(a instanceof xp)return{value:a.iterator(),recurse:!1};if(ru(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return H5(n,rs.SHORTEST)},t)}function i8(e){if(e===null)return null;let t=e[0];return W5(t)?{value:o8(e),recurse:!1}:{value:null,recurse:!0}}function o8(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ce?Ft(e):bn(e)}var WE=class extends xp{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bh='"',rc=Symbol("out"),gS=Symbol("field"),Vh=Symbol("quote"),Nx=Symbol("quoteafterquote"),bS=Symbol("quoteinquote"),BE=class extends xp{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new WE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(n))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new VE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},u8=class UE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new UE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Xo.fromPixels(this.webcamVideoElement)}catch(n){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(n)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(n){throw new Error(`Error thrown cropping the video: ${n.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return O(()=>{let n=Gt(re(t,"float32"),0),a;a=Qn.cropAndResize(n,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=a.shape;return W(a,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},GE=class{},HE=class extends sn{split(e){return new p8(this,e)}},p8=class extends HE{constructor(e,t){super(),this.upstream=e,this.impl=new c8(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},c8=class extends B1{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},d8=class extends sn{decodeUTF8(){return new h8(this)}},h8=class extends HE{constructor(e){super(),this.upstream=e,this.impl=new m8(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},m8=class extends B1{constructor(e){if(super(),this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=QS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},jE=class extends d8{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(G().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function f8(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=g8(e));let s=await(n||w.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new jE(i,t)}else throw new Error(s.statusText)}var g8=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function qE(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var KE=class extends GE{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(qE(this.input)&&G().get("IS_NODE")){let e=Fv();this.input=e.readFileSync(this.input.slice(7))}return new jE(this.input,this.options)}},XE=class extends GE{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return qE(this.url)?new KE(this.url,this.fileOptions).iterator():f8(this.url,this.fileOptions)}};function b8(e,t={}){return new BE(new XE(e),t)}function y8(e){let t=W1(e);return Yn(async()=>t)}function x8(e){return Yn(async()=>{let t=await e();return W1(()=>t.next())})}async function v8(e,t){return u8.create(e,t)}async function w8(e){return l8.create(e)}var k8="4.21.0";function ge(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var I8=gr.whereImpl,V1=class YE extends Pc{nextDataId(){return YE.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new $m(this,Ea())}write(t,n,a){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&T.warn(` +`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bh='"',rc=Symbol("out"),gS=Symbol("field"),Vh=Symbol("quote"),Nx=Symbol("quoteafterquote"),bS=Symbol("quoteinquote"),BE=class extends xp{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new WE(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r14||!Number.isInteger(n))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!G().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let n=new VE(t);return await n.start(),n}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(a){throw new Error(`Error thrown while initializing video stream: ${a.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let n=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,n.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,n,a=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(a.freqDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(a.timeDataQueue);n=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:n},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],n=[],a=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:t,timeDataQueue:n}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),n.push(this.timeData.slice())),++a===this.numFrames&&(clearInterval(s),r({freqDataQueue:t,timeDataQueue:n}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let n=t[0].length,a=new Float32Array(t.length*n);return t.forEach((r,s)=>a.set(r,s*n)),a}getTensorFromAudioDataArray(t,n){let a=new Float32Array(w.sizeFromShape(n));return a.set(t,a.length-t.length),bn(a,n)}},u8=class UE extends sn{constructor(t,n){if(super(),this.webcamVideoElement=t,this.webcamConfig=n,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=je([0],"int32"),this.webcamConfig.centerCrop){let a=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-a)/2,i=(1-r)/2,o=s+a,l=r+i;this.cropBox=Aa([i,s,l,o],[1,4])}else this.cropBox=Aa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,n={}){if(!G().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!n.resizeWidth||!n.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=n.resizeWidth,t.height=n.resizeHeight}let a=new UE(t,n);return await a.start(),a}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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iX={kernelName:hu,backendName:"cpu",kernelFunc:sX},oX=lt(Ai,e=>Math.asin(e)),lX={kernelName:Ai,backendName:"cpu",kernelFunc:oX},uX=lt(Fi,e=>Math.asinh(e)),pX={kernelName:Fi,backendName:"cpu",kernelFunc:uX},cX=lt($i,e=>Math.atan(e)),dX={kernelName:$i,backendName:"cpu",kernelFunc:cX},hX=Mt((e,t)=>Math.atan2(e,t)),mX=Zt(Ri,hX),fX={kernelName:Ri,backendName:"cpu",kernelFunc:mX},gX=lt(Di,e=>Math.atanh(e)),bX={kernelName:Di,backendName:"cpu",kernelFunc:gX};function nk(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,p=r.effectiveFilterHeight,d=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=Pe(r.outShape,n),g=f.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let v=0;vq?q=ie:s==="avg"&&(K+=ie,Z++)}if(isNaN(q))break}let J=S+M*x+C;g[J]=s==="avg"?K/Z:q}}}return f}function G_(e,t,n,a,r=!1,s=!1){let i=Pe(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,u=a.dilationHeight,p=a.dilationWidth,d=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=Pe(t,n,e);for(let g=0;gD&&(D=H,r?$=s?((g*a.inHeight+S)*a.inWidth+B)*a.inChannels+b:(S*a.inWidth+B)*a.inChannels+b:$=M*c+U)}}i.set($,g,y,N,b)}}return i}function H_(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,p=r.dilationHeight,d=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,b=r.padInfo.left,y=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Pe(r.outShape,n),v=x.values,I=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[3]*r.outShape[4],_=r.outShape[4];for(let F=0;Fye?ye=gt:s==="avg"&&(ke+=gt,Se++),isNaN(ye))break}if(isNaN(ye))break}if(isNaN(ye))break}let Le=ue+S;v[Le]=s==="avg"?ke/Math.max(Se,1):ye}}}}return x}function yX(e,t){let n=Pe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=M&&(M=ee,B=H*p*d+K*p+J)}}}n.set(B,f,b,I,F,g)}}}return n}function xX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Pe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var SX={kernelName:Wc,backendName:"cpu",kernelFunc:IX};function 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Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),QX={kernelName:Vi,backendName:"cpu",kernelFunc:JX},eY=lt(Ui,e=>Math.cosh(e)),tY={kernelName:Ui,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:b,padInfo:y}=h,x=y.left,v=y.top,I=h.outChannels/h.inChannels,N=new Wt(h.outShape,r.dtype),C=n.data.get(r.dataId).values,_=n.data.get(s.dataId).values,F=N.values;for(let D=0;D=h.inHeight)continue;let K=H*d[0],Z=$+q*p[1];for(let J=0;J=h.inWidth)continue;let ie=K+te*d[1],ve=Z+se*h.inChannels,ue=ee,ye=ie;for(let ke=0;ke{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(a.dataId).values,p=a.shape.length,d=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:b,outWidth:y,padInfo:x,strideHeight:v,strideWidth:I,filterHeight:N,filterWidth:C,dilationHeight:_,dilationWidth:F,outShape:D}=T.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=w.sizeFromShape(D),S=D.length,M=w.getArrayFromDType(a.dtype,$);for(let B=0;B=0&&te=0&&ieJ&&(J=ye)}}}let ee=w.locToIndex([B,U,q,Z],S,w.computeStrides(D));M[ee]=J}}}return{dataId:l.write(w.toTypedArray(M,a.dtype),D,a.dtype),shape:D,dtype:a.dtype}}},vY={kernelName:ql,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${ql}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ee,J=te)}}}$[Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},wY={kernelName:jl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,p=w.toNestedArray(a.shape,u.data.get(a.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:b,padInfo:y,strideHeight:x,strideWidth:v,filterHeight:I,filterWidth:N,dilationHeight:C,dilationWidth:_,outShape:F}=T.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===F.length,()=>`Error in ${jl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let D=w.toNestedArray(F,u.data.get(s.dataId).values),$=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let S=0;S=0&&ae=0&&seK&&(K=ie,Z=ae,J=se)}}}$[S][Z][J][q]+=D[S][M][U][q]}}}return{dataId:u.write(w.toTypedArray($,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function kY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{canvas:s,options:i}=a,{contextOptions:o,imageOptions:l}=i||{},u=(l==null?void 0:l.alpha)||1,p=(o==null?void 0:o.contextType)||"2d";if(p!=="2d")throw new Error(`Context type ${o.contextType} is not supported by the CPU backend.`);let d=s.getContext(p,(o==null?void 0:o.contextAttributes)||{});if(d==null)throw new Error(`Could not get the context with ${p} type.`);let[c,h]=r.shape.slice(0,2),m=r.shape.length===2?1:r.shape[2],f=n.data.get(r.dataId).values,g=r.dtype==="float32"?255:1,b=new Uint8ClampedArray(h*c*4);for(let x=0;x1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${C}.`)}else if(r.dtype==="int32"&&(C<0||C>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${C}.`);m===1?(v[0]=C*g,v[1]=C*g,v[2]=C*g):v[N]=C*g}let I=x*4;b[I+0]=Math.round(v[0]),b[I+1]=Math.round(v[1]),b[I+2]=Math.round(v[2]),b[I+3]=Math.round(v[3])}s.width=h,s.height=c;let y=new ImageData(b,h,c);return d.putImageData(y,0,0),r}var IY={kernelName:zm,backendName:"cpu",kernelFunc:kY};function Rd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"sum");let o;r.dtype==="bool"?o=ws({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):o=hr({inputs:{x:r},backend:n});let l=o.shape.length,u=w.parseAxisParam(s,o.shape),p=T.getAxesPermutation(u,l),d=u,c=o;p!=null&&(c=Vn({inputs:{x:o},backend:n,attrs:{perm:p}}),d=T.getInnerMostAxes(d.length,l)),T.assertAxesAreInnerMostDims("sum",d,c.shape.length);let[h,m]=T.computeOutAndReduceShapes(c.shape,d),f=T.upcastType(c.dtype,"int32"),g=km(n,h,f),b=w.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(c.dataId).values;for(let v=0;v=0&&(c=Rd({inputs:{x:c},backend:n,attrs:{axis:u[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var TY={kernelName:Wm,backendName:"cpu",kernelFunc:NY};function CY(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;ge([a,r],"eluGrad");let s=new Float32Array(w.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l=0?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",s)}var EY={kernelName:Su,backendName:"cpu",kernelFunc:CY},_Y=T.ERF_P,AY=T.ERF_A1,FY=T.ERF_A2,$Y=T.ERF_A3,DY=T.ERF_A4,RY=T.ERF_A5,MY=lt(Xi,e=>{let t=Math.sign(e),n=Math.abs(e),a=1/(1+_Y*n);return t*(1-((((RY*a+DY)*a+$Y)*a+FY)*a+AY)*a*Math.exp(-n*n))}),OY={kernelName:Xi,backendName:"cpu",kernelFunc:MY};function Nm(e){let{inputs:t,backend:n,attrs:a}=e,{input:r}=t,{dim:s}=a,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),xt({inputs:{x:r},backend:n,attrs:{shape:o}})}var PY={kernelName:Tu,backendName:"cpu",kernelFunc:Nm},LY=Mt((e,t)=>e/t),ak=Zt(qi,LY),gv={kernelName:qi,backendName:"cpu",kernelFunc:ak};function K_(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],p=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",p),c=w.getTypedArrayFromDType("float32",p);for(let g=0;g{let{image:a}=e,r=n,s=w.getTypedArrayFromDType(a.dtype,w.sizeFromShape(a.shape)),[i,o,l,u]=a.shape,p=r.data.get(a.dataId).values;for(let d=0;d=0&&y=0,()=>`GatherV2: the index value ${I} is not in [0, ${p-1}]`)}let d=o;o==null&&(d=0);let c=w.sizeFromShape(s.shape),h=T.segment_util.collectGatherOpShapeInfo(r,s,l,d),m=xt({inputs:{x:r},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=xt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),g=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],b=n.bufferSync(f),y=n.bufferSync(m),x=c_(y,b,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,x.dtype,x.values)}var e7={kernelName:Eu,backendName:"cpu",kernelFunc:QY};function t7(e){let{inputs:t,backend:n}=e,{input:a}=t,r=w.sizeFromShape(a.shape),s=a.shape[a.shape.length-1],i=r/s,o=xt({inputs:{x:a},backend:n,attrs:{shape:[i,s]}}),l=K_(o,!0,n),u=xt({inputs:{x:l},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var n7={kernelName:Vm,backendName:"cpu",kernelFunc:t7},a7=lt(ao,e=>Number.isFinite(e)?1:0,"bool"),r7={kernelName:ao,backendName:"cpu",kernelFunc:a7},s7=lt(ro,e=>Math.abs(e)===1/0?1:0,"bool"),i7={kernelName:ro,backendName:"cpu",kernelFunc:s7},o7=lt(so,e=>Number.isNaN(e)?1:0,"bool"),l7={kernelName:so,backendName:"cpu",kernelFunc:o7};function u7(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=g_(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var p7={kernelName:Du,backendName:"cpu",kernelFunc:u7},c7=lt(lo,e=>Math.log1p(e)),d7={kernelName:lo,backendName:"cpu",kernelFunc:c7},h7=Mt((e,t)=>e&&t),m7=Zt(Ru,h7,null,"bool"),f7={kernelName:Ru,backendName:"cpu",kernelFunc:m7},g7=lt(Mu,e=>e?0:1,"bool"),b7={kernelName:Mu,backendName:"cpu",kernelFunc:g7},y7=Mt((e,t)=>e||t),x7=Zt(Ou,y7,null,"bool"),v7={kernelName:Ou,backendName:"cpu",kernelFunc:x7};function w7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;ge(r,"LRN");let u=r.shape[3],p=u-1,d=n.data.get(r.dataId).values,c=w.sizeFromShape(r.shape),h=new Float32Array(c);function m(f){let g=f%u,b=f-g+Math.max(0,g-s),y=f-g+Math.min(g+s,p),x=0;for(;b<=y;b++){let v=d[b];x+=v*v}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=T.computePool2DInfo(r.shape,s,i,u,o,l),d;if(p.filterWidth===1&&p.filterHeight===1&&w.arraysEqual(p.inShape,p.outShape))d=hr({inputs:{x:r},backend:n});else{let c=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),m=nk(c,r.shape,r.dtype,h,p,"max");d=n.makeTensorInfo(p.outShape,r.dtype,m.values)}return d}var C7={kernelName:ho,backendName:"cpu",kernelFunc:T7};function E7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=a;ge(r,"maxPool3d");let p=T.computePool3DInfo(r.shape,s,i,1,o,l,u),d=n.data.get(r.dataId).values,c=H_(d,r.shape,r.dtype,w.computeStrides(r.shape),p,"max");return n.makeTensorInfo(c.shape,"float32",c.values)}var _7={kernelName:Lu,backendName:"cpu",kernelFunc:E7};function A7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=a;ge([r,s],"maxPool3DGrad");let p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=n.bufferSync(s),c=yX(d,p),h=p.strideDepth,m=p.strideHeight,f=p.strideWidth,g=p.dilationDepth,b=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterDepth,v=p.effectiveFilterHeight,I=p.effectiveFilterWidth,N=x-1-p.padInfo.front,C=I-1-p.padInfo.left,_=v-1-p.padInfo.top,F=Pe(s.shape,"float32"),D=n.bufferSync(r);for(let $=0;$=p.outDepth||Math.floor(ee)!==ee))for(let ae=0;ae=p.outHeight||Math.floor(te)!==te))for(let se=0;se=p.outWidth||Math.floor(ie)!==ie)continue;let ve=x*v*I-1-c.get($,ee,te,ie,S),ue=J*v*I+ae*I+se,ye=ve===ue?1:0;if(ye===0)continue;let ke=D.get($,ee,te,ie,S);Z+=ke*ye}}}F.set(Z,$,M,B,U,S)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var F7={kernelName:qc,backendName:"cpu",kernelFunc:A7};function $7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ge([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:p,dimRoundingMode:d}=a,c=T.computePool2DInfo(o.shape,l,u,1,p,d),h=n.data.get(o.dataId).values,m=Pe(c.outShape,o.dtype,G_(h,o.shape,o.dtype,c).values),f=c.strideHeight,g=c.strideWidth,b=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,I=v-1-c.padInfo.left,N=x-1-c.padInfo.top,C=Pe(o.shape,"float32"),_=n.data.get(r.dataId).values,F=Pe(r.shape,"float32",_);for(let D=0;D=c.outHeight||Math.floor(K)!==K))for(let Z=0;Z=c.outWidth||Math.floor(J)!==J)continue;let ee=x*v-1-m.get(D,K,J,$),ae=q*v+Z,te=ee===ae?1:0;if(te===0)continue;let se=F.get(D,K,J,$);H+=se*te}}C.set(H,D,S,M,$)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var D7={kernelName:jc,backendName:"cpu",kernelFunc:$7};function R7(e,t,n,a,r){let s=w.computeStrides(t),i=nk(e,t,n,s,r,"max"),o=G_(e,t,n,r,!0,a);return[i.values,o.values]}var M7={kernelName:Kc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;ge(a,"MaxPoolWithArgmax");let u=l.data.get(a.dataId).values,p=T.computePool2DInfo(a.shape,r,s,[1,1],i),[d,c]=R7(u,a.shape,a.dtype,o,p),h=l.write(d,p.outShape,a.dtype),m=l.write(c,p.outShape,a.dtype);return[{dataId:h,shape:p.outShape,dtype:a.dtype},{dataId:m,shape:p.outShape,dtype:"int32"}]}};function O7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=w.parseAxisParam(s,r.shape),l=T.computeOutAndReduceShapes(r.shape,o)[1],u=w.sizeFromShape(l),p=[],d=n.makeTensorInfo([],"float32",new Float32Array([u]));p.push(d);let c=ws({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});p.push(c);let h=ak({inputs:{a:c,b:d},backend:n});p.push(h);let m=Rd({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var P7={kernelName:mo,backendName:"cpu",kernelFunc:O7};function L7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ge(r,"min");let o=w.parseAxisParam(s,r.shape),l=o,u=T.getAxesPermutation(l,r.shape.length),p=r;u!=null&&(p=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}),l=T.getInnerMostAxes(l.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",l,p.shape.length);let[d,c]=T.computeOutAndReduceShapes(p.shape,l),h=w.sizeFromShape(c),m=w.makeZerosTypedArray(w.sizeFromShape(d),p.dtype),f=n.data.get(p.dataId).values;for(let b=0;by[0]+r.shape[x]+y[1]),l=s.map(y=>y[0]),u=s.map((y,x)=>y[0]+r.shape[x]),p=i==="reflect"?0:1,d=n.data.get(r.dataId).values,c=r.shape.length,h=w.computeStrides(r.shape),m=w.sizeFromShape(o),f=o.length,g=w.computeStrides(o),b=w.getTypedArrayFromDType(r.dtype,m);for(let y=0;y=u[I]&&(x[I]=(u[I]-1)*2-x[I]+p);x=x.map((I,N)=>I-l[N]);let v=w.locToIndex(x,c,h);b[y]=d[v]}return{dataId:n.write(b,o,r.dtype),shape:o,dtype:r.dtype}}var B7={kernelName:bo,backendName:"cpu",kernelFunc:W7},V7=Mt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),U7=Zt(yo,V7),G7={kernelName:yo,backendName:"cpu",kernelFunc:U7},H7=Is(Fm());function Y_(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=r.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=w.parseAxisParam([o],r.shape),u=X_({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=T.expandShapeToKeepDim(u.shape,l),d=xt({inputs:{x:u},backend:n,attrs:{shape:p}}),c=ek({inputs:{a:r,b:d},backend:n}),h=i_({inputs:{x:c},backend:n}),m=Rd({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=ak({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var j7={kernelName:Bo,backendName:"cpu",kernelFunc:Y_};function q7(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a;ge(r,"multinomial");let l=o?r:Y_({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],p=l.shape[1],d=n.data.get(l.dataId).values,c=[u,s],h=w.makeZerosTypedArray(w.sizeFromShape(c),"int32");for(let m=0;m=0&&d[c]{w.assertShapesMatch(s,p.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===p.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(p=>{let d=Nm({inputs:{input:p},backend:n,attrs:{dim:r}});return o.push(d),d}),u=ou({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var lZ={kernelName:ju,backendName:"cpu",kernelFunc:J_};function uZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;ge(r,"pad");let o=s.map((b,y)=>b[0]+r.shape[y]+b[1]),l=s.map(b=>b[0]),u=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),d=r.shape.length,c=w.computeStrides(r.shape),h=w.sizeFromShape(o),m=o.length,f=w.computeStrides(o),g=w.getTypedArrayFromDType(r.dtype,h);i!==0&&g.fill(i);for(let 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x}function yX(e,t){let n=Pe(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f=M&&(M=ee,B=H*p*d+K*p+J)}}}n.set(B,f,b,I,F,g)}}}return n}function xX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ge(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,u=1;w.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=T.computePool3DInfo(s.shape,i,o,1,l,u),d=p.strideDepth,c=p.strideHeight,h=p.strideWidth,m=p.filterDepth,f=p.filterHeight,g=p.filterWidth,b=p.dilationDepth,y=p.dilationHeight,x=p.dilationWidth,v=p.effectiveFilterDepth,I=p.effectiveFilterHeight,N=p.effectiveFilterWidth,C=v-1-p.padInfo.front,_=N-1-p.padInfo.left,F=I-1-p.padInfo.top,D=Pe(s.shape,"float32"),$=1/(m*f*g),S=n.bufferSync(r);for(let M=0;M=p.outDepth||Math.floor(te)!==te))for(let se=0;se=p.outHeight||Math.floor(ie)!==ie))for(let ve=0;ve=p.outWidth||Math.floor(ue)!==ue)continue;let ye=S.get(M,te,ie,ue,B);ee+=ye}}}D.set(ee*$,M,U,H,q,B)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var SX={kernelName:Wc,backendName:"cpu",kernelFunc:IX};function NX(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ge([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=a,p=T.computePool2DInfo(i.shape,o,l,1,u),d=p.strideHeight,c=p.strideWidth,h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,b=p.effectiveFilterHeight,y=p.effectiveFilterWidth,x=y-1-p.padInfo.left,v=b-1-p.padInfo.top,I=Pe(i.shape,"float32"),N=1/(h*m),C=n.data.get(r.dataId).values,_=Pe(r.shape,"float32",C);for(let F=0;F=p.outHeight||Math.floor(q)!==q))for(let K=0;K=p.outWidth||Math.floor(Z)!==Z)continue;let J=_.get(F,q,Z,D);U+=J}}I.set(U*N,F,$,S,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var TX={kernelName:zc,backendName:"cpu",kernelFunc:NX};function CX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;w.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((b,y)=>b*y),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),p=T.getReshapedPermuted(r.shape,s,o),d=T.getSliceBeginCoords(i,s.length),c=T.getSliceSize(p,i,s.length),h=xt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),f=xt({inputs:{x:m},backend:n,attrs:{shape:p}}),g=wi({inputs:{x:f},backend:n,attrs:{begin:d,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var AX={kernelName:fu,backendName:"cpu",kernelFunc:_X};function FX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,u=H1(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var $X={kernelName:gu,backendName:"cpu",kernelFunc:FX};function DX(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var RX={kernelName:Bc,backendName:"cpu",kernelFunc:DX},MX=lt(Ns,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;uf.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(f=>w.sizeFromShape(f.shape)>0);if(l.length===1)return hr({inputs:{x:l[0]},backend:n});if(l[0].dtype==="complex64"){let f=l.map(v=>vi({inputs:{input:v},backend:n})),g=l.map(v=>iu({inputs:{input:v},backend:n})),b=ou({inputs:f,backend:n,attrs:{axis:s}}),y=ou({inputs:g,backend:n,attrs:{axis:s}}),x=Jn({inputs:{real:b,imag:y},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),x}let u=l.map(f=>{let g=[-1,w.sizeFromShape(f.shape.slice(s))];return xt({inputs:{x:f},backend:n,attrs:{shape:g}})}),p=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=T.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,c=j1(p,o,t[0].dtype,d),h=T.computeOutShape(l.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var WX={kernelName:yu,backendName:"cpu",kernelFunc:ou};function j_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:p}=a;ge([r,s],"conv2d");let d=T.convertConv2DDataFormat(l),c=T.computeConv2DInfo(r.shape,s.shape,i,u,o,p,!1,d),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,b=c.padInfo.left,y=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Wt(c.outShape,r.dtype),I=w.computeStrides(r.shape),N=w.computeStrides(s.shape),C=I[0],_=x?I[1]:I[2],F=x?I[2]:1,D=x?1:I[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],M=x?v.strides[2]:1,B=x?1:v.strides[1],U=n.data.get(r.dataId).values,H=n.data.get(s.dataId).values,q=v.values;for(let K=0;K=c.inHeight)continue;let ve=se*N[0],ue=Z+ie*_;for(let ye=0;ye=c.inWidth)continue;let mt=ve+Le*N[1],st=ue+Ge*F,at=mt;for(let rt=0;rt=u.inDepth)continue;let K=H*F[0],Z=$+q*_[1];for(let J=0;J=u.inHeight)continue;let ie=K+te*F[1],ve=Z+se*_[2];for(let ue=0;ue=u.inWidth)continue;let Ge=ie+Se*F[2],mt=ve+Le*u.inChannels,st=Ge;for(let at=0;atMath.cos(e)),QX={kernelName:Vi,backendName:"cpu",kernelFunc:JX},eY=lt(Ui,e=>Math.cosh(e)),tY={kernelName:Ui,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=a,[p,d,c,h]=r.shape,m=s.shape[0],[f,g]=o,b=Pe([m,f,g,h],"float32"),y=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,I=w.computeStrides(r.shape),N=w.computeStrides(b.shape);for(let C=0;C=p)continue;let B=f>1?($-F)*(d-1)/(f-1):0,U=g>1?(S-D)*(c-1)/(g-1):0;for(let H=0;H1?F*(d-1)+H*B:.5*(F+$)*(d-1);if(q<0||q>d-1){for(let K=0;K1?D*(c-1)+ee*U:.5*(D+S)*(c-1);if(ae<0||ae>c-1){for(let ve=0;ve1?D*(c-1)+K*U:.5*(D+S)*(c-1);if(Z<0||Z>c-1){for(let ae=0;aeb+m-y-1:(b,y)=>b+y;for(let b=0;bb+m-y-1:(b,y)=>b+y;for(let b=0;b`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],p=r.shape[3],d=l*s,c=u*s,h=p/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*d*c*h),g=0;for(let b=0;b`Error in depthwiseConv2d: Either strides or dilations must be 1. 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b=0;bv+l[I]),x=w.locToIndex(y,m,f);g[x]=u[b]}return{dataId:n.write(g,o,r.dtype),shape:o,dtype:r.dtype}}var Q_={kernelName:wo,backendName:"cpu",kernelFunc:uZ},pZ=Mt((e,t)=>Math.pow(e,t)),cZ=Zt(ko,pZ),dZ={kernelName:ko,backendName:"cpu",kernelFunc:cZ};function hZ(e){let{inputs:t,backend:n,attrs:a}=e,{paramsNestedSplits:r,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:o}=a,l=r.map(b=>n.data.get(b.dataId).values),u=r.map(b=>b.shape),p=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,[c,h,m]=S_(l,u,p,s.shape,s.dtype,d,i.shape,o),f=c.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(m,s.dtype,h);return f.concat([g])}var mZ={kernelName:Gm,backendName:"cpu",kernelFunc:hZ};function fZ(e){let{inputs:t,backend:n}=e,{starts:a,limits:r,deltas:s}=t,i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[u,p]=N_(i,a.shape,a.dtype,o,r.shape,l,s.shape),d=n.makeTensorInfo([u.length],"int32",u),c=n.makeTensorInfo([p.length],a.dtype,p);return[d,c]}var gZ={kernelName:Hm,backendName:"cpu",kernelFunc:fZ};function bZ(e){let{inputs:t,backend:n,attrs:a}=e,{shape:r,values:s,defaultValue:i,rowPartitionTensors:o}=t,{rowPartitionTypes:l}=a,u=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,c=o.map(g=>n.data.get(g.dataId).values),h=o.map(g=>g.shape),[m,f]=T_(u,r.shape,p,s.shape,s.dtype,d,i.shape,c,h,l);return n.makeTensorInfo(m,s.dtype,f)}var yZ={kernelName:jm,backendName:"cpu",kernelFunc:bZ};function xZ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,dtype:s,step:i}=n,o=X1(a,r,i,s);return t.makeTensorInfo([o.length],s,o)}var vZ={kernelName:Xc,backendName:"cpu",kernelFunc:xZ},wZ=lt(No,e=>1/e),kZ={kernelName:No,backendName:"cpu",kernelFunc:wZ};function IZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ge(r,"resizeBilinear");let l=w.computeStrides(r.shape),[u,p]=o,[d,c,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(w.sizeFromShape([d,u,p,m])),b=[s&&u>1?c-1:c,s&&p>1?h-1:h],y=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=0,v=b[0]/y[0],I=b[1]/y[1];for(let N=0;N1?u-1:u,i&&h>1?p-1:p],g=[i&&c>1?c-1:c,i&&h>1?h-1:h],b=f[0]/g[0],y=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let I=0;I1?c-1:c,s&&p>1?h-1:h],y=[s&&u>1?u-1:u,s&&p>1?p-1:p],x=b[0]/y[0],v=b[1]/y[1],I=0;for(let N=0;N1?p-1:p,i&&m>1?d-1:d],y=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=b[0]/y[0],v=b[1]/y[1],I=1/x,N=1/v,C=Math.ceil(I)*2+2,_=Math.ceil(N)*2+2;for(let F=0;F=h)continue;let te=D+ae*l[1],se=ae*x,ie=Math.min(p-1,i?Math.round(se):Math.floor(se));if($===ie)for(let ve=0;ve<_;ve++){let ue=ve+K;if(ue<0||ue>=m)continue;let ye=te+ue*l[2],ke=ue*v,Se=Math.min(d-1,i?Math.round(ke):Math.floor(ke));U===Se&&(J+=g[ye+Z])}}f[H+Z]=J}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var AZ={kernelName:Ku,backendName:"cpu",kernelFunc:_Z};function FZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ge(r,"reverse");let i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return hr({inputs:{x:r},backend:n});let l=new Wt(r.shape,r.dtype),u=n.bufferSync(r);for(let p=0;pc[h]=r.shape[h]-1-c[h]),l.set(u.get(...c),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var $Z={kernelName:Ao,backendName:"cpu",kernelFunc:FZ},DZ={kernelName:pp,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=w.getTypedArrayFromDType(a.dtype,w.sizeFromShape(a.shape)),[u,p,d,c]=a.shape,[h,m]=T.getImageCenter(i,p,d),f=255,g=Math.sin(r),b=Math.cos(r),y=o.data.get(a.dataId).values;for(let x=0;x=0&&M=0&&B{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2===0?t:t+1}),MZ={kernelName:Fo,backendName:"cpu",kernelFunc:RZ};function OZ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:u,strides:p,outputSize:d}=T.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=ri(h,m,i,d,u,l,o,p,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var PZ={kernelName:Yu,backendName:"cpu",kernelFunc:OZ};function LZ(e,t){let n=0,a=e.length,r=0;for(;n1||r.shape.length===1?1:w.sizeFromShape(r.shape.slice(1));for(let m=0;me>=0?jZ*e:HZ*(Math.exp(e)-1)),KZ={kernelName:Do,backendName:"cpu",kernelFunc:qZ},XZ=lt(Oo,e=>e<0?-1:e>0?1:0),YZ={kernelName:Oo,backendName:"cpu",kernelFunc:XZ},ZZ=lt(Ro,e=>Math.sin(e)),JZ={kernelName:Ro,backendName:"cpu",kernelFunc:ZZ},QZ=lt(Mo,e=>Math.sinh(e)),eJ={kernelName:Mo,backendName:"cpu",kernelFunc:QZ},tJ=11920928955078125e-23,kS=Math.log(tJ)+2,nJ=lt(Lo,e=>{let t=e>-kS,n=em.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=wee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new gee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new eee(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new JA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new nQ(o):l=new tQ(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=eQ(t,p,d),h=this.getAndSaveBinary(c,()=>J9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||Q9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yee:xee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=xA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new iQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=See(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ea().makeTensorFromDataId(u,n,a,l)}};gk.nextDataId=0;function See(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew gk,2);var Tee={forceHalfFloat:eF},bk=` + `}},bee=gr.whereImpl,yee=1e-7,xee=1e-4,Tx={};function vee(e){return e in Tx||(Tx[e]={}),Tx[e]}var wee=G().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),kee=600;function Iee(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*kee/1024/1024}var gk=class QA extends Pc{nextDataId(){return QA.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let n;if(t!=null){if(t instanceof em)n=t;else{let a=ja(G().getNumber("WEBGL_VERSION"),t);n=new em(a)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let a=ja(G().getNumber("WEBGL_VERSION"));n=new em(a),this.binaryCache=vee(G().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=n,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new nee(this.gpgpu),this.numMBBeforeWarning=Iee(),this.texData=new $m(this,Ea())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,n,a,r,s,i){let o=this.makeTensorInfo(n,a),l=this.texData.get(o.dataId);l.isPacked=!1,l.texture={texture:t,texShape:[r,s]},l.texShape=[r,s];let u=dc(n),p=new NS(u,!1,i),d=this.runWebGLProgram(p,[o],a,[[r,s]]);return d.shape=n,l.texture=null,this.disposeIntermediateTensorInfo(o),d.dataId}write(t,n,a){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(t),a==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:n,dtype:a,values:t,usage:da.UPLOAD,refCount:1}),r}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let n=this.texData.get(t);n.refCount++}decRef(t){if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--}}move(t,n,a,r,s){if(G().getBool("DEBUG")&&this.checkNumericalProblems(n),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:a,dtype:r,values:n,usage:da.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let n=this.texData.get(t),{values:a,dtype:r,complexTensorInfos:s,slice:i,shape:o,isPacked:l}=n;if(i!=null){let c;l?c=new ss(o,es):c=new ir(o,es);let h=this.runWebGLProgram(c,[{dataId:t,shape:o,dtype:r}],r),m=this.readSync(h.dataId);return this.disposeIntermediateTensorInfo(h),m}if(a!=null)return this.convertAndCacheOnCPU(t);if(r==="string")return a;let u=this.activeTimers!=null,p;u&&(p=w.now());let d;if(r==="complex64"){let c=this.readSync(s.real.dataId),h=this.readSync(s.imag.dataId);d=T.mergeRealAndImagArrays(c,h)}else d=this.getValuesFromTexture(t);return u&&(this.downloadWaitMs+=w.now()-p),this.convertAndCacheOnCPU(t,d)}async read(t){if(this.pendingRead.has(t)){let m=this.pendingRead.get(t);return new Promise(f=>m.push(f))}let n=this.texData.get(t),{values:a,shape:r,slice:s,dtype:i,complexTensorInfos:o,isPacked:l}=n;if(s!=null){let m;l?m=new ss(r,es):m=new ir(r,es);let f=this.runWebGLProgram(m,[{dataId:t,shape:r,dtype:i}],i),g=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),g}if(a!=null)return this.convertAndCacheOnCPU(t);if(G().getBool("DEBUG")&&!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(i!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(t);let m=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(m.texture.texture,...Gh(r))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(i==="complex64"){let m=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=m[0],g=m[1];d=T.mergeRealAndImagArrays(f,g)}else if(u==null)d=this.getValuesFromTexture(t);else{let m=w.sizeFromShape(r);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,m)}if(p!=null&&this.disposeIntermediateTensorInfo(p),u!=null){let m=this.gpgpu.gl;de(m,()=>m.deleteBuffer(u))}let c=this.convertAndCacheOnCPU(t,d),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(m=>m(c)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ea().removeDataId(t,this),this.pendingDeletes--),c}readToGPU(t,n={}){let a=this.texData.get(t),{values:r,shape:s,slice:i,dtype:o,isPacked:l,texture:u}=a;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let h;l?h=new ss(s,es):h=new ir(s,es);let m=this.runWebGLProgram(h,[{dataId:t,shape:s,dtype:o}],o),f=this.readToGPU(m,n);return this.disposeIntermediateTensorInfo(m),f}if(u==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let p=this.decode(t,n.customTexShape),d=Ea().makeTensorFromTensorInfo(p),c=this.texData.get(p.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(t){let n=this.readSync(t.dataId);if(t.dtype==="string")try{let a=n.map(r=>w.decodeString(r));return Pe(t.shape,t.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Pe(t.shape,t.dtype,n)}checkNumericalProblems(t){if(t!=null)for(let n=0;n0}time(t){let n=this.activeTimers,a=[],r=!1;this.programTimersStack==null?(this.programTimersStack=a,r=!0):this.activeTimers.push(a),this.activeTimers=a,t();let s=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),i=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=n,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);o.kernelMs=w.sum(l),o.getExtraProfileInfo=()=>l.map((u,p)=>({name:i[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(t){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=w.now(),t)}async getQueryTime(t){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let n=t;return n.endMs-n.startMs}disposeData(t,n=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(n?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!n&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:a}=this.texData.get(t);return a!=null&&(this.disposeData(a.real.dataId,n),this.disposeData(a.imag.dataId,n)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:n,dtype:a,texShape:r,usage:s,isPacked:i,slice:o}=this.texData.get(t),l=o&&o.origDataId||t,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),n!=null&&(this.numBytesInGPU-=this.computeBytes(r,a),this.textureManager.releaseTexture(n,r,s,i)));let p=this.texData.get(t);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,n=wee){return G().getBool("WEBGL_CPU_FORWARD")&&t.every(a=>this.texData.get(a.dataId).texture==null&&w.sizeFromShape(a.shape)0&&w.isString(a[0])){let s=a.map(i=>w.encodeString(i));r=this.write(s,t,n)}else r=this.write(a,t,n);return this.texData.get(r).usage=null,{dataId:r,shape:t,dtype:n}}makeOutput(t,n,a){return Ea().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,a),this)}unpackTensor(t){let n=new gee(t.shape);return this.runWebGLProgram(n,[t],t.dtype)}packTensor(t){let n=new eee(t.shape);return this.runWebGLProgram(n,[t],t.dtype,null,!0)}packedReshape(t,n){let a=[ki(t.shape),...Ii(t.shape)],r={dtype:t.dtype,shape:a,dataId:t.dataId},s=[ki(n),...Ii(n)],i=new JA(s,a),o=!0,l=[a],u=this.runWebGLProgram(i,[r],t.dtype,l,o);return{dataId:u.dataId,shape:n,dtype:u.dtype}}decode(t,n){let a=this.texData.get(t),{isPacked:r,shape:s,dtype:i}=a;if(n!=null){let c=w.sizeFromShape(s),h=n[0]*n[1]*4;w.assert(c<=h,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dc(s),l;r?l=new nQ(o):l=new tQ(o);let u=!0,p=[n!=null?n:Gh(o)],d=this.runWebGLProgram(l,[{shape:o,dtype:i,dataId:t}],i,p,u,n);return{dtype:i,shape:s,dataId:d.dataId}}runWebGLProgram(t,n,a,r,s=!1,i){let o=this.makeTensorInfo(t.outputShape,a),l=this.texData.get(o.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===Fc.DENSE){let b=i!=null?i:Gh(t.outputShape);l.texShape=b.map(y=>y*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),w.sizeFromShape(o.shape)===0)return l.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],p=n.map(b=>{if(b.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(b.dataId);if(y.texture==null){if(!t.packedInputs&&w.sizeFromShape(b.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:b.shape,texData:null,isUniform:!0,uniformValues:y.values};t.packedInputs&&(y.isPacked=!0,y.shape=b.shape)}if(this.uploadToGPU(b.dataId),!!y.isPacked!=!!t.packedInputs)b=y.isPacked?this.unpackTensor(b):this.packTensor(b),u.push(b),y=this.texData.get(b.dataId);else if(y.isPacked&&!$c(y.shape,b.shape)){let x=b,v=b.shape;b.shape=y.shape,b=this.packedReshape(b,v),u.push(b),y=this.texData.get(b.dataId),x.shape=v}return{shape:b.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let d={shape:o.shape,texData:l,isUniform:!1},c=eQ(t,p,d),h=this.getAndSaveBinary(c,()=>J9(this.gpgpu,t,p,d)),m=this.activeTimers!=null,f;m&&(f=this.startTimer()),G().get("ENGINE_COMPILE_ONLY")||Q9(this.gpgpu,h,p,d,r),u.forEach(b=>this.disposeIntermediateTensorInfo(b)),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)}));let g=G().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let b=w.now();b-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=b)}if(!G().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let b=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),b}return o}compileAndRun(t,n,a,r,s=!1){return a=a||n[0].dtype,this.runWebGLProgram(t,n,a,r,s)}getAndSaveBinary(t,n){return t in this.binaryCache||(this.binaryCache[t]=n()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=G().getBool("DEBUG");G().set("DEBUG",!1);let n=this.abs(xe(1e-8)).dataSync()[0];if(G().set("DEBUG",t),n>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?yee:xee}uploadToGPU(t){let n=this.texData.get(t),{shape:a,dtype:r,values:s,texture:i,usage:o,isPacked:l}=n;if(i!=null)return;let u=this.activeTimers!=null,p;u&&(p=w.now());let d=n.texShape;if(d==null&&(d=xA(a,l),n.texShape=d),s!=null){let c=dc(a),h,m=d[1],f=d[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(l||!g)&&([m,f]=vp(d[0],d[1])),l?h=new iQ(c,g):h=new NS(c,g);let b=g?[f,m]:d,y=this.makeTensorInfo(b,r),x=this.texData.get(y.dataId);g?x.usage=da.PIXELS:x.usage=da.UPLOAD,x.texShape=b,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),m,f,s);let v=[[f,m]],I=this.runWebGLProgram(h,[y],r,v,!0),N=this.texData.get(I.dataId);n.texShape=N.texShape,n.isPacked=N.isPacked,n.usage=N.usage,G().get("ENGINE_COMPILE_ONLY")?this.disposeData(I.dataId):(n.texture=N.texture,n.values=null,this.texData.delete(I.dataId)),this.disposeIntermediateTensorInfo(y),u&&(this.uploadWaitMs+=w.now()-p)}else{let c=this.acquireTexture(d,o,r,l);n.texture=c}}convertAndCacheOnCPU(t,n){let a=this.texData.get(t),{dtype:r}=a;return n!=null&&(a.values=See(n,r)),a.values}acquireTexture(t,n,a,r){if(this.numBytesInGPU+=this.computeBytes(t,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,n,r)}computeBytes(t,n){return t[0]*t[1]*w.bytesPerElement(n)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,n]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(n));return Promise.all(t)}else{for(let[,n]of Object.entries(this.binaryCache)){let a=new Promise(r=>{try{this.checkCompletion_(n),r(!0)}catch(s){throw s}});t.push(a)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await s0(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ik(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:n,customUniformLocations:a,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:o,outTexShapeLocation:l}=AA(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=n,t.customUniformLocations=a,t.infLoc=r,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=o,t.outTexShapeLocation=l}}createTensorFromGPUData(t,n,a){t.channels=t.channels||"RGBA";let{texture:r,height:s,width:i,channels:o}=t,l=Ea().backend;if(!l.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let u=l.writeTexture(r,n,a,s,i,o);return Ea().makeTensorFromDataId(u,n,a,l)}};gk.nextDataId=0;function See(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;anew gk,2);var Tee={forceHalfFloat:eF},bk=` if (isnan(a)) return a; if (isnan(b)) return b; `,Si=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=vn(this.outputShape.length),this.userCode=` @@ -5006,4 +5006,4 @@ return a / b;`,aie=` } `}};function 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Oc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Oc||(Oc={}));var AF;function uue(e){AF=e.wasm.cwrap(li,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function pue(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:p,leakyreluAlpha:d}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let _=n.dataIdMap.get(i.dataId);if(_.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${_.shape.length}.`);m=_.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Oc[p];if(g==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the 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Got ${r.dtype}, ${s.dtype}, and ${i.dtype}`);let p=T.computeDilation2DInfo(r.shape,s.shape,o,l,"NHWC",u),d=n.makeOutput(s.shape,s.dtype);return a$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(s.dataId).id,n.dataIdMap.get(i.dataId).id,n.dataIdMap.get(d.dataId).id,Qe[r.dtype],p.batchSize,p.inChannels,p.inHeight,p.inWidth,p.outHeight,p.outWidth,p.strideHeight,p.strideWidth,p.dilationHeight,p.dilationWidth,p.filterHeight,p.filterWidth,p.padInfo.top,p.padInfo.left),d}var lce={kernelName:ql,backendName:"wasm",setupFunc:ice,kernelFunc:oce},r$;function uce(e){r$=e.wasm.cwrap(jl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pce(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,dy:i}=t,{strides:o,pad:l,dilations:u}=a;if(r.dtype!==s.dtype||r.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. 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Uce={kernelName:Eu,backendName:"wasm",setupFunc:Bce,kernelFunc:Vce},Gce=!1,Hce=Ut(Au,Gce,"bool"),jce=!1,qce=Ut(to,jce,"bool"),Kce=Xe(ao,"bool"),Xce=Xe(ro,"bool"),Yce=Xe(so,"bool"),h$;function Zce(e){h$=e.wasm.cwrap(io,null,["number","number","number","number"])}function Jce(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;h$(r,Qe[t.dtype],n,i)}return s}var Qce={kernelName:io,backendName:"wasm",setupFunc:Zce,kernelFunc:Jce},ede=!1,tde=Ut(Fu,ede,"bool"),nde=!1,ade=Ut($u,nde,"bool"),m$;function rde(e){m$=e.wasm.cwrap(Du,null,["number","number","number","number"])}function sde(e){let{attrs:t,backend:n}=e,{start:a,stop:r,num:s}=t,i=Math.floor(s),o=n.makeOutput([i],"float32");return m$(n.dataIdMap.get(o.dataId).id,a,r,i),o}var ide={kernelName:Du,backendName:"wasm",setupFunc:rde,kernelFunc:sde},ode=Xe(oo),lde=Xe(lo),ude=!1,pde=Ut(Ru,ude,"bool"),cde=Xe(Mu),dde=!1,hde=Ut(Ou,dde,"bool"),mde=!1,fde=Ut(mN,mde,"bool"),f$;function gde(e){f$=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number"])}function bde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a;if(r.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let u=n.makeOutput(r.shape,r.dtype);return f$(n.dataIdMap.get(r.dataId).id,n.dataIdMap.get(u.dataId).id,r.shape[3],s,i,o,l),u}var yde={kernelName:uo,backendName:"wasm",setupFunc:gde,kernelFunc:bde},g$;function xde(e){g$=e.wasm.cwrap(Pu,null,["number","number","number","number","number","number","number","number","number"])}function vde(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:p}=a;if(r.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad 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Sde={kernelName:po,backendName:"wasm",setupFunc:kde,kernelFunc:Ide},Nde=!1,Tde=Ut(co,Nde),y$;function Cde(e){y$=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ede(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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Kd=class extends pn{constructor(t=new vg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof vr?this.faceFeatureExtractor.forwardInput(t):t,r=xa(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=jd(r,n.fc.age).as1D(),i=jd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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p=`mobilenetv1/conv_${u}`,d=`MobilenetV1/Conv2d_${u}_depthwise`,c=`${p}/depthwise_conv`,h=`${p}/pointwise_conv`,m=n(`${d}/depthwise_weights`,4,`${c}/filters`),f=n(`${d}/BatchNorm/gamma`,1,`${c}/batch_norm_scale`),g=n(`${d}/BatchNorm/beta`,1,`${c}/batch_norm_offset`),b=n(`${d}/BatchNorm/moving_mean`,1,`${c}/batch_norm_mean`),y=n(`${d}/BatchNorm/moving_variance`,1,`${c}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:b,batch_norm_variance:y},pointwise_conv:a("MobilenetV1",u,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(u,p){let d=n(`${u}/weights`,4,`${p}/filters`),c=n(`${u}/biases`,1,`${p}/bias`);return{filters:d,bias:c}}function o(u){let p=i(`Prediction/BoxPredictor_${u}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${u}/box_encoding_predictor`),d=i(`Prediction/BoxPredictor_${u}/ClassPredictor`,`prediction_layer/box_predictor_${u}/class_predictor`);return{box_encoding_predictor:p,class_predictor:d}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function TD(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=wge(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Wr(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return _n(e,t),{params:s,paramMappings:t}}function Oa(e,t,n){return O(()=>{let a=$t(e,t.filters,n,"same");return a=X(a,t.batch_norm_offset),an(a,0,6)})}var kge=.0010000000474974513;function Ige(e,t,n){return O(()=>{let a=As(e,t.filters,n,"same");return a=_s(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,kge),an(a,0,6)})}function Sge(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function CD(e,t){return O(()=>{let n,a=Oa(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=Sge(o);a=Ige(a,s.depthwise_conv,l),a=Oa(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function Nge(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),u=Math.min(a[n][1],a[n][3]),p=Math.max(a[n][0],a[n][2]),d=Math.max(a[n][1],a[n][3]),c=(i-r)*(o-s),h=(p-l)*(d-u);if(c<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,u),g=Math.min(i,p),b=Math.min(o,d),y=Math.max(g-m,0)*Math.max(b-f,0);return y/(c+h-y)}function ED(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((p,d)=>({score:p,boxIndex:d})).filter(p=>p.score>r).sort((p,d)=>d.score-p.score),l=p=>p<=a?1:0,u=[];return o.forEach(p=>{if(u.length>=i)return;let d=p.score;for(let c=u.length-1;c>=0;--c){let h=Nge(e,p.boxIndex,u[c]);if(h!==0&&(p.score*=l(h),p.score<=r))break}d===p.score&&u.push(p.boxIndex)}),u}function Tge(e){let t=dt(De(e,[1,0])),n=[pe(t[2],t[0]),pe(t[3],t[1])],a=[X(t[0],he(n[0],2)),X(t[1],he(n[1],2))];return{sizes:n,centers:a}}function Cge(e,t){let{sizes:n,centers:a}=Tge(e),r=dt(De(t,[1,0])),s=he(z(mn(he(r[2],5)),n[0]),2),i=X(z(he(r[0],10),n[0]),a[0]),o=he(z(mn(he(r[3],5)),n[1]),2),l=X(z(he(r[1],10),n[1]),a[1]);return De(Ft([pe(i,s),pe(l,o),X(i,s),X(l,o)]),[1,0])}function _D(e,t,n){return O(()=>{let a=e.shape[0],r=Cge(W(On(n.extra_dim,[a,1,1]),[-1,4]),W(e,[-1,4]));r=W(r,[a,r.shape[0]/a,4]);let s=ma(Ve(t,[0,0,1],[-1,-1,-1])),i=Ve(s,[0,0,0],[-1,-1,1]);i=W(i,[a,i.shape[1]]);let o=dt(r),l=dt(i);return{boxes:o,scores:l}})}function xl(e,t){return O(()=>{let n=e.shape[0],a=W(fl(e,t.box_encoding_predictor),[n,-1,1,4]),r=W(fl(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function AD(e,t,n){return O(()=>{let a=Oa(e,n.conv_0,[1,1]),r=Oa(a,n.conv_1,[2,2]),s=Oa(r,n.conv_2,[1,1]),i=Oa(s,n.conv_3,[2,2]),o=Oa(i,n.conv_4,[1,1]),l=Oa(o,n.conv_5,[2,2]),u=Oa(l,n.conv_6,[1,1]),p=Oa(u,n.conv_7,[2,2]),d=xl(t,n.box_predictor_0),c=xl(e,n.box_predictor_1),h=xl(r,n.box_predictor_2),m=xl(i,n.box_predictor_3),f=xl(l,n.box_predictor_4),g=xl(p,n.box_predictor_5),b=et([d.boxPredictionEncoding,c.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),y=et([d.classPrediction,c.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:b,classPredictions:y}})}var Ia=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ws=class extends pn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=CD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=AD(s.out,s.conv11,n.prediction_layer);return _D(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TD(t)}extractParams(t){return ND(t)}};function FD(e){let t=new Ws;return t.extractWeights(e),t}function Ege(e){return FD(e)}var Jk=class extends Ws{};var $D=.4,DD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],MD=[117.001,114.697,97.404],OD="tiny_yolov2_model",PD="tiny_yolov2_separable_conv_model";var Cg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Cg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Cg(t.x)&&Cg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ds(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function _ge(e,t){let n=Mp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function LD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=_ge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Age(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function zD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Age(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Eg=class Eg extends pn{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return zD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Eg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return LD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Eg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Eg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$D,classes:["face"],...t?{anchors:RD,meanRgb:MD}:{anchors:DD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return 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ln{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var Zfe=.5,Jfe=.43,Qfe=.45,sa=class{constructor(t,n,a=new Ue(0,0)){let{width:r,height:s}=n;this._imgDims=new Un(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new Ue(r,s)).add(a))}get shift(){return new Ue(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Ue(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Ue(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof Tt?t.box.floor():new ln(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=d=>r.sub(d).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/Qfe),l=sl(t),u=Math.floor(Math.max(0,l.x-Zfe*o)),p=Math.floor(Math.max(0,l.y-Jfe*o));return new ll(u,p,Math.min(o,this.imageWidth+u),Math.min(o,this.imageHeight+p))}alignMinBbox(t){let n=Ek(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Fk=class extends sa{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],sl([t[3],t[4]])]}};var ul=class extends sa{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(sl)}};var Ap=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${rl(this.distance)})`:""}`}};var Fp=class extends ln{static assertIsValidLabeledBox(t,n){if(ln.assertIsValidBox(t,n),!Xa(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t),this._label=n}get label(){return this._label}};var zs=class e{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new e(t.label,n)}};var $k=class extends Fp{static assertIsValidPredictedBox(t,n){if(Fp.assertIsValidLabeledBox(t,n),!_p(t.score)||!_p(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n),this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function xr(e){return e.detection instanceof Tt}function pl(e,t){return{...e,...{detection:t}}}function Dk(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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Mk()?Ok(Dk()):Wd()?Ok(Rk()):null}function tge(e){if(un||Pk(),!un)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=un.Canvas,Image:n=un.Image}=e;un.Canvas=t,un.Image=n,un.createCanvasElement=e.createCanvasElement||(()=>new t),un.createImageElement=e.createImageElement||(()=>new n),un.ImageData=e.ImageData||un.ImageData,un.Video=e.Video||un.Video,un.fetch=e.fetch||un.fetch,un.readFile=e.readFile||un.readFile}var tt={getEnv:ege,setEnv:Ok,initialize:Pk,createBrowserEnv:Dk,createFileSystem:og,createNodejsEnv:Rk,monkeyPatch:tge,isBrowser:Mk,isNodejs:Wd};Pk();function cl(e){return!tt.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=tt.getEnv();if(e instanceof n)return e;let a=cl(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d",{willReadFrequently:!0});if(!r)throw new Error("resolveContext2d 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i={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new $p({...i,...s})}},Bd=class{constructor(t,n={}){this.box=new ln(t),this.options=new lg(n)}draw(t){let n=Gn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:u}=this.options;u&&new dl([u],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function nge(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof Tt?a.score:xr(a)?a.detection.score:void 0,s=a instanceof Tt?a.box:xr(a)?a.detection.box:new ln(a),i=r?`${rl(r)}`:void 0;new Bd(s,{label:i}).draw(e)})}function Vd(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function zk(e){return new Promise((t,n)=>{if(e instanceof tt.getEnv().Canvas||Vd(e)){t(null);return}function a(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){s.currentTarget&&(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function Wk(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=tt.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function hl(e){let{Image:t,Video:n}=tt.getEnv();return e instanceof t?new Un(e.naturalWidth,e.naturalHeight):e instanceof n?new Un(e.videoWidth,e.videoHeight):new Un(e.width,e.height)}function ml({width:e,height:t}){let{createCanvasElement:n}=tt.getEnv(),a=n();return a.width=e,a.height=t,a}function Ud(e,t){let{ImageData:n}=tt.getEnv();if(!(e instanceof n)&&!Vd(e))throw new Error("createCanvasFromMedia - media has not finished 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vr=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if(Wr(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ka(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof tt.getEnv().Canvas?a:Ud(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return yr(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),a=this.getInputHeight(t);return Nk({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,O(()=>{let a=yr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof Ce){let o=ka(i)?i:Gt(i);return o=Ak(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qn.resizeBilinear(o,[t,t],!1,!1)),o.as3D(t,t,3)}if(i instanceof tt.getEnv().Canvas)return Xo.fromPixels(Vk(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return Ft(a.map(s=>re(s,"float32"))).as4D(this.batchSize,t,t,3)})}};async function vt(e){if(e instanceof vr)return e;let t=Array.isArray(e)?e:[e];if(!t.length)throw new Error("toNetInput - empty array passed as input");let n=r=>Array.isArray(e)?` at input index ${r}:`:"",a=t.map(cl);return a.forEach((r,s)=>{if(!ug(r)&&!Wr(r)&&!ka(r))throw typeof t[s]=="string"?new Error(`toNetInput -${n(s)} string passed, but could not resolve HTMLElement for element id ${t[s]}`):new Error(`toNetInput -${n(s)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(ka(r)){let i=r.shape[0];if(i!==1)throw new Error(`toNetInput -${n(s)} tf.Tensor4D with batchSize ${i} passed, but not supported in input array`)}}),await Promise.all(a.map(r=>ug(r)&&zk(r))),new vr(a,Array.isArray(e))}async function Dp(e,t){let{Canvas:n}=tt.getEnv(),a=e;if(!(e instanceof n)){let i=await vt(e);if(i.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let o=i.getInput(0);a=o instanceof n?o:await Bk(o)}let r=Gn(a);return t.map(i=>i instanceof Tt?i.forSize(a.width,a.height).box.floor():i).map(i=>i.clipAtImageBorders(a.width,a.height)).map(({x:i,y:o,width:l,height:u})=>{let p=ml({width:l,height:u});return l>0&&u>0&&Gn(p).putImageData(r.getImageData(i,o,l,u),0,0),p})}async function Rp(e,t){if(!Wr(e)&&!ka(e))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(ka(e)&&e.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return O(()=>{let[n,a,r]=e.shape.slice(ka(e)?1:0);return t.map(o=>o instanceof Tt?o.forSize(a,n).box:o).map(o=>o.clipAtImageBorders(a,n)).filter(o=>o.width>0&&o.height>0).map(({x:o,y:l,width:u,height:p})=>qo(e.as3D(n,a,r),[l,o,0],[p,u,r]))})}async function Br(e,t){let{fetch:n}=tt.getEnv(),a=await n(e,t);if(!(a.status<400))throw new Error(`failed to fetch: (${a.status}) ${a.statusText}, from url: ${a.url}`);return a}async function age(e){let t=await Br(e),n=await t.blob();if(!n.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${n.type}, for url: ${t.url}`);return Wk(n)}async function Uk(e){return(await Br(e)).json()}async function rge(e){return new Float32Array(await(await Br(e)).arrayBuffer())}function lD(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToVideo - expected buf to be of type: Blob"));let a=tt.getEnv().createVideoElement();a.oncanplay=()=>t(a),a.onerror=n,a.playsInline=!0,a.muted=!0,a.src=URL.createObjectURL(e),a.play()})}async function sge(e){let t=await Br(e),n=await t.blob();if(!n.type.startsWith("video/"))throw new Error(`fetchVideo - expected blob type to be of type video/*, instead have: ${n.type}, for url: ${t.url}`);return lD(n)}function pg(e,t){let n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let a=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(a,"");let r=e.split("/").filter(o=>o),s=e.endsWith(".json")?r[r.length-1]:n,i=a+(e.endsWith(".json")?r.slice(0,r.length-1):r).join("/");return i=e.startsWith("/")?`/${i}`:i,{modelBaseUri:i,manifestUri:i==="/"?`/${s}`:`${i}/${s}`}}async function Gk(e,t){let{manifestUri:n,modelBaseUri:a}=pg(e,t),r=await Uk(n);return jt.loadWeights(r,a)}function ige(e,t,n=!1){let{width:a,height:r}=n?hl(t):t;return e.width=a,e.height=r,{width:a,height:r}}var pn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:a}=this.traversePropertyPath(t);return 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Float32Array){this.extractWeights(t);return}await this.loadFromUri(t)}async loadFromUri(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let n=await Gk(t,this.getDefaultModelName());this.loadFromWeightMap(n)}async loadFromDisk(t){if(t&&typeof t!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);let{readFile:n}=tt.getEnv(),{manifestUri:a,modelBaseUri:r}=pg(t,this.getDefaultModelName()),s=u=>Promise.all(u.map(p=>n(p).then(d=>typeof d=="string"?Buffer.from(d):d.buffer))),i=jt.weightsLoaderFactory(s),o=JSON.parse((await n(a)).toString()),l=await i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeightMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let 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a=re(t.toBatchTensor(112,!0),"float32"),s=Ya(a,[122.782,117.001,104.298]).div(255),i=Gd(s,n.dense0,!0);return i=Gd(i,n.dense1),i=Gd(i,n.dense2),i=Gd(i,n.dense3),i=xa(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return pD(t)}extractParams(t){return uD(t)}};function jd(e,t){return O(()=>X($e(e,t.weights),t.bias))}function cD(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=An(e),o=dg(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function dD(e){let t=[],n=ia(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return _n(e,t),{params:r,paramMappings:t}}function gg(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var zp=class extends pn{constructor(t,n){super(t),this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof vr?this.faceFeatureExtractor.forwardInput(t):t;return jd(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return cD(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=gg(t);return this.faceFeatureExtractor.loadFromWeightMap(n),dD(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var Hk=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Vr=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Hk.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Hk.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var qd=class extends zp{constructor(t=new Lp){super("FaceExpressionNet",t)}forwardInput(t){return O(()=>qa(this.runNet(t)))}async forward(t){return this.forwardInput(await vt(t))}async predictExpressions(t){let n=await vt(t),a=await this.forwardInput(n),r=await Promise.all(dt(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Vr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function jk(e){return e.expressions instanceof Vr}function bg(e,t){return{...e,...{expressions:t}}}function oge(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Vr?s:jk(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(d=>d.probability>n),u=xr(s)?s.detection.box.bottomLeft:a||new Ue(0,0);new dl(l.map(d=>`${d.expression} (${rl(d.probability)})`),u).draw(e)})}function gl(e){return xr(e)&&e.landmarks instanceof sa&&e.unshiftedLandmarks instanceof sa&&e.alignedRect instanceof Tt}function lge(e){let t=l=>l*180/Math.PI,n=(l,u)=>Math.sqrt((l.x-u.x)**2+(l.y-u.y)**2),a={roll:void 0,pitch:void 0,yaw:void 0},r=(l,u,p)=>{let d=Math.floor(l.x-u.x),c=Math.floor(u.x-p.x);return d-c},s=(l,u)=>{let p=Math.hypot(u.x-l.x,u.y-l.y),d=u.y-l.y,c=Math.asin(d/p),h=t(c),m=Math.floor(90-h),f=u.x-l.x<0?-1:1;return m*f},i=(l,u,p)=>{let d=n(l,p),c=new 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l=a(i,i,`${o}/separable_conv0`),u=a(i,i,`${o}/separable_conv1`),p=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function mD(e,t){let n=[],{extractWeights:a,getRemainingWeights:r}=An(e),{extractConvParams:s,extractSeparableConvParams:i,extractReductionBlockParams:o,extractMainBlockParams:l}=dge(a,n),u=s(3,32,3,"entry_flow/conv_in"),p=o(32,64,"entry_flow/reduction_block_0"),d=o(64,128,"entry_flow/reduction_block_1"),c={conv_in:u,reduction_block_0:p,reduction_block_1:d},h={};yr(t,0,1).forEach(b=>{h[`main_block_${b}`]=l(128,`middle_flow/main_block_${b}`)});let m=o(128,256,"exit_flow/reduction_block"),f=i(256,512,"exit_flow/separable_conv"),g={reduction_block:m,separable_conv:f};if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:n,params:{entry_flow:c,middle_flow:h,exit_flow:g}}}function hge(e,t){let n=ia(e,t),a=mg(n),r=Pp(n);function s(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=a(`${o}/expansion_conv`);return{separable_conv0:l,separable_conv1:u,expansion_conv:p}}function i(o){let l=r(`${o}/separable_conv0`),u=r(`${o}/separable_conv1`),p=r(`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:u,separable_conv2:p}}return{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}}function fD(e,t){let n=[],{extractConvParams:a,extractSeparableConvParams:r,extractReductionBlockParams:s,extractMainBlockParams:i}=hge(e,n),o=a("entry_flow/conv_in"),l=s("entry_flow/reduction_block_0"),u=s("entry_flow/reduction_block_1"),p={conv_in:o,reduction_block_0:l,reduction_block_1:u},d={};yr(t,0,1).forEach(f=>{d[`main_block_${f}`]=i(`middle_flow/main_block_${f}`)});let c=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:c,separable_conv:h};return _n(e,n),{params:{entry_flow:p,middle_flow:d,exit_flow:m},paramMappings:n}}function gD(e,t,n){return X($t(e,t.filters,n,"same"),t.bias)}function Kk(e,t,n=!0){let a=n?Ke(e):e;return a=Hn(a,t.separable_conv0,[1,1]),a=Hn(Ke(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=X(a,gD(e,t.expansion_conv,[2,2])),a}function mge(e,t){let n=Hn(Ke(e),t.separable_conv0,[1,1]);return n=Hn(Ke(n),t.separable_conv1,[1,1]),n=Hn(Ke(n),t.separable_conv2,[1,1]),n=X(n,e),n}var vg=class extends pn{constructor(t){super("TinyXception"),this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return O(()=>{let a=re(t.toBatchTensor(112,!0),"float32"),s=Ya(a,[122.782,117.001,104.298]).div(255),i=Ke(gD(s,n.entry_flow.conv_in,[2,2]));return i=Kk(i,n.entry_flow.reduction_block_0,!1),i=Kk(i,n.entry_flow.reduction_block_1),yr(this._numMainBlocks,0,1).forEach(o=>{i=mge(i,n.middle_flow[`main_block_${o}`])}),i=Kk(i,n.exit_flow.reduction_block),i=Ke(Hn(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await vt(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return fD(t,this._numMainBlocks)}extractParams(t){return mD(t,this._numMainBlocks)}};function bD(e){let t=[],{extractWeights:n,getRemainingWeights:a}=An(e),r=dg(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function yD(e){let t=[],n=ia(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return _n(e,t),{params:r,paramMappings:t}}var wg=(n=>(n.FEMALE="female",n.MALE="male",n))(wg||{});var Kd=class extends pn{constructor(t=new vg(2)){super("AgeGenderNet"),this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return O(()=>{let a=t instanceof vr?this.faceFeatureExtractor.forwardInput(t):t,r=xa(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=jd(r,n.fc.age).as1D(),i=jd(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return O(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:qa(a)}})}async forward(t){return this.forwardInput(await vt(t))}async predictAgeAndGender(t){let n=await vt(t),a=await this.forwardInput(n),r=dt(a.age),s=dt(a.gender),i=r.map((l,u)=>({ageTensor:l,genderTensor:s[u]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:u})=>{let p=l.dataSync()[0],d=u.dataSync()[0],c=d>.5,h=c?"male":"female",m=c?d:1-d;return l.dispose(),u.dispose(),{age:p,gender:h,genderProbability:m}}));return 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this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ws=class extends pn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return O(()=>{let a=re(t.toBatchTensor(512,!1),"float32"),r=pe(he(a,127.5),1),s=CD(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=AD(s.out,s.conv11,n.prediction_layer);return _D(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await vt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new Ia(n),s=await vt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],u=o[0];for(let x=1;x{let[v,I]=[Math.max(0,b[x][0]),Math.min(1,b[x][2])].map(_=>_*g),[N,C]=[Math.max(0,b[x][1]),Math.min(1,b[x][3])].map(_=>_*f);return new Tt(p[x],new ll(N,v,C-N,I-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return TD(t)}extractParams(t){return ND(t)}};function FD(e){let t=new Ws;return t.extractWeights(e),t}function Ege(e){return FD(e)}var Jk=class extends Ws{};var $D=.4,DD=[new Ue(.738768,.874946),new Ue(2.42204,2.65704),new Ue(4.30971,7.04493),new Ue(10.246,4.59428),new Ue(12.6868,11.8741)],RD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],MD=[117.001,114.697,97.404],OD="tiny_yolov2_model",PD="tiny_yolov2_separable_conv_model";var Cg=e=>typeof e=="number";function Qk(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Cg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Cg(t.x)&&Cg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Cg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Vp(e){return O(()=>{let t=z(e,xe(.10000000149011612));return X(Ke(pe(e,t)),t)})}function Ur(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=pe(n,t.bn.sub),n=z(n,t.bn.truediv),n=X(n,t.conv.bias),Vp(n)})}function Gr(e,t){return O(()=>{let n=va(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ds(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=X(n,t.bias),Vp(n)})}function _ge(e,t){let n=Mp(e,t);function a(i,o){let l=je(e(i)),u=je(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:u}}function r(i,o,l){let u=n(i,o,3,`${l}/conv`),p=a(o,`${l}/bn`);return{conv:u,bn:p}}let s=Op(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function LD(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=An(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:u}=_ge(r,i),p;if(t.withSeparableConvs){let[d,c,h,m,f,g,b,y,x]=a,v=t.isFirstLayerConv2d?o(d,c,3,"conv0"):u(d,c,"conv0"),I=u(c,h,"conv1"),N=u(h,m,"conv2"),C=u(m,f,"conv3"),_=u(f,g,"conv4"),F=u(g,b,"conv5"),D=y?u(b,y,"conv6"):void 0,$=x?u(y,x,"conv7"):void 0,S=o(x||y||b,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}else{let[d,c,h,m,f,g,b,y,x]=a,v=l(d,c,"conv0"),I=l(c,h,"conv1"),N=l(h,m,"conv2"),C=l(m,f,"conv3"),_=l(f,g,"conv4"),F=l(g,b,"conv5"),D=l(b,y,"conv6"),$=l(y,x,"conv7"),S=o(x,5*n,1,"conv8");p={conv0:v,conv1:I,conv2:N,conv3:C,conv4:_,conv5:F,conv6:D,conv7:$,conv8:S}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:i}}function Age(e,t){let n=ia(e,t);function a(o){let l=n(`${o}/sub`,1),u=n(`${o}/truediv`,1);return{sub:l,truediv:u}}function r(o){let l=n(`${o}/filters`,4),u=n(`${o}/bias`,1);return{filters:l,bias:u}}function s(o){let l=r(`${o}/conv`),u=a(`${o}/bn`);return{conv:l,bn:u}}let i=Pp(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function zD(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Age(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return _n(e,n),{params:i,paramMappings:n}}var Ja=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Eg=class Eg extends pn{constructor(t){super("TinyYolov2"),Qk(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Ur(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Ur(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Ur(a,n.conv6),a=Ur(a,n.conv7),fl(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Vp(fl(t,n.conv0,"valid",!1)):Gr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Gr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Gr(a,n.conv6):a,a=n.conv7?Gr(a,n.conv7):a,fl(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let r=re(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?Ya(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await vt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ja(n),s=await vt(t),i=await this.forwardInput(s,a),o=O(()=>dt(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},u=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let p=u.map(g=>g.box),d=u.map(g=>g.score),c=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return _k(p.map(g=>g.rescale(a)),d,this.config.iouThreshold,!0).map(g=>new ol(d[g],c[g],h[g],p[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return zD(t,this.config)}extractParams(t){let n=this.config.filterSizes||Eg.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return LD(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,u=t.shape[1],p=this.config.anchors.length,[d,c,h]=O(()=>{let b=t.reshape([u,u,p,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,p,4]),x=b.slice([0,0,0,4],[u,u,p,1]),v=this.withClassScores?qa(b.slice([0,0,0,5],[u,u,p,this.config.classes.length]),3):xe(0);return[y,x,v]}),m=[],f=await c.array(),g=await d.array();for(let b=0;ba){let I=(y+zd(g[b][y][x][0]))/u*o,N=(b+zd(g[b][y][x][1]))/u*l,C=Math.exp(g[b][y][x][2])*this.config.anchors[x].x/u*o,_=Math.exp(g[b][y][x][3])*this.config.anchors[x].y/u*l,F=I-C/2,D=N-_/2,$={row:b,col:y,anchor:x},{classScore:S,label:M}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};m.push({box:new il(F,D,F+C,D+_),score:v,classScore:v*S,label:M,...$})}}return d.dispose(),c.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}};Eg.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Up=Eg;var vl=class extends Up{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:$D,classes:["face"],...t?{anchors:RD,meanRgb:MD}:{anchors:DD,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?PD:OD}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function Fge(e,t=!0){let n=new vl(t);return n.extractWeights(e),n}var Zd=class extends Ja{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var Sa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function wl(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>gl(l)?r(l):l.detection),i=a||(t instanceof Ce?await Rp(t,s):await Dp(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ce&&l.dispose()),o}async function Gp(e,t,n,a,r){return wl([e],t,async s=>n(s[0]),a,r)}var WD=.4,BD=[new Ue(1.603231,2.094468),new Ue(6.041143,7.080126),new Ue(2.882459,3.518061),new Ue(4.266906,5.178857),new Ue(9.041765,10.66308)],VD=[117.001,114.697,97.404];var kl=class extends Up{constructor(){let t={withSeparableConvs:!0,iouThreshold:WD,classes:["face"],anchors:BD,meanRgb:VD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new Tt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var nt={ssdMobilenetv1:new Ws,tinyFaceDetector:new kl,tinyYolov2:new vl,faceLandmark68Net:new bl,faceLandmark68TinyNet:new Xd,faceRecognitionNet:new yl,faceExpressionNet:new qd,ageGenderNet:new Kd},UD=(e,t)=>nt.ssdMobilenetv1.locateFaces(e,t),$ge=(e,t)=>nt.tinyFaceDetector.locateFaces(e,t),Dge=(e,t)=>nt.tinyYolov2.locateFaces(e,t),GD=e=>nt.faceLandmark68Net.detectLandmarks(e),Rge=e=>nt.faceLandmark68TinyNet.detectLandmarks(e),Mge=e=>nt.faceRecognitionNet.computeFaceDescriptor(e),Oge=e=>nt.faceExpressionNet.predictExpressions(e),Pge=e=>nt.ageGenderNet.predictAgeAndGender(e),HD=e=>nt.ssdMobilenetv1.load(e),Lge=e=>nt.tinyFaceDetector.load(e),zge=e=>nt.tinyYolov2.load(e),Wge=e=>nt.faceLandmark68Net.load(e),Bge=e=>nt.faceLandmark68TinyNet.load(e),Vge=e=>nt.faceRecognitionNet.load(e),Uge=e=>nt.faceExpressionNet.load(e),Gge=e=>nt.ageGenderNet.load(e),Hge=HD,jge=UD,qge=GD;var _g=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Il=class extends _g{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>bg(a,n[r]))}withAgeAndGender(){return new Nl(this,this.input)}},Sl=class extends _g{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceExpressionNet.predictExpressions(a),this.extractedFaces);return bg(t,n)}withAgeAndGender(){return new Tl(this,this.input)}},Bs=class extends Il{withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Vs=class extends Sl{withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Ag=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.extractedFaces=r}},Nl=class extends Ag{async run(){let t=await this.parentTask,n=await wl(t,this.input,async a=>Promise.all(a.map(r=>nt.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Ng(Tg(a,i,o),s)})}withFaceExpressions(){return new Il(this,this.input)}},Tl=class extends Ag{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Gp(t,this.input,s=>nt.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Ng(Tg(t,a,r),n)}withFaceExpressions(){return new Sl(this,this.input)}},Us=class extends Nl{withFaceExpressions(){return new Bs(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},Gs=class extends Tl{withFaceExpressions(){return new Vs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var Jd=class extends Sa{constructor(n,a){super();this.parentTask=n;this.input=a}},Hr=class extends Jd{async run(){let t=await this.parentTask;return(await wl(t,this.input,a=>Promise.all(a.map(r=>nt.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Sg(t[r],a))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}},jr=class extends Jd{async run(){let t=await this.parentTask;if(!t)return;let n=await Gp(t,this.input,a=>nt.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Sg(t,n)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}};var Qd=class extends Sa{constructor(n,a,r){super();this.parentTask=n;this.input=a;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?nt.faceLandmark68TinyNet:nt.faceLandmark68Net}},eh=class extends Qd{async run(){let t=await this.parentTask,n=t.map(i=>i.detection),a=this.input instanceof Ce?await Rp(this.input,n):await Dp(this.input,n),r=await Promise.all(a.map(i=>this.landmarkNet.detectLandmarks(i)));return a.forEach(i=>i instanceof Ce&&i.dispose()),t.filter((i,o)=>r[o]).map((i,o)=>Wp(i,r[o]))}withFaceExpressions(){return new Bs(this,this.input)}withAgeAndGender(){return new Us(this,this.input)}withFaceDescriptors(){return new Hr(this,this.input)}},th=class extends Qd{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ce?await Rp(this.input,[n]):await Dp(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ce&&s.dispose()),Wp(t,r)}withFaceExpressions(){return new Vs(this,this.input)}withAgeAndGender(){return new Gs(this,this.input)}withFaceDescriptor(){return new jr(this,this.input)}};var nh=class extends Sa{constructor(n,a=new Ia){super();this.input=n;this.options=a}},Hp=class extends nh{async run(){let{input:t,options:n}=this,a;if(n instanceof Zd)a=nt.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Ia)a=nt.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ja)a=nt.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(a=>t(a.map(r=>pl({},r)))).catch(a=>n(a))})}withFaceLandmarks(t=!1){return new eh(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Il(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Nl(this.runAndExtendWithFaceDetections(),this.input)}},ah=class extends nh{async run(){let t=await new Hp(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?pl({},n):void 0)})}withFaceLandmarks(t=!1){return new th(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Sl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Tl(this.runAndExtendWithFaceDetection(),this.input)}};function Kge(e,t=new Ia){return new ah(e,t)}function Fg(e,t=new Ia){return new Hp(e,t)}async function jD(e,t){return Fg(e,new 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rn(o,t,e,r){let{extractWeights:n,getRemainingWeights:s}=k(o),a=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:m}=fa(n,a),p;if(t.withSeparableConvs){let[u,f,l,b,T,h,g,P,y]=r,I=t.isFirstLayerConv2d?i(u,f,3,"conv0"):m(u,f,"conv0"),j=m(f,l,"conv1"),tt=m(l,b,"conv2"),it=m(b,T,"conv3"),q=m(T,h,"conv4"),Pt=m(h,g,"conv5"),wt=P?m(g,P,"conv6"):void 0,Ft=y?m(P,y,"conv7"):void 0,ie=i(y||P||g,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}else{let[u,f,l,b,T,h,g,P,y]=r,I=c(u,f,"conv0"),j=c(f,l,"conv1"),tt=c(l,b,"conv2"),it=c(b,T,"conv3"),q=c(T,h,"conv4"),Pt=c(h,g,"conv5"),wt=c(g,P,"conv6"),Ft=c(P,y,"conv7"),ie=i(y,5*e,1,"conv8");p={conv0:I,conv1:j,conv2:tt,conv3:it,conv4:q,conv5:Pt,conv6:wt,conv7:Ft,conv8:ie}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:p,paramMappings:a}}function la(o,t){let e=Y(o,t);function r(i){let c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=No;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node-wasm.js b/dist/face-api.node-wasm.js index d08f9b9..227fac2 100644 --- a/dist/face-api.node-wasm.js +++ b/dist/face-api.node-wasm.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var dn=Object.create;var tr=Object.defineProperty;var hn=Object.getOwnPropertyDescriptor;var bn=Object.getOwnPropertyNames;var gn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var vn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Wr=(o,t)=>{for(var e in t)tr(o,e,{get:t[e],enumerable:!0})},_o=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of bn(t))!xn.call(o,n)&&n!==e&&tr(o,n,{get:()=>t[n],enumerable:!(r=hn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?dn(gn(o)):{},_o(t||!o||!o.__esModule?tr(e,"default",{value:o,enumerable:!0}):e,o)),yn=o=>_o(tr({},"__esModule",{value:!0}),o);var x=vn((Ya,rr)=>{"use strict";var 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Nr=class Nr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return 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n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||Nr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Nr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Nr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var an=.4,sn=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],cn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),pn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),un=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=un,Ca=mn,Ia=pn;var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Lr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Mr(Cr(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=fn;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function nn(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Nr=class Nr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return nn(t,this.config)}extractParams(t){let e=this.config.filterSizes||Nr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return on(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Nr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Nr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:Zo,classes:["face"],...t?{anchors:Qo,meanRgb:tn}:{anchors:Ko,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?rn:en}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var an=.4,sn=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],cn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:an,classes:["face"],anchors:sn,meanRgb:cn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},mn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),pn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),un=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=un,Ca=mn,Ia=pn;var Lr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Lr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>vr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Lr{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return vr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Sr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Sr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Mr(Cr(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Sr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Mr(Cr(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Er(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Er(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Ar(o,t=new X){return new Ie(o,t)}async function fn(o,t){return Ar(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Ar(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=fn;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function ln(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>ln(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=Lo;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/face-api.node.js b/dist/face-api.node.js index 8589ca5..95f1c39 100644 --- a/dist/face-api.node.js +++ b/dist/face-api.node.js @@ -4,4 +4,4 @@ author: ' */ -"use strict";var ln=Object.create;var tr=Object.defineProperty;var dn=Object.getOwnPropertyDescriptor;var hn=Object.getOwnPropertyNames;var bn=Object.getPrototypeOf,gn=Object.prototype.hasOwnProperty;var xn=(o,t)=>()=>(t||o((t={exports:{}}).exports,t),t.exports),Sr=(o,t)=>{for(var e in t)tr(o,e,{get:t[e],enumerable:!0})},_o=(o,t,e,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of hn(t))!gn.call(o,n)&&n!==e&&tr(o,n,{get:()=>t[n],enumerable:!(r=dn(t,n))||r.enumerable});return o};var v=(o,t,e)=>(e=o!=null?ln(bn(o)):{},_o(t||!o||!o.__esModule?tr(e,"default",{value:o,enumerable:!0}):e,o)),vn=o=>_o(tr({},"__esModule",{value:!0}),o);var x=xn((Ya,Br)=>{"use strict";var 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st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return 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n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var 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c=e(`${i}/sub`,1),m=e(`${i}/truediv`,1);return{sub:c,truediv:m}}function n(i){let c=e(`${i}/filters`,4),m=e(`${i}/bias`,1);return{filters:c,bias:m}}function s(i){let c=n(`${i}/conv`),m=r(`${i}/bn`);return{conv:c,bn:m}}let a=ge(e);return{extractConvParams:n,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}}function on(o,t){let e=[],{extractConvParams:r,extractConvWithBatchNormParams:n,extractSeparableConvParams:s}=la(o,e),a;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;a={conv0:t.isFirstLayerConv2d?r("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:i>7?s("conv6"):void 0,conv7:i>8?s("conv7"):void 0,conv8:r("conv8")}}else a={conv0:n("conv0"),conv1:n("conv1"),conv2:n("conv2"),conv3:n("conv3"),conv4:n("conv4"),conv5:n("conv5"),conv6:n("conv6"),conv7:n("conv7"),conv8:r("conv8")};return W(o,e),{params:a,paramMappings:e}}var st=class{constructor({inputSize:t,scoreThreshold:e}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=e||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Cr=class Cr extends A{constructor(t){super("TinyYolov2"),ho(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,e){let r=vt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=vt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=vt(r,e.conv6),r=vt(r,e.conv7),Jt(r,e.conv8,"valid",!1)}runMobilenet(t,e){let r=this.config.isFirstLayerConv2d?Ee(Jt(t,e.conv0,"valid",!1)):yt(t,e.conv0);return r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv1),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv2),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv3),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv4),r=C.maxPool(r,[2,2],[2,2],"same"),r=yt(r,e.conv5),r=C.maxPool(r,[2,2],[1,1],"same"),r=e.conv6?yt(r,e.conv6):r,r=e.conv7?yt(r,e.conv7):r,Jt(r,e.conv8,"valid",!1)}forwardInput(t,e){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return C.tidy(()=>{let n=C.cast(t.toBatchTensor(e,!1),"float32");return n=this.config.meanRgb?rt(n,this.config.meanRgb):n,n=n.div(255),this.config.withSeparableConvs?this.runMobilenet(n,r):this.runTinyYolov2(n,r)})}async forward(t,e){return this.forwardInput(await D(t),e)}async detect(t,e={}){let{inputSize:r,scoreThreshold:n}=new st(e),s=await D(t),a=await this.forwardInput(s,r),i=C.tidy(()=>C.unstack(a)[0].expandDims()),c={width:s.getInputWidth(0),height:s.getInputHeight(0)},m=await this.extractBoxes(i,s.getReshapedInputDimensions(0),n);a.dispose(),i.dispose();let p=m.map(h=>h.box),u=m.map(h=>h.score),f=m.map(h=>h.classScore),l=m.map(h=>this.config.classes[h.label]);return Vr(p.map(h=>h.rescale(r)),u,this.config.iouThreshold,!0).map(h=>new Ht(u[h],f[h],l[h],p[h],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return on(t,this.config)}extractParams(t){let e=this.config.filterSizes||Cr.DEFAULT_FILTER_SIZES,r=e?e.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return rn(t,this.config,this.boxEncodingSize,e)}async extractBoxes(t,e,r){let{width:n,height:s}=e,a=Math.max(n,s),i=a/n,c=a/s,m=t.shape[1],p=this.config.anchors.length,[u,f,l]=C.tidy(()=>{let g=t.reshape([m,m,p,this.boxEncodingSize]),P=g.slice([0,0,0,0],[m,m,p,4]),y=g.slice([0,0,0,4],[m,m,p,1]),I=this.withClassScores?C.softmax(g.slice([0,0,0,5],[m,m,p,this.config.classes.length]),3):C.scalar(0);return[P,y,I]}),b=[],T=await f.array(),h=await u.array();for(let g=0;gr){let j=(P+Ne(h[g][P][y][0]))/m*i,tt=(g+Ne(h[g][P][y][1]))/m*c,it=Math.exp(h[g][P][y][2])*this.config.anchors[y].x/m*i,q=Math.exp(h[g][P][y][3])*this.config.anchors[y].y/m*c,Pt=j-it/2,wt=tt-q/2,Ft={row:g,col:P,anchor:y},{classScore:ie,label:yo}=this.withClassScores?await this.extractPredictedClass(l,Ft):{classScore:1,label:0};b.push({box:new Ot(Pt,wt,Pt+it,wt+q),score:I,classScore:I*ie,label:yo,...Ft})}}return u.dispose(),f.dispose(),l.dispose(),b}async extractPredictedClass(t,e){let{row:r,col:n,anchor:s}=e,a=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>a[r][n][s][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}};Cr.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Me=Cr;var te=class extends Me{constructor(t=!0){let e={withSeparableConvs:t,iouThreshold:qo,classes:["face"],...t?{anchors:Ko,meanRgb:Qo}:{anchors:Zo,withClassScores:!0}};super(e)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?en:tn}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function da(o,t=!0){let e=new te(t);return e.extractWeights(o),e}var je=class extends st{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var J=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};var Xe=v(x());var go=v(x());async function ee(o,t,e,r,n=({alignedRect:s})=>s){let s=o.map(c=>qt(c)?n(c):c.detection),a=r||(t instanceof go.Tensor?await le(t,s):await fe(t,s)),i=await e(a);return a.forEach(c=>c instanceof go.Tensor&&c.dispose()),i}async function Ce(o,t,e,r,n){return ee([o],t,async s=>e(s[0]),r,n)}var nn=.4,an=[new _(1.603231,2.094468),new _(6.041143,7.080126),new _(2.882459,3.518061),new _(4.266906,5.178857),new _(9.041765,10.66308)],sn=[117.001,114.697,97.404];var re=class extends Me{constructor(){let t={withSeparableConvs:!0,iouThreshold:nn,classes:["face"],anchors:an,meanRgb:sn,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,e){return(await this.detect(t,e)).map(n=>new M(n.score,n.relativeBox,{width:n.imageWidth,height:n.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var F={ssdMobilenetv1:new It,tinyFaceDetector:new re,tinyYolov2:new te,faceLandmark68Net:new Zt,faceLandmark68TinyNet:new ze,faceRecognitionNet:new Kt,faceExpressionNet:new Oe,ageGenderNet:new He},cn=(o,t)=>F.ssdMobilenetv1.locateFaces(o,t),ha=(o,t)=>F.tinyFaceDetector.locateFaces(o,t),ba=(o,t)=>F.tinyYolov2.locateFaces(o,t),mn=o=>F.faceLandmark68Net.detectLandmarks(o),ga=o=>F.faceLandmark68TinyNet.detectLandmarks(o),xa=o=>F.faceRecognitionNet.computeFaceDescriptor(o),va=o=>F.faceExpressionNet.predictExpressions(o),ya=o=>F.ageGenderNet.predictAgeAndGender(o),pn=o=>F.ssdMobilenetv1.load(o),_a=o=>F.tinyFaceDetector.load(o),Ta=o=>F.tinyYolov2.load(o),Pa=o=>F.faceLandmark68Net.load(o),wa=o=>F.faceLandmark68TinyNet.load(o),Fa=o=>F.faceRecognitionNet.load(o),Da=o=>F.faceExpressionNet.load(o),Ea=o=>F.ageGenderNet.load(o),Ma=pn,Ca=cn,Ia=mn;var Ir=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},oe=class extends Ir{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.faceExpressionNet.predictExpressions(n))),this.extractedFaces);return t.map((r,n)=>gr(r,e[n]))}withAgeAndGender(){return new ae(this,this.input)}},ne=class extends Ir{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceExpressionNet.predictExpressions(r),this.extractedFaces);return gr(t,e)}withAgeAndGender(){return new se(this,this.input)}},St=class extends oe{withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},At=class extends ne{withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Nr=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.extractedFaces=n}},ae=class extends Nr{async run(){let t=await this.parentTask,e=await ee(t,this.input,async r=>Promise.all(r.map(n=>F.ageGenderNet.predictAgeAndGender(n))),this.extractedFaces);return t.map((r,n)=>{let{age:s,gender:a,genderProbability:i}=e[n];return Dr(Er(r,a,i),s)})}withFaceExpressions(){return new oe(this,this.input)}},se=class extends Nr{async run(){let t=await this.parentTask;if(!t)return;let{age:e,gender:r,genderProbability:n}=await Ce(t,this.input,s=>F.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Dr(Er(t,r,n),e)}withFaceExpressions(){return new ne(this,this.input)}},Wt=class extends ae{withFaceExpressions(){return new St(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},kt=class extends se{withFaceExpressions(){return new At(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ue=class extends J{constructor(e,r){super();this.parentTask=e;this.input=r}},_t=class extends Ue{async run(){let t=await this.parentTask;return(await ee(t,this.input,r=>Promise.all(r.map(n=>F.faceRecognitionNet.computeFaceDescriptor(n))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,n)=>Fr(t[n],r))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}},Tt=class extends Ue{async run(){let t=await this.parentTask;if(!t)return;let e=await Ce(t,this.input,r=>F.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Fr(t,e)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}};var Je=class extends J{constructor(e,r,n){super();this.parentTask=e;this.input=r;this.useTinyLandmarkNet=n}get landmarkNet(){return this.useTinyLandmarkNet?F.faceLandmark68TinyNet:F.faceLandmark68Net}},qe=class extends Je{async run(){let t=await this.parentTask,e=t.map(a=>a.detection),r=this.input instanceof Xe.Tensor?await le(this.input,e):await fe(this.input,e),n=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Xe.Tensor&&a.dispose()),t.filter((a,i)=>n[i]).map((a,i)=>Pe(a,n[i]))}withFaceExpressions(){return new St(this,this.input)}withAgeAndGender(){return new Wt(this,this.input)}withFaceDescriptors(){return new _t(this,this.input)}},Ze=class extends Je{async run(){let t=await this.parentTask;if(!t)return;let{detection:e}=t,r=this.input instanceof Xe.Tensor?await le(this.input,[e]):await fe(this.input,[e]),n=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(s=>s instanceof Xe.Tensor&&s.dispose()),Pe(t,n)}withFaceExpressions(){return new At(this,this.input)}withAgeAndGender(){return new kt(this,this.input)}withFaceDescriptor(){return new Tt(this,this.input)}};var Ke=class extends J{constructor(e,r=new X){super();this.input=e;this.options=r}},Ie=class extends Ke{async run(){let{input:t,options:e}=this,r;if(e instanceof je)r=F.tinyFaceDetector.locateFaces(t,e);else if(e instanceof X)r=F.ssdMobilenetv1.locateFaces(t,e);else if(e instanceof st)r=F.tinyYolov2.locateFaces(t,e);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,e)=>{this.run().then(r=>t(r.map(n=>Vt({},n)))).catch(r=>e(r))})}withFaceLandmarks(t=!1){return new qe(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new oe(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ae(this.runAndExtendWithFaceDetections(),this.input)}},Qe=class extends Ke{async run(){let t=await new Ie(this.input,this.options),e=t[0];return t.forEach(r=>{r.score>e.score&&(e=r)}),e}runAndExtendWithFaceDetection(){return new Promise(async t=>{let e=await this.run();t(e?Vt({},e):void 0)})}withFaceLandmarks(t=!1){return new Ze(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new ne(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new se(this.runAndExtendWithFaceDetection(),this.input)}};function Na(o,t=new X){return new Qe(o,t)}function Lr(o,t=new X){return new Ie(o,t)}async function un(o,t){return Lr(o,new X(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function La(o,t={}){return Lr(o,new st(t)).withFaceLandmarks().withFaceDescriptors()}var Sa=un;function xo(o,t){if(o.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let e=Array.from(o),r=Array.from(t);return Math.sqrt(e.map((n,s)=>n-r[s]).reduce((n,s)=>n+s*s,0))}var vo=class o{constructor(t,e=.6){this._distanceThreshold=e;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let n=1,s=()=>`person ${n++}`;this._labeledDescriptors=r.map(a=>{if(a instanceof Et)return a;if(a instanceof Float32Array)return new Et(s(),[a]);if(a.descriptor&&a.descriptor instanceof Float32Array)return new Et(s(),[a.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor | Float32Array | Array | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,e){return e.map(r=>xo(r,t)).reduce((r,n)=>r+n,0)/(e.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:e,label:r})=>new me(r,this.computeMeanDistance(t,e))).reduce((e,r)=>e.distancet.toJSON())}}static fromJSON(t){let e=t.labeledDescriptors.map(r=>Et.fromJSON(r));return new o(e,t.distanceThreshold)}};function Aa(o){let t=new re;return t.extractWeights(o),t}function fn(o,t){let{width:e,height:r}=new R(t.width,t.height);if(e<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:e,height:r})}`);if(Array.isArray(o))return o.map(n=>fn(n,{width:e,height:r}));if(qt(o)){let n=o.detection.forSize(e,r),s=o.unshiftedLandmarks.forSize(n.box.width,n.box.height);return Pe(Vt(o,n),s)}return pt(o)?Vt(o,o.detection.forSize(e,r)):o instanceof H||o instanceof M?o.forSize(e,r):o}var ka=No;0&&(module.exports={AgeGenderNet,BoundingBox,Box,ComposableTask,ComputeAllFaceDescriptorsTask,ComputeFaceDescriptorsTaskBase,ComputeSingleFaceDescriptorTask,DetectAllFaceLandmarksTask,DetectAllFacesTask,DetectFaceLandmarksTaskBase,DetectFacesTaskBase,DetectSingleFaceLandmarksTask,DetectSingleFaceTask,Dimensions,FACE_EXPRESSION_LABELS,FaceDetection,FaceDetectionNet,FaceExpressionNet,FaceExpressions,FaceLandmark68Net,FaceLandmark68TinyNet,FaceLandmarkNet,FaceLandmarks,FaceLandmarks5,FaceLandmarks68,FaceMatch,FaceMatcher,FaceRecognitionNet,Gender,LabeledBox,LabeledFaceDescriptors,NetInput,NeuralNetwork,ObjectDetection,Point,PredictedBox,Rect,SsdMobilenetv1,SsdMobilenetv1Options,TinyFaceDetector,TinyFaceDetectorOptions,TinyYolov2,TinyYolov2Options,allFaces,allFacesSsdMobilenetv1,allFacesTinyYolov2,awaitMediaLoaded,bufferToImage,computeFaceDescriptor,createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw,env,euclideanDistance,extendWithAge,extendWithFaceDescriptor,extendWithFaceDetection,extendWithFaceExpressions,extendWithFaceLandmarks,extendWithGender,extractFaceTensors,extractFaces,fetchImage,fetchJson,fetchNetWeights,fetchOrThrow,fetchVideo,getContext2dOrThrow,getMediaDimensions,imageTensorToCanvas,imageToSquare,inverseSigmoid,iou,isMediaElement,isMediaLoaded,isWithAge,isWithFaceDetection,isWithFaceExpressions,isWithFaceLandmarks,isWithGender,loadAgeGenderModel,loadFaceDetectionModel,loadFaceExpressionModel,loadFaceLandmarkModel,loadFaceLandmarkTinyModel,loadFaceRecognitionModel,loadSsdMobilenetv1Model,loadTinyFaceDetectorModel,loadTinyYolov2Model,loadWeightMap,locateFaces,matchDimensions,minBbox,nets,nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid,ssdMobilenetv1,tf,tinyFaceDetector,tinyYolov2,toNetInput,utils,validateConfig,version}); diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js index 3eb9d39..58f90f4 100644 --- a/dist/tfjs.esm.js +++ b/dist/tfjs.esm.js @@ -29,7 +29,7 @@ Expected: ${s}.`)}typeof expect!="undefined"&&expect().nothing()}function jX(r,t ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,"data","sparseSegmentSum"),o=C(t,"indices","sparseSegmentSum","int32"),s=C(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(su,i)}var b2=k({sparseSegmentSum_:tY});function eY(r,t,e,n,o,s,i,a){let u=C(r,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype 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this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className="Sequential";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className="elu";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className="selu";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className="relu";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className="relu6";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className="linear";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className="sigmoid";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className="hardSigmoid";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className="softplus";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className="softsign";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className="tanh";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className="softmax";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className="logSoftmax";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className="gelu";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className="gelu_new";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className="mish";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className="swish";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function xi(r){if(r==null){let t={};return t.className="linear",t.config={},QN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:"L1L2"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function ve(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="ReLU";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className="LeakyReLU";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Softmax";Q.registerClass(rf);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Dx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",r.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className="Conv2D";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className="Conv3D";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yi(u,m,c,this.padding),h=yi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=om(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=yi(e[o],u,i,this.padding),e[s]=yi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className="Conv2DTranspose";Q.registerClass(of);var sf=class extends Hc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className="Conv3DTranspose";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className="SeparableConv";var af=class extends Eb{constructor(t){super(2,t)}};af.className="SeparableConv2D";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className="Conv1D";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className="Cropping2D";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className="UpSampling2D";Q.registerClass(cf);function CJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className="DepthwiseConv2D";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className="RNN";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className="SimpleRNNCell";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className="SimpleRNN";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className="GRU";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className="LSTM";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className="ConvRNN2D";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Me(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xc.className="ConvLSTM2DCell";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className="ConvLSTM2D";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className="Dropout";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className="SpatialDropout1D";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className="Dense";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className="Activation";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className="RepeatVector";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className="Reshape";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Permute";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className="Masking";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=rn(n,"int32"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Embedding";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className="Dot";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className="GaussianNoise";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className="GaussianDropout";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className="BatchNormalization";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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s==="max"?i=ku(r,t,e,a):i=xu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=ek(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof 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Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return 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this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className="Sequential";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className="elu";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className="selu";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className="relu";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className="relu6";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className="linear";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className="sigmoid";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className="hardSigmoid";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className="softplus";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className="softsign";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className="tanh";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className="softmax";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className="logSoftmax";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className="gelu";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className="gelu_new";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className="mish";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className="swish";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function xi(r){if(r==null){let t={};return t.className="linear",t.config={},QN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:"L1L2"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function ve(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="ReLU";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className="LeakyReLU";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Softmax";Q.registerClass(rf);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Dx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",r.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className="Conv2D";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className="Conv3D";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yi(u,m,c,this.padding),h=yi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=om(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=yi(e[o],u,i,this.padding),e[s]=yi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className="Conv2DTranspose";Q.registerClass(of);var sf=class extends Hc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className="Conv3DTranspose";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length{t=St(t);let n;if(this.rank===1)throw new _t("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className="SeparableConv";var af=class extends Eb{constructor(t){super(2,t)}};af.className="SeparableConv2D";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className="Conv1D";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className="Cropping2D";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className="UpSampling2D";Q.registerClass(cf);function CJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className="DepthwiseConv2D";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;xr(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;na.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;oDe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className="RNN";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className="SimpleRNNCell";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className="SimpleRNN";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className="GRU";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className="LSTM";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;is!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;aDe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className="ConvRNN2D";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Me(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xc.className="ConvLSTM2DCell";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className="ConvLSTM2D";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);if(0Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className="Dropout";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className="SpatialDropout1D";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className="Dense";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className="Activation";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className="RepeatVector";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className="Reshape";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Permute";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className="Masking";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=rn(n,"int32"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Embedding";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length1)throw new z(`Can not merge tensors with different batch sizes. 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Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className="Dot";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className="GaussianNoise";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className="GaussianDropout";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let 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t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className="BatchNormalization";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new 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n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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s==="max"?i=ku(r,t,e,a):i=xu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=ek(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"max")}};Lf.className="MaxPooling1D";Q.registerClass(Lf);var zf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};zf.className="AveragePooling1D";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"max")}};Bf.className="MaxPooling2D";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};Vf.className="AveragePooling2D";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"max")}};Gf.className="MaxPooling3D";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"avg")}};Wf.className="AveragePooling3D";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Uf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Uf.className="GlobalAveragePooling1D";Q.registerClass(Uf);var Hf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Hf.className="GlobalMaxPooling1D";Q.registerClass(Hf);var Ob=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Ne(n,[1,2]):Ne(n,[2,3])})}};qf.className="GlobalAveragePooling2D";Q.registerClass(qf);var Kf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};Kf.className="GlobalMaxPooling2D";Q.registerClass(Kf);var Pb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},jf=class extends Pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),nk((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};jf.className="TimeDistributed";Q.registerClass(jf);function EJ(r){da(eR,"BidirectionalMergeMode",r)}var AJ="concat",Xf=class extends Pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?AJ:t.mergeMode,EJ(this.mergeMode),t.weights)throw new _t("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode==="concat"?a=_m([o,s]):this.mergeMode==="sum"?a=K(o,s):this.mergeMode==="ave"?a=$(.5,K(o,s)):this.mergeMode==="mul"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Xf.className="Bidirectional";Q.registerClass(Xf);var Yf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=rn(t,"float32")),K($(t,this.scale),this.offset)))}};Yf.className="Rescaling";Q.registerClass(Yf);var{resizeBilinear:DJ,cropAndResize:$J}=fn,Zf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N{let s=DJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Zf.className="CenterCrop";Q.registerClass(Zf);function ZR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=rn(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=mh(a,n,e,i):u=mh(a,[],e,i),t!=="tfIdf")return u;if(n)return $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var Jf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=rn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count. Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ZR(t,this.outputMode,this.numTokens,n)})}};Jf.className="CategoryEncoding";Q.registerClass(Jf);var FJ=["bilinear","nearest"],JR=new Set(FJ),Qf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(JR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...JR]} are supported`)})}};Qf.className="Resizing";Q.registerClass(Qf);var Bh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Bh.className="RandomSeed";var Vh=class extends Et{constructor(t){super(t),this.randomGenerator=new Bh(t.seed)}getConfig(){let t={seed:this.randomGenerator.seed},e=super.getConfig();return Object.assign(t,e),t}};Vh.className="BaseRandomLayer";var OJ=["bilinear","nearest"],QR=new Set(OJ),td=class extends Vh{constructor(t){super(t);let{factor:e,interpolation:n="bilinear"}=t;if(this.factor=e,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new z(`Invalid factor: ${this.factor}. 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EF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let 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o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a{let o=v("axis",r,t,e),s=v("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let 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s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>fF(i,a,u));case"basic_math":return o(()=>dF(i,a,u));case"control":return wF(i,a,u);case"convolution":return o(()=>CF(i,a,u));case"creation":return o(()=>vF(i,a,u));case"dynamic":return SF(i,a,u);case"evaluation":return o(()=>NF(i,a,u));case"image":return o(()=>_F(i,a,u));case"graph":return o(()=>kF(i,a,u));case"logical":return o(()=>EF(i,a,u));case"matrices":return o(()=>AF(i,a,u));case"normalization":return o(()=>DF(i,a,u));case"ragged":return o(()=>$F(i,a,u));case"reduction":return o(()=>RF(i,a,u));case"slice_join":return o(()=>FF(i,a,u));case"sparse":return o(()=>OF(i,a,u));case"spectral":return o(()=>MF(i,a,u));case"string":return o(()=>PF(i,a,u));case"transformation":return o(()=>LF(i,a,u));case"hash_table":return TF(i,a,u,n);case"custom":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt="?tfjs-format=file",gtt="model.json",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function xtt(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof r=="string"&&(r=btt(r));let n=new qh(r,t,e);return await n.load(),n}function ytt(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(r instanceof Array){let[n,o]=r;if(!n)throw new Error("modelJSON must be the first element of the array");if(!o||!(o instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Mr.getWeightSpecs(n.weightsManifest),i=Mr.getModelArtifactsForJSONSync(n,s,o);t=Mr.fromMemorySync(i)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=Mr.fromMemorySync(r);else throw new Error("Unknown model format");let e=new qh(t);return e.load(),e}function btt(r){return r.endsWith("/")||(r=r+"/"),`${r}${gtt}${htt}`}var VF="4.21.0";var 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e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries 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n=v("tensorListId",r,t,e),o=e.getTensorList(n.id),s=v("dtype",r,t,e),i=v("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=v("tensorListId",r,t,e),o=v("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=v("tensorListId",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=v("tensor",r,t,e),o=v("elementShape",r,t,e),s=v("lengths",r,t,e),i=bF(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=v("tensorListId",r,t,e),o=e.getTensorList(n.id);return[pt(o.size(),"int32")]}case"TensorListResize":{let n=v("tensorListId",r,t,e),o=v("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function 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o=v("strides",r,t,e),s=Gh(r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilations",r,t,e);return[n.conv2d(v("x",r,t,e),v("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=IF(r,t,e);return[n.fused.conv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=IF(r,t,e);return[n.fused.depthwiseConv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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performance."),console.log(u);for(let c=0;ct.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a{let o=[];for(let s=0;s{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v("keyDType",r,t,e),i=v("valueDType",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let 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o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let 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o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a{let o=v("axis",r,t,e),s=v("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>fF(i,a,u));case"basic_math":return o(()=>dF(i,a,u));case"control":return wF(i,a,u);case"convolution":return o(()=>CF(i,a,u));case"creation":return o(()=>vF(i,a,u));case"dynamic":return SF(i,a,u);case"evaluation":return o(()=>NF(i,a,u));case"image":return o(()=>_F(i,a,u));case"graph":return o(()=>kF(i,a,u));case"logical":return o(()=>EF(i,a,u));case"matrices":return o(()=>AF(i,a,u));case"normalization":return o(()=>DF(i,a,u));case"ragged":return o(()=>$F(i,a,u));case"reduction":return o(()=>RF(i,a,u));case"slice_join":return o(()=>FF(i,a,u));case"sparse":return o(()=>OF(i,a,u));case"spectral":return o(()=>MF(i,a,u));case"string":return o(()=>PF(i,a,u));case"transformation":return o(()=>LF(i,a,u));case"hash_table":return TF(i,a,u,n);case"custom":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;ee.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;at[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt="?tfjs-format=file",gtt="model.json",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function xtt(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof r=="string"&&(r=btt(r));let n=new qh(r,t,e);return await n.load(),n}function ytt(r){if(r==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(r instanceof Array){let[n,o]=r;if(!n)throw new Error("modelJSON must be the first element of the array");if(!o||!(o instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Mr.getWeightSpecs(n.weightsManifest),i=Mr.getModelArtifactsForJSONSync(n,s,o);t=Mr.fromMemorySync(i)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=Mr.fromMemorySync(r);else throw new Error("Unknown model format");let e=new qh(t);return e.load(),e}function btt(r){return r.endsWith("/")||(r=r+"/"),`${r}${gtt}${htt}`}var VF="4.22.0";var aO={};Kt(aO,{CSVDataset:()=>id,Dataset:()=>Ii,FileDataSource:()=>ud,TextLineDataset:()=>sd,URLDataSource:()=>cd,array:()=>ZF,csv:()=>rO,func:()=>nO,generator:()=>oO,microphone:()=>iO,version_data:()=>Jk,webcam:()=>sO,zip:()=>JF});var YF=Gl(hh());var KF=Gl(hh());function GF(r,t){return ow(r,t)}function ow(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(Vu(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=ow(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function WF(r,t=Rk){return UF(r,t)}function UF(r,t,e=new Set){let n=r[0];if(e.has(n))throw new Error("Circular references are not supported.");let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(Vu(n)){let s=Array.isArray(n)?[]:{};e.add(n);for(let i in n){let a=r.map(l=>l[i]),u=UF(a,t,e);s[i]=u}return e.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function Rk(r){return r===null?null:Vu(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function sw(r,t){let e=new Map;ow(r,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(y.isPromise(s)){let i=await s;e.set(o,i)}}return ow(r,t,e)}function Vu(r){let t=!1;if(L().get("IS_BROWSER"))t=r instanceof TextDecoder;else{let{StringDecoder:e}=$k();t=r instanceof e}return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Lt)&&!(r instanceof Promise)&&!t)}function HF(r){return r==null||wtt(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Lt||y.isTypedArray(r)}function wtt(r){return r===null||typeof r!="object"&&typeof r!="function"}function qF(r){return GF(r,Itt)}function Itt(r){return r instanceof Lt?{value:r.clone(),recurse:!1}:Vu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var od=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),n=this.get(e);return this.set(e,this.pop()),n}};var Kh=class r extends od{constructor(){super(r.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;oe===!0)}rowMajorBatch(t,e=!0){return new zk(this,t,e)}columnMajorBatch(t,e=!0,n=Rk){return this.rowMajorBatch(t,e).map(s=>WF(s,n))}concatenate(t,e){return new aw(qk([this,t]),e)}take(t){return t<0||t==null?this:new Lk(this,t)}skip(t){return t<0||t==null?this:new Pk(this,t)}prefetch(t){return new lw(this,t)}shuffle(t,e){return new Hk(this,t,e)}serial(){return new Mk(this)}},Fk=class extends er{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:qF(t),done:!1}}},Ok=class extends er{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Mk=class extends er{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Pk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zk=class extends er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Dl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Dl||(Dl={}));var Uk=class extends er{constructor(t,e=Dl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof er?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await sw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Dl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Dl.SHORTEST:return{value:null,done:!0};case Dl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},lw=class extends er{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new od(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Hk=class extends lw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=KF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var Ii=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),En(async()=>(await n.iterator()).columnMajorBatch(t,e,Ctt),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,En(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,En(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return En(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return En(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return En(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,En(async()=>{let o=jh(async()=>({value:await e.iterator(),done:!1}));return jF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=YF.alea(e||y.now().toString());return En(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,En(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ii.MAX_BUFFER_SIZE=1e4;function En(r,t=null){return new class extends Ii{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function ZF(r){return En(async()=>qk(r),r.length)}function JF(r){if(!Vu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e{let e=await sw(r,n=>{if(n instanceof Ii)return{value:n.iterator(),recurse:!1};if(Vu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return XF(e,Dl.SHORTEST)},t)}function Ctt(r){if(r===null)return null;let t=r[0];return HF(t)?{value:vtt(r),recurse:!1}:{value:null,recurse:!0}}function vtt(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Lt?Fe(r):ir(r)}var sd=class extends Ii{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var uw='"',Xh=Symbol("out"),QF=Symbol("field"),cw=Symbol("quote"),Kk=Symbol("quoteafterquote"),tO=Symbol("quoteinquote"),id=class extends Ii{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new sd(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new r(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=je(J(t,"float32"),0),n;n=fn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ad=class{};var Yh=class extends er{split(t){return new jk(this,t)}},jk=class extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=$k();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var ld=class extends fw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function eO(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=Stt(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new ld(i,t)}else throw new Error(s.statusText)}var Stt=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function dw(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var ud=class extends ad{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(dw(this.input)&&L().get("IS_NODE")){let t=hw();this.input=t.readFileSync(this.input.slice(7))}return new ld(this.input,this.options)}};var cd=class extends ad{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return dw(this.url)?new ud(this.url,this.fileOptions).iterator():eO(this.url,this.fileOptions)}};function rO(r,t={}){return new id(new cd(r),t)}function nO(r){let t=jh(r);return En(async()=>t)}function oO(r){return En(async()=>{let t=await r();return jh(()=>t.next())})}async function sO(r,t){return mw.create(r,t)}async function iO(r){return pw.create(r)}var Jk="4.21.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Ntt=Xr.whereImpl,pd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Bn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().get("IS_NODE")&&S.warn(` +`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var uw='"',Xh=Symbol("out"),QF=Symbol("field"),cw=Symbol("quote"),Kk=Symbol("quoteafterquote"),tO=Symbol("quoteinquote"),id=class extends Ii{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new sd(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new r(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=je(J(t,"float32"),0),n;n=fn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ad=class{};var Yh=class extends er{split(t){return new jk(this,t)}},jk=class extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=$k();this.decoder=new 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i=o.strideDepth,a=o.strideHeight,u=o.strideWidth,l=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,x=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=wt(o.outShape,e),I=w.values,N=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],A=o.outShape[3]*o.outShape[4],D=o.outShape[4];for(let F=0;FRt?Rt=Ae:s==="avg"&&(Dt+=Ae,Ht++),isNaN(Rt))break}if(isNaN(Rt))break}if(isNaN(Rt))break}let qt=Ct+G;I[qt]=s==="avg"?Dt/Math.max(Ht,1):Rt}}}}return w}function dM(r,t){let e=wt(t.outShape,"int32"),n=t.strideDepth,o=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,a=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,m=t.padInfo.front,f=t.padInfo.top,d=t.padInfo.left;for(let h=0;h=W&&(W=st,q=j*c*p+Z*c+nt)}}}e.set(q,h,x,N,F,g)}}}return e}function yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,M=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,ft,Ct,q);st+=Rt}}}M.set(st*V,W,H,j,Y,q)}return e.makeTensorInfo(M.shape,M.dtype,M.values)}var xM={kernelName:Hl,backendName:"cpu",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var yM={kernelName:Ul,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization 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e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var CM={kernelName:ql,backendName:"cpu",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:"cpu",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:"cpu",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:"cpu",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype==="int32"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var KM={kernelName:Qd,backendName:"cpu",kernelFunc:Uet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=md(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var XM={kernelName:Fp,backendName:"cpu",kernelFunc:Het};function qet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var YM={kernelName:La,backendName:"cpu",kernelFunc:qet};var Ket=S.ERF_P,jet=S.ERF_A1,Xet=S.ERF_A2,Yet=S.ERF_A3,Zet=S.ERF_A4,Jet=S.ERF_A5,Qet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ket*e);return t*(1-((((Jet*n+Zet)*n+Yet)*n+Xet)*n+jet)*n*Math.exp(-e*e))}),ZM={kernelName:us,backendName:"cpu",kernelFunc:Qet};function yd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var JM={kernelName:Mi,backendName:"cpu",kernelFunc:yd};var trt=Qt((r,t)=>r/t),tg=oe(as,trt),eg={kernelName:as,backendName:"cpu",kernelFunc:tg};function $w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m=0&&w=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=yw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var sP={kernelName:Pi,backendName:"cpu",kernelFunc:urt};function crt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var iP={kernelName:Mp,backendName:"cpu",kernelFunc:crt};var prt=At(gs,r=>Number.isFinite(r)?1:0,"bool"),aP={kernelName:gs,backendName:"cpu",kernelFunc:prt};var mrt=At(xs,r=>Math.abs(r)===1/0?1:0,"bool"),lP={kernelName:xs,backendName:"cpu",kernelFunc:mrt};var frt=At(ys,r=>Number.isNaN(r)?1:0,"bool"),uP={kernelName:ys,backendName:"cpu",kernelFunc:frt};function drt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bw(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var cP={kernelName:Ha,backendName:"cpu",kernelFunc:drt};var hrt=At(Is,r=>Math.log1p(r)),pP={kernelName:Is,backendName:"cpu",kernelFunc:hrt};var grt=Qt((r,t)=>r&&t),xrt=oe(qa,grt,null,"bool"),mP={kernelName:qa,backendName:"cpu",kernelFunc:xrt};var yrt=At(Ka,r=>r?0:1,"bool"),fP={kernelName:Ka,backendName:"cpu",kernelFunc:yrt};var brt=Qt((r,t)=>r||t),wrt=oe(ja,brt,null,"bool"),dP={kernelName:ja,backendName:"cpu",kernelFunc:wrt};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Zr({inputs:{x:o},backend:e});else{let m=e.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=xd(m,o.shape,o.dtype,f,c,"max");p=e.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var yP={kernelName:Ns,backendName:"cpu",kernelFunc:vrt};function Srt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n;tt(o,"maxPool3d");let c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.data.get(o.dataId).values,m=Dw(p,o.shape,o.dtype,y.computeStrides(o.shape),c,"max");return e.makeTensorInfo(m.shape,"float32",m.values)}var bP={kernelName:Li,backendName:"cpu",kernelFunc:Srt};function Nrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n;tt([o,s],"maxPool3DGrad");let c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.bufferSync(s),m=dM(p,c),f=c.strideDepth,d=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,w=c.effectiveFilterDepth,I=c.effectiveFilterHeight,N=c.effectiveFilterWidth,E=w-1-c.padInfo.front,A=N-1-c.padInfo.left,D=I-1-c.padInfo.top,F=wt(s.shape,"float32"),M=e.bufferSync(o);for(let V=0;V=c.outDepth||Math.floor(st)!==st))for(let lt=0;lt=c.outHeight||Math.floor(ot)!==ot))for(let it=0;it=c.outWidth||Math.floor(ft)!==ft)continue;let gt=w*I*N-1-m.get(V,st,ot,ft,G),Ct=nt*I*N+lt*N+it,Rt=gt===Ct?1:0;if(Rt===0)continue;let Dt=M.get(V,st,ot,ft,G);et+=Dt*Rt}}}F.set(et,V,W,q,H,G)}return e.makeTensorInfo(F.shape,F.dtype,F.values)}var wP={kernelName:tu,backendName:"cpu",kernelFunc:Nrt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;tt([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=e.data.get(a.dataId).values,d=wt(m.outShape,a.dtype,Aw(f,a.shape,a.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,w=m.effectiveFilterHeight,I=m.effectiveFilterWidth,N=I-1-m.padInfo.left,E=w-1-m.padInfo.top,A=wt(a.shape,"float32"),D=e.data.get(o.dataId).values,F=wt(o.shape,"float32",D);for(let M=0;M=m.outHeight||Math.floor(Z)!==Z))for(let et=0;et=m.outWidth||Math.floor(nt)!==nt)continue;let st=w*I-1-d.get(M,Z,nt,V),lt=Y*I+et,ot=st===lt?1:0;if(ot===0)continue;let it=F.get(M,Z,nt,V);j+=it*ot}}A.set(j,M,G,W,V)}return e.makeTensorInfo(A.shape,A.dtype,A.values)}var IP={kernelName:Ql,backendName:"cpu",kernelFunc:krt};function CP(r,t,e,n,o){let s=y.computeStrides(t),i=xd(r,t,e,s,o,"max"),a=Aw(r,t,e,o,!0,n);return[i.values,a.values]}var vP={kernelName:eu,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;tt(n,"MaxPoolWithArgmax");let l=u.data.get(n.dataId).values,c=S.computePool2DInfo(n.shape,o,s,[1,1],i),[p,m]=CP(l,n.shape,n.dtype,a,c),f=u.write(p,c.outShape,n.dtype),d=u.write(m,c.outShape,n.dtype);return[{dataId:f,shape:c.outShape,dtype:n.dtype},{dataId:d,shape:c.outShape,dtype:"int32"}]}};function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=y.parseAxisParam(s,o.shape),l=S.computeOutAndReduceShapes(o.shape,a)[1],c=y.sizeFromShape(l),p=[],m=e.makeTensorInfo([],"float32",new Float32Array([c]));p.push(m);let f=Fo({inputs:{x:o},backend:e,attrs:{dtype:"float32"}});p.push(f);let d=tg({inputs:{a:f,b:m},backend:e});p.push(d);let h=$l({inputs:{x:d},backend:e,attrs:{axis:s,keepDims:i}});return p.forEach(g=>e.disposeIntermediateTensorInfo(g)),h}var SP={kernelName:ks,backendName:"cpu",kernelFunc:Trt};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"min");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=We({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;xw[0]+o.shape[I]+w[1]),u=s.map(w=>w[0]),l=s.map((w,I)=>w[0]+o.shape[I]),c=i==="reflect"?0:1,p=e.data.get(o.dataId).values,m=o.shape.length,f=y.computeStrides(o.shape),d=y.sizeFromShape(a),h=a.length,g=y.computeStrides(a),x=y.getTypedArrayFromDType(o.dtype,d);for(let w=0;w=l[E]&&(I[E]=(l[E]-1)*2-I[E]+c);I=I.map((E,A)=>E-u[A]);let N=y.locToIndex(I,m,f);x[w]=p[N]}return{dataId:e.write(x,a,o.dtype),shape:a,dtype:o.dtype}}var kP={kernelName:Es,backendName:"cpu",kernelFunc:Ert};var Art=Qt((r,t)=>{let e=r%t;return r<0&&t<0||r>=0&&t>=0?e:(e+t)%t}),Drt=oe(As,Art),TP={kernelName:As,backendName:"cpu",kernelFunc:Drt};var EP=Gl(hh());function LT(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=o.shape.length,a=s;if(a===-1&&(a=i-1),a!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${a}`);let u=y.parseAxisParam([a],o.shape),l=PT({inputs:{x:o},backend:e,attrs:{reductionIndices:u,keepDims:!1}}),c=S.expandShapeToKeepDim(l.shape,u),p=te({inputs:{x:l},backend:e,attrs:{shape:c}}),m=Jh({inputs:{a:o,b:p},backend:e}),f=aT({inputs:{x:m},backend:e}),d=$l({inputs:{x:f},backend:e,attrs:{axis:u,keepDims:!1}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=tg({inputs:{a:f,b:h},backend:e});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var _P={kernelName:Qs,backendName:"cpu",kernelFunc:LT};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;tt(o,"multinomial");let u=a?o:LT({inputs:{logits:o},backend:e,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],p=e.data.get(u.dataId).values,m=[l,s],f=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let d=0;d=0&&p[m]{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=yd({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Gu({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var LP={kernelName:Vi,backendName:"cpu",kernelFunc:zT};function Brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;tt(o,"pad");let a=s.map((b,w)=>b[0]+o.shape[w]+b[1]),u=s.map(b=>b[0]),l=e.data.get(o.dataId).values,c=y.sizeFromShape(o.shape),p=o.shape.length,m=y.computeStrides(o.shape),f=y.sizeFromShape(a),d=a.length,h=y.computeStrides(a),g=y.getTypedArrayFromDType(o.dtype,f);i!==0&&g.fill(i);for(let 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yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,M=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,ft,Ct,q);st+=Rt}}}M.set(st*V,W,H,j,Y,q)}return e.makeTensorInfo(M.shape,M.dtype,M.values)}var xM={kernelName:Hl,backendName:"cpu",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var yM={kernelName:Ul,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var bM={kernelName:ds,backendName:"cpu",kernelFunc:Cet};function vet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:"cpu",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:"cpu",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var CM={kernelName:ql,backendName:"cpu",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;lh.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:"cpu",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;beMath.cos(r)),RM={kernelName:rs,backendName:"cpu",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:"cpu",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;ltx+d-b-1:(x,b)=>x+b;for(let x=0;xx+d-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H=0&&it=0&>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W=0&&ot=0&&ftet&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 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Logits was rank ${i} and dim was ${a}`);let u=y.parseAxisParam([a],o.shape),l=PT({inputs:{x:o},backend:e,attrs:{reductionIndices:u,keepDims:!1}}),c=S.expandShapeToKeepDim(l.shape,u),p=te({inputs:{x:l},backend:e,attrs:{shape:c}}),m=Jh({inputs:{a:o,b:p},backend:e}),f=aT({inputs:{x:m},backend:e}),d=$l({inputs:{x:f},backend:e,attrs:{axis:u,keepDims:!1}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=tg({inputs:{a:f,b:h},backend:e});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var _P={kernelName:Qs,backendName:"cpu",kernelFunc:LT};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;tt(o,"multinomial");let u=a?o:LT({inputs:{logits:o},backend:e,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],p=e.data.get(u.dataId).values,m=[l,s],f=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let d=0;d=0&&p[m]{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=yd({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Gu({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var LP={kernelName:Vi,backendName:"cpu",kernelFunc:zT};function Brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;tt(o,"pad");let a=s.map((b,w)=>b[0]+o.shape[w]+b[1]),u=s.map(b=>b[0]),l=e.data.get(o.dataId).values,c=y.sizeFromShape(o.shape),p=o.shape.length,m=y.computeStrides(o.shape),f=y.sizeFromShape(a),d=a.length,h=y.computeStrides(a),g=y.getTypedArrayFromDType(o.dtype,f);i!==0&&g.fill(i);for(let b=0;bE+u[A]),N=y.locToIndex(I,d,h);g[N]=l[b]}return{dataId:e.write(g,a,o.dtype),shape:a,dtype:o.dtype}}var Rw={kernelName:Rs,backendName:"cpu",kernelFunc:Brt};var Vrt=Qt((r,t)=>Math.pow(r,t)),Grt=oe(Fs,Vrt),zP={kernelName:Fs,backendName:"cpu",kernelFunc:Grt};function Wrt(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.data.get(x.dataId).values),l=o.map(x=>x.shape),c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,[m,f,d]=Iw(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var BP={kernelName:Lp,backendName:"cpu",kernelFunc:Wrt};function Urt(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=Cw(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var VP={kernelName:zp,backendName:"cpu",kernelFunc:Urt};function Hrt(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.data.get(o.dataId).values,c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,m=a.map(g=>e.data.get(g.dataId).values),f=a.map(g=>g.shape),[d,h]=vw(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var GP={kernelName:Bp,backendName:"cpu",kernelFunc:Hrt};function qrt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,dtype:s,step:i}=e,a=tp(n,o,i,s);return t.makeTensorInfo([a.length],s,a)}var WP={kernelName:ru,backendName:"cpu",kernelFunc:qrt};var Krt=At(Ps,r=>1/r),UP={kernelName:Ps,backendName:"cpu",kernelFunc:Krt};function jrt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n;tt(o,"resizeBilinear");let u=y.computeStrides(o.shape),[l,c]=a,[p,m,f,d]=o.shape,h=e.data.get(o.dataId).values,g=new Float32Array(y.sizeFromShape([p,l,c,d])),x=[s&&l>1?m-1:m,s&&c>1?f-1:f],b=[s&&l>1?l-1:l,s&&c>1?c-1:c],w=0,I=x[0]/b[0],N=x[1]/b[1];for(let E=0;E1?l-1:l,i&&f>1?c-1:c],g=[i&&m>1?m-1:m,i&&f>1?f-1:f],x=h[0]/g[0],b=h[1]/g[1],w=e.data.get(s.dataId).values,I=0;for(let N=0;N1?m-1:m,s&&c>1?f-1:f],b=[s&&l>1?l-1:l,s&&c>1?c-1:c],w=x[0]/b[0],I=x[1]/b[1],N=0;for(let E=0;E1?c-1:c,i&&d>1?p-1:p],b=[i&&f>1?f-1:f,i&&d>1?d-1:d],w=x[0]/b[0],I=x[1]/b[1],N=1/w,E=1/I,A=Math.ceil(N)*2+2,D=Math.ceil(E)*2+2;for(let F=0;F=f)continue;let ot=M+lt*u[1],it=lt*w,ft=Math.min(c-1,i?Math.round(it):Math.floor(it));if(V===ft)for(let gt=0;gt=d)continue;let Rt=ot+Ct*u[2],Dt=Ct*I,Ht=Math.min(p-1,i?Math.round(Dt):Math.floor(Dt));H===Ht&&(nt+=g[Rt+et])}}h[j+et]=nt}}}}return e.makeTensorInfo(o.shape,o.dtype,h)}var jP={kernelName:el,backendName:"cpu",kernelFunc:Zrt};function Jrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n;tt(o,"reverse");let i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return Zr({inputs:{x:o},backend:e});let u=new le(o.shape,o.dtype),l=e.bufferSync(o);for(let c=0;cm[f]=o.shape[f]-1-m[f]),u.set(l.get(...m),...p)}return e.makeTensorInfo(u.shape,u.dtype,u.values)}var XP={kernelName:Gs,backendName:"cpu",kernelFunc:Jrt};var YP={kernelName:pl,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[l,c,p,m]=n.shape,[f,d]=S.getImageCenter(i,c,p),h=255,g=Math.sin(o),x=Math.cos(o),b=a.data.get(n.dataId).values;for(let I=0;I=0&&q=0&&H{let t=Math.floor(r);return r-t<.5?Math.floor(r):r-t>.5?Math.ceil(r):t%2===0?t:t+1}),ZP={kernelName:Ws,backendName:"cpu",kernelFunc:Qrt};function tnt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=!0,f=e.bufferSync(o),d=e.bufferSync(s),h=Ci(f,d,i,p,l,u,a,c,0,m);return e.makeTensorInfo(i,h.dtype,h.values)}var JP={kernelName:nl,backendName:"cpu",kernelFunc:tnt};function ent(r,t){let e=0,n=r.length,o=0;for(;e1||o.shape.length===1?1:y.sizeFromShape(o.shape.slice(1));for(let d=0;dr>=0?int*r:snt*(Math.exp(r)-1)),rL={kernelName:Hs,backendName:"cpu",kernelFunc:ant};var lnt=At(js,r=>r<0?-1:r>0?1:0),nL={kernelName:js,backendName:"cpu",kernelFunc:lnt};var unt=At(qs,r=>Math.sin(r)),oL={kernelName:qs,backendName:"cpu",kernelFunc:unt};var cnt=At(Ks,r=>Math.sinh(r)),sL={kernelName:Ks,backendName:"cpu",kernelFunc:cnt};var pnt=11920928955078125e-23,iL=Math.log(pnt)+2,mnt=At(Ys,r=>{let t=r>-iL,e=rd.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=` + `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber("WEBGL_VERSION"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new pp(n),this.binaryCache=Kot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;nnew Dd,2);var T$e={forceHalfFloat:Qz};var $d=` if (isnan(a)) return a; if (isnan(b)) return b; `;var $n=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=` @@ -5006,4 +5006,4 @@ return a / b;`,Fat=` } `}};function aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new TC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=K1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),j=j1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(j),g(q,N,j,A,D)},x=g(d,"unsortedSegmentSum",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return 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OW={kernelName:Oa,backendName:"wasm",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,["number","number","number","number","number","number"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:"wasm",setupFunc:ect,kernelFunc:rct};var LW;function 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ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:"wasm",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:"wasm",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. 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$ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:"wasm",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,"bool");var Fct=!1,CU=ee(hs,Fct,"bool");var vU=yt(gs,"bool");var SU=yt(xs,"bool");var NU=yt(ys,"bool");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,["number","number","number","number"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:"wasm",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,"bool");var Lct=!1,EU=ee(Ua,Lct,"bool");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,["number","number","number","number"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:"wasm",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,"bool");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,"bool");var Wct=!1,PU=ee(E_,Wct,"bool");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:"wasm",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:"wasm",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,["number","number","number","number"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:"wasm",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=rq.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:"")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error("WASM backend not initialized.");return i_}var Hmt="4.22.0";var qmt=2;Xp("wasm",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq="4.22.0",Kmt="4.22.0",jmt="4.22.0",Xmt="4.22.0",Ymt="4.22.0",Zmt={tfjs:sq,"tfjs-core":sq,"tfjs-converter":Kmt,"tfjs-backend-cpu":jmt,"tfjs-backend-webgl":Xmt,"tfjs-backend-wasm":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike}; diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js index 0e48bd6..6c80804 100644 --- a/dist/tfjs.version.js +++ b/dist/tfjs.version.js @@ -4,4 +4,4 @@ author: ' */ -var e="4.21.0";var s="4.21.0";var t="4.21.0";var n="4.21.0";var i="4.21.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; +var e="4.22.0";var s="4.22.0";var t="4.22.0";var n="4.22.0";var i="4.22.0";var w={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":i};export{w as version}; diff --git a/package.json b/package.json index f4d45df..a9ca0ee 100644 --- a/package.json +++ b/package.json @@ -44,26 +44,26 @@ ], "devDependencies": { "@canvas/image": "^2.0.0", - "@microsoft/api-extractor": "^7.47.7", - "@tensorflow/tfjs": "^4.21.0", - "@tensorflow/tfjs-backend-cpu": "^4.21.0", - "@tensorflow/tfjs-backend-wasm": "^4.21.0", - "@tensorflow/tfjs-backend-webgl": "^4.21.0", - "@tensorflow/tfjs-backend-webgpu": "4.21.0", - "@tensorflow/tfjs-converter": "^4.21.0", - "@tensorflow/tfjs-core": "^4.21.0", - "@tensorflow/tfjs-data": "^4.21.0", - "@tensorflow/tfjs-layers": "^4.21.0", - "@tensorflow/tfjs-node": "^4.21.0", - "@tensorflow/tfjs-node-gpu": "^4.21.0", - "@types/node": "^22.5.4", + "@microsoft/api-extractor": "^7.49.2", + "@tensorflow/tfjs": "^4.22.0", + "@tensorflow/tfjs-backend-cpu": "^4.22.0", + "@tensorflow/tfjs-backend-wasm": "^4.22.0", + "@tensorflow/tfjs-backend-webgl": "^4.22.0", + "@tensorflow/tfjs-backend-webgpu": "4.22.0", + "@tensorflow/tfjs-converter": "^4.22.0", + "@tensorflow/tfjs-core": "^4.22.0", + "@tensorflow/tfjs-data": "^4.22.0", + "@tensorflow/tfjs-layers": "^4.22.0", + "@tensorflow/tfjs-node": "^4.22.0", + "@tensorflow/tfjs-node-gpu": "^4.22.0", + "@types/node": "^22.13.1", "@types/offscreencanvas": "^2019.7.3", "@typescript-eslint/eslint-plugin": "^8.5.0", "@typescript-eslint/parser": "^8.5.0", - "@vladmandic/build": "^0.10.1", + "@vladmandic/build": "^0.10.2", "@vladmandic/pilogger": "^0.5.1", "ajv": "^8.17.1", - "esbuild": "^0.23.1", + "esbuild": "^0.24.2", "eslint": "8.57.0", "eslint-config-airbnb-base": "^15.0.0", "eslint-plugin-import": "^2.30.0", @@ -73,8 +73,8 @@ "node-fetch": "^3.3.2", "rimraf": "^6.0.1", "seedrandom": "^3.0.5", - "tslib": "^2.7.0", - "typedoc": "^0.26.7", - "typescript": "5.5.4" + "tslib": "^2.8.1", + "typedoc": "^0.27.6", + "typescript": "5.7.3" } } diff --git a/src/NeuralNetwork.ts b/src/NeuralNetwork.ts index d60c615..8b2d43f 100644 --- a/src/NeuralNetwork.ts +++ b/src/NeuralNetwork.ts @@ -103,6 +103,7 @@ export abstract class NeuralNetwork { const { readFile } = env.getEnv(); const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName()); const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(filePaths.map((fp) => readFile(fp).then((buf) => (typeof buf === 'string' ? Buffer.from(buf) : buf.buffer)))); + // @ts-ignore async-vs-sync mismatch const loadWeights = tf['io'].weightsLoaderFactory(fetchWeightsFromDisk); const manifest = JSON.parse((await readFile(manifestUri)).toString()); const weightMap = await loadWeights(manifest, modelBaseUri); diff --git a/typedoc/assets/hierarchy.js b/typedoc/assets/hierarchy.js new file mode 100644 index 0000000..fbe3128 --- /dev/null +++ b/typedoc/assets/hierarchy.js @@ -0,0 +1 @@ +window.hierarchyData = "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" \ No newline at end of file diff --git a/typedoc/assets/icons.js b/typedoc/assets/icons.js index e88e8ca..58882d7 100644 --- a/typedoc/assets/icons.js +++ b/typedoc/assets/icons.js @@ -3,7 +3,7 @@ function addIcons() { if (document.readyState === "loading") return document.addEventListener("DOMContentLoaded", addIcons); const svg = document.body.appendChild(document.createElementNS("http://www.w3.org/2000/svg", "svg")); - svg.innerHTML = `""`; + svg.innerHTML = `MMNEPVFCICPMFPCPTTAAATR`; svg.style.display = "none"; if (location.protocol === "file:") updateUseElements(); } diff --git a/typedoc/assets/icons.svg b/typedoc/assets/icons.svg index e371b8b..50ad579 100644 --- a/typedoc/assets/icons.svg +++ b/typedoc/assets/icons.svg @@ -1 +1 @@ - \ No newline at end of file +MMNEPVFCICPMFPCPTTAAATR \ No newline at end of file diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js index 21a5d74..4f59cd9 100644 --- a/typedoc/assets/main.js +++ b/typedoc/assets/main.js @@ -1,9 +1,9 @@ "use strict"; -window.translations={"copy":"Copy","copied":"Copied!","normally_hidden":"This member is normally hidden due to your filter settings."}; -"use strict";(()=>{var Pe=Object.create;var ie=Object.defineProperty;var Oe=Object.getOwnPropertyDescriptor;var _e=Object.getOwnPropertyNames;var Re=Object.getPrototypeOf,Me=Object.prototype.hasOwnProperty;var Fe=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var De=(t,e,n,r)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of _e(e))!Me.call(t,i)&&i!==n&&ie(t,i,{get:()=>e[i],enumerable:!(r=Oe(e,i))||r.enumerable});return t};var Ae=(t,e,n)=>(n=t!=null?Pe(Re(t)):{},De(e||!t||!t.__esModule?ie(n,"default",{value:t,enumerable:!0}):n,t));var ue=Fe((ae,le)=>{(function(){var t=function(e){var n=new t.Builder;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),n.searchPipeline.add(t.stemmer),e.call(n,n),n.build()};t.version="2.3.9";t.utils={},t.utils.warn=function(e){return function(n){e.console&&console.warn&&console.warn(n)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var n=Object.create(null),r=Object.keys(e),i=0;i0){var d=t.utils.clone(n)||{};d.position=[a,u],d.index=s.length,s.push(new t.Token(r.slice(a,o),d))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,n){n in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn(`Function is not registered with pipeline. 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"; \ No newline at end of file diff --git a/typedoc/assets/style.css b/typedoc/assets/style.css index 9d619a6..7f80f3d 100644 --- a/typedoc/assets/style.css +++ b/typedoc/assets/style.css @@ -1,91 +1,247 @@ -:root { - /* Light */ - --light-color-background: #f2f4f8; - --light-color-background-secondary: #eff0f1; - --light-color-warning-text: #222; - --light-color-background-warning: #e6e600; - --light-color-icon-background: var(--light-color-background); - --light-color-accent: #c5c7c9; - --light-color-active-menu-item: var(--light-color-accent); - --light-color-text: #222; - --light-color-text-aside: #6e6e6e; - --light-color-link: #1f70c2; - --light-color-focus-outline: #3584e4; - - --light-color-ts-keyword: #056bd6; - --light-color-ts-project: #b111c9; - --light-color-ts-module: var(--light-color-ts-project); - --light-color-ts-namespace: var(--light-color-ts-project); - --light-color-ts-enum: #7e6f15; - --light-color-ts-enum-member: var(--light-color-ts-enum); - --light-color-ts-variable: #4760ec; - --light-color-ts-function: #572be7; - --light-color-ts-class: #1f70c2; - --light-color-ts-interface: #108024; - --light-color-ts-constructor: var(--light-color-ts-class); - --light-color-ts-property: var(--light-color-ts-variable); - --light-color-ts-method: var(--light-color-ts-function); - --light-color-ts-call-signature: var(--light-color-ts-method); - --light-color-ts-index-signature: var(--light-color-ts-property); - --light-color-ts-constructor-signature: var(--light-color-ts-constructor); - --light-color-ts-parameter: var(--light-color-ts-variable); - /* type literal not included as links will never be generated to it */ - --light-color-ts-type-parameter: #a55c0e; - --light-color-ts-accessor: var(--light-color-ts-property); - --light-color-ts-get-signature: var(--light-color-ts-accessor); - --light-color-ts-set-signature: var(--light-color-ts-accessor); - --light-color-ts-type-alias: #d51270; - /* reference not included as links will be colored with the kind that it points to */ - --light-color-document: #000000; - - --light-external-icon: url("data:image/svg+xml;utf8,"); - --light-color-scheme: light; - - /* Dark */ - --dark-color-background: #2b2e33; - --dark-color-background-secondary: #1e2024; - --dark-color-background-warning: #bebe00; - --dark-color-warning-text: #222; - --dark-color-icon-background: var(--dark-color-background-secondary); - --dark-color-accent: #9096a2; - --dark-color-active-menu-item: #5d5d6a; - --dark-color-text: #f5f5f5; - --dark-color-text-aside: #dddddd; - --dark-color-link: #00aff4; - --dark-color-focus-outline: #4c97f2; - - --dark-color-ts-keyword: #3399ff; - --dark-color-ts-project: #e358ff; - --dark-color-ts-module: var(--dark-color-ts-project); - --dark-color-ts-namespace: var(--dark-color-ts-project); - --dark-color-ts-enum: #f4d93e; - --dark-color-ts-enum-member: var(--dark-color-ts-enum); - --dark-color-ts-variable: #798dff; - --dark-color-ts-function: #a280ff; - --dark-color-ts-class: #8ac4ff; - --dark-color-ts-interface: #6cff87; - --dark-color-ts-constructor: var(--dark-color-ts-class); - --dark-color-ts-property: var(--dark-color-ts-variable); - --dark-color-ts-method: var(--dark-color-ts-function); - --dark-color-ts-call-signature: var(--dark-color-ts-method); - --dark-color-ts-index-signature: var(--dark-color-ts-property); - --dark-color-ts-constructor-signature: var(--dark-color-ts-constructor); - --dark-color-ts-parameter: var(--dark-color-ts-variable); - /* type literal not included as links will never be generated to it */ - --dark-color-ts-type-parameter: #e07d13; - --dark-color-ts-accessor: var(--dark-color-ts-property); - --dark-color-ts-get-signature: var(--dark-color-ts-accessor); - --dark-color-ts-set-signature: var(--dark-color-ts-accessor); - --dark-color-ts-type-alias: #ff6492; - /* reference not included as links will be colored with the kind that it points to */ - --dark-color-document: #ffffff; - - --dark-external-icon: url("data:image/svg+xml;utf8,"); - --dark-color-scheme: dark; -} - -@media (prefers-color-scheme: light) { +@layer typedoc { :root { + /* Light */ + --light-color-background: #f2f4f8; + --light-color-background-secondary: #eff0f1; + --light-color-warning-text: #222; + --light-color-background-warning: #e6e600; + --light-color-accent: #c5c7c9; + --light-color-active-menu-item: var(--light-color-accent); + --light-color-text: #222; + --light-color-text-aside: #6e6e6e; + + --light-color-icon-background: var(--light-color-background); + --light-color-icon-text: var(--light-color-text); + + --light-color-comment-tag-text: var(--light-color-text); + --light-color-comment-tag: var(--light-color-background); + + --light-color-link: #1f70c2; + --light-color-focus-outline: #3584e4; + + --light-color-ts-keyword: #056bd6; + --light-color-ts-project: #b111c9; + --light-color-ts-module: var(--light-color-ts-project); + --light-color-ts-namespace: var(--light-color-ts-project); + --light-color-ts-enum: #7e6f15; + --light-color-ts-enum-member: var(--light-color-ts-enum); + --light-color-ts-variable: #4760ec; + --light-color-ts-function: #572be7; + --light-color-ts-class: #1f70c2; + --light-color-ts-interface: #108024; + --light-color-ts-constructor: var(--light-color-ts-class); + --light-color-ts-property: #9f5f30; + --light-color-ts-method: #be3989; + --light-color-ts-reference: #ff4d82; + --light-color-ts-call-signature: var(--light-color-ts-method); + --light-color-ts-index-signature: var(--light-color-ts-property); + --light-color-ts-constructor-signature: var( + --light-color-ts-constructor + ); + --light-color-ts-parameter: var(--light-color-ts-variable); + /* type literal not included as links will never be generated to it */ + --light-color-ts-type-parameter: #a55c0e; + --light-color-ts-accessor: #c73c3c; + --light-color-ts-get-signature: var(--light-color-ts-accessor); + --light-color-ts-set-signature: var(--light-color-ts-accessor); + --light-color-ts-type-alias: #d51270; + /* reference not included as links will be colored with the kind that it points to */ + --light-color-document: #000000; + + --light-color-alert-note: #0969d9; + --light-color-alert-tip: #1a7f37; + --light-color-alert-important: #8250df; + --light-color-alert-warning: #9a6700; + --light-color-alert-caution: #cf222e; + + --light-external-icon: url("data:image/svg+xml;utf8,"); + --light-color-scheme: light; + + /* Dark */ + --dark-color-background: #2b2e33; + --dark-color-background-secondary: #1e2024; + --dark-color-background-warning: #bebe00; + --dark-color-warning-text: #222; + --dark-color-accent: #9096a2; + --dark-color-active-menu-item: #5d5d6a; + --dark-color-text: #f5f5f5; + --dark-color-text-aside: #dddddd; + + --dark-color-icon-background: var(--dark-color-background-secondary); + --dark-color-icon-text: var(--dark-color-text); + + --dark-color-comment-tag-text: var(--dark-color-text); + --dark-color-comment-tag: var(--dark-color-background); + + --dark-color-link: #00aff4; + --dark-color-focus-outline: #4c97f2; + + --dark-color-ts-keyword: #3399ff; + --dark-color-ts-project: #e358ff; + --dark-color-ts-module: var(--dark-color-ts-project); + --dark-color-ts-namespace: var(--dark-color-ts-project); + --dark-color-ts-enum: #f4d93e; + --dark-color-ts-enum-member: var(--dark-color-ts-enum); + --dark-color-ts-variable: #798dff; + --dark-color-ts-function: #a280ff; + --dark-color-ts-class: #8ac4ff; + --dark-color-ts-interface: #6cff87; + --dark-color-ts-constructor: var(--dark-color-ts-class); + --dark-color-ts-property: #ff984d; + --dark-color-ts-method: #ff4db8; + --dark-color-ts-reference: #ff4d82; + --dark-color-ts-call-signature: var(--dark-color-ts-method); + --dark-color-ts-index-signature: var(--dark-color-ts-property); + --dark-color-ts-constructor-signature: var(--dark-color-ts-constructor); + --dark-color-ts-parameter: var(--dark-color-ts-variable); + /* type literal not included as links will never be generated to it */ + --dark-color-ts-type-parameter: #e07d13; + --dark-color-ts-accessor: #ff6060; + --dark-color-ts-get-signature: var(--dark-color-ts-accessor); + --dark-color-ts-set-signature: var(--dark-color-ts-accessor); + --dark-color-ts-type-alias: #ff6492; + /* reference not included as links will be colored with the kind that it points to */ + --dark-color-document: #ffffff; + + --dark-color-alert-note: #0969d9; + --dark-color-alert-tip: #1a7f37; + --dark-color-alert-important: #8250df; + --dark-color-alert-warning: #9a6700; + --dark-color-alert-caution: #cf222e; + + --dark-external-icon: url("data:image/svg+xml;utf8,"); + --dark-color-scheme: dark; + } + + @media (prefers-color-scheme: light) { + :root { + --color-background: var(--light-color-background); + --color-background-secondary: var( + --light-color-background-secondary + ); + --color-background-warning: var(--light-color-background-warning); + --color-warning-text: var(--light-color-warning-text); + --color-accent: var(--light-color-accent); + --color-active-menu-item: var(--light-color-active-menu-item); + --color-text: var(--light-color-text); + --color-text-aside: var(--light-color-text-aside); + + --color-icon-background: var(--light-color-icon-background); + --color-icon-text: var(--light-color-icon-text); + + --color-comment-tag-text: var(--light-color-text); + --color-comment-tag: var(--light-color-background); + + --color-link: var(--light-color-link); + --color-focus-outline: var(--light-color-focus-outline); + + --color-ts-keyword: var(--light-color-ts-keyword); + --color-ts-project: var(--light-color-ts-project); + --color-ts-module: var(--light-color-ts-module); + --color-ts-namespace: var(--light-color-ts-namespace); + --color-ts-enum: var(--light-color-ts-enum); + --color-ts-enum-member: var(--light-color-ts-enum-member); + --color-ts-variable: var(--light-color-ts-variable); + --color-ts-function: var(--light-color-ts-function); + --color-ts-class: var(--light-color-ts-class); + --color-ts-interface: var(--light-color-ts-interface); + --color-ts-constructor: var(--light-color-ts-constructor); + --color-ts-property: var(--light-color-ts-property); + --color-ts-method: var(--light-color-ts-method); + --color-ts-reference: var(--light-color-ts-reference); + --color-ts-call-signature: var(--light-color-ts-call-signature); + --color-ts-index-signature: var(--light-color-ts-index-signature); + --color-ts-constructor-signature: var( + --light-color-ts-constructor-signature + ); + --color-ts-parameter: var(--light-color-ts-parameter); + --color-ts-type-parameter: var(--light-color-ts-type-parameter); + --color-ts-accessor: var(--light-color-ts-accessor); + --color-ts-get-signature: var(--light-color-ts-get-signature); + --color-ts-set-signature: var(--light-color-ts-set-signature); + --color-ts-type-alias: var(--light-color-ts-type-alias); + --color-document: var(--light-color-document); + + --color-alert-note: var(--light-color-alert-note); + --color-alert-tip: var(--light-color-alert-tip); + --color-alert-important: var(--light-color-alert-important); + --color-alert-warning: var(--light-color-alert-warning); + --color-alert-caution: var(--light-color-alert-caution); + + --external-icon: var(--light-external-icon); + --color-scheme: var(--light-color-scheme); + } + } + + @media (prefers-color-scheme: dark) { + :root { + --color-background: var(--dark-color-background); + --color-background-secondary: var( + --dark-color-background-secondary + ); + --color-background-warning: var(--dark-color-background-warning); + --color-warning-text: var(--dark-color-warning-text); + --color-accent: var(--dark-color-accent); + --color-active-menu-item: var(--dark-color-active-menu-item); + --color-text: var(--dark-color-text); + --color-text-aside: var(--dark-color-text-aside); + + --color-icon-background: var(--dark-color-icon-background); + --color-icon-text: var(--dark-color-icon-text); + + --color-comment-tag-text: var(--dark-color-text); + --color-comment-tag: var(--dark-color-background); + + --color-link: var(--dark-color-link); + --color-focus-outline: var(--dark-color-focus-outline); + + --color-ts-keyword: var(--dark-color-ts-keyword); + --color-ts-project: var(--dark-color-ts-project); + --color-ts-module: var(--dark-color-ts-module); + --color-ts-namespace: var(--dark-color-ts-namespace); + --color-ts-enum: var(--dark-color-ts-enum); + --color-ts-enum-member: var(--dark-color-ts-enum-member); + --color-ts-variable: var(--dark-color-ts-variable); + --color-ts-function: var(--dark-color-ts-function); + --color-ts-class: var(--dark-color-ts-class); + --color-ts-interface: var(--dark-color-ts-interface); + --color-ts-constructor: var(--dark-color-ts-constructor); + --color-ts-property: var(--dark-color-ts-property); + --color-ts-method: var(--dark-color-ts-method); + --color-ts-reference: var(--dark-color-ts-reference); + --color-ts-call-signature: var(--dark-color-ts-call-signature); + --color-ts-index-signature: var(--dark-color-ts-index-signature); + --color-ts-constructor-signature: var( + --dark-color-ts-constructor-signature + ); + --color-ts-parameter: var(--dark-color-ts-parameter); + --color-ts-type-parameter: var(--dark-color-ts-type-parameter); + --color-ts-accessor: var(--dark-color-ts-accessor); + --color-ts-get-signature: var(--dark-color-ts-get-signature); + --color-ts-set-signature: var(--dark-color-ts-set-signature); + --color-ts-type-alias: var(--dark-color-ts-type-alias); + --color-document: var(--dark-color-document); + + --color-alert-note: var(--dark-color-alert-note); + --color-alert-tip: var(--dark-color-alert-tip); + --color-alert-important: var(--dark-color-alert-important); + --color-alert-warning: var(--dark-color-alert-warning); + --color-alert-caution: var(--dark-color-alert-caution); + + --external-icon: var(--dark-external-icon); + --color-scheme: var(--dark-color-scheme); + } + } + + html { + color-scheme: var(--color-scheme); + } + + body { + margin: 0; + } + + :root[data-theme="light"] { --color-background: var(--light-color-background); --color-background-secondary: var(--light-color-background-secondary); --color-background-warning: var(--light-color-background-warning); @@ -95,10 +251,16 @@ --color-active-menu-item: var(--light-color-active-menu-item); --color-text: var(--light-color-text); --color-text-aside: var(--light-color-text-aside); + --color-icon-text: var(--light-color-icon-text); + + --color-comment-tag-text: var(--light-color-text); + --color-comment-tag: var(--light-color-background); + --color-link: var(--light-color-link); --color-focus-outline: var(--light-color-focus-outline); --color-ts-keyword: var(--light-color-ts-keyword); + --color-ts-project: var(--light-color-ts-project); --color-ts-module: var(--light-color-ts-module); --color-ts-namespace: var(--light-color-ts-namespace); --color-ts-enum: var(--light-color-ts-enum); @@ -110,6 +272,7 @@ --color-ts-constructor: var(--light-color-ts-constructor); --color-ts-property: var(--light-color-ts-property); --color-ts-method: var(--light-color-ts-method); + --color-ts-reference: var(--light-color-ts-reference); --color-ts-call-signature: var(--light-color-ts-call-signature); --color-ts-index-signature: var(--light-color-ts-index-signature); --color-ts-constructor-signature: var( @@ -123,13 +286,17 @@ --color-ts-type-alias: var(--light-color-ts-type-alias); --color-document: var(--light-color-document); + --color-note: var(--light-color-note); + --color-tip: var(--light-color-tip); + --color-important: var(--light-color-important); + --color-warning: var(--light-color-warning); + --color-caution: var(--light-color-caution); + --external-icon: var(--light-external-icon); --color-scheme: var(--light-color-scheme); } -} -@media (prefers-color-scheme: dark) { - :root { + :root[data-theme="dark"] { --color-background: var(--dark-color-background); --color-background-secondary: var(--dark-color-background-secondary); --color-background-warning: var(--dark-color-background-warning); @@ -139,10 +306,16 @@ --color-active-menu-item: var(--dark-color-active-menu-item); --color-text: var(--dark-color-text); --color-text-aside: var(--dark-color-text-aside); + --color-icon-text: var(--dark-color-icon-text); + + --color-comment-tag-text: var(--dark-color-text); + --color-comment-tag: var(--dark-color-background); + --color-link: var(--dark-color-link); --color-focus-outline: var(--dark-color-focus-outline); --color-ts-keyword: var(--dark-color-ts-keyword); + --color-ts-project: var(--dark-color-ts-project); --color-ts-module: var(--dark-color-ts-module); --color-ts-namespace: var(--dark-color-ts-namespace); --color-ts-enum: var(--dark-color-ts-enum); @@ -154,6 +327,7 @@ --color-ts-constructor: var(--dark-color-ts-constructor); --color-ts-property: var(--dark-color-ts-property); --color-ts-method: var(--dark-color-ts-method); + --color-ts-reference: var(--dark-color-ts-reference); --color-ts-call-signature: var(--dark-color-ts-call-signature); --color-ts-index-signature: var(--dark-color-ts-index-signature); --color-ts-constructor-signature: var( @@ -167,1282 +341,1270 @@ --color-ts-type-alias: var(--dark-color-ts-type-alias); --color-document: var(--dark-color-document); + --color-note: var(--dark-color-note); + --color-tip: var(--dark-color-tip); + --color-important: var(--dark-color-important); + --color-warning: var(--dark-color-warning); + --color-caution: var(--dark-color-caution); + --external-icon: var(--dark-external-icon); --color-scheme: var(--dark-color-scheme); } -} -html { - color-scheme: var(--color-scheme); -} - -body { - margin: 0; -} - -:root[data-theme="light"] { - --color-background: var(--light-color-background); - --color-background-secondary: var(--light-color-background-secondary); - --color-background-warning: var(--light-color-background-warning); - --color-warning-text: var(--light-color-warning-text); - --color-icon-background: var(--light-color-icon-background); - --color-accent: var(--light-color-accent); - --color-active-menu-item: var(--light-color-active-menu-item); - --color-text: var(--light-color-text); - --color-text-aside: var(--light-color-text-aside); - --color-link: var(--light-color-link); - --color-focus-outline: var(--light-color-focus-outline); - - --color-ts-keyword: var(--light-color-ts-keyword); - --color-ts-module: var(--light-color-ts-module); - --color-ts-namespace: var(--light-color-ts-namespace); - --color-ts-enum: var(--light-color-ts-enum); - --color-ts-enum-member: var(--light-color-ts-enum-member); - --color-ts-variable: var(--light-color-ts-variable); - --color-ts-function: var(--light-color-ts-function); - --color-ts-class: var(--light-color-ts-class); - --color-ts-interface: var(--light-color-ts-interface); - --color-ts-constructor: var(--light-color-ts-constructor); - --color-ts-property: var(--light-color-ts-property); - --color-ts-method: var(--light-color-ts-method); - --color-ts-call-signature: var(--light-color-ts-call-signature); - --color-ts-index-signature: var(--light-color-ts-index-signature); - --color-ts-constructor-signature: var( - --light-color-ts-constructor-signature - ); - --color-ts-parameter: var(--light-color-ts-parameter); - --color-ts-type-parameter: var(--light-color-ts-type-parameter); - --color-ts-accessor: var(--light-color-ts-accessor); - --color-ts-get-signature: var(--light-color-ts-get-signature); - --color-ts-set-signature: var(--light-color-ts-set-signature); - --color-ts-type-alias: var(--light-color-ts-type-alias); - --color-document: var(--light-color-document); - - --external-icon: var(--light-external-icon); - --color-scheme: var(--light-color-scheme); -} - -:root[data-theme="dark"] { - --color-background: var(--dark-color-background); - --color-background-secondary: var(--dark-color-background-secondary); - --color-background-warning: var(--dark-color-background-warning); - --color-warning-text: var(--dark-color-warning-text); - --color-icon-background: var(--dark-color-icon-background); - --color-accent: var(--dark-color-accent); - --color-active-menu-item: var(--dark-color-active-menu-item); - --color-text: var(--dark-color-text); - --color-text-aside: var(--dark-color-text-aside); - --color-link: var(--dark-color-link); - --color-focus-outline: var(--dark-color-focus-outline); - - --color-ts-keyword: var(--dark-color-ts-keyword); - --color-ts-module: var(--dark-color-ts-module); - --color-ts-namespace: var(--dark-color-ts-namespace); - --color-ts-enum: var(--dark-color-ts-enum); - --color-ts-enum-member: var(--dark-color-ts-enum-member); - --color-ts-variable: var(--dark-color-ts-variable); - --color-ts-function: var(--dark-color-ts-function); - --color-ts-class: var(--dark-color-ts-class); - --color-ts-interface: var(--dark-color-ts-interface); - --color-ts-constructor: var(--dark-color-ts-constructor); - --color-ts-property: var(--dark-color-ts-property); - --color-ts-method: var(--dark-color-ts-method); - --color-ts-call-signature: var(--dark-color-ts-call-signature); - --color-ts-index-signature: var(--dark-color-ts-index-signature); - --color-ts-constructor-signature: var( - --dark-color-ts-constructor-signature - ); - --color-ts-parameter: var(--dark-color-ts-parameter); - --color-ts-type-parameter: var(--dark-color-ts-type-parameter); - --color-ts-accessor: var(--dark-color-ts-accessor); - --color-ts-get-signature: var(--dark-color-ts-get-signature); - --color-ts-set-signature: var(--dark-color-ts-set-signature); - --color-ts-type-alias: var(--dark-color-ts-type-alias); - --color-document: var(--dark-color-document); - - --external-icon: var(--dark-external-icon); - --color-scheme: var(--dark-color-scheme); -} - -*:focus-visible, -.tsd-accordion-summary:focus-visible svg { - outline: 2px solid var(--color-focus-outline); -} - -.always-visible, -.always-visible .tsd-signatures { - display: inherit !important; -} - -h1, -h2, -h3, -h4, -h5, -h6 { - line-height: 1.2; -} - -h1 { - font-size: 1.875rem; - margin: 0.67rem 0; -} - -h2 { - font-size: 1.5rem; - margin: 0.83rem 0; -} - -h3 { - font-size: 1.25rem; - margin: 1rem 0; -} - -h4 { - font-size: 1.05rem; - margin: 1.33rem 0; -} - -h5 { - font-size: 1rem; - margin: 1.5rem 0; -} - -h6 { - font-size: 0.875rem; - margin: 2.33rem 0; -} - -dl, -menu, -ol, -ul { - margin: 1em 0; -} - -dd { - margin: 0 0 0 40px; -} - -.container { - max-width: 1700px; - padding: 0 2rem; -} - -/* Footer */ -footer { - border-top: 1px solid var(--color-accent); - padding-top: 1rem; - padding-bottom: 1rem; - max-height: 3.5rem; -} -footer > p { - margin: 0 1em; -} - -.container-main { - margin: 0 auto; - /* toolbar, footer, margin */ - min-height: calc(100vh - 41px - 56px - 4rem); -} - -@keyframes fade-in { - from { - opacity: 0; + *:focus-visible, + .tsd-accordion-summary:focus-visible svg { + outline: 2px solid var(--color-focus-outline); } - to { + + .always-visible, + .always-visible .tsd-signatures { + display: inherit !important; + } + + h1, + h2, + h3, + h4, + h5, + h6 { + line-height: 1.2; + } + + h1 { + font-size: 1.875rem; + margin: 0.67rem 0; + } + + h2 { + font-size: 1.5rem; + margin: 0.83rem 0; + } + + h3 { + font-size: 1.25rem; + margin: 1rem 0; + } + + h4 { + font-size: 1.05rem; + margin: 1.33rem 0; + } + + h5 { + font-size: 1rem; + margin: 1.5rem 0; + } + + h6 { + font-size: 0.875rem; + margin: 2.33rem 0; + } + + dl, + menu, + ol, + ul { + margin: 1em 0; + } + + dd { + margin: 0 0 0 34px; + } + + .container { + max-width: 1700px; + padding: 0 2rem; + } + + /* Footer */ + footer { + border-top: 1px solid var(--color-accent); + padding-top: 1rem; + padding-bottom: 1rem; + max-height: 3.5rem; + } + footer > p { + margin: 0 1em; + } + + .container-main { + margin: 0 auto; + /* toolbar, footer, margin */ + min-height: calc(100vh - 41px - 56px - 4rem); + } + + @keyframes fade-in { + from { + opacity: 0; + } + to { + opacity: 1; + } + } + @keyframes fade-out { + from { + opacity: 1; + visibility: visible; + } + to { + opacity: 0; + } + } + @keyframes fade-in-delayed { + 0% { + opacity: 0; + } + 33% { + opacity: 0; + } + 100% { + opacity: 1; + } + } + @keyframes fade-out-delayed { + 0% { + opacity: 1; + visibility: visible; + } + 66% { + opacity: 0; + } + 100% { + opacity: 0; + } + } + @keyframes pop-in-from-right { + from { + transform: translate(100%, 0); + } + to { + transform: translate(0, 0); + } + } + @keyframes pop-out-to-right { + from { + transform: translate(0, 0); + visibility: visible; + } + to { + transform: translate(100%, 0); + } + } + body { + background: var(--color-background); + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", + Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; + font-size: 16px; + color: var(--color-text); + } + + a { + color: var(--color-link); + text-decoration: none; + } + a:hover { + text-decoration: underline; + } + a.external[target="_blank"] { + background-image: var(--external-icon); + background-position: top 3px right; + background-repeat: no-repeat; + padding-right: 13px; + } + a.tsd-anchor-link { + color: var(--color-text); + } + + code, + pre { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + padding: 0.2em; + margin: 0; + font-size: 0.875rem; + border-radius: 0.8em; + } + + pre { + position: relative; + white-space: pre-wrap; + word-wrap: break-word; + padding: 10px; + border: 1px solid var(--color-accent); + margin-bottom: 8px; + } + pre code { + padding: 0; + font-size: 100%; + } + pre > button { + position: absolute; + top: 10px; + right: 10px; + opacity: 0; + transition: opacity 0.1s; + box-sizing: border-box; + } + pre:hover > button, + pre > button.visible { opacity: 1; } -} -@keyframes fade-out { - from { - opacity: 1; - visibility: visible; + + blockquote { + margin: 1em 0; + padding-left: 1em; + border-left: 4px solid gray; } - to { + + .tsd-typography { + line-height: 1.333em; + } + .tsd-typography ul { + list-style: square; + padding: 0 0 0 20px; + margin: 0; + } + .tsd-typography .tsd-index-panel h3, + .tsd-index-panel .tsd-typography h3, + .tsd-typography h4, + .tsd-typography h5, + .tsd-typography h6 { + font-size: 1em; + } + .tsd-typography h5, + .tsd-typography h6 { + font-weight: normal; + } + .tsd-typography p, + .tsd-typography ul, + .tsd-typography ol { + margin: 1em 0; + } + .tsd-typography table { + border-collapse: collapse; + border: none; + } + .tsd-typography td, + .tsd-typography th { + padding: 6px 13px; + border: 1px solid var(--color-accent); + } + .tsd-typography thead, + .tsd-typography tr:nth-child(even) { + background-color: var(--color-background-secondary); + } + + .tsd-alert { + padding: 8px 16px; + margin-bottom: 16px; + border-left: 0.25em solid var(--alert-color); + } + .tsd-alert blockquote > :last-child, + .tsd-alert > :last-child { + margin-bottom: 0; + } + .tsd-alert-title { + color: var(--alert-color); + display: inline-flex; + align-items: center; + } + .tsd-alert-title span { + margin-left: 4px; + } + + .tsd-alert-note { + --alert-color: var(--color-alert-note); + } + .tsd-alert-tip { + --alert-color: var(--color-alert-tip); + } + .tsd-alert-important { + --alert-color: var(--color-alert-important); + } + .tsd-alert-warning { + --alert-color: var(--color-alert-warning); + } + .tsd-alert-caution { + --alert-color: var(--color-alert-caution); + } + + .tsd-breadcrumb { + margin: 0; + padding: 0; + color: var(--color-text-aside); + } + .tsd-breadcrumb a { + color: var(--color-text-aside); + text-decoration: none; + } + .tsd-breadcrumb a:hover { + text-decoration: underline; + } + .tsd-breadcrumb li { + display: inline; + } + .tsd-breadcrumb li:after { + content: " / "; + } + + .tsd-comment-tags { + display: flex; + flex-direction: column; + } + dl.tsd-comment-tag-group { + display: flex; + align-items: center; + overflow: hidden; + margin: 0.5em 0; + } + dl.tsd-comment-tag-group dt { + display: flex; + margin-right: 0.5em; + font-size: 0.875em; + font-weight: normal; + } + dl.tsd-comment-tag-group dd { + margin: 0; + } + code.tsd-tag { + padding: 0.25em 0.4em; + border: 0.1em solid var(--color-accent); + margin-right: 0.25em; + font-size: 70%; + } + h1 code.tsd-tag:first-of-type { + margin-left: 0.25em; + } + + dl.tsd-comment-tag-group dd:before, + dl.tsd-comment-tag-group dd:after { + content: " "; + } + dl.tsd-comment-tag-group dd pre, + dl.tsd-comment-tag-group dd:after { + clear: both; + } + dl.tsd-comment-tag-group p { + margin: 0; + } + + .tsd-panel.tsd-comment .lead { + font-size: 1.1em; + line-height: 1.333em; + margin-bottom: 2em; + } + .tsd-panel.tsd-comment .lead:last-child { + margin-bottom: 0; + } + + .tsd-filter-visibility h4 { + font-size: 1rem; + padding-top: 0.75rem; + padding-bottom: 0.5rem; + margin: 0; + } + .tsd-filter-item:not(:last-child) { + margin-bottom: 0.5rem; + } + .tsd-filter-input { + display: flex; + width: -moz-fit-content; + width: fit-content; + align-items: center; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + cursor: pointer; + } + .tsd-filter-input input[type="checkbox"] { + cursor: pointer; + position: absolute; + width: 1.5em; + height: 1.5em; opacity: 0; } -} -@keyframes fade-in-delayed { - 0% { - opacity: 0; + .tsd-filter-input input[type="checkbox"]:disabled { + pointer-events: none; } - 33% { - opacity: 0; - } - 100% { - opacity: 1; - } -} -@keyframes fade-out-delayed { - 0% { - opacity: 1; - visibility: visible; - } - 66% { - opacity: 0; - } - 100% { - opacity: 0; - } -} -@keyframes pop-in-from-right { - from { - transform: translate(100%, 0); - } - to { - transform: translate(0, 0); - } -} -@keyframes pop-out-to-right { - from { - transform: translate(0, 0); - visibility: visible; - } - to { - transform: translate(100%, 0); - } -} -body { - background: var(--color-background); - font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", - Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; - font-size: 16px; - color: var(--color-text); -} - -a { - color: var(--color-link); - text-decoration: none; -} -a:hover { - text-decoration: underline; -} -a.external[target="_blank"] { - background-image: var(--external-icon); - background-position: top 3px right; - background-repeat: no-repeat; - padding-right: 13px; -} -a.tsd-anchor-link { - color: var(--color-text); -} - -code, -pre { - font-family: Menlo, Monaco, Consolas, "Courier New", monospace; - padding: 0.2em; - margin: 0; - font-size: 0.875rem; - border-radius: 0.8em; -} - -pre { - position: relative; - white-space: pre; - white-space: pre-wrap; - word-wrap: break-word; - padding: 10px; - border: 1px solid var(--color-accent); -} -pre code { - padding: 0; - font-size: 100%; -} -pre > button { - position: absolute; - top: 10px; - right: 10px; - opacity: 0; - transition: opacity 0.1s; - box-sizing: border-box; -} -pre:hover > button, -pre > button.visible { - opacity: 1; -} - -blockquote { - margin: 1em 0; - padding-left: 1em; - border-left: 4px solid gray; -} - -.tsd-typography { - line-height: 1.333em; -} -.tsd-typography ul { - list-style: square; - padding: 0 0 0 20px; - margin: 0; -} -.tsd-typography .tsd-index-panel h3, -.tsd-index-panel .tsd-typography h3, -.tsd-typography h4, -.tsd-typography h5, -.tsd-typography h6 { - font-size: 1em; -} -.tsd-typography h5, -.tsd-typography h6 { - font-weight: normal; -} -.tsd-typography p, -.tsd-typography ul, -.tsd-typography ol { - margin: 1em 0; -} -.tsd-typography table { - border-collapse: collapse; - border: none; -} -.tsd-typography td, -.tsd-typography th { - padding: 6px 13px; - border: 1px solid var(--color-accent); -} -.tsd-typography thead, -.tsd-typography tr:nth-child(even) { - background-color: var(--color-background-secondary); -} - -.tsd-breadcrumb { - margin: 0; - padding: 0; - color: var(--color-text-aside); -} -.tsd-breadcrumb a { - color: var(--color-text-aside); - text-decoration: none; -} -.tsd-breadcrumb a:hover { - text-decoration: underline; -} -.tsd-breadcrumb li { - display: inline; -} -.tsd-breadcrumb li:after { - content: " / "; -} - -.tsd-comment-tags { - display: flex; - flex-direction: column; -} -dl.tsd-comment-tag-group { - display: flex; - align-items: center; - overflow: hidden; - margin: 0.5em 0; -} -dl.tsd-comment-tag-group dt { - display: flex; - margin-right: 0.5em; - font-size: 0.875em; - font-weight: normal; -} -dl.tsd-comment-tag-group dd { - margin: 0; -} -code.tsd-tag { - padding: 0.25em 0.4em; - border: 0.1em solid var(--color-accent); - margin-right: 0.25em; - font-size: 70%; -} -h1 code.tsd-tag:first-of-type { - margin-left: 0.25em; -} - -dl.tsd-comment-tag-group dd:before, -dl.tsd-comment-tag-group dd:after { - content: " "; -} -dl.tsd-comment-tag-group dd pre, -dl.tsd-comment-tag-group dd:after { - clear: both; -} -dl.tsd-comment-tag-group p { - margin: 0; -} - -.tsd-panel.tsd-comment .lead { - font-size: 1.1em; - line-height: 1.333em; - margin-bottom: 2em; -} -.tsd-panel.tsd-comment .lead:last-child { - margin-bottom: 0; -} - -.tsd-filter-visibility h4 { - font-size: 1rem; - padding-top: 0.75rem; - padding-bottom: 0.5rem; - margin: 0; -} -.tsd-filter-item:not(:last-child) { - margin-bottom: 0.5rem; -} -.tsd-filter-input { - display: flex; - width: -moz-fit-content; - width: fit-content; - align-items: center; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - cursor: pointer; -} -.tsd-filter-input input[type="checkbox"] { - cursor: pointer; - position: absolute; - width: 1.5em; - height: 1.5em; - opacity: 0; -} -.tsd-filter-input input[type="checkbox"]:disabled { - pointer-events: none; -} -.tsd-filter-input svg { - cursor: pointer; - width: 1.5em; - height: 1.5em; - margin-right: 0.5em; - border-radius: 0.33em; - /* Leaving this at full opacity breaks event listeners on Firefox. + .tsd-filter-input svg { + cursor: pointer; + width: 1.5em; + height: 1.5em; + margin-right: 0.5em; + border-radius: 0.33em; + /* Leaving this at full opacity breaks event listeners on Firefox. Don't remove unless you know what you're doing. */ - opacity: 0.99; -} -.tsd-filter-input input[type="checkbox"]:focus-visible + svg { - outline: 2px solid var(--color-focus-outline); -} -.tsd-checkbox-background { - fill: var(--color-accent); -} -input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { - stroke: var(--color-text); -} -.tsd-filter-input input:disabled ~ svg > .tsd-checkbox-background { - fill: var(--color-background); - stroke: var(--color-accent); - stroke-width: 0.25rem; -} -.tsd-filter-input input:disabled ~ svg > .tsd-checkbox-checkmark { - stroke: var(--color-accent); -} - -.settings-label { - font-weight: bold; - text-transform: uppercase; - display: inline-block; -} - -.tsd-filter-visibility .settings-label { - margin: 0.75rem 0 0.5rem 0; -} - -.tsd-theme-toggle .settings-label { - margin: 0.75rem 0.75rem 0 0; -} - -.tsd-hierarchy { - list-style: square; - margin: 0; -} -.tsd-hierarchy .target { - font-weight: bold; -} - -.tsd-full-hierarchy:not(:last-child) { - margin-bottom: 1em; - padding-bottom: 1em; - border-bottom: 1px solid var(--color-accent); -} -.tsd-full-hierarchy, -.tsd-full-hierarchy ul { - list-style: none; - margin: 0; - padding: 0; -} -.tsd-full-hierarchy ul { - padding-left: 1.5rem; -} -.tsd-full-hierarchy a { - padding: 0.25rem 0 !important; - font-size: 1rem; - display: inline-flex; - align-items: center; - color: var(--color-text); -} - -.tsd-panel-group.tsd-index-group { - margin-bottom: 0; -} -.tsd-index-panel .tsd-index-list { - list-style: none; - line-height: 1.333em; - margin: 0; - padding: 0.25rem 0 0 0; - overflow: hidden; - display: grid; - grid-template-columns: repeat(3, 1fr); - column-gap: 1rem; - grid-template-rows: auto; -} -@media (max-width: 1024px) { - .tsd-index-panel .tsd-index-list { - grid-template-columns: repeat(2, 1fr); + opacity: 0.99; } -} -@media (max-width: 768px) { - .tsd-index-panel .tsd-index-list { - grid-template-columns: repeat(1, 1fr); + .tsd-filter-input input[type="checkbox"]:focus-visible + svg { + outline: 2px solid var(--color-focus-outline); + } + .tsd-checkbox-background { + fill: var(--color-accent); + } + input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark { + stroke: var(--color-text); + } + .tsd-filter-input input:disabled ~ svg > .tsd-checkbox-background { + fill: var(--color-background); + stroke: var(--color-accent); + stroke-width: 0.25rem; + } + .tsd-filter-input input:disabled ~ svg > .tsd-checkbox-checkmark { + stroke: var(--color-accent); } -} -.tsd-index-panel .tsd-index-list li { - -webkit-page-break-inside: avoid; - -moz-page-break-inside: avoid; - -ms-page-break-inside: avoid; - -o-page-break-inside: avoid; - page-break-inside: avoid; -} -.tsd-flag { - display: inline-block; - padding: 0.25em 0.4em; - border-radius: 4px; - color: var(--color-comment-tag-text); - background-color: var(--color-comment-tag); - text-indent: 0; - font-size: 75%; - line-height: 1; - font-weight: normal; -} - -.tsd-anchor { - position: relative; - top: -100px; -} - -.tsd-member { - position: relative; -} -.tsd-member .tsd-anchor + h3 { - display: flex; - align-items: center; - margin-top: 0; - margin-bottom: 0; - border-bottom: none; -} - -.tsd-navigation.settings { - margin: 1rem 0; -} -.tsd-navigation > a, -.tsd-navigation .tsd-accordion-summary { - width: calc(100% - 0.25rem); - display: flex; - align-items: center; -} -.tsd-navigation a, -.tsd-navigation summary > span, -.tsd-page-navigation a { - display: flex; - width: calc(100% - 0.25rem); - align-items: center; - padding: 0.25rem; - color: var(--color-text); - text-decoration: none; - box-sizing: border-box; -} -.tsd-navigation a.current, -.tsd-page-navigation a.current { - background: var(--color-active-menu-item); -} -.tsd-navigation a:hover, -.tsd-page-navigation a:hover { - text-decoration: underline; -} -.tsd-navigation ul, -.tsd-page-navigation ul { - margin-top: 0; - margin-bottom: 0; - padding: 0; - list-style: none; -} -.tsd-navigation li, -.tsd-page-navigation li { - padding: 0; - max-width: 100%; -} -.tsd-navigation .tsd-nav-link { - display: none; -} -.tsd-nested-navigation { - margin-left: 3rem; -} -.tsd-nested-navigation > li > details { - margin-left: -1.5rem; -} -.tsd-small-nested-navigation { - margin-left: 1.5rem; -} -.tsd-small-nested-navigation > li > details { - margin-left: -1.5rem; -} - -.tsd-page-navigation-section { - margin-left: 10px; -} -.tsd-page-navigation-section > summary { - padding: 0.25rem; -} -.tsd-page-navigation-section > div { - margin-left: 20px; -} -.tsd-page-navigation ul { - padding-left: 1.75rem; -} - -#tsd-sidebar-links a { - margin-top: 0; - margin-bottom: 0.5rem; - line-height: 1.25rem; -} -#tsd-sidebar-links a:last-of-type { - margin-bottom: 0; -} - -a.tsd-index-link { - padding: 0.25rem 0 !important; - font-size: 1rem; - line-height: 1.25rem; - display: inline-flex; - align-items: center; - color: var(--color-text); -} -.tsd-accordion-summary { - list-style-type: none; /* hide marker on non-safari */ - outline: none; /* broken on safari, so just hide it */ -} -.tsd-accordion-summary::-webkit-details-marker { - display: none; /* hide marker on safari */ -} -.tsd-accordion-summary, -.tsd-accordion-summary a { - -moz-user-select: none; - -webkit-user-select: none; - -ms-user-select: none; - user-select: none; - - cursor: pointer; -} -.tsd-accordion-summary a { - width: calc(100% - 1.5rem); -} -.tsd-accordion-summary > * { - margin-top: 0; - margin-bottom: 0; - padding-top: 0; - padding-bottom: 0; -} -.tsd-accordion .tsd-accordion-summary > svg { - margin-left: 0.25rem; - vertical-align: text-top; -} -.tsd-index-content > :not(:first-child) { - margin-top: 0.75rem; -} -.tsd-index-heading { - margin-top: 1.5rem; - margin-bottom: 0.75rem; -} - -.tsd-kind-icon { - margin-right: 0.5rem; - width: 1.25rem; - height: 1.25rem; - min-width: 1.25rem; - min-height: 1.25rem; -} -.tsd-kind-icon path { - transform-origin: center; - transform: scale(1.1); -} -.tsd-signature > .tsd-kind-icon { - margin-right: 0.8rem; -} - -.tsd-panel { - margin-bottom: 2.5rem; -} -.tsd-panel.tsd-member { - margin-bottom: 4rem; -} -.tsd-panel:empty { - display: none; -} -.tsd-panel > h1, -.tsd-panel > h2, -.tsd-panel > h3 { - margin: 1.5rem -1.5rem 0.75rem -1.5rem; - padding: 0 1.5rem 0.75rem 1.5rem; -} -.tsd-panel > h1.tsd-before-signature, -.tsd-panel > h2.tsd-before-signature, -.tsd-panel > h3.tsd-before-signature { - margin-bottom: 0; - border-bottom: none; -} - -.tsd-panel-group { - margin: 2rem 0; -} -.tsd-panel-group.tsd-index-group { - margin: 2rem 0; -} -.tsd-panel-group.tsd-index-group details { - margin: 2rem 0; -} -.tsd-panel-group > .tsd-accordion-summary { - margin-bottom: 1rem; -} - -#tsd-search { - transition: background-color 0.2s; -} -#tsd-search .title { - position: relative; - z-index: 2; -} -#tsd-search .field { - position: absolute; - left: 0; - top: 0; - right: 2.5rem; - height: 100%; -} -#tsd-search .field input { - box-sizing: border-box; - position: relative; - top: -50px; - z-index: 1; - width: 100%; - padding: 0 10px; - opacity: 0; - outline: 0; - border: 0; - background: transparent; - color: var(--color-text); -} -#tsd-search .field label { - position: absolute; - overflow: hidden; - right: -40px; -} -#tsd-search .field input, -#tsd-search .title, -#tsd-toolbar-links a { - transition: opacity 0.2s; -} -#tsd-search .results { - position: absolute; - visibility: hidden; - top: 40px; - width: 100%; - margin: 0; - padding: 0; - list-style: none; - box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); -} -#tsd-search .results li { - background-color: var(--color-background); - line-height: initial; - padding: 4px; -} -#tsd-search .results li:nth-child(even) { - background-color: var(--color-background-secondary); -} -#tsd-search .results li.state { - display: none; -} -#tsd-search .results li.current:not(.no-results), -#tsd-search .results li:hover:not(.no-results) { - background-color: var(--color-accent); -} -#tsd-search .results a { - display: flex; - align-items: center; - padding: 0.25rem; - box-sizing: border-box; -} -#tsd-search .results a:before { - top: 10px; -} -#tsd-search .results span.parent { - color: var(--color-text-aside); - font-weight: normal; -} -#tsd-search.has-focus { - background-color: var(--color-accent); -} -#tsd-search.has-focus .field input { - top: 0; - opacity: 1; -} -#tsd-search.has-focus .title, -#tsd-search.has-focus #tsd-toolbar-links a { - z-index: 0; - opacity: 0; -} -#tsd-search.has-focus .results { - visibility: visible; -} -#tsd-search.loading .results li.state.loading { - display: block; -} -#tsd-search.failure .results li.state.failure { - display: block; -} - -#tsd-toolbar-links { - position: absolute; - top: 0; - right: 2rem; - height: 100%; - display: flex; - align-items: center; - justify-content: flex-end; -} -#tsd-toolbar-links a { - margin-left: 1.5rem; -} -#tsd-toolbar-links a:hover { - text-decoration: underline; -} - -.tsd-signature { - margin: 0 0 1rem 0; - padding: 1rem 0.5rem; - border: 1px solid var(--color-accent); - font-family: Menlo, Monaco, Consolas, "Courier New", monospace; - font-size: 14px; - overflow-x: auto; -} - -.tsd-signature-keyword { - color: var(--color-ts-keyword); - font-weight: normal; -} - -.tsd-signature-symbol { - color: var(--color-text-aside); - font-weight: normal; -} - -.tsd-signature-type { - font-style: italic; - font-weight: normal; -} - -.tsd-signatures { - padding: 0; - margin: 0 0 1em 0; - list-style-type: none; -} -.tsd-signatures .tsd-signature { - margin: 0; - border-color: var(--color-accent); - border-width: 1px 0; - transition: background-color 0.1s; -} -.tsd-signatures .tsd-index-signature:not(:last-child) { - margin-bottom: 1em; -} -.tsd-signatures .tsd-index-signature .tsd-signature { - border-width: 1px; -} -.tsd-description .tsd-signatures .tsd-signature { - border-width: 1px; -} - -ul.tsd-parameter-list, -ul.tsd-type-parameter-list { - list-style: square; - margin: 0; - padding-left: 20px; -} -ul.tsd-parameter-list > li.tsd-parameter-signature, -ul.tsd-type-parameter-list > li.tsd-parameter-signature { - list-style: none; - margin-left: -20px; -} -ul.tsd-parameter-list h5, -ul.tsd-type-parameter-list h5 { - font-size: 16px; - margin: 1em 0 0.5em 0; -} -.tsd-sources { - margin-top: 1rem; - font-size: 0.875em; -} -.tsd-sources a { - color: var(--color-text-aside); - text-decoration: underline; -} -.tsd-sources ul { - list-style: none; - padding: 0; -} - -.tsd-page-toolbar { - position: sticky; - z-index: 1; - top: 0; - left: 0; - width: 100%; - color: var(--color-text); - background: var(--color-background-secondary); - border-bottom: 1px var(--color-accent) solid; - transition: transform 0.3s ease-in-out; -} -.tsd-page-toolbar a { - color: var(--color-text); - text-decoration: none; -} -.tsd-page-toolbar a.title { - font-weight: bold; -} -.tsd-page-toolbar a.title:hover { - text-decoration: underline; -} -.tsd-page-toolbar .tsd-toolbar-contents { - display: flex; - justify-content: space-between; - height: 2.5rem; - margin: 0 auto; -} -.tsd-page-toolbar .table-cell { - position: relative; - white-space: nowrap; - line-height: 40px; -} -.tsd-page-toolbar .table-cell:first-child { - width: 100%; -} -.tsd-page-toolbar .tsd-toolbar-icon { - box-sizing: border-box; - line-height: 0; - padding: 12px 0; -} - -.tsd-widget { - display: inline-block; - overflow: hidden; - opacity: 0.8; - height: 40px; - transition: - opacity 0.1s, - background-color 0.2s; - vertical-align: bottom; - cursor: pointer; -} -.tsd-widget:hover { - opacity: 0.9; -} -.tsd-widget.active { - opacity: 1; - background-color: var(--color-accent); -} -.tsd-widget.no-caption { - width: 40px; -} -.tsd-widget.no-caption:before { - margin: 0; -} - -.tsd-widget.options, -.tsd-widget.menu { - display: none; -} -input[type="checkbox"] + .tsd-widget:before { - background-position: -120px 0; -} -input[type="checkbox"]:checked + .tsd-widget:before { - background-position: -160px 0; -} - -img { - max-width: 100%; -} - -.tsd-anchor-icon { - display: inline-flex; - align-items: center; - margin-left: 0.5rem; - vertical-align: middle; - color: var(--color-text); -} - -.tsd-anchor-icon svg { - width: 1em; - height: 1em; - visibility: hidden; -} - -.tsd-anchor-link:hover > .tsd-anchor-icon svg { - visibility: visible; -} - -.deprecated { - text-decoration: line-through !important; -} - -.warning { - padding: 1rem; - color: var(--color-warning-text); - background: var(--color-background-warning); -} - -.tsd-kind-project { - color: var(--color-ts-project); -} -.tsd-kind-module { - color: var(--color-ts-module); -} -.tsd-kind-namespace { - color: var(--color-ts-namespace); -} -.tsd-kind-enum { - color: var(--color-ts-enum); -} -.tsd-kind-enum-member { - color: var(--color-ts-enum-member); -} -.tsd-kind-variable { - color: var(--color-ts-variable); -} -.tsd-kind-function { - color: var(--color-ts-function); -} -.tsd-kind-class { - color: var(--color-ts-class); -} -.tsd-kind-interface { - color: var(--color-ts-interface); -} -.tsd-kind-constructor { - color: var(--color-ts-constructor); -} -.tsd-kind-property { - color: var(--color-ts-property); -} -.tsd-kind-method { - color: var(--color-ts-method); -} -.tsd-kind-call-signature { - color: var(--color-ts-call-signature); -} -.tsd-kind-index-signature { - color: var(--color-ts-index-signature); -} -.tsd-kind-constructor-signature { - color: var(--color-ts-constructor-signature); -} -.tsd-kind-parameter { - color: var(--color-ts-parameter); -} -.tsd-kind-type-literal { - color: var(--color-ts-type-literal); -} -.tsd-kind-type-parameter { - color: var(--color-ts-type-parameter); -} -.tsd-kind-accessor { - color: var(--color-ts-accessor); -} -.tsd-kind-get-signature { - color: var(--color-ts-get-signature); -} -.tsd-kind-set-signature { - color: var(--color-ts-set-signature); -} -.tsd-kind-type-alias { - color: var(--color-ts-type-alias); -} - -/* if we have a kind icon, don't color the text by kind */ -.tsd-kind-icon ~ span { - color: var(--color-text); -} - -* { - scrollbar-width: thin; - scrollbar-color: var(--color-accent) var(--color-icon-background); -} - -*::-webkit-scrollbar { - width: 0.75rem; -} - -*::-webkit-scrollbar-track { - background: var(--color-icon-background); -} - -*::-webkit-scrollbar-thumb { - background-color: var(--color-accent); - border-radius: 999rem; - border: 0.25rem solid var(--color-icon-background); -} - -/* mobile */ -@media (max-width: 769px) { - .tsd-widget.options, - .tsd-widget.menu { + .settings-label { + font-weight: bold; + text-transform: uppercase; display: inline-block; } - .container-main { - display: flex; - } - html .col-content { - float: none; - max-width: 100%; - width: 100%; - } - html .col-sidebar { - position: fixed !important; - overflow-y: auto; - -webkit-overflow-scrolling: touch; - z-index: 1024; - top: 0 !important; - bottom: 0 !important; - left: auto !important; - right: 0 !important; - padding: 1.5rem 1.5rem 0 0; - width: 75vw; - visibility: hidden; - background-color: var(--color-background); - transform: translate(100%, 0); - } - html .col-sidebar > *:last-child { - padding-bottom: 20px; - } - html .overlay { - content: ""; - display: block; - position: fixed; - z-index: 1023; - top: 0; - left: 0; - right: 0; - bottom: 0; - background-color: rgba(0, 0, 0, 0.75); - visibility: hidden; + .tsd-filter-visibility .settings-label { + margin: 0.75rem 0 0.5rem 0; } - .to-has-menu .overlay { - animation: fade-in 0.4s; + .tsd-theme-toggle .settings-label { + margin: 0.75rem 0.75rem 0 0; } - .to-has-menu .col-sidebar { - animation: pop-in-from-right 0.4s; + .tsd-hierarchy h4 label:hover span { + text-decoration: underline; } - .from-has-menu .overlay { - animation: fade-out 0.4s; + .tsd-hierarchy { + list-style: square; + margin: 0; + } + .tsd-hierarchy-target { + font-weight: bold; + } + .tsd-hierarchy-toggle { + color: var(--color-link); + cursor: pointer; } - .from-has-menu .col-sidebar { - animation: pop-out-to-right 0.4s; + .tsd-full-hierarchy:not(:last-child) { + margin-bottom: 1em; + padding-bottom: 1em; + border-bottom: 1px solid var(--color-accent); } - - .has-menu body { - overflow: hidden; + .tsd-full-hierarchy, + .tsd-full-hierarchy ul { + list-style: none; + margin: 0; + padding: 0; } - .has-menu .overlay { - visibility: visible; + .tsd-full-hierarchy ul { + padding-left: 1.5rem; } - .has-menu .col-sidebar { - visibility: visible; - transform: translate(0, 0); - display: flex; - flex-direction: column; - gap: 1.5rem; - max-height: 100vh; - padding: 1rem 2rem; + .tsd-full-hierarchy a { + padding: 0.25rem 0 !important; + font-size: 1rem; + display: inline-flex; + align-items: center; + color: var(--color-text); } - .has-menu .tsd-navigation { - max-height: 100%; + .tsd-full-hierarchy svg[data-dropdown] { + cursor: pointer; } - #tsd-toolbar-links { + .tsd-full-hierarchy svg[data-dropdown="false"] { + transform: rotate(-90deg); + } + .tsd-full-hierarchy svg[data-dropdown="false"] ~ ul { display: none; } - .tsd-navigation .tsd-nav-link { - display: flex; - } -} -/* one sidebar */ -@media (min-width: 770px) { - .container-main { + .tsd-panel-group.tsd-index-group { + margin-bottom: 0; + } + .tsd-index-panel .tsd-index-list { + list-style: none; + line-height: 1.333em; + margin: 0; + padding: 0.25rem 0 0 0; + overflow: hidden; display: grid; - grid-template-columns: minmax(0, 1fr) minmax(0, 2fr); - grid-template-areas: "sidebar content"; - margin: 2rem auto; + grid-template-columns: repeat(3, 1fr); + column-gap: 1rem; + grid-template-rows: auto; + } + @media (max-width: 1024px) { + .tsd-index-panel .tsd-index-list { + grid-template-columns: repeat(2, 1fr); + } + } + @media (max-width: 768px) { + .tsd-index-panel .tsd-index-list { + grid-template-columns: repeat(1, 1fr); + } + } + .tsd-index-panel .tsd-index-list li { + -webkit-page-break-inside: avoid; + -moz-page-break-inside: avoid; + -ms-page-break-inside: avoid; + -o-page-break-inside: avoid; + page-break-inside: avoid; } - .col-sidebar { - grid-area: sidebar; + .tsd-flag { + display: inline-block; + padding: 0.25em 0.4em; + border-radius: 4px; + color: var(--color-comment-tag-text); + background-color: var(--color-comment-tag); + text-indent: 0; + font-size: 75%; + line-height: 1; + font-weight: normal; } - .col-content { - grid-area: content; - padding: 0 1rem; + + .tsd-anchor { + position: relative; + top: -100px; } -} -@media (min-width: 770px) and (max-width: 1399px) { - .col-sidebar { - max-height: calc(100vh - 2rem - 42px); - overflow: auto; - position: sticky; - top: 42px; - padding-top: 1rem; + + .tsd-member { + position: relative; } - .site-menu { + .tsd-member .tsd-anchor + h3 { + display: flex; + align-items: center; + margin-top: 0; + margin-bottom: 0; + border-bottom: none; + } + + .tsd-navigation.settings { + margin: 1rem 0; + } + .tsd-navigation > a, + .tsd-navigation .tsd-accordion-summary { + width: calc(100% - 0.25rem); + display: flex; + align-items: center; + } + .tsd-navigation a, + .tsd-navigation summary > span, + .tsd-page-navigation a { + display: flex; + width: calc(100% - 0.25rem); + align-items: center; + padding: 0.25rem; + color: var(--color-text); + text-decoration: none; + box-sizing: border-box; + } + .tsd-navigation a.current, + .tsd-page-navigation a.current { + background: var(--color-active-menu-item); + } + .tsd-navigation a:hover, + .tsd-page-navigation a:hover { + text-decoration: underline; + } + .tsd-navigation ul, + .tsd-page-navigation ul { + margin-top: 0; + margin-bottom: 0; + padding: 0; + list-style: none; + } + .tsd-navigation li, + .tsd-page-navigation li { + padding: 0; + max-width: 100%; + } + .tsd-navigation .tsd-nav-link { + display: none; + } + .tsd-nested-navigation { + margin-left: 3rem; + } + .tsd-nested-navigation > li > details { + margin-left: -1.5rem; + } + .tsd-small-nested-navigation { + margin-left: 1.5rem; + } + .tsd-small-nested-navigation > li > details { + margin-left: -1.5rem; + } + + .tsd-page-navigation-section { + margin-left: 10px; + } + .tsd-page-navigation-section > summary { + padding: 0.25rem; + } + .tsd-page-navigation-section > div { + margin-left: 20px; + } + .tsd-page-navigation ul { + padding-left: 1.75rem; + } + + #tsd-sidebar-links a { + margin-top: 0; + margin-bottom: 0.5rem; + line-height: 1.25rem; + } + #tsd-sidebar-links a:last-of-type { + margin-bottom: 0; + } + + a.tsd-index-link { + padding: 0.25rem 0 !important; + font-size: 1rem; + line-height: 1.25rem; + display: inline-flex; + align-items: center; + color: var(--color-text); + } + .tsd-accordion-summary { + list-style-type: none; /* hide marker on non-safari */ + outline: none; /* broken on safari, so just hide it */ + } + .tsd-accordion-summary::-webkit-details-marker { + display: none; /* hide marker on safari */ + } + .tsd-accordion-summary, + .tsd-accordion-summary a { + -moz-user-select: none; + -webkit-user-select: none; + -ms-user-select: none; + user-select: none; + + cursor: pointer; + } + .tsd-accordion-summary a { + width: calc(100% - 1.5rem); + } + .tsd-accordion-summary > * { + margin-top: 0; + margin-bottom: 0; + padding-top: 0; + padding-bottom: 0; + } + .tsd-accordion .tsd-accordion-summary > svg { + margin-left: 0.25rem; + vertical-align: text-top; + } + /* + We need to be careful to target the arrow indicating whether the accordion + is open, but not any other SVGs included in the details element. +*/ + .tsd-accordion:not([open]) > .tsd-accordion-summary > svg:first-child, + .tsd-accordion:not([open]) > .tsd-accordion-summary > h1 > svg:first-child, + .tsd-accordion:not([open]) > .tsd-accordion-summary > h2 > svg:first-child, + .tsd-accordion:not([open]) > .tsd-accordion-summary > h3 > svg:first-child, + .tsd-accordion:not([open]) > .tsd-accordion-summary > h4 > svg:first-child { + transform: rotate(-90deg); + } + .tsd-index-content > :not(:first-child) { + margin-top: 0.75rem; + } + .tsd-index-heading { + margin-top: 1.5rem; + margin-bottom: 0.75rem; + } + + .tsd-no-select { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + } + .tsd-kind-icon { + margin-right: 0.5rem; + width: 1.25rem; + height: 1.25rem; + min-width: 1.25rem; + min-height: 1.25rem; + } + .tsd-signature > .tsd-kind-icon { + margin-right: 0.8rem; + } + + .tsd-panel { + margin-bottom: 2.5rem; + } + .tsd-panel.tsd-member { + margin-bottom: 4rem; + } + .tsd-panel:empty { + display: none; + } + .tsd-panel > h1, + .tsd-panel > h2, + .tsd-panel > h3 { + margin: 1.5rem -1.5rem 0.75rem -1.5rem; + padding: 0 1.5rem 0.75rem 1.5rem; + } + .tsd-panel > h1.tsd-before-signature, + .tsd-panel > h2.tsd-before-signature, + .tsd-panel > h3.tsd-before-signature { + margin-bottom: 0; + border-bottom: none; + } + + .tsd-panel-group { + margin: 2rem 0; + } + .tsd-panel-group.tsd-index-group { + margin: 2rem 0; + } + .tsd-panel-group.tsd-index-group details { + margin: 2rem 0; + } + .tsd-panel-group > .tsd-accordion-summary { + margin-bottom: 1rem; + } + + #tsd-search { + transition: background-color 0.2s; + } + #tsd-search .title { + position: relative; + z-index: 2; + } + #tsd-search .field { + position: absolute; + left: 0; + top: 0; + right: 2.5rem; + height: 100%; + } + #tsd-search .field input { + box-sizing: border-box; + position: relative; + top: -50px; + z-index: 1; + width: 100%; + padding: 0 10px; + opacity: 0; + outline: 0; + border: 0; + background: transparent; + color: var(--color-text); + } + #tsd-search .field label { + position: absolute; + overflow: hidden; + right: -40px; + } + #tsd-search .field input, + #tsd-search .title, + #tsd-toolbar-links a { + transition: opacity 0.2s; + } + #tsd-search .results { + position: absolute; + visibility: hidden; + top: 40px; + width: 100%; + margin: 0; + padding: 0; + list-style: none; + box-shadow: 0 0 4px rgba(0, 0, 0, 0.25); + } + #tsd-search .results li { + background-color: var(--color-background); + line-height: initial; + padding: 4px; + } + #tsd-search .results li:nth-child(even) { + background-color: var(--color-background-secondary); + } + #tsd-search .results li.state { + display: none; + } + #tsd-search .results li.current:not(.no-results), + #tsd-search .results li:hover:not(.no-results) { + background-color: var(--color-accent); + } + #tsd-search .results a { + display: flex; + align-items: center; + padding: 0.25rem; + box-sizing: border-box; + } + #tsd-search .results a:before { + top: 10px; + } + #tsd-search .results span.parent { + color: var(--color-text-aside); + font-weight: normal; + } + #tsd-search.has-focus { + background-color: var(--color-accent); + } + #tsd-search.has-focus .field input { + top: 0; + opacity: 1; + } + #tsd-search.has-focus .title, + #tsd-search.has-focus #tsd-toolbar-links a { + z-index: 0; + opacity: 0; + } + #tsd-search.has-focus .results { + visibility: visible; + } + #tsd-search.loading .results li.state.loading { + display: block; + } + #tsd-search.failure .results li.state.failure { + display: block; + } + + #tsd-toolbar-links { + position: absolute; + top: 0; + right: 2rem; + height: 100%; + display: flex; + align-items: center; + justify-content: flex-end; + } + #tsd-toolbar-links a { + margin-left: 1.5rem; + } + #tsd-toolbar-links a:hover { + text-decoration: underline; + } + + .tsd-signature { + margin: 0 0 1rem 0; + padding: 1rem 0.5rem; + border: 1px solid var(--color-accent); + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 14px; + overflow-x: auto; + } + + .tsd-signature-keyword { + color: var(--color-ts-keyword); + font-weight: normal; + } + + .tsd-signature-symbol { + color: var(--color-text-aside); + font-weight: normal; + } + + .tsd-signature-type { + font-style: italic; + font-weight: normal; + } + + .tsd-signatures { + padding: 0; + margin: 0 0 1em 0; + list-style-type: none; + } + .tsd-signatures .tsd-signature { + margin: 0; + border-color: var(--color-accent); + border-width: 1px 0; + transition: background-color 0.1s; + } + .tsd-signatures .tsd-index-signature:not(:last-child) { + margin-bottom: 1em; + } + .tsd-signatures .tsd-index-signature .tsd-signature { + border-width: 1px; + } + .tsd-description .tsd-signatures .tsd-signature { + border-width: 1px; + } + + ul.tsd-parameter-list, + ul.tsd-type-parameter-list { + list-style: square; + margin: 0; + padding-left: 20px; + } + ul.tsd-parameter-list > li.tsd-parameter-signature, + ul.tsd-type-parameter-list > li.tsd-parameter-signature { + list-style: none; + margin-left: -20px; + } + ul.tsd-parameter-list h5, + ul.tsd-type-parameter-list h5 { + font-size: 16px; + margin: 1em 0 0.5em 0; + } + .tsd-sources { margin-top: 1rem; + font-size: 0.875em; } -} - -/* two sidebars */ -@media (min-width: 1200px) { - .container-main { - grid-template-columns: minmax(0, 1fr) minmax(0, 2.5fr) minmax(0, 20rem); - grid-template-areas: "sidebar content toc"; + .tsd-sources a { + color: var(--color-text-aside); + text-decoration: underline; + } + .tsd-sources ul { + list-style: none; + padding: 0; } - .col-sidebar { - display: contents; - } - - .page-menu { - grid-area: toc; - padding-left: 1rem; - } - .site-menu { - grid-area: sidebar; - } - - .site-menu { - margin-top: 1rem 0; - } - - .page-menu, - .site-menu { - max-height: calc(100vh - 2rem - 42px); - overflow: auto; + .tsd-page-toolbar { position: sticky; - top: 42px; + z-index: 1; + top: 0; + left: 0; + width: 100%; + color: var(--color-text); + background: var(--color-background-secondary); + border-bottom: 1px var(--color-accent) solid; + transition: transform 0.3s ease-in-out; + } + .tsd-page-toolbar a { + color: var(--color-text); + text-decoration: none; + } + .tsd-page-toolbar a.title { + font-weight: bold; + } + .tsd-page-toolbar a.title:hover { + text-decoration: underline; + } + .tsd-page-toolbar .tsd-toolbar-contents { + display: flex; + justify-content: space-between; + height: 2.5rem; + margin: 0 auto; + } + .tsd-page-toolbar .table-cell { + position: relative; + white-space: nowrap; + line-height: 40px; + } + .tsd-page-toolbar .table-cell:first-child { + width: 100%; + } + .tsd-page-toolbar .tsd-toolbar-icon { + box-sizing: border-box; + line-height: 0; + padding: 12px 0; + } + + .tsd-widget { + display: inline-block; + overflow: hidden; + opacity: 0.8; + height: 40px; + transition: + opacity 0.1s, + background-color 0.2s; + vertical-align: bottom; + cursor: pointer; + } + .tsd-widget:hover { + opacity: 0.9; + } + .tsd-widget.active { + opacity: 1; + background-color: var(--color-accent); + } + .tsd-widget.no-caption { + width: 40px; + } + .tsd-widget.no-caption:before { + margin: 0; + } + + .tsd-widget.options, + .tsd-widget.menu { + display: none; + } + input[type="checkbox"] + .tsd-widget:before { + background-position: -120px 0; + } + input[type="checkbox"]:checked + .tsd-widget:before { + background-position: -160px 0; + } + + img { + max-width: 100%; + } + + .tsd-member-summary-name { + display: inline-flex; + align-items: center; + padding: 0.25rem; + text-decoration: none; + } + + .tsd-anchor-icon { + display: inline-flex; + align-items: center; + margin-left: 0.5rem; + color: var(--color-text); + } + + .tsd-anchor-icon svg { + width: 1em; + height: 1em; + visibility: hidden; + } + + .tsd-member-summary-name:hover > .tsd-anchor-icon svg, + .tsd-anchor-link:hover > .tsd-anchor-icon svg { + visibility: visible; + } + + .deprecated { + text-decoration: line-through !important; + } + + .warning { + padding: 1rem; + color: var(--color-warning-text); + background: var(--color-background-warning); + } + + .tsd-kind-project { + color: var(--color-ts-project); + } + .tsd-kind-module { + color: var(--color-ts-module); + } + .tsd-kind-namespace { + color: var(--color-ts-namespace); + } + .tsd-kind-enum { + color: var(--color-ts-enum); + } + .tsd-kind-enum-member { + color: var(--color-ts-enum-member); + } + .tsd-kind-variable { + color: var(--color-ts-variable); + } + .tsd-kind-function { + color: var(--color-ts-function); + } + .tsd-kind-class { + color: var(--color-ts-class); + } + .tsd-kind-interface { + color: var(--color-ts-interface); + } + .tsd-kind-constructor { + color: var(--color-ts-constructor); + } + .tsd-kind-property { + color: var(--color-ts-property); + } + .tsd-kind-method { + color: var(--color-ts-method); + } + .tsd-kind-reference { + color: var(--color-ts-reference); + } + .tsd-kind-call-signature { + color: var(--color-ts-call-signature); + } + .tsd-kind-index-signature { + color: var(--color-ts-index-signature); + } + .tsd-kind-constructor-signature { + color: var(--color-ts-constructor-signature); + } + .tsd-kind-parameter { + color: var(--color-ts-parameter); + } + .tsd-kind-type-parameter { + color: var(--color-ts-type-parameter); + } + .tsd-kind-accessor { + color: var(--color-ts-accessor); + } + .tsd-kind-get-signature { + color: var(--color-ts-get-signature); + } + .tsd-kind-set-signature { + color: var(--color-ts-set-signature); + } + .tsd-kind-type-alias { + color: var(--color-ts-type-alias); + } + + /* if we have a kind icon, don't color the text by kind */ + .tsd-kind-icon ~ span { + color: var(--color-text); + } + + * { + scrollbar-width: thin; + scrollbar-color: var(--color-accent) var(--color-icon-background); + } + + *::-webkit-scrollbar { + width: 0.75rem; + } + + *::-webkit-scrollbar-track { + background: var(--color-icon-background); + } + + *::-webkit-scrollbar-thumb { + background-color: var(--color-accent); + border-radius: 999rem; + border: 0.25rem solid var(--color-icon-background); + } + + /* mobile */ + @media (max-width: 769px) { + .tsd-widget.options, + .tsd-widget.menu { + display: inline-block; + } + + .container-main { + display: flex; + } + html .col-content { + float: none; + max-width: 100%; + width: 100%; + } + html .col-sidebar { + position: fixed !important; + overflow-y: auto; + -webkit-overflow-scrolling: touch; + z-index: 1024; + top: 0 !important; + bottom: 0 !important; + left: auto !important; + right: 0 !important; + padding: 1.5rem 1.5rem 0 0; + width: 75vw; + visibility: hidden; + background-color: var(--color-background); + transform: translate(100%, 0); + } + html .col-sidebar > *:last-child { + padding-bottom: 20px; + } + html .overlay { + content: ""; + display: block; + position: fixed; + z-index: 1023; + top: 0; + left: 0; + right: 0; + bottom: 0; + background-color: rgba(0, 0, 0, 0.75); + visibility: hidden; + } + + .to-has-menu .overlay { + animation: fade-in 0.4s; + } + + .to-has-menu .col-sidebar { + animation: pop-in-from-right 0.4s; + } + + .from-has-menu .overlay { + animation: fade-out 0.4s; + } + + .from-has-menu .col-sidebar { + animation: pop-out-to-right 0.4s; + } + + .has-menu body { + overflow: hidden; + } + .has-menu .overlay { + visibility: visible; + } + .has-menu .col-sidebar { + visibility: visible; + transform: translate(0, 0); + display: flex; + flex-direction: column; + gap: 1.5rem; + max-height: 100vh; + padding: 1rem 2rem; + } + .has-menu .tsd-navigation { + max-height: 100%; + } + #tsd-toolbar-links { + display: none; + } + .tsd-navigation .tsd-nav-link { + display: flex; + } + } + + /* one sidebar */ + @media (min-width: 770px) { + .container-main { + display: grid; + grid-template-columns: minmax(0, 1fr) minmax(0, 2fr); + grid-template-areas: "sidebar content"; + margin: 2rem auto; + } + + .col-sidebar { + grid-area: sidebar; + } + .col-content { + grid-area: content; + padding: 0 1rem; + } + } + @media (min-width: 770px) and (max-width: 1399px) { + .col-sidebar { + max-height: calc(100vh - 2rem - 42px); + overflow: auto; + position: sticky; + top: 42px; + padding-top: 1rem; + } + .site-menu { + margin-top: 1rem; + } + } + + /* two sidebars */ + @media (min-width: 1200px) { + .container-main { + grid-template-columns: minmax(0, 1fr) minmax(0, 2.5fr) minmax( + 0, + 20rem + ); + grid-template-areas: "sidebar content toc"; + } + + .col-sidebar { + display: contents; + } + + .page-menu { + grid-area: toc; + padding-left: 1rem; + } + .site-menu { + grid-area: sidebar; + } + + .site-menu { + margin-top: 1rem; + } + + .page-menu, + .site-menu { + max-height: calc(100vh - 2rem - 42px); + overflow: auto; + position: sticky; + top: 42px; + } } } diff --git a/typedoc/classes/AgeGenderNet.html b/typedoc/classes/AgeGenderNet.html index 56efe0b..99b98b4 100644 --- a/typedoc/classes/AgeGenderNet.html +++ b/typedoc/classes/AgeGenderNet.html @@ -1,27 +1,27 @@ -AgeGenderNet | @vladmandic/face-api - v1.7.14

Class AgeGenderNet

Hierarchy (view full)

Constructors

constructor +AgeGenderNet | @vladmandic/face-api - v1.7.14

Class AgeGenderNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings +

Accessors

faceFeatureExtractor +isLoaded +paramMappings params

Methods

dispose -extractClassifierParams -extractWeights +extractClassifierParams +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -predictAgeAndGender -reassignParamFromPath -runNet -serializeParams +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +predictAgeAndGender +reassignParamFromPath +runNet +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

predictAgeAndGender

reassignParamFromPath

runNet

serializeParams

variable

Settings

Member Visibility

On This Page

Constructors
Properties
Accessors
Methods
+

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

predictAgeAndGender

reassignParamFromPath

runNet

serializeParams

variable

Settings

Member Visibility

On This Page

Constructors
Properties
Accessors
Methods
diff --git a/typedoc/classes/BoundingBox.html b/typedoc/classes/BoundingBox.html index 0d406ff..7354c5d 100644 --- a/typedoc/classes/BoundingBox.html +++ b/typedoc/classes/BoundingBox.html @@ -1,26 +1,26 @@ -BoundingBox | @vladmandic/face-api - v1.7.14

Class BoundingBox

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Constructors

constructor +BoundingBox | @vladmandic/face-api - v1.7.14

Class BoundingBox

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

area bottom -bottomLeft -bottomRight +bottomLeft +bottomRight height left right top -topLeft -topRight +topLeft +topRight width x y

Methods

calibrate -clipAtImageBorders +clipAtImageBorders floor pad -padAtBorders +padAtBorders rescale round shift -toSquare -assertIsValidBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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+toSquare +assertIsValidBox +isRect +

Constructors

constructor

  • new BoundingBox(
        left: number,
        top: number,
        right: number,
        bottom: number,
        allowNegativeDimensions?: boolean,
    ): BoundingBox

  • Parameters

    • left: number
    • top: number
    • right: number
    • bottom: number
    • allowNegativeDimensions: boolean = false

    Returns BoundingBox

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(
        imageHeight: number,
        imageWidth: number,
    ): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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diff --git a/typedoc/classes/Box.html b/typedoc/classes/Box.html index 5fdd9ee..c8e0eb3 100644 --- a/typedoc/classes/Box.html +++ b/typedoc/classes/Box.html @@ -1,26 +1,26 @@ -Box | @vladmandic/face-api - v1.7.14

Class Box<BoxType>

Type Parameters

  • BoxType = any

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Constructors

constructor +Box | @vladmandic/face-api - v1.7.14

Class Box<BoxType>

Type Parameters

  • BoxType = any

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

area bottom -bottomLeft -bottomRight +bottomLeft +bottomRight height left right top -topLeft -topRight +topLeft +topRight width x y

Methods

calibrate -clipAtImageBorders +clipAtImageBorders floor pad -padAtBorders +padAtBorders rescale round shift -toSquare -assertIsValidBox -isRect -

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(imageHeight, imageWidth): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

    • dx: number
    • dy: number
    • edx: number
    • edy: number
    • ex: number
    • ey: number
    • h: number
    • w: number
    • x: number
    • y: number

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(box, callee, allowNegativeDimensions?): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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Methods
+toSquare +assertIsValidBox +isRect +

Constructors

constructor

Accessors

area

bottom

bottomLeft

bottomRight

height

left

right

top

topLeft

topRight

width

x

y

Methods

calibrate

clipAtImageBorders

floor

pad

padAtBorders

  • padAtBorders(
        imageHeight: number,
        imageWidth: number,
    ): {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

  • Parameters

    • imageHeight: number
    • imageWidth: number

    Returns {
        dx: number;
        dy: number;
        edx: number;
        edy: number;
        ex: number;
        ey: number;
        h: number;
        w: number;
        x: number;
        y: number;
    }

rescale

round

shift

toSquare

StaticassertIsValidBox

  • assertIsValidBox(
        box: any,
        callee: string,
        allowNegativeDimensions?: boolean,
    ): void

  • Parameters

    • box: any
    • callee: string
    • allowNegativeDimensions: boolean = false

    Returns void

StaticisRect

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diff --git a/typedoc/classes/ComposableTask.html b/typedoc/classes/ComposableTask.html index d515fb0..829e1bc 100644 --- a/typedoc/classes/ComposableTask.html +++ b/typedoc/classes/ComposableTask.html @@ -1,4 +1,4 @@ -ComposableTask | @vladmandic/face-api - v1.7.14

Class ComposableTask<T>

Type Parameters

  • T

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Constructors

constructor +ComposableTask | @vladmandic/face-api - v1.7.14

Class ComposableTask<T>

Type Parameters

  • T

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

constructor

Methods

run

then

  • then(onfulfilled): Promise<T>

  • Parameters

    • onfulfilled: ((value: T) => T | PromiseLike<T>)
        • (value): T | PromiseLike<T>

        • Parameters

          • value: T

          Returns T | PromiseLike<T>

    Returns Promise<T>

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diff --git a/typedoc/classes/ComputeAllFaceDescriptorsTask.html b/typedoc/classes/ComputeAllFaceDescriptorsTask.html index 2b9d320..a17ab82 100644 --- a/typedoc/classes/ComputeAllFaceDescriptorsTask.html +++ b/typedoc/classes/ComputeAllFaceDescriptorsTask.html @@ -1,6 +1,6 @@ -ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.14

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +ComputeAllFaceDescriptorsTask | @vladmandic/face-api - v1.7.14

Class ComputeAllFaceDescriptorsTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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+withAgeAndGender +withFaceExpressions +

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constructor

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run

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withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html index 4629e33..ffc9fcc 100644 --- a/typedoc/classes/ComputeFaceDescriptorsTaskBase.html +++ b/typedoc/classes/ComputeFaceDescriptorsTaskBase.html @@ -1,4 +1,4 @@ -ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.14

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (view full)

Constructors

constructor +ComputeFaceDescriptorsTaskBase | @vladmandic/face-api - v1.7.14

Class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

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Methods

run

then

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diff --git a/typedoc/classes/ComputeSingleFaceDescriptorTask.html b/typedoc/classes/ComputeSingleFaceDescriptorTask.html index 471a7eb..9feaf37 100644 --- a/typedoc/classes/ComputeSingleFaceDescriptorTask.html +++ b/typedoc/classes/ComputeSingleFaceDescriptorTask.html @@ -1,6 +1,6 @@ -ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.14

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +ComputeSingleFaceDescriptorTask | @vladmandic/face-api - v1.7.14

Class ComputeSingleFaceDescriptorTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

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+withAgeAndGender +withFaceExpressions +

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constructor

Methods

run

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withAgeAndGender

withFaceExpressions

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diff --git a/typedoc/classes/DetectAllFaceLandmarksTask.html b/typedoc/classes/DetectAllFaceLandmarksTask.html index 79e6c0a..d3f704d 100644 --- a/typedoc/classes/DetectAllFaceLandmarksTask.html +++ b/typedoc/classes/DetectAllFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +DetectAllFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectAllFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceDescriptors -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptors

withFaceExpressions

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+withAgeAndGender +withFaceDescriptors +withFaceExpressions +

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constructor

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run

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withFaceDescriptors

withFaceExpressions

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diff --git a/typedoc/classes/DetectAllFacesTask.html b/typedoc/classes/DetectAllFacesTask.html index 740f238..4c12daf 100644 --- a/typedoc/classes/DetectAllFacesTask.html +++ b/typedoc/classes/DetectAllFacesTask.html @@ -1,7 +1,7 @@ -DetectAllFacesTask | @vladmandic/face-api - v1.7.14

Class DetectAllFacesTask

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Constructors

constructor +DetectAllFacesTask | @vladmandic/face-api - v1.7.14

Class DetectAllFacesTask

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceExpressions -withFaceLandmarks -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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+withAgeAndGender +withFaceExpressions +withFaceLandmarks +

Constructors

constructor

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run

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withAgeAndGender

withFaceExpressions

withFaceLandmarks

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diff --git a/typedoc/classes/DetectFaceLandmarksTaskBase.html b/typedoc/classes/DetectFaceLandmarksTaskBase.html index ec51461..21056f1 100644 --- a/typedoc/classes/DetectFaceLandmarksTaskBase.html +++ b/typedoc/classes/DetectFaceLandmarksTaskBase.html @@ -1,4 +1,4 @@ -DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (view full)

Constructors

constructor +DetectFaceLandmarksTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFaceLandmarksTaskBase<TReturn, TParentReturn>

Type Parameters

  • TReturn
  • TParentReturn

Hierarchy (View Summary)

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constructor

Methods

run then -

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/DetectFacesTaskBase.html b/typedoc/classes/DetectFacesTaskBase.html index cd24d0a..1d8a404 100644 --- a/typedoc/classes/DetectFacesTaskBase.html +++ b/typedoc/classes/DetectFacesTaskBase.html @@ -1,4 +1,4 @@ -DetectFacesTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

Hierarchy (view full)

Constructors

constructor +DetectFacesTaskBase | @vladmandic/face-api - v1.7.14

Class DetectFacesTaskBase<TReturn>

Type Parameters

  • TReturn

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -

Constructors

constructor

Methods

run

then

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diff --git a/typedoc/classes/DetectSingleFaceLandmarksTask.html b/typedoc/classes/DetectSingleFaceLandmarksTask.html index c76c736..a7b0ed4 100644 --- a/typedoc/classes/DetectSingleFaceLandmarksTask.html +++ b/typedoc/classes/DetectSingleFaceLandmarksTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (view full)

Constructors

constructor +DetectSingleFaceLandmarksTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceLandmarksTask<TSource>

Type Parameters

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceDescriptor -withFaceExpressions -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceDescriptor

withFaceExpressions

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+withAgeAndGender +withFaceDescriptor +withFaceExpressions +

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constructor

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run

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withAgeAndGender

withFaceDescriptor

withFaceExpressions

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diff --git a/typedoc/classes/DetectSingleFaceTask.html b/typedoc/classes/DetectSingleFaceTask.html index 7c7ee69..c32c67a 100644 --- a/typedoc/classes/DetectSingleFaceTask.html +++ b/typedoc/classes/DetectSingleFaceTask.html @@ -1,7 +1,7 @@ -DetectSingleFaceTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceTask

Hierarchy (view full)

Constructors

constructor +DetectSingleFaceTask | @vladmandic/face-api - v1.7.14

Class DetectSingleFaceTask

Hierarchy (View Summary)

Constructors

constructor

Methods

run then -withAgeAndGender -withFaceExpressions -withFaceLandmarks -

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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+withAgeAndGender +withFaceExpressions +withFaceLandmarks +

Constructors

constructor

Methods

run

then

withAgeAndGender

withFaceExpressions

withFaceLandmarks

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diff --git a/typedoc/classes/Dimensions.html b/typedoc/classes/Dimensions.html index b665f59..d32e411 100644 --- a/typedoc/classes/Dimensions.html +++ b/typedoc/classes/Dimensions.html @@ -1,5 +1,5 @@ -Dimensions | @vladmandic/face-api - v1.7.14

Class Dimensions

Implements

Constructors

constructor +Dimensions | @vladmandic/face-api - v1.7.14

Class Dimensions

Implements

Constructors

constructor

Accessors

height width

Methods

reverse -

Constructors

constructor

Accessors

height

width

Methods

reverse

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+

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constructor

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height

width

Methods

reverse

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diff --git a/typedoc/classes/FaceDetection.html b/typedoc/classes/FaceDetection.html index 524b231..8db6a8a 100644 --- a/typedoc/classes/FaceDetection.html +++ b/typedoc/classes/FaceDetection.html @@ -1,11 +1,11 @@ -FaceDetection | @vladmandic/face-api - v1.7.14

Class FaceDetection

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Constructors

constructor +FaceDetection | @vladmandic/face-api - v1.7.14

Class FaceDetection

Hierarchy (View Summary)

Implements

Constructors

constructor

Accessors

box -className -classScore -imageDims -imageHeight -imageWidth -relativeBox +className +classScore +imageDims +imageHeight +imageWidth +relativeBox score -

Methods

forSize -

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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+

Methods

forSize +

Constructors

constructor

Accessors

box

className

classScore

imageDims

imageHeight

imageWidth

relativeBox

score

Methods

forSize

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diff --git a/typedoc/classes/FaceDetectionNet.html b/typedoc/classes/FaceDetectionNet.html index eef46f4..183bfb7 100644 --- a/typedoc/classes/FaceDetectionNet.html +++ b/typedoc/classes/FaceDetectionNet.html @@ -1,23 +1,23 @@ -FaceDetectionNet | @vladmandic/face-api - v1.7.14

Class FaceDetectionNet

Hierarchy (view full)

Constructors

constructor +FaceDetectionNet | @vladmandic/face-api - v1.7.14

Class FaceDetectionNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name -

Accessors

isLoaded -paramMappings +

Accessors

isLoaded +paramMappings params

Methods

dispose -extractWeights +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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Accessors
Methods
+

Constructors

constructor

Properties

_name

_name: any

Accessors

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadFromDisk

loadFromUri

loadFromWeightMap

locateFaces

reassignParamFromPath

serializeParams

variable

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diff --git a/typedoc/classes/FaceExpressionNet.html b/typedoc/classes/FaceExpressionNet.html index 07c13bc..0ec140c 100644 --- a/typedoc/classes/FaceExpressionNet.html +++ b/typedoc/classes/FaceExpressionNet.html @@ -1,27 +1,27 @@ -FaceExpressionNet | @vladmandic/face-api - v1.7.14

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor +FaceExpressionNet | @vladmandic/face-api - v1.7.14

Class FaceExpressionNet

Hierarchy

  • FaceProcessor<FaceFeatureExtractorParams>
    • FaceExpressionNet

Constructors

constructor

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings +

Accessors

faceFeatureExtractor +isLoaded +paramMappings params

Methods

dispose -extractClassifierParams -extractWeights +extractClassifierParams +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -predictExpressions -reassignParamFromPath -runNet -serializeParams +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +predictExpressions +reassignParamFromPath +runNet +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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+

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

dispose

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

predictExpressions

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceExpressions.html b/typedoc/classes/FaceExpressions.html index d2663e9..7f730ea 100644 --- a/typedoc/classes/FaceExpressions.html +++ b/typedoc/classes/FaceExpressions.html @@ -1,4 +1,4 @@ -FaceExpressions | @vladmandic/face-api - v1.7.14

Class FaceExpressions

Constructors

constructor +FaceExpressions | @vladmandic/face-api - v1.7.14

Class FaceExpressions

Constructors

constructor

Properties

angry disgusted fearful @@ -6,5 +6,5 @@ neutral sad surprised -

Methods

asSortedArray -

Constructors

constructor

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): {
        expression:
            | "neutral"
            | "happy"
            | "sad"
            | "angry"
            | "fearful"
            | "disgusted"
            | "surprised";
        probability: number;
    }[]

  • Returns {
        expression:
            | "neutral"
            | "happy"
            | "sad"
            | "angry"
            | "fearful"
            | "disgusted"
            | "surprised";
        probability: number;
    }[]

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+

Methods

asSortedArray +

Constructors

constructor

Properties

angry

angry: number = 0

disgusted

disgusted: number = 0

fearful

fearful: number = 0

happy

happy: number = 0

neutral

neutral: number = 0

sad

sad: number = 0

surprised

surprised: number = 0

Methods

asSortedArray

  • asSortedArray(): {
        expression: | "neutral"
        | "happy"
        | "sad"
        | "angry"
        | "fearful"
        | "disgusted"
        | "surprised";
        probability: number;
    }[]

  • Returns {
        expression:
            | "neutral"
            | "happy"
            | "sad"
            | "angry"
            | "fearful"
            | "disgusted"
            | "surprised";
        probability: number;
    }[]

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diff --git a/typedoc/classes/FaceLandmark68Net.html b/typedoc/classes/FaceLandmark68Net.html index 76a8bb8..1a5dddd 100644 --- a/typedoc/classes/FaceLandmark68Net.html +++ b/typedoc/classes/FaceLandmark68Net.html @@ -1,28 +1,28 @@ -FaceLandmark68Net | @vladmandic/face-api - v1.7.14

Class FaceLandmark68Net

Hierarchy (view full)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor +FaceLandmark68Net | @vladmandic/face-api - v1.7.14

Class FaceLandmark68Net

Hierarchy (View Summary)

  • FaceLandmark68NetBase<FaceFeatureExtractorParams>

Constructors

constructor

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings +

Accessors

faceFeatureExtractor +isLoaded +paramMappings params -

Methods

detectLandmarks +

Methods

detectLandmarks dispose -extractClassifierParams -extractWeights +extractClassifierParams +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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Methods
+

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceLandmark68TinyNet.html b/typedoc/classes/FaceLandmark68TinyNet.html index 1d713b0..8ad8446 100644 --- a/typedoc/classes/FaceLandmark68TinyNet.html +++ b/typedoc/classes/FaceLandmark68TinyNet.html @@ -1,28 +1,28 @@ -FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.14

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor +FaceLandmark68TinyNet | @vladmandic/face-api - v1.7.14

Class FaceLandmark68TinyNet

Hierarchy

  • FaceLandmark68NetBase<TinyFaceFeatureExtractorParams>
    • FaceLandmark68TinyNet

Constructors

constructor

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings +

Accessors

faceFeatureExtractor +isLoaded +paramMappings params -

Methods

detectLandmarks +

Methods

detectLandmarks dispose -extractClassifierParams -extractWeights +extractClassifierParams +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

  • extractClassifierParams(weights): {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

  • Parameters

    • weights: Float32Array

    Returns {
        paramMappings: ParamMapping[];
        params: NetParams;
    }

    • paramMappings: ParamMapping[]
    • params: NetParams

extractWeights

  • extractWeights(weights): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

  • getFrozenParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getParamFromPath

getParamList

  • getParamList(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

getTrainableParams

  • getTrainableParams(): {
        path: string;
        tensor: Tensor<Rank>;
    }[]

  • Returns {
        path: string;
        tensor: Tensor<Rank>;
    }[]

load

  • load(weightsOrUrl): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

loadFromDisk

  • loadFromDisk(filePath): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath, tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

Settings

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Properties
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Methods
+

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

  • get isLoaded(): boolean

  • Returns boolean

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

  • dispose(throwOnRedispose?: boolean): void

  • Parameters

    • throwOnRedispose: boolean = true

    Returns void

extractClassifierParams

  • extractClassifierParams(
        weights: Float32Array,
    ): { paramMappings: ParamMapping[]; params: NetParams }

  • Parameters

    • weights: Float32Array

    Returns { paramMappings: ParamMapping[]; params: NetParams }

extractWeights

  • extractWeights(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

  • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

  • Parameters

    • weightsOrUrl: undefined | string | Float32Array

    Returns Promise<void>

loadClassifierParams

  • loadClassifierParams(weights: Float32Array): void

  • Parameters

    • weights: Float32Array

    Returns void

loadFromDisk

  • loadFromDisk(filePath: undefined | string): Promise<void>

  • Parameters

    • filePath: undefined | string

    Returns Promise<void>

loadFromUri

  • loadFromUri(uri: undefined | string): Promise<void>

  • Parameters

    • uri: undefined | string

    Returns Promise<void>

loadFromWeightMap

postProcess

reassignParamFromPath

  • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

  • Parameters

    Returns void

runNet

serializeParams

  • serializeParams(): Float32Array

  • Returns Float32Array

variable

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diff --git a/typedoc/classes/FaceLandmarkNet.html b/typedoc/classes/FaceLandmarkNet.html index 2162302..6ed132c 100644 --- a/typedoc/classes/FaceLandmarkNet.html +++ b/typedoc/classes/FaceLandmarkNet.html @@ -1,28 +1,28 @@ -FaceLandmarkNet | @vladmandic/face-api - v1.7.14

Class FaceLandmarkNet

Hierarchy (view full)

Constructors

constructor +FaceLandmarkNet | @vladmandic/face-api - v1.7.14

Class FaceLandmarkNet

Hierarchy (View Summary)

Constructors

constructor

Properties

_name -

Accessors

faceFeatureExtractor -isLoaded -paramMappings +

Accessors

faceFeatureExtractor +isLoaded +paramMappings params -

Methods

detectLandmarks +

Methods

detectLandmarks dispose -extractClassifierParams -extractWeights +extractClassifierParams +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadClassifierParams -loadFromDisk -loadFromUri -loadFromWeightMap -postProcess -reassignParamFromPath -runNet -serializeParams +loadClassifierParams +loadFromDisk +loadFromUri +loadFromWeightMap +postProcess +reassignParamFromPath +runNet +serializeParams variable -

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

postProcess

reassignParamFromPath

runNet

serializeParams

variable

Settings

Member Visibility

On This Page

Constructors
Properties
Accessors
Methods
+

Constructors

constructor

Properties

_name

_name: any

Accessors

faceFeatureExtractor

  • get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>

  • Returns IFaceFeatureExtractor<TExtractorParams>

isLoaded

paramMappings

  • get paramMappings(): ParamMapping[]

  • Returns ParamMapping[]

params

Methods

detectLandmarks

dispose

extractClassifierParams

extractWeights

forward

forwardInput

freeze

getFrozenParams

getParamFromPath

getParamList

getTrainableParams

load

loadClassifierParams

loadFromDisk

loadFromUri

loadFromWeightMap

postProcess

reassignParamFromPath

runNet

serializeParams

variable

Settings

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Constructors
Properties
Accessors
Methods
diff --git a/typedoc/classes/FaceLandmarks.html b/typedoc/classes/FaceLandmarks.html index 02b415c..ac2f1e6 100644 --- a/typedoc/classes/FaceLandmarks.html +++ b/typedoc/classes/FaceLandmarks.html @@ -1,19 +1,19 @@ -FaceLandmarks | @vladmandic/face-api - v1.7.14

Class FaceLandmarks

Hierarchy (view full)

Implements

Constructors

constructor -

Accessors

imageHeight -imageWidth +FaceLandmarks | @vladmandic/face-api - v1.7.14

Class FaceLandmarks

Hierarchy (View Summary)

Implements

Constructors

constructor +

Accessors

imageHeight +imageWidth positions -relativePositions +relativePositions shift

Methods

align -forSize -shiftBy -shiftByPoint -

Constructors

constructor

Accessors

imageHeight

imageWidth

positions

relativePositions

shift

Methods

align

  • align(detection?, options?): Box<any>

  • Aligns the face landmarks after face detection from the relative positions of the faces +forSize +shiftBy +shiftByPoint +

Constructors

constructor

Accessors

imageHeight

imageWidth

positions

relativePositions

shift

Methods

align

  • align(
        detection?: null | IRect | IBoundingBox | FaceDetection,
        options?: { minBoxPadding?: number; useDlibAlignment?: boolean },
    ): Box

  • Aligns the face landmarks after face detection from the relative positions of the faces bounding box, or it's current shift. This function should be used to align the face images after face detection has been performed, before they are passed to the face recognition net. This will make the computed face descriptor more accurate.

    -

    Parameters

    • Optionaldetection:
          | null
          | IRect
          | IBoundingBox
          | FaceDetection

      (optional) The bounding box of the face or the face detection result. If +

      Parameters

      • Optionaldetection: null | IRect | IBoundingBox | FaceDetection

        (optional) The bounding box of the face or the face detection result. If no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

        -
      • options: {
            minBoxPadding?: number;
            useDlibAlignment?: boolean;
        } = {}
        • OptionalminBoxPadding?: number
        • OptionaluseDlibAlignment?: boolean

      Returns Box<any>

      The bounding box of the aligned face.

      -

forSize

shiftBy

shiftByPoint

Settings

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+
  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    +

    forSize

    shiftBy

    shiftByPoint

    Settings

    Member Visibility

    On This Page

    Constructors
    Accessors
    Methods
    diff --git a/typedoc/classes/FaceLandmarks5.html b/typedoc/classes/FaceLandmarks5.html index abd0a5e..6e5cd53 100644 --- a/typedoc/classes/FaceLandmarks5.html +++ b/typedoc/classes/FaceLandmarks5.html @@ -1,19 +1,19 @@ -FaceLandmarks5 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks5

    Hierarchy (view full)

    Constructors

    constructor -

    Accessors

    imageHeight -imageWidth +FaceLandmarks5 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks5

    Hierarchy (View Summary)

    Constructors

    constructor +

    Accessors

    imageHeight +imageWidth positions -relativePositions +relativePositions shift

    Methods

    align -forSize -shiftBy -shiftByPoint -

    Constructors

    constructor

    Accessors

    imageHeight

    imageWidth

    positions

    relativePositions

    shift

    Methods

    align

    • align(detection?, options?): Box<any>

    • Aligns the face landmarks after face detection from the relative positions of the faces +forSize +shiftBy +shiftByPoint +

    Constructors

    constructor

    Accessors

    imageHeight

    imageWidth

    positions

    relativePositions

    shift

    Methods

    align

    • align(
          detection?: null | IRect | IBoundingBox | FaceDetection,
          options?: { minBoxPadding?: number; useDlibAlignment?: boolean },
      ): Box

    • Aligns the face landmarks after face detection from the relative positions of the faces bounding box, or it's current shift. This function should be used to align the face images after face detection has been performed, before they are passed to the face recognition net. This will make the computed face descriptor more accurate.

      -

      Parameters

      • Optionaldetection:
            | null
            | IRect
            | IBoundingBox
            | FaceDetection

        (optional) The bounding box of the face or the face detection result. If +

        Parameters

        • Optionaldetection: null | IRect | IBoundingBox | FaceDetection

          (optional) The bounding box of the face or the face detection result. If no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

          -
        • options: {
              minBoxPadding?: number;
              useDlibAlignment?: boolean;
          } = {}
          • OptionalminBoxPadding?: number
          • OptionaluseDlibAlignment?: boolean

        Returns Box<any>

        The bounding box of the aligned face.

        -

    forSize

    shiftBy

    shiftByPoint

    Settings

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    Constructors
    Accessors
    Methods
    +
  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    +

    forSize

    shiftBy

    shiftByPoint

    Settings

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    Constructors
    Accessors
    Methods
    diff --git a/typedoc/classes/FaceLandmarks68.html b/typedoc/classes/FaceLandmarks68.html index 053d003..95aa4e2 100644 --- a/typedoc/classes/FaceLandmarks68.html +++ b/typedoc/classes/FaceLandmarks68.html @@ -1,26 +1,26 @@ -FaceLandmarks68 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks68

    Hierarchy (view full)

    Constructors

    constructor -

    Accessors

    imageHeight -imageWidth +FaceLandmarks68 | @vladmandic/face-api - v1.7.14

    Class FaceLandmarks68

    Hierarchy (View Summary)

    Constructors

    constructor +

    Accessors

    imageHeight +imageWidth positions -relativePositions +relativePositions shift

    Methods

    align -forSize -getJawOutline -getLeftEye -getLeftEyeBrow -getMouth -getNose -getRightEye -getRightEyeBrow -shiftBy -shiftByPoint -

    Constructors

    constructor

    Accessors

    imageHeight

    imageWidth

    positions

    relativePositions

    shift

    Methods

    align

    Constructors

    constructor

    Accessors

    imageHeight

    imageWidth

    positions

    relativePositions

    shift

    Methods

    align

    • align(
          detection?: null | IRect | IBoundingBox | FaceDetection,
          options?: { minBoxPadding?: number; useDlibAlignment?: boolean },
      ): Box

    • Aligns the face landmarks after face detection from the relative positions of the faces bounding box, or it's current shift. This function should be used to align the face images after face detection has been performed, before they are passed to the face recognition net. This will make the computed face descriptor more accurate.

      -

      Parameters

      • Optionaldetection:
            | null
            | IRect
            | IBoundingBox
            | FaceDetection

        (optional) The bounding box of the face or the face detection result. If +

        Parameters

        • Optionaldetection: null | IRect | IBoundingBox | FaceDetection

          (optional) The bounding box of the face or the face detection result. If no argument was passed the position of the face landmarks are assumed to be relative to it's current shift.

          -
        • options: {
              minBoxPadding?: number;
              useDlibAlignment?: boolean;
          } = {}
          • OptionalminBoxPadding?: number
          • OptionaluseDlibAlignment?: boolean

        Returns Box<any>

        The bounding box of the aligned face.

        -

    forSize

    getJawOutline

    getLeftEye

    getLeftEyeBrow

    getMouth

    getNose

    getRightEye

    getRightEyeBrow

    shiftBy

    shiftByPoint

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    +
  • options: { minBoxPadding?: number; useDlibAlignment?: boolean } = {}

    • Returns Box

    The bounding box of the aligned face.

    +

    forSize

    getJawOutline

    getLeftEye

    getLeftEyeBrow

    getMouth

    getNose

    getRightEye

    getRightEyeBrow

    shiftBy

    shiftByPoint

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    diff --git a/typedoc/classes/FaceMatch.html b/typedoc/classes/FaceMatch.html index 9bc7975..2b9036e 100644 --- a/typedoc/classes/FaceMatch.html +++ b/typedoc/classes/FaceMatch.html @@ -1,6 +1,6 @@ -FaceMatch | @vladmandic/face-api - v1.7.14

    Class FaceMatch

    Implements

    Constructors

    constructor +FaceMatch | @vladmandic/face-api - v1.7.14

    Class FaceMatch

    Implements

    Constructors

    constructor

    Accessors

    distance label -

    Methods

    toString -

    Constructors

    constructor

    Accessors

    distance

    label

    Methods

    toString

    • toString(withDistance?): string

    • Returns a string representation of an object.

      -

      Parameters

      • withDistance: boolean = true

      Returns string

    Settings

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    +

    Methods

    toString +

    Constructors

    constructor

    Accessors

    distance

    label

    Methods

    toString

    • toString(withDistance?: boolean): string

    • Returns a string representation of an object.

      +

      Parameters

      • withDistance: boolean = true

      Returns string

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    diff --git a/typedoc/classes/FaceMatcher.html b/typedoc/classes/FaceMatcher.html index dd7502a..9aca31a 100644 --- a/typedoc/classes/FaceMatcher.html +++ b/typedoc/classes/FaceMatcher.html @@ -1,9 +1,9 @@ -FaceMatcher | @vladmandic/face-api - v1.7.14

    Class FaceMatcher

    Constructors

    constructor -

    Accessors

    distanceThreshold -labeledDescriptors -

    Methods

    computeMeanDistance -findBestMatch -matchDescriptor -toJSON -fromJSON -

    Constructors

    constructor

    Accessors

    distanceThreshold

    labeledDescriptors

    Methods

    computeMeanDistance

    • computeMeanDistance(queryDescriptor, descriptors): number

    • Parameters

      • queryDescriptor: Float32Array
      • descriptors: Float32Array[]

      Returns number

    findBestMatch

    matchDescriptor

    toJSON

    StaticfromJSON

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    +FaceMatcher | @vladmandic/face-api - v1.7.14

    Class FaceMatcher

    Constructors

    constructor +

    Accessors

    distanceThreshold +labeledDescriptors +

    Methods

    computeMeanDistance +findBestMatch +matchDescriptor +toJSON +fromJSON +

    Constructors

    constructor

    Accessors

    distanceThreshold

    labeledDescriptors

    Methods

    computeMeanDistance

    • computeMeanDistance(
          queryDescriptor: Float32Array,
          descriptors: Float32Array[],
      ): number

    • Parameters

      • queryDescriptor: Float32Array
      • descriptors: Float32Array[]

      Returns number

    findBestMatch

    matchDescriptor

    toJSON

    StaticfromJSON

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    diff --git a/typedoc/classes/FaceRecognitionNet.html b/typedoc/classes/FaceRecognitionNet.html index 7c59ca6..c97b40d 100644 --- a/typedoc/classes/FaceRecognitionNet.html +++ b/typedoc/classes/FaceRecognitionNet.html @@ -1,23 +1,23 @@ -FaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Class FaceRecognitionNet

    Hierarchy (view full)

    Constructors

    constructor +FaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Class FaceRecognitionNet

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name -

    Accessors

    isLoaded -paramMappings +

    Accessors

    isLoaded +paramMappings params -

    Methods

    computeFaceDescriptor +

    Methods

    computeFaceDescriptor dispose -extractWeights +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -reassignParamFromPath -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +reassignParamFromPath +serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    Methods

    computeFaceDescriptor

    dispose

    extractWeights

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

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    Accessors
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    +

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    Methods

    computeFaceDescriptor

    dispose

    extractWeights

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

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    diff --git a/typedoc/classes/LabeledBox.html b/typedoc/classes/LabeledBox.html index 843d1be..51e0198 100644 --- a/typedoc/classes/LabeledBox.html +++ b/typedoc/classes/LabeledBox.html @@ -1,28 +1,28 @@ -LabeledBox | @vladmandic/face-api - v1.7.14

    Class LabeledBox

    Hierarchy (view full)

    Constructors

    constructor +LabeledBox | @vladmandic/face-api - v1.7.14

    Class LabeledBox

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    area bottom -bottomLeft -bottomRight +bottomLeft +bottomRight height label left right top -topLeft -topRight +topLeft +topRight width x y

    Methods

    calibrate -clipAtImageBorders +clipAtImageBorders floor pad -padAtBorders +padAtBorders rescale round shift -toSquare -assertIsValidBox -assertIsValidLabeledBox -isRect -

    Constructors

    constructor

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    height

    label

    left

    right

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(imageHeight, imageWidth): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

      • dx: number
      • dy: number
      • edx: number
      • edy: number
      • ex: number
      • ey: number
      • h: number
      • w: number
      • x: number
      • y: number

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(box, callee, allowNegativeDimensions?): void

    • Parameters

      • box: any
      • callee: string
      • allowNegativeDimensions: boolean = false

      Returns void

    StaticassertIsValidLabeledBox

    StaticisRect

    Settings

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    Constructors
    Accessors
    Methods
    +toSquare +assertIsValidBox +assertIsValidLabeledBox +isRect +

    Constructors

    constructor

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    height

    label

    left

    right

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(
          imageHeight: number,
          imageWidth: number,
      ): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

      • box: any
      • callee: string
      • allowNegativeDimensions: boolean = false

      Returns void

    StaticassertIsValidLabeledBox

    • assertIsValidLabeledBox(box: any, callee: string): void

    • Parameters

      • box: any
      • callee: string

      Returns void

    StaticisRect

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    diff --git a/typedoc/classes/LabeledFaceDescriptors.html b/typedoc/classes/LabeledFaceDescriptors.html index 83a8c2d..3cebdba 100644 --- a/typedoc/classes/LabeledFaceDescriptors.html +++ b/typedoc/classes/LabeledFaceDescriptors.html @@ -1,6 +1,6 @@ -LabeledFaceDescriptors | @vladmandic/face-api - v1.7.14

    Class LabeledFaceDescriptors

    Constructors

    constructor +LabeledFaceDescriptors | @vladmandic/face-api - v1.7.14

    Class LabeledFaceDescriptors

    Constructors

    constructor

    Accessors

    descriptors label -

    Methods

    toJSON -fromJSON -

    Constructors

    constructor

    Accessors

    descriptors

    label

    Methods

    toJSON

    StaticfromJSON

    Settings

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    Constructors
    Accessors
    Methods
    +

    Methods

    toJSON +fromJSON +

    Constructors

    constructor

    Accessors

    descriptors

    label

    Methods

    toJSON

    StaticfromJSON

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    diff --git a/typedoc/classes/NetInput.html b/typedoc/classes/NetInput.html index 5e66e3d..8748754 100644 --- a/typedoc/classes/NetInput.html +++ b/typedoc/classes/NetInput.html @@ -1,19 +1,19 @@ -NetInput | @vladmandic/face-api - v1.7.14

    Class NetInput

    Constructors

    constructor -

    Accessors

    batchSize +NetInput | @vladmandic/face-api - v1.7.14

    Class NetInput

    Constructors

    constructor +

    Accessors

    batchSize canvases -imageTensors -inputDimensions -inputSize -isBatchInput -reshapedInputDimensions -

    Methods

    getInput -getInputDimensions -getInputHeight -getInputWidth -getReshapedInputDimensions -toBatchTensor -

    Constructors

    constructor

    Accessors

    batchSize

    canvases

    imageTensors

    inputDimensions

    inputSize

    isBatchInput

    reshapedInputDimensions

    Methods

    getInput

    getInputDimensions

    • getInputDimensions(batchIdx): number[]

    • Parameters

      • batchIdx: number

      Returns number[]

    getInputHeight

    getInputWidth

    getReshapedInputDimensions

    toBatchTensor

    Methods

    getInput +getInputDimensions +getInputHeight +getInputWidth +getReshapedInputDimensions +toBatchTensor +

    Constructors

    constructor

    Accessors

    batchSize

    canvases

    imageTensors

    inputDimensions

    inputSize

    isBatchInput

    reshapedInputDimensions

    Methods

    getInput

    getInputDimensions

    • getInputDimensions(batchIdx: number): number[]

    • Parameters

      • batchIdx: number

      Returns number[]

    getInputHeight

    • getInputHeight(batchIdx: number): number

    • Parameters

      • batchIdx: number

      Returns number

    getInputWidth

    • getInputWidth(batchIdx: number): number

    • Parameters

      • batchIdx: number

      Returns number

    getReshapedInputDimensions

    toBatchTensor

    • toBatchTensor(inputSize: number, isCenterInputs?: boolean): Tensor4D

    • Create a batch tensor from all input canvases and tensors with size [batchSize, inputSize, inputSize, 3].

      Parameters

      • inputSize: number

        Height and width of the tensor.

      • isCenterInputs: boolean = true

      Returns Tensor4D

      The batch tensor.

      -

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    +

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    diff --git a/typedoc/classes/NeuralNetwork.html b/typedoc/classes/NeuralNetwork.html index 0ed02c7..a8e2fd7 100644 --- a/typedoc/classes/NeuralNetwork.html +++ b/typedoc/classes/NeuralNetwork.html @@ -1,20 +1,20 @@ -NeuralNetwork | @vladmandic/face-api - v1.7.14

    Class NeuralNetwork<TNetParams>Abstract

    Type Parameters

    • TNetParams

    Hierarchy (view full)

    Constructors

    constructor +NeuralNetwork | @vladmandic/face-api - v1.7.14

    Class NeuralNetwork<TNetParams>Abstract

    Type Parameters

    • TNetParams

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name -

    Accessors

    isLoaded -paramMappings +

    Accessors

    isLoaded +paramMappings params

    Methods

    dispose -extractWeights +extractWeights freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -reassignParamFromPath -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +reassignParamFromPath +serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    params

    Methods

    dispose

    • dispose(throwOnRedispose?): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

    Settings

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    Accessors
    Methods
    +

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    params

    Methods

    dispose

    • dispose(throwOnRedispose?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    reassignParamFromPath

    serializeParams

    variable

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    diff --git a/typedoc/classes/ObjectDetection.html b/typedoc/classes/ObjectDetection.html index 04c1e96..9cbc0ec 100644 --- a/typedoc/classes/ObjectDetection.html +++ b/typedoc/classes/ObjectDetection.html @@ -1,11 +1,11 @@ -ObjectDetection | @vladmandic/face-api - v1.7.14

    Class ObjectDetection

    Hierarchy (view full)

    Constructors

    constructor +ObjectDetection | @vladmandic/face-api - v1.7.14

    Class ObjectDetection

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    box -className -classScore -imageDims -imageHeight -imageWidth -relativeBox +className +classScore +imageDims +imageHeight +imageWidth +relativeBox score -

    Methods

    forSize -

    Constructors

    constructor

    Accessors

    box

    className

    classScore

    imageDims

    imageHeight

    imageWidth

    relativeBox

    score

    Methods

    forSize

    Settings

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    Accessors
    Methods
    +

    Methods

    forSize +

    Constructors

    constructor

    Accessors

    box

    className

    classScore

    imageDims

    imageHeight

    imageWidth

    relativeBox

    score

    Methods

    forSize

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    diff --git a/typedoc/classes/Point.html b/typedoc/classes/Point.html index 591d571..12ffce2 100644 --- a/typedoc/classes/Point.html +++ b/typedoc/classes/Point.html @@ -1,4 +1,4 @@ -Point | @vladmandic/face-api - v1.7.14

    Class Point

    Implements

    Constructors

    constructor +Point | @vladmandic/face-api - v1.7.14

    Class Point

    Implements

    Constructors

    constructor

    Accessors

    x y

    Methods

    abs @@ -8,4 +8,4 @@ magnitude mul sub -

    Constructors

    constructor

    Accessors

    x

    y

    Methods

    abs

    add

    div

    floor

    magnitude

    mul

    sub

    Settings

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    +

    Constructors

    constructor

    Accessors

    x

    y

    Methods

    abs

    add

    div

    floor

    magnitude

    mul

    sub

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    diff --git a/typedoc/classes/PredictedBox.html b/typedoc/classes/PredictedBox.html index 523d3fe..12ee8a5 100644 --- a/typedoc/classes/PredictedBox.html +++ b/typedoc/classes/PredictedBox.html @@ -1,31 +1,31 @@ -PredictedBox | @vladmandic/face-api - v1.7.14

    Class PredictedBox

    Hierarchy (view full)

    Constructors

    constructor +PredictedBox | @vladmandic/face-api - v1.7.14

    Class PredictedBox

    Hierarchy (View Summary)

    Constructors

    constructor

    Accessors

    area bottom -bottomLeft -bottomRight -classScore +bottomLeft +bottomRight +classScore height label left right score top -topLeft -topRight +topLeft +topRight width x y

    Methods

    calibrate -clipAtImageBorders +clipAtImageBorders floor pad -padAtBorders +padAtBorders rescale round shift -toSquare -assertIsValidBox -assertIsValidLabeledBox -assertIsValidPredictedBox -isRect -

    Constructors

    constructor

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    classScore

    height

    label

    left

    right

    score

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(imageHeight, imageWidth): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

      • dx: number
      • dy: number
      • edx: number
      • edy: number
      • ex: number
      • ey: number
      • h: number
      • w: number
      • x: number
      • y: number

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    StaticassertIsValidLabeledBox

    StaticassertIsValidPredictedBox

    StaticisRect

    Settings

    Member Visibility

    On This Page

    Constructors
    Accessors
    Methods
    +toSquare +assertIsValidBox +assertIsValidLabeledBox +assertIsValidPredictedBox +isRect +

    Constructors

    constructor

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    classScore

    height

    label

    left

    right

    score

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(
          imageHeight: number,
          imageWidth: number,
      ): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

      • box: any
      • callee: string
      • allowNegativeDimensions: boolean = false

      Returns void

    StaticassertIsValidLabeledBox

    StaticassertIsValidPredictedBox

    StaticisRect

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    diff --git a/typedoc/classes/Rect.html b/typedoc/classes/Rect.html index aa098d8..3956c7a 100644 --- a/typedoc/classes/Rect.html +++ b/typedoc/classes/Rect.html @@ -1,26 +1,26 @@ -Rect | @vladmandic/face-api - v1.7.14

    Class Rect

    Hierarchy (view full)

    Implements

    Constructors

    constructor +Rect | @vladmandic/face-api - v1.7.14

    Class Rect

    Hierarchy (View Summary)

    Implements

    Constructors

    constructor

    Accessors

    area bottom -bottomLeft -bottomRight +bottomLeft +bottomRight height left right top -topLeft -topRight +topLeft +topRight width x y

    Methods

    calibrate -clipAtImageBorders +clipAtImageBorders floor pad -padAtBorders +padAtBorders rescale round shift -toSquare -assertIsValidBox -isRect -

    Constructors

    constructor

    • new Rect(x, y, width, height, allowNegativeDimensions?): Rect

    • Parameters

      • x: number
      • y: number
      • width: number
      • height: number
      • allowNegativeDimensions: boolean = false

      Returns Rect

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    height

    left

    right

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(imageHeight, imageWidth): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

      • dx: number
      • dy: number
      • edx: number
      • edy: number
      • ex: number
      • ey: number
      • h: number
      • w: number
      • x: number
      • y: number

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(box, callee, allowNegativeDimensions?): void

    • Parameters

      • box: any
      • callee: string
      • allowNegativeDimensions: boolean = false

      Returns void

    StaticisRect

    Settings

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    Accessors
    Methods
    +toSquare +assertIsValidBox +isRect +

    Constructors

    constructor

    • new Rect(
          x: number,
          y: number,
          width: number,
          height: number,
          allowNegativeDimensions?: boolean,
      ): Rect

    • Parameters

      • x: number
      • y: number
      • width: number
      • height: number
      • allowNegativeDimensions: boolean = false

      Returns Rect

    Accessors

    area

    bottom

    bottomLeft

    bottomRight

    height

    left

    right

    top

    topLeft

    topRight

    width

    x

    y

    Methods

    calibrate

    clipAtImageBorders

    floor

    pad

    padAtBorders

    • padAtBorders(
          imageHeight: number,
          imageWidth: number,
      ): {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    • Parameters

      • imageHeight: number
      • imageWidth: number

      Returns {
          dx: number;
          dy: number;
          edx: number;
          edy: number;
          ex: number;
          ey: number;
          h: number;
          w: number;
          x: number;
          y: number;
      }

    rescale

    round

    shift

    toSquare

    StaticassertIsValidBox

    • assertIsValidBox(
          box: any,
          callee: string,
          allowNegativeDimensions?: boolean,
      ): void

    • Parameters

      • box: any
      • callee: string
      • allowNegativeDimensions: boolean = false

      Returns void

    StaticisRect

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    diff --git a/typedoc/classes/SsdMobilenetv1.html b/typedoc/classes/SsdMobilenetv1.html index 8972148..db2f22b 100644 --- a/typedoc/classes/SsdMobilenetv1.html +++ b/typedoc/classes/SsdMobilenetv1.html @@ -1,23 +1,23 @@ -SsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1

    Hierarchy (view full)

    Constructors

    constructor +SsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1

    Hierarchy (View Summary)

    Constructors

    constructor

    Properties

    _name -

    Accessors

    isLoaded -paramMappings +

    Accessors

    isLoaded +paramMappings params

    Methods

    dispose -extractWeights +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    Methods

    dispose

    extractWeights

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    serializeParams

    variable

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Accessors
    Methods
    +

    Constructors

    constructor

    Properties

    _name

    _name: any

    Accessors

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    Methods

    dispose

    extractWeights

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    loadFromUri

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    serializeParams

    variable

    Settings

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    Accessors
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    diff --git a/typedoc/classes/SsdMobilenetv1Options.html b/typedoc/classes/SsdMobilenetv1Options.html index 2579cba..739dbe6 100644 --- a/typedoc/classes/SsdMobilenetv1Options.html +++ b/typedoc/classes/SsdMobilenetv1Options.html @@ -1,4 +1,4 @@ -SsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1Options

    Constructors

    constructor -

    Accessors

    maxResults -minConfidence -

    Constructors

    constructor

    Accessors

    maxResults

    minConfidence

    Settings

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    Accessors
    +SsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Class SsdMobilenetv1Options

    Constructors

    constructor +

    Accessors

    maxResults +minConfidence +

    Constructors

    constructor

    Accessors

    maxResults

    minConfidence

    Settings

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    diff --git a/typedoc/classes/TinyFaceDetector.html b/typedoc/classes/TinyFaceDetector.html index 8b2de8c..bd5da1a 100644 --- a/typedoc/classes/TinyFaceDetector.html +++ b/typedoc/classes/TinyFaceDetector.html @@ -1,31 +1,31 @@ -TinyFaceDetector | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetector

    Hierarchy

    • TinyYolov2Base
      • TinyFaceDetector

    Constructors

    constructor +TinyFaceDetector | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetector

    Hierarchy

    • TinyYolov2Base
      • TinyFaceDetector

    Constructors

    constructor

    Properties

    _name -DEFAULT_FILTER_SIZES +DEFAULT_FILTER_SIZES

    Accessors

    anchors -boxEncodingSize +boxEncodingSize config -isLoaded -paramMappings +isLoaded +paramMappings params -withClassScores +withClassScores

    Methods

    detect dispose -extractWeights +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -runMobilenet -runTinyYolov2 -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +runMobilenet +runTinyYolov2 +serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    StaticDEFAULT_FILTER_SIZES

    DEFAULT_FILTER_SIZES: number[] = ...

    Accessors

    anchors

    boxEncodingSize

    config

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    withClassScores

    Methods

    detect

    dispose

    • dispose(throwOnRedispose?): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    • getFrozenParams(): {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    • Returns {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    getParamFromPath

    getParamList

    getTrainableParams

    • getTrainableParams(): {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    • Returns {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    load

    • load(weightsOrUrl): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath, tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Accessors
    Methods
    +

    Constructors

    constructor

    Properties

    _name

    _name: any

    StaticDEFAULT_FILTER_SIZES

    DEFAULT_FILTER_SIZES: number[] = ...

    Accessors

    anchors

    boxEncodingSize

    config

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    withClassScores

    Methods

    detect

    dispose

    • dispose(throwOnRedispose?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

    Settings

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    Accessors
    Methods
    diff --git a/typedoc/classes/TinyFaceDetectorOptions.html b/typedoc/classes/TinyFaceDetectorOptions.html index b0fd4d8..2245337 100644 --- a/typedoc/classes/TinyFaceDetectorOptions.html +++ b/typedoc/classes/TinyFaceDetectorOptions.html @@ -1,4 +1,4 @@ -TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetectorOptions

    Hierarchy (view full)

    Constructors

    constructor -

    Accessors

    inputSize -scoreThreshold -

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

    Settings

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    Constructors
    Accessors
    +TinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Class TinyFaceDetectorOptions

    Hierarchy (View Summary)

    Constructors

    constructor +

    Accessors

    inputSize +scoreThreshold +

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

    Settings

    Member Visibility

    On This Page

    Constructors
    Accessors
    diff --git a/typedoc/classes/TinyYolov2.html b/typedoc/classes/TinyYolov2.html index 0c99d99..b9017d6 100644 --- a/typedoc/classes/TinyYolov2.html +++ b/typedoc/classes/TinyYolov2.html @@ -1,32 +1,32 @@ -TinyYolov2 | @vladmandic/face-api - v1.7.14

    Class TinyYolov2

    Hierarchy

    • TinyYolov2Base
      • TinyYolov2

    Constructors

    constructor +TinyYolov2 | @vladmandic/face-api - v1.7.14

    Class TinyYolov2

    Hierarchy

    • TinyYolov2Base
      • TinyYolov2

    Constructors

    constructor

    Properties

    _name -DEFAULT_FILTER_SIZES +DEFAULT_FILTER_SIZES

    Accessors

    anchors -boxEncodingSize +boxEncodingSize config -isLoaded -paramMappings +isLoaded +paramMappings params -withClassScores -withSeparableConvs +withClassScores +withSeparableConvs

    Methods

    detect dispose -extractWeights +extractWeights forward -forwardInput +forwardInput freeze -getFrozenParams -getParamFromPath -getParamList -getTrainableParams +getFrozenParams +getParamFromPath +getParamList +getTrainableParams load -loadFromDisk -loadFromUri -loadFromWeightMap -locateFaces -reassignParamFromPath -runMobilenet -runTinyYolov2 -serializeParams +loadFromDisk +loadFromUri +loadFromWeightMap +locateFaces +reassignParamFromPath +runMobilenet +runTinyYolov2 +serializeParams variable -

    Constructors

    constructor

    Properties

    _name

    _name: any

    StaticDEFAULT_FILTER_SIZES

    DEFAULT_FILTER_SIZES: number[] = ...

    Accessors

    anchors

    boxEncodingSize

    config

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    withClassScores

    withSeparableConvs

    Methods

    detect

    dispose

    • dispose(throwOnRedispose?): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    • getFrozenParams(): {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    • Returns {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    getParamFromPath

    getParamList

    getTrainableParams

    • getTrainableParams(): {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    • Returns {
          path: string;
          tensor: Tensor<Rank>;
      }[]

    load

    • load(weightsOrUrl): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath, tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

    Settings

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    On This Page

    Constructors
    Properties
    Accessors
    Methods
    +

    Constructors

    constructor

    Properties

    _name

    _name: any

    StaticDEFAULT_FILTER_SIZES

    DEFAULT_FILTER_SIZES: number[] = ...

    Accessors

    anchors

    boxEncodingSize

    config

    isLoaded

    paramMappings

    • get paramMappings(): ParamMapping[]

    • Returns ParamMapping[]

    params

    withClassScores

    withSeparableConvs

    Methods

    detect

    dispose

    • dispose(throwOnRedispose?: boolean): void

    • Parameters

      • throwOnRedispose: boolean = true

      Returns void

    extractWeights

    • extractWeights(weights: Float32Array): void

    • Parameters

      • weights: Float32Array

      Returns void

    forward

    forwardInput

    freeze

    getFrozenParams

    getParamFromPath

    getParamList

    getTrainableParams

    load

    • load(weightsOrUrl: undefined | string | Float32Array): Promise<void>

    • Parameters

      • weightsOrUrl: undefined | string | Float32Array

      Returns Promise<void>

    loadFromDisk

    • loadFromDisk(filePath: undefined | string): Promise<void>

    • Parameters

      • filePath: undefined | string

      Returns Promise<void>

    loadFromUri

    • loadFromUri(uri: undefined | string): Promise<void>

    • Parameters

      • uri: undefined | string

      Returns Promise<void>

    loadFromWeightMap

    locateFaces

    reassignParamFromPath

    • reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void

    • Parameters

      Returns void

    runMobilenet

    runTinyYolov2

    serializeParams

    • serializeParams(): Float32Array

    • Returns Float32Array

    variable

    Settings

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    Properties
    Accessors
    Methods
    diff --git a/typedoc/classes/TinyYolov2Options.html b/typedoc/classes/TinyYolov2Options.html index 49bc7a1..ca17271 100644 --- a/typedoc/classes/TinyYolov2Options.html +++ b/typedoc/classes/TinyYolov2Options.html @@ -1,4 +1,4 @@ -TinyYolov2Options | @vladmandic/face-api - v1.7.14

    Class TinyYolov2Options

    Hierarchy (view full)

    Constructors

    constructor -

    Accessors

    inputSize -scoreThreshold -

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

    Settings

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    Constructors
    Accessors
    +TinyYolov2Options | @vladmandic/face-api - v1.7.14

    Class TinyYolov2Options

    Hierarchy (View Summary)

    Constructors

    constructor +

    Accessors

    inputSize +scoreThreshold +

    Constructors

    constructor

    Accessors

    inputSize

    scoreThreshold

    Settings

    Member Visibility

    On This Page

    Constructors
    Accessors
    diff --git a/typedoc/classes/draw.DrawBox.html b/typedoc/classes/draw.DrawBox.html index 6d96dc9..949b356 100644 --- a/typedoc/classes/draw.DrawBox.html +++ b/typedoc/classes/draw.DrawBox.html @@ -1,5 +1,5 @@ -DrawBox | @vladmandic/face-api - v1.7.14

    Class DrawBox

    Constructors

    constructor +DrawBox | @vladmandic/face-api - v1.7.14

    Class DrawBox

    Constructors

    constructor

    Properties

    box options

    Methods

    draw -

    Constructors

    constructor

    Properties

    box

    box: Box<any>

    options

    options: DrawBoxOptions

    Methods

    draw

    • draw(canvasArg): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    Settings

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    Properties
    Methods
    +

    Constructors

    constructor

    Properties

    box

    box: Box

    options

    options: DrawBoxOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    Settings

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    Methods
    diff --git a/typedoc/classes/draw.DrawBoxOptions.html b/typedoc/classes/draw.DrawBoxOptions.html index ae63065..6dbd435 100644 --- a/typedoc/classes/draw.DrawBoxOptions.html +++ b/typedoc/classes/draw.DrawBoxOptions.html @@ -1,6 +1,6 @@ -DrawBoxOptions | @vladmandic/face-api - v1.7.14

    Class DrawBoxOptions

    Constructors

    constructor -

    Properties

    boxColor -drawLabelOptions +DrawBoxOptions | @vladmandic/face-api - v1.7.14

    Class DrawBoxOptions

    Constructors

    constructor +

    Properties

    boxColor +drawLabelOptions label? -lineWidth -

    Constructors

    constructor

    Properties

    boxColor

    boxColor: string

    drawLabelOptions

    drawLabelOptions: DrawTextFieldOptions

    Optionallabel

    label?: string

    lineWidth

    lineWidth: number

    Settings

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    Constructors
    Properties
    +lineWidth +

    Constructors

    constructor

    Properties

    boxColor

    boxColor: string

    drawLabelOptions

    drawLabelOptions: DrawTextFieldOptions

    Optionallabel

    label?: string

    lineWidth

    lineWidth: number

    Settings

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    Properties
    diff --git a/typedoc/classes/draw.DrawFaceLandmarks.html b/typedoc/classes/draw.DrawFaceLandmarks.html index a818ad0..6d1b0e6 100644 --- a/typedoc/classes/draw.DrawFaceLandmarks.html +++ b/typedoc/classes/draw.DrawFaceLandmarks.html @@ -1,5 +1,5 @@ -DrawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarks

    Constructors

    constructor -

    Properties

    faceLandmarks +DrawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarks

    Constructors

    constructor +

    Properties

    faceLandmarks options

    Methods

    draw -

    Constructors

    constructor

    Properties

    faceLandmarks

    faceLandmarks: FaceLandmarks

    options

    options: DrawFaceLandmarksOptions

    Methods

    draw

    Settings

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    Properties
    Methods
    +

    Constructors

    constructor

    Properties

    faceLandmarks

    faceLandmarks: FaceLandmarks

    options

    options: DrawFaceLandmarksOptions

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    Settings

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    Methods
    diff --git a/typedoc/classes/draw.DrawFaceLandmarksOptions.html b/typedoc/classes/draw.DrawFaceLandmarksOptions.html index 288d263..d0f6b43 100644 --- a/typedoc/classes/draw.DrawFaceLandmarksOptions.html +++ b/typedoc/classes/draw.DrawFaceLandmarksOptions.html @@ -1,8 +1,8 @@ -DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarksOptions

    Constructors

    constructor -

    Properties

    drawLines -drawPoints -lineColor -lineWidth -pointColor -pointSize -

    Constructors

    constructor

    Properties

    drawLines

    drawLines: boolean

    drawPoints

    drawPoints: boolean

    lineColor

    lineColor: string

    lineWidth

    lineWidth: number

    pointColor

    pointColor: string

    pointSize

    pointSize: number

    Settings

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    +DrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Class DrawFaceLandmarksOptions

    Constructors

    constructor +

    Properties

    drawLines +drawPoints +lineColor +lineWidth +pointColor +pointSize +

    Constructors

    constructor

    Properties

    drawLines

    drawLines: boolean

    drawPoints

    drawPoints: boolean

    lineColor

    lineColor: string

    lineWidth

    lineWidth: number

    pointColor

    pointColor: string

    pointSize

    pointSize: number

    Settings

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    Constructors
    Properties
    diff --git a/typedoc/classes/draw.DrawTextField.html b/typedoc/classes/draw.DrawTextField.html index ea20cdc..509c78a 100644 --- a/typedoc/classes/draw.DrawTextField.html +++ b/typedoc/classes/draw.DrawTextField.html @@ -1,9 +1,9 @@ -DrawTextField | @vladmandic/face-api - v1.7.14

    Class DrawTextField

    Constructors

    constructor +DrawTextField | @vladmandic/face-api - v1.7.14

    Class DrawTextField

    Constructors

    constructor

    Properties

    anchor options text

    Methods

    draw -getUpperLeft -measureHeight -measureWidth -

    Constructors

    constructor

    Properties

    anchor

    anchor: IPoint

    options

    options: DrawTextFieldOptions

    text

    text: string[]

    Methods

    draw

    • draw(canvasArg): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    getUpperLeft

    measureHeight

    measureWidth

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Methods
    +getUpperLeft +measureHeight +measureWidth +

    Constructors

    constructor

    Properties

    anchor

    anchor: IPoint

    options

    options: DrawTextFieldOptions

    text

    text: string[]

    Methods

    draw

    • draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns void

    getUpperLeft

    measureHeight

    measureWidth

    • measureWidth(ctx: CanvasRenderingContext2D): number

    • Parameters

      • ctx: CanvasRenderingContext2D

      Returns number

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Methods
    diff --git a/typedoc/classes/draw.DrawTextFieldOptions.html b/typedoc/classes/draw.DrawTextFieldOptions.html index b700429..ed73204 100644 --- a/typedoc/classes/draw.DrawTextFieldOptions.html +++ b/typedoc/classes/draw.DrawTextFieldOptions.html @@ -1,8 +1,8 @@ -DrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Class DrawTextFieldOptions

    Implements

    Constructors

    constructor -

    Properties

    anchorPosition -backgroundColor -fontColor -fontSize -fontStyle +DrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Class DrawTextFieldOptions

    Implements

    Constructors

    constructor +

    Properties

    anchorPosition +backgroundColor +fontColor +fontSize +fontStyle padding -

    Constructors

    constructor

    Properties

    anchorPosition

    anchorPosition: AnchorPosition

    backgroundColor

    backgroundColor: string

    fontColor

    fontColor: string

    fontSize

    fontSize: number

    fontStyle

    fontStyle: string

    padding

    padding: number

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    +

    Constructors

    constructor

    Properties

    anchorPosition

    anchorPosition: AnchorPosition

    backgroundColor

    backgroundColor: string

    fontColor

    fontColor: string

    fontSize

    fontSize: number

    fontStyle

    fontStyle: string

    padding

    padding: number

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    diff --git a/typedoc/classes/tf.Tensor-1.html b/typedoc/classes/tf.Tensor-1.html index 014be3d..d92e1cf 100644 --- a/typedoc/classes/tf.Tensor-1.html +++ b/typedoc/classes/tf.Tensor-1.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/face-api - v1.7.14

    Class Tensor<R>

    A tf.Tensor object represents an immutable, multidimensional array of +Tensor | @vladmandic/face-api - v1.7.14

    Class Tensor<R>

    A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.

    For performance reasons, functions that create tensors do not necessarily perform a copy of the data passed to them (e.g. if the data is passed as a @@ -6,18 +6,18 @@ perform a copy of the data passed to them (e.g. if the data is passed as a a feature and is not supported. To avoid this behavior, use the tensor before changing the input data or create a copy with copy = tf.add(yourTensor, 0).

    See tf.tensor for details on how to create a tf.Tensor.

    -

    Doc

    Type Parameters

    Hierarchy (view full)

    Implements

    • TensorInfo

    Constructors

    constructor -

    Properties

    dataId +

    Doc

    Type Parameters

    Hierarchy (View Summary)

    Implements

    • TensorInfo

    Constructors

    constructor +

    Properties

    dataId dtype id kept -kerasMask? -rankType -scopeId +kerasMask? +rankType +scopeId shape size strides -

    Accessors

    isDisposed +

    Accessors

    isDisposed rank

    Methods

    abs acos @@ -25,98 +25,98 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0) add all any -argMax -argMin +argMax +argMin array -arraySync -as1D -as2D -as3D -as4D -as5D +arraySync +as1D +as2D +as3D +as4D +as5D asin asinh -asScalar -asType +asScalar +asType atan atan2 atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo +avgPool +batchNorm +batchToSpaceND +broadcastTo buffer -bufferSync +bufferSync bytes cast ceil -clipByValue +clipByValue clone concat conv1d conv2d -conv2dTranspose +conv2dTranspose cos cosh cumprod cumsum data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d +dataSync +dataToGPU +depthToSpace +depthwiseConv2d dilation2d dispose div -divNoNan +divNoNan dot elu equal erf -euclideanNorm +euclideanNorm exp -expandDims +expandDims expm1 fft flatten floor -floorDiv +floorDiv gather greater -greaterEqual +greaterEqual ifft irfft -isFinite -isInf -isNaN -leakyRelu +isFinite +isInf +isNaN +leakyRelu less -lessEqual -localResponseNormalization +lessEqual +localResponseNormalization log log1p -logicalAnd -logicalNot -logicalOr -logicalXor -logSigmoid -logSoftmax -logSumExp -matMul +logicalAnd +logicalNot +logicalOr +logicalXor +logSigmoid +logSoftmax +logSumExp +matMul max maximum -maxPool +maxPool mean min minimum -mirrorPad +mirrorPad mod mul neg norm -notEqual -oneHot -onesLike +notEqual +oneHot +onesLike pad pool pow @@ -127,15 +127,15 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0) relu relu6 reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor +reshapeAs +resizeBilinear +resizeNearestNeighbor reverse rfft round rsqrt selu -separableConv2d +separableConv2d sigmoid sign sin @@ -143,59 +143,59 @@ changing the input data or create a copy with copy = tf.add(yourTensor, 0) slice softmax softplus -spaceToBatchND +spaceToBatchND split sqrt square -squaredDifference +squaredDifference squeeze stack step -stridedSlice +stridedSlice sub sum tan tanh -throwIfDisposed +throwIfDisposed tile -toBool -toFloat -toInt +toBool +toFloat +toInt topk -toString +toString transpose unique -unsortedSegmentSum +unsortedSegmentSum unstack variable where -zerosLike -

    Constructors

    constructor

    • new Tensor<R>(shape, dtype, dataId, id): Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]
      • dtype: keyof DataTypeMap
      • dataId: object
      • id: number

      Returns Tensor<R>

    Properties

    dataId

    dataId: object

    Id of the bucket holding the data for this tensor. Multiple arrays can +zerosLike +

    Constructors

    constructor

    • new Tensor<R extends Rank = Rank>(
          shape: ShapeMap[R],
          dtype: keyof DataTypeMap,
          dataId: object,
          id: number,
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]
      • dtype: keyof DataTypeMap
      • dataId: object
      • id: number

      Returns tf.Tensor<R>

    Properties

    dataId

    dataId: object

    Id of the bucket holding the data for this tensor. Multiple arrays can point to the same bucket (e.g. when calling array.reshape()).

    -

    Readonlydtype

    dtype: keyof DataTypeMap

    The data type for the array.

    -

    Readonlyid

    id: number

    Unique id of this tensor.

    -

    kept

    kept: boolean

    Whether this tensor has been globally kept.

    -

    OptionalkerasMask

    kerasMask?: Tensor<Rank>

    The keras mask that some keras layers attach to the tensor

    -

    ReadonlyrankType

    rankType: R

    The rank type for the array (see Rank enum).

    -

    scopeId

    scopeId: number

    The id of the scope this tensor is being tracked in.

    -

    Readonlyshape

    shape: ShapeMap[R]

    The shape of the tensor.

    -

    Readonlysize

    size: number

    Number of elements in the tensor.

    -

    Readonlystrides

    strides: number[]

    Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ +

    Readonlydtype

    dtype: keyof DataTypeMap

    The data type for the array.

    +

    Readonlyid

    id: number

    Unique id of this tensor.

    +

    kept

    kept: boolean

    Whether this tensor has been globally kept.

    +

    OptionalkerasMask

    kerasMask?: tf.Tensor

    The keras mask that some keras layers attach to the tensor

    +

    ReadonlyrankType

    rankType: R

    The rank type for the array (see Rank enum).

    +

    scopeId

    scopeId: number

    The id of the scope this tensor is being tracked in.

    +

    Readonlyshape

    shape: ShapeMap[R]

    The shape of the tensor.

    +

    Readonlysize

    size: number

    Number of elements in the tensor.

    +

    Readonlystrides

    strides: number[]

    Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ numpy.ndarray.strides.html

    -

    Accessors

    isDisposed

    • get isDisposed(): boolean

    • Returns boolean

    rank

    • get rank(): number

    • Returns number

    Methods

    abs

    • abs<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acos

    • acos<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acosh

    • acosh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    add

    • add<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    all

    • all<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    any

    • any<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    argMax

    • argMax<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    argMin

    • argMin<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    array

    • array(): Promise<ArrayMap[R]>

    • Returns the tensor data as a nested array. The transfer of data is done +

    Accessors

    isDisposed

    • get isDisposed(): boolean

    • Returns boolean

    rank

    • get rank(): number

    • Returns number

    Methods

    abs

    • abs<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acos

    • acos<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acosh

    • acosh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    add

    all

    • all<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    any

    • any<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    argMax

    • argMax<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    argMin

    • argMin<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    array

    • array(): Promise<ArrayMap[R]>

    • Returns the tensor data as a nested array. The transfer of data is done asynchronously.

      -

      Returns Promise<ArrayMap[R]>

      Doc

    arraySync

    • arraySync(): ArrayMap[R]

    • Returns the tensor data as a nested array. The transfer of data is done +

      Returns Promise<ArrayMap[R]>

      Doc

    arraySync

    • arraySync(): ArrayMap[R]

    • Returns the tensor data as a nested array. The transfer of data is done synchronously.

      -

      Returns ArrayMap[R]

      Doc

    as1D

    • as1D<T>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    as2D

    • as2D<T>(rows, columns): Tensor2D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number

      Returns Tensor2D

    as3D

    • as3D<T>(rows, columns, depth): Tensor3D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number

      Returns Tensor3D

    as4D

    • as4D<T>(rows, columns, depth, depth2): Tensor4D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number

      Returns Tensor4D

    as5D

    • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number
      • depth3: number

      Returns Tensor5D

    asin

    • asin<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asinh

    • asinh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asScalar

    • asScalar<T>(): Scalar

    • Type Parameters

      Returns Scalar

    asType

    • asType<T>(this, dtype): T

    • Type Parameters

      Parameters

      • this: T
      • dtype: keyof DataTypeMap

      Returns T

    atan

    • atan<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    atan2

    • atan2<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    atanh

    • atanh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    avgPool

    • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    batchNorm

    batchToSpaceND

    • batchToSpaceND<R>(blockShape, crops): Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • crops: number[][]

      Returns Tensor<R>

    broadcastTo

    • broadcastTo<R>(shape): Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]

      Returns Tensor<R>

    buffer

    • buffer<D>(): Promise<TensorBuffer<R, D>>

    • Returns a promise of tf.TensorBuffer that holds the underlying data.

      -

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns Promise<TensorBuffer<R, D>>

      Doc

    bufferSync

    • bufferSync<D>(): TensorBuffer<R, D>

    • Returns a tf.TensorBuffer that holds the underlying data.

      -

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns TensorBuffer<R, D>

      Doc

    bytes

    • bytes(): Promise<Uint8Array | Uint8Array[]>

    • Returns the underlying bytes of the tensor's data.

      -

      Returns Promise<Uint8Array | Uint8Array[]>

    cast

    • cast<T>(dtype): T

    • Type Parameters

      Parameters

      • dtype: keyof DataTypeMap

      Returns T

    ceil

    • ceil<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    clipByValue

    • clipByValue<T>(min, max): Tensor<Rank>

    • Type Parameters

      Parameters

      • min: number
      • max: number

      Returns Tensor<Rank>

    clone

    • clone<T>(this): T

    • Returns a copy of the tensor. See tf.clone for details.

      -

      Type Parameters

      Parameters

      • this: T

      Returns T

      Doc

    concat

    • concat<T>(tensors, axis?): T

    • Type Parameters

      Parameters

      Returns T

    conv1d

    • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • stride: number
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldataFormat: "NWC" | "NCW"
      • Optionaldilation: number
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2d

    • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2dTranspose

    • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • outputShape: [number, number, number, number] | [number, number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    cos

    • cos<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cosh

    • cosh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cumprod

    • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns Tensor<R>

    cumsum

    • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns Tensor<R>

    data

    • data<D>(): Promise<DataTypeMap[D]>

    • Asynchronously downloads the values from the tf.Tensor. Returns a +

      Returns ArrayMap[R]

      Doc

    as1D

    • as1D<T extends tf.Tensor>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    as2D

    • as2D<T extends tf.Tensor>(rows: number, columns: number): Tensor2D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number

      Returns Tensor2D

    as3D

    • as3D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
      ): Tensor3D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number

      Returns Tensor3D

    as4D

    • as4D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
          depth2: number,
      ): Tensor4D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number

      Returns Tensor4D

    as5D

    • as5D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
          depth2: number,
          depth3: number,
      ): Tensor5D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number
      • depth3: number

      Returns Tensor5D

    asin

    • asin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asinh

    • asinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asScalar

    • asScalar<T extends tf.Tensor>(): Scalar

    • Type Parameters

      Returns Scalar

    asType

    • asType<T extends tf.Tensor>(this: T, dtype: keyof DataTypeMap): T

    • Type Parameters

      Parameters

      • this: T
      • dtype: keyof DataTypeMap

      Returns T

    atan

    • atan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    atan2

    atanh

    • atanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    avgPool

    • avgPool<T extends Tensor4D | Tensor3D>(
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    batchNorm

    batchToSpaceND

    • batchToSpaceND<R extends Rank>(
          blockShape: number[],
          crops: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • crops: number[][]

      Returns tf.Tensor<R>

    broadcastTo

    • broadcastTo<R extends Rank>(shape: ShapeMap[R]): tf.Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]

      Returns tf.Tensor<R>

    buffer

    • buffer<D extends keyof DataTypeMap = "float32">(): Promise<TensorBuffer<R, D>>

    • Returns a promise of tf.TensorBuffer that holds the underlying data.

      +

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns Promise<TensorBuffer<R, D>>

      Doc

    bufferSync

    • bufferSync<D extends keyof DataTypeMap = "float32">(): TensorBuffer<R, D>

    • Returns a tf.TensorBuffer that holds the underlying data.

      +

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns TensorBuffer<R, D>

      Doc

    bytes

    • bytes(): Promise<Uint8Array | Uint8Array[]>

    • Returns the underlying bytes of the tensor's data.

      +

      Returns Promise<Uint8Array | Uint8Array[]>

    cast

    • cast<T extends tf.Tensor>(dtype: keyof DataTypeMap): T

    • Type Parameters

      Parameters

      • dtype: keyof DataTypeMap

      Returns T

    ceil

    • ceil<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    clipByValue

    • clipByValue<T extends tf.Tensor>(min: number, max: number): tf.Tensor

    • Type Parameters

      Parameters

      • min: number
      • max: number

      Returns tf.Tensor

    clone

    • clone<T extends tf.Tensor>(this: T): T

    • Returns a copy of the tensor. See tf.clone for details.

      +

      Type Parameters

      Parameters

      • this: T

      Returns T

      Doc

    concat

    • concat<T extends tf.Tensor>(tensors: T | (TensorLike | T)[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    conv1d

    • conv1d<T extends Tensor2D | Tensor3D>(
          filter: Tensor3D | TensorLike3D,
          stride: number,
          pad: number | "valid" | "same" | ExplicitPadding,
          dataFormat?: "NWC" | "NCW",
          dilation?: number,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • stride: number
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldataFormat: "NWC" | "NCW"
      • Optionaldilation: number
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2d

    • conv2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2dTranspose

    • conv2dTranspose<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          outputShape: [number, number, number, number] | [number, number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • outputShape: [number, number, number, number] | [number, number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    cos

    • cos<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cosh

    • cosh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cumprod

    • cumprod<R extends Rank>(
          axis?: number,
          exclusive?: boolean,
          reverse?: boolean,
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns tf.Tensor<R>

    cumsum

    • cumsum<R extends Rank>(
          axis?: number,
          exclusive?: boolean,
          reverse?: boolean,
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns tf.Tensor<R>

    data

    • data<D extends keyof DataTypeMap = NumericDataType>(): Promise<DataTypeMap[D]>

    • Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

      -

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns Promise<DataTypeMap[D]>

      Doc

    dataSync

    • dataSync<D>(): DataTypeMap[D]

    • Synchronously downloads the values from the tf.Tensor. This blocks the +

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns Promise<DataTypeMap[D]>

      Doc

    dataSync

    • dataSync<D extends keyof DataTypeMap = NumericDataType>(): DataTypeMap[D]

    • Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

      -

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns DataTypeMap[D]

      Doc

    dataToGPU

    • dataToGPU(options?): GPUData

    • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns DataTypeMap[D]

      Doc

    dataToGPU

    • dataToGPU(options?: DataToGPUWebGLOption): GPUData

    • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() and data(), this method prevents data from being downloaded to CPU.

      For WebGL backend, the data will be stored on a densely packed texture. This means that the texture will use the RGBA channels to store value.

      @@ -217,9 +217,9 @@ texShape: [number, number] // [height, width] Remember to dispose the GPUData after it is used by `res.tensorRef.dispose()`.       -

      Doc

    depthToSpace

    • depthToSpace<T>(blockSize, dataFormat): T

    • Type Parameters

      Parameters

      • blockSize: number
      • dataFormat: "NHWC" | "NCHW"

      Returns T

    depthwiseConv2d

    • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    dilation2d

    • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilations: number | [number, number]
      • OptionaldataFormat: "NHWC"

      Returns T

    dispose

    • dispose(): void

    • Disposes tf.Tensor from memory.

      -

      Returns void

      Doc

    div

    • div<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    divNoNan

    • divNoNan<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    dot

    elu

    • elu<T>(): T

    • Type Parameters

      Returns T

    equal

    • equal<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    erf

    • erf<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    euclideanNorm

    • euclideanNorm<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    exp

    • exp<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    expandDims

    • expandDims<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    expm1

    • expm1<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    fft

    • fft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    flatten

    • flatten<T>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    floor

    • floor<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    floorDiv

    • floorDiv<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    gather

    • gather<T>(this, indices, axis?, batchDims?): T

    • Type Parameters

      Parameters

      Returns T

    greater

    • greater<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    greaterEqual

    • greaterEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    ifft

    • ifft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    irfft

    • irfft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    isFinite

    • isFinite<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isInf

    • isInf<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isNaN

    • isNaN<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    leakyRelu

    • leakyRelu<T>(alpha): T

    • Type Parameters

      Parameters

      • alpha: number

      Returns T

    less

    • less<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    lessEqual

    • lessEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    localResponseNormalization

    • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

    • Type Parameters

      Parameters

      • OptionaldepthRadius: number
      • Optionalbias: number
      • Optionalalpha: number
      • Optionalbeta: number

      Returns T

    log

    • log<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    log1p

    • log1p<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logicalAnd

    • logicalAnd<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logicalNot

    • logicalNot<T>(): T

    • Type Parameters

      Returns T

    logicalOr

    • logicalOr<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logicalXor

    • logicalXor<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logSigmoid

    • logSigmoid<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logSoftmax

    • logSoftmax<T>(this, axis?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns T

    logSumExp

    • logSumExp<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    matMul

    • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    max

    • max<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    maximum

    • maximum<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    maxPool

    • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    mean

    • mean<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    min

    • min<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    minimum

    • minimum<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    mirrorPad

    • mirrorPad<T>(paddings, mode): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • mode: "reflect" | "symmetric"

      Returns T

    mod

    • mod<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    mul

    • mul<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    neg

    • neg<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    norm

    • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

    • Type Parameters

      Parameters

      • Optionalord: number | "euclidean" | "fro"
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns Tensor<Rank>

    notEqual

    • notEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    oneHot

    • oneHot(depth, onValue, offValue): Tensor<Rank>

    • Parameters

      • depth: number
      • onValue: number
      • offValue: number

      Returns Tensor<Rank>

    onesLike

    • onesLike<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    pad

    • pad<T>(paddings, constantValue?): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • OptionalconstantValue: number

      Returns T

    pool

    • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • windowShape: number | [number, number]
      • poolingType: "avg" | "max"
      • padding:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldiationRate: number | [number, number]
      • Optionalstrides: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    pow

    • pow<T>(exp): T

    • Type Parameters

      Parameters

      Returns T

    prelu

    • prelu<T>(alpha): T

    • Type Parameters

      Parameters

      Returns T

    print

    • print(verbose?): void

    • Prints the tf.Tensor. See tf.print for details.

      +

      Doc

    depthToSpace

    • depthToSpace<T extends Tensor4D>(
          blockSize: number,
          dataFormat: "NHWC" | "NCHW",
      ): T

    • Type Parameters

      Parameters

      • blockSize: number
      • dataFormat: "NHWC" | "NCHW"

      Returns T

    depthwiseConv2d

    • depthwiseConv2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    dilation2d

    • dilation2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor3D | TensorLike3D,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilations?: number | [number, number],
          dataFormat?: "NHWC",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilations: number | [number, number]
      • OptionaldataFormat: "NHWC"

      Returns T

    dispose

    • dispose(): void

    • Disposes tf.Tensor from memory.

      +

      Returns void

      Doc

    div

    divNoNan

    dot

    elu

    • elu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    equal

    erf

    • erf<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    euclideanNorm

    • euclideanNorm<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    exp

    • exp<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    expandDims

    • expandDims<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    expm1

    • expm1<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    fft

    • fft<T extends tf.Tensor>(this: tf.Tensor): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    flatten

    • flatten<T extends tf.Tensor>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    floor

    • floor<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    floorDiv

    gather

    • gather<T extends tf.Tensor>(
          this: T,
          indices: TensorLike | tf.Tensor,
          axis?: number,
          batchDims?: number,
      ): T

    • Type Parameters

      Parameters

      Returns T

    greater

    greaterEqual

    • greaterEqual<T extends tf.Tensor>(b: TensorLike | tf.Tensor): T

    • Type Parameters

      Parameters

      Returns T

    ifft

    • ifft<T extends tf.Tensor>(this: tf.Tensor): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    irfft

    • irfft<T extends tf.Tensor>(this: tf.Tensor): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    isFinite

    • isFinite<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isInf

    • isInf<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isNaN

    • isNaN<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    leakyRelu

    • leakyRelu<T extends tf.Tensor>(alpha: number): T

    • Type Parameters

      Parameters

      • alpha: number

      Returns T

    less

    lessEqual

    localResponseNormalization

    • localResponseNormalization<T extends tf.Tensor>(
          depthRadius?: number,
          bias?: number,
          alpha?: number,
          beta?: number,
      ): T

    • Type Parameters

      Parameters

      • OptionaldepthRadius: number
      • Optionalbias: number
      • Optionalalpha: number
      • Optionalbeta: number

      Returns T

    log

    • log<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    log1p

    • log1p<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logicalAnd

    • logicalAnd<T extends tf.Tensor>(b: TensorLike | tf.Tensor): T

    • Type Parameters

      Parameters

      Returns T

    logicalNot

    • logicalNot<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    logicalOr

    logicalXor

    • logicalXor<T extends tf.Tensor>(b: TensorLike | tf.Tensor): T

    • Type Parameters

      Parameters

      Returns T

    logSigmoid

    • logSigmoid<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logSoftmax

    • logSoftmax<T extends tf.Tensor>(this: T, axis?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns T

    logSumExp

    • logSumExp<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    matMul

    • matMul<T extends tf.Tensor>(
          b: TensorLike | tf.Tensor,
          transposeA?: boolean,
          transposeB?: boolean,
      ): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    max

    • max<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    maximum

    maxPool

    • maxPool<T extends Tensor4D | Tensor3D>(
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    mean

    • mean<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    min

    • min<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    minimum

    mirrorPad

    • mirrorPad<T extends tf.Tensor>(
          paddings: [number, number][],
          mode: "reflect" | "symmetric",
      ): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • mode: "reflect" | "symmetric"

      Returns T

    mod

    mul

    neg

    • neg<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    norm

    • norm<T extends tf.Tensor>(
          ord?: number | "euclidean" | "fro",
          axis?: number | number[],
          keepDims?: boolean,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • Optionalord: number | "euclidean" | "fro"
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns tf.Tensor

    notEqual

    oneHot

    • oneHot(depth: number, onValue: number, offValue: number): tf.Tensor

    • Parameters

      • depth: number
      • onValue: number
      • offValue: number

      Returns tf.Tensor

    onesLike

    • onesLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    pad

    • pad<T extends tf.Tensor>(
          paddings: [number, number][],
          constantValue?: number,
      ): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • OptionalconstantValue: number

      Returns T

    pool

    • pool<T extends Tensor4D | Tensor3D>(
          windowShape: number | [number, number],
          poolingType: "avg" | "max",
          padding: number | "valid" | "same" | ExplicitPadding,
          diationRate?: number | [number, number],
          strides?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • windowShape: number | [number, number]
      • poolingType: "avg" | "max"
      • padding: number | "valid" | "same" | ExplicitPadding
      • OptionaldiationRate: number | [number, number]
      • Optionalstrides: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    pow

    prelu

    • prelu<T extends tf.Tensor>(alpha: TensorLike | T): T

    • Type Parameters

      Parameters

      Returns T

    print

    • print(verbose?: boolean): void

    • Prints the tf.Tensor. See tf.print for details.

      Parameters

      • Optionalverbose: boolean

        Whether to print verbose information about the tensor, including dtype and size.

        -

      Returns void

      Doc

    prod

    • prod<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    reciprocal

    • reciprocal<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T>(shape): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T>(x): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfPixelCenters: boolean

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfFloatCenters: boolean

      Returns T

    reverse

    • reverse<T>(this, axis?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]

      Returns T

    rfft

    • rfft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    round

    • round<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

    • Type Parameters

      Parameters

      • depthwiseFilter: Tensor4D | TensorLike4D
      • pointwiseFilter: Tensor4D | TensorLike
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilation: number | [number, number]
      • OptionaldataFormat: "NHWC" | "NCHW"

      Returns T

    sigmoid

    • sigmoid<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T>(this, begin, size?): T

    • Type Parameters

      Parameters

      • this: T
      • begin: number | number[]
      • Optionalsize: number | number[]

      Returns T

    softmax

    • softmax<T>(this, dim?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • paddings: number[][]

      Returns Tensor<R>

    split

    • split<T>(numOrSizeSplits, axis?): T[]

    • Type Parameters

      Parameters

      • numOrSizeSplits: number | number[]
      • Optionalaxis: number

      Returns T[]

    sqrt

    • sqrt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    • squaredDifference<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    squeeze

    • squeeze<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T>(x, axis?): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T>(this, alpha?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

    • Type Parameters

      Parameters

      • this: Tensor<Rank>
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns Tensor<Rank>

    sub

    • sub<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    sum

    • sum<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    tan

    • tan<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T>(b): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T>(this, k?, sorted?): {
          indices: T;
          values: T;
      }

    • Type Parameters

      Parameters

      • this: T
      • Optionalk: number
      • Optionalsorted: boolean

      Returns {
          indices: T;
          values: T;
      }

      • indices: T
      • values: T

    toString

    • toString(verbose?): string

    • Returns a human-readable description of the tensor. Useful for logging.

      -

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T>(perm?): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T>(this, axis?): {
          indices: T;
          values: T;
      }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns {
          indices: T;
          values: T;
      }

      • indices: T
      • values: T

    unsortedSegmentSum

    • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

    • Type Parameters

      Parameters

      • this: T
      • segmentIds: Tensor1D | TensorLike1D
      • numSegments: number

      Returns T

    unstack

    • unstack<T>(axis?): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(trainable?, name?, dtype?): Variable<R>

    • Parameters

      • Optionaltrainable: boolean
      • Optionalname: string
      • Optionaldtype: keyof DataTypeMap

      Returns Variable<R>

    where

    • where<T>(condition, x): T

    • Type Parameters

      Parameters

      Returns T

    zerosLike

    • zerosLike<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Accessors
    Methods
    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    +

    Returns void

    Doc

    prod

    • prod<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    reciprocal

    • reciprocal<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T extends tf.Tensor>(shape: number[]): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T extends tf.Tensor>(x: T): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfPixelCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfPixelCenters: boolean

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfFloatCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfFloatCenters: boolean

      Returns T

    reverse

    • reverse<T extends tf.Tensor>(this: T, axis?: number | number[]): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]

      Returns T

    rfft

    • rfft<T extends tf.Tensor>(this: tf.Tensor): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    round

    • round<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T extends Tensor4D | Tensor3D>(
          depthwiseFilter: Tensor4D | TensorLike4D,
          pointwiseFilter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilation?: number | [number, number],
          dataFormat?: "NHWC" | "NCHW",
      ): T

    • Type Parameters

      Parameters

      • depthwiseFilter: Tensor4D | TensorLike4D
      • pointwiseFilter: Tensor4D | TensorLike
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilation: number | [number, number]
      • OptionaldataFormat: "NHWC" | "NCHW"

      Returns T

    sigmoid

    • sigmoid<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T extends tf.Tensor>(
          this: T,
          begin: number | number[],
          size?: number | number[],
      ): T

    • Type Parameters

      Parameters

      • this: T
      • begin: number | number[]
      • Optionalsize: number | number[]

      Returns T

    softmax

    • softmax<T extends tf.Tensor>(this: T, dim?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R extends Rank>(
          blockShape: number[],
          paddings: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • paddings: number[][]

      Returns tf.Tensor<R>

    split

    • split<T extends tf.Tensor>(
          numOrSizeSplits: number | number[],
          axis?: number,
      ): T[]

    • Type Parameters

      Parameters

      • numOrSizeSplits: number | number[]
      • Optionalaxis: number

      Returns T[]

    sqrt

    • sqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    • squaredDifference<T extends tf.Tensor>(b: TensorLike | tf.Tensor): T

    • Type Parameters

      Parameters

      Returns T

    squeeze

    • squeeze<T extends tf.Tensor>(axis?: number[]): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T extends tf.Tensor>(x: tf.Tensor | tf.Tensor[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T extends tf.Tensor>(this: T, alpha?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T extends tf.Tensor>(
          this: tf.Tensor,
          begin: number[],
          end: number[],
          strides: number[],
          beginMask?: number,
          endMask?: number,
          ellipsisMask?: number,
          newAxisMask?: number,
          shrinkAxisMask?: number,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • this: tf.Tensor
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns tf.Tensor

    sub

    sum

    • sum<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    tan

    • tan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T extends tf.Tensor>(b: number[]): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T extends tf.Tensor>(
          this: T,
          k?: number,
          sorted?: boolean,
      ): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalk: number
      • Optionalsorted: boolean

      Returns { indices: T; values: T }

    toString

    • toString(verbose?: boolean): string

    • Returns a human-readable description of the tensor. Useful for logging.

      +

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • segmentIds: Tensor1D | TensorLike1D
      • numSegments: number

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

      • Optionaltrainable: boolean
      • Optionalname: string
      • Optionaldtype: keyof DataTypeMap

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
    Accessors
    Methods
    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    diff --git a/typedoc/classes/tf.Variable.html b/typedoc/classes/tf.Variable.html index 56761bf..f1e668d 100644 --- a/typedoc/classes/tf.Variable.html +++ b/typedoc/classes/tf.Variable.html @@ -1,18 +1,18 @@ -Variable | @vladmandic/face-api - v1.7.14

    Class Variable<R>

    A mutable tf.Tensor, useful for persisting state, e.g. for training.

    -

    Doc

    Type Parameters

    Hierarchy (view full)

    Constructors

    constructor -

    Properties

    dataId +Variable | @vladmandic/face-api - v1.7.14

    Class Variable<R>

    A mutable tf.Tensor, useful for persisting state, e.g. for training.

    +

    Doc

    Type Parameters

    Hierarchy (View Summary)

    Constructors

    constructor +

    Properties

    dataId dtype id kept -kerasMask? +kerasMask? name -rankType -scopeId +rankType +scopeId shape size strides trainable -

    Accessors

    isDisposed +

    Accessors

    isDisposed rank

    Methods

    abs acos @@ -20,99 +20,99 @@ add all any -argMax -argMin +argMax +argMin array -arraySync -as1D -as2D -as3D -as4D -as5D +arraySync +as1D +as2D +as3D +as4D +as5D asin asinh -asScalar +asScalar assign -asType +asType atan atan2 atanh -avgPool -batchNorm -batchToSpaceND -broadcastTo +avgPool +batchNorm +batchToSpaceND +broadcastTo buffer -bufferSync +bufferSync bytes cast ceil -clipByValue +clipByValue clone concat conv1d conv2d -conv2dTranspose +conv2dTranspose cos cosh cumprod cumsum data -dataSync -dataToGPU -depthToSpace -depthwiseConv2d +dataSync +dataToGPU +depthToSpace +depthwiseConv2d dilation2d dispose div -divNoNan +divNoNan dot elu equal erf -euclideanNorm +euclideanNorm exp -expandDims +expandDims expm1 fft flatten floor -floorDiv +floorDiv gather greater -greaterEqual +greaterEqual ifft irfft -isFinite -isInf -isNaN -leakyRelu +isFinite +isInf +isNaN +leakyRelu less -lessEqual -localResponseNormalization +lessEqual +localResponseNormalization log log1p -logicalAnd -logicalNot -logicalOr -logicalXor -logSigmoid -logSoftmax -logSumExp -matMul +logicalAnd +logicalNot +logicalOr +logicalXor +logSigmoid +logSoftmax +logSumExp +matMul max maximum -maxPool +maxPool mean min minimum -mirrorPad +mirrorPad mod mul neg norm -notEqual -oneHot -onesLike +notEqual +oneHot +onesLike pad pool pow @@ -123,15 +123,15 @@ relu relu6 reshape -reshapeAs -resizeBilinear -resizeNearestNeighbor +reshapeAs +resizeBilinear +resizeNearestNeighbor reverse rfft round rsqrt selu -separableConv2d +separableConv2d sigmoid sign sin @@ -139,62 +139,62 @@ slice softmax softplus -spaceToBatchND +spaceToBatchND split sqrt square -squaredDifference +squaredDifference squeeze stack step -stridedSlice +stridedSlice sub sum tan tanh -throwIfDisposed +throwIfDisposed tile -toBool -toFloat -toInt +toBool +toFloat +toInt topk -toString +toString transpose unique -unsortedSegmentSum +unsortedSegmentSum unstack variable where -zerosLike -

    Constructors

    constructor

    • new Variable<R>(initialValue, trainable, name, tensorId): Variable<R>

    • Type Parameters

      Parameters

      • initialValue: Tensor<R>
      • trainable: boolean
      • name: string
      • tensorId: number

      Returns Variable<R>

    Properties

    dataId

    dataId: object

    Id of the bucket holding the data for this tensor. Multiple arrays can +zerosLike +

    Constructors

    constructor

    • new Variable<R extends Rank = Rank>(
          initialValue: tf.Tensor<R>,
          trainable: boolean,
          name: string,
          tensorId: number,
      ): Variable<R>

    • Type Parameters

      Parameters

      • initialValue: tf.Tensor<R>
      • trainable: boolean
      • name: string
      • tensorId: number

      Returns Variable<R>

    Properties

    dataId

    dataId: object

    Id of the bucket holding the data for this tensor. Multiple arrays can point to the same bucket (e.g. when calling array.reshape()).

    -

    Readonlydtype

    dtype: keyof DataTypeMap

    The data type for the array.

    -

    Readonlyid

    id: number

    Unique id of this tensor.

    -

    kept

    kept: boolean

    Whether this tensor has been globally kept.

    -

    OptionalkerasMask

    kerasMask?: Tensor<Rank>

    The keras mask that some keras layers attach to the tensor

    -

    name

    name: string

    ReadonlyrankType

    rankType: R

    The rank type for the array (see Rank enum).

    -

    scopeId

    scopeId: number

    The id of the scope this tensor is being tracked in.

    -

    Readonlyshape

    shape: ShapeMap[R]

    The shape of the tensor.

    -

    Readonlysize

    size: number

    Number of elements in the tensor.

    -

    Readonlystrides

    strides: number[]

    Number of elements to skip in each dimension when indexing. See -https://docs.scipy.org/doc/numpy/reference/generated/\ +

    Readonlydtype

    dtype: keyof DataTypeMap

    The data type for the array.

    +

    Readonlyid

    id: number

    Unique id of this tensor.

    +

    kept

    kept: boolean

    Whether this tensor has been globally kept.

    +

    OptionalkerasMask

    kerasMask?: tf.Tensor

    The keras mask that some keras layers attach to the tensor

    +

    name

    name: string

    ReadonlyrankType

    rankType: R

    The rank type for the array (see Rank enum).

    +

    scopeId

    scopeId: number

    The id of the scope this tensor is being tracked in.

    +

    Readonlyshape

    shape: ShapeMap[R]

    The shape of the tensor.

    +

    Readonlysize

    size: number

    Number of elements in the tensor.

    +

    Readonlystrides

    strides: number[]

    Number of elements to skip in each dimension when indexing. See +https://docs.scipy.org/doc/numpy/reference/generated/\ numpy.ndarray.strides.html

    -

    trainable

    trainable: boolean

    Accessors

    isDisposed

    • get isDisposed(): boolean

    • Returns boolean

    rank

    • get rank(): number

    • Returns number

    Methods

    abs

    • abs<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acos

    • acos<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acosh

    • acosh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    add

    • add<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    all

    • all<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    any

    • any<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    argMax

    • argMax<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    argMin

    • argMin<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    array

    • array(): Promise<ArrayMap[R]>

    • Returns the tensor data as a nested array. The transfer of data is done +

    trainable

    trainable: boolean

    Accessors

    isDisposed

    • get isDisposed(): boolean

    • Returns boolean

    rank

    • get rank(): number

    • Returns number

    Methods

    abs

    • abs<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acos

    • acos<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    acosh

    • acosh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    add

    all

    • all<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    any

    • any<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    argMax

    • argMax<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    argMin

    • argMin<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    array

    • array(): Promise<ArrayMap[R]>

    • Returns the tensor data as a nested array. The transfer of data is done asynchronously.

      -

      Returns Promise<ArrayMap[R]>

      Doc

    arraySync

    • arraySync(): ArrayMap[R]

    • Returns the tensor data as a nested array. The transfer of data is done +

      Returns Promise<ArrayMap[R]>

      Doc

    arraySync

    • arraySync(): ArrayMap[R]

    • Returns the tensor data as a nested array. The transfer of data is done synchronously.

      -

      Returns ArrayMap[R]

      Doc

    as1D

    • as1D<T>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    as2D

    • as2D<T>(rows, columns): Tensor2D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number

      Returns Tensor2D

    as3D

    • as3D<T>(rows, columns, depth): Tensor3D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number

      Returns Tensor3D

    as4D

    • as4D<T>(rows, columns, depth, depth2): Tensor4D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number

      Returns Tensor4D

    as5D

    • as5D<T>(rows, columns, depth, depth2, depth3): Tensor5D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number
      • depth3: number

      Returns Tensor5D

    asin

    • asin<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asinh

    • asinh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asScalar

    • asScalar<T>(): Scalar

    • Type Parameters

      Returns Scalar

    assign

    • assign(newValue): void

    • Assign a new tf.Tensor to this variable. The new tf.Tensor must have +

      Returns ArrayMap[R]

      Doc

    as1D

    • as1D<T extends tf.Tensor>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    as2D

    • as2D<T extends tf.Tensor>(rows: number, columns: number): Tensor2D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number

      Returns Tensor2D

    as3D

    • as3D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
      ): Tensor3D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number

      Returns Tensor3D

    as4D

    • as4D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
          depth2: number,
      ): Tensor4D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number

      Returns Tensor4D

    as5D

    • as5D<T extends tf.Tensor>(
          rows: number,
          columns: number,
          depth: number,
          depth2: number,
          depth3: number,
      ): Tensor5D

    • Type Parameters

      Parameters

      • rows: number
      • columns: number
      • depth: number
      • depth2: number
      • depth3: number

      Returns Tensor5D

    asin

    • asin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asinh

    • asinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    asScalar

    • asScalar<T extends tf.Tensor>(): Scalar

    • Type Parameters

      Returns Scalar

    assign

    • assign(newValue: tf.Tensor<R>): void

    • Assign a new tf.Tensor to this variable. The new tf.Tensor must have the same shape and dtype as the old tf.Tensor.

      -

      Parameters

      • newValue: Tensor<R>

        New tensor to be assigned to this variable.

        -

      Returns void

      Doc

    asType

    • asType<T>(this, dtype): T

    • Type Parameters

      Parameters

      • this: T
      • dtype: keyof DataTypeMap

      Returns T

    atan

    • atan<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    atan2

    • atan2<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    atanh

    • atanh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    avgPool

    • avgPool<T>(filterSize, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    batchNorm

    batchToSpaceND

    • batchToSpaceND<R>(blockShape, crops): Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • crops: number[][]

      Returns Tensor<R>

    broadcastTo

    • broadcastTo<R>(shape): Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]

      Returns Tensor<R>

    buffer

    • buffer<D>(): Promise<TensorBuffer<R, D>>

    • Returns a promise of tf.TensorBuffer that holds the underlying data.

      -

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns Promise<TensorBuffer<R, D>>

      Doc

    bufferSync

    • bufferSync<D>(): TensorBuffer<R, D>

    • Returns a tf.TensorBuffer that holds the underlying data.

      -

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns TensorBuffer<R, D>

      Doc

    bytes

    • bytes(): Promise<Uint8Array | Uint8Array[]>

    • Returns the underlying bytes of the tensor's data.

      -

      Returns Promise<Uint8Array | Uint8Array[]>

    cast

    • cast<T>(dtype): T

    • Type Parameters

      Parameters

      • dtype: keyof DataTypeMap

      Returns T

    ceil

    • ceil<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    clipByValue

    • clipByValue<T>(min, max): Tensor<Rank>

    • Type Parameters

      Parameters

      • min: number
      • max: number

      Returns Tensor<Rank>

    clone

    • clone<T>(this): T

    • Returns a copy of the tensor. See tf.clone for details.

      -

      Type Parameters

      Parameters

      • this: T

      Returns T

      Doc

    concat

    • concat<T>(tensors, axis?): T

    • Type Parameters

      Parameters

      Returns T

    conv1d

    • conv1d<T>(filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • stride: number
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldataFormat: "NWC" | "NCW"
      • Optionaldilation: number
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2d

    • conv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2dTranspose

    • conv2dTranspose<T>(filter, outputShape, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • outputShape: [number, number, number, number] | [number, number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    cos

    • cos<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cosh

    • cosh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cumprod

    • cumprod<R>(axis?, exclusive?, reverse?): Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns Tensor<R>

    cumsum

    • cumsum<R>(axis?, exclusive?, reverse?): Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns Tensor<R>

    data

    • data<D>(): Promise<DataTypeMap[D]>

    • Asynchronously downloads the values from the tf.Tensor. Returns a +

      Parameters

      • newValue: tf.Tensor<R>

        New tensor to be assigned to this variable.

        +

      Returns void

      Doc

    asType

    • asType<T extends tf.Tensor>(this: T, dtype: keyof DataTypeMap): T

    • Type Parameters

      Parameters

      • this: T
      • dtype: keyof DataTypeMap

      Returns T

    atan

    • atan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    atan2

    atanh

    • atanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    avgPool

    • avgPool<T extends Tensor4D | Tensor3D>(
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    batchNorm

    batchToSpaceND

    • batchToSpaceND<R extends Rank>(
          blockShape: number[],
          crops: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • crops: number[][]

      Returns tf.Tensor<R>

    broadcastTo

    • broadcastTo<R extends Rank>(shape: ShapeMap[R]): tf.Tensor<R>

    • Type Parameters

      Parameters

      • shape: ShapeMap[R]

      Returns tf.Tensor<R>

    buffer

    • buffer<D extends keyof DataTypeMap = "float32">(): Promise<TensorBuffer<R, D>>

    • Returns a promise of tf.TensorBuffer that holds the underlying data.

      +

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns Promise<TensorBuffer<R, D>>

      Doc

    bufferSync

    • bufferSync<D extends keyof DataTypeMap = "float32">(): TensorBuffer<R, D>

    • Returns a tf.TensorBuffer that holds the underlying data.

      +

      Type Parameters

      • D extends keyof DataTypeMap = "float32"

      Returns TensorBuffer<R, D>

      Doc

    bytes

    • bytes(): Promise<Uint8Array | Uint8Array[]>

    • Returns the underlying bytes of the tensor's data.

      +

      Returns Promise<Uint8Array | Uint8Array[]>

    cast

    • cast<T extends tf.Tensor>(dtype: keyof DataTypeMap): T

    • Type Parameters

      Parameters

      • dtype: keyof DataTypeMap

      Returns T

    ceil

    • ceil<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    clipByValue

    • clipByValue<T extends tf.Tensor>(min: number, max: number): tf.Tensor

    • Type Parameters

      Parameters

      • min: number
      • max: number

      Returns tf.Tensor

    clone

    • clone<T extends tf.Tensor>(this: T): T

    • Returns a copy of the tensor. See tf.clone for details.

      +

      Type Parameters

      Parameters

      • this: T

      Returns T

      Doc

    concat

    • concat<T extends tf.Tensor>(tensors: T | (TensorLike | T)[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    conv1d

    • conv1d<T extends Tensor2D | Tensor3D>(
          filter: Tensor3D | TensorLike3D,
          stride: number,
          pad: number | "valid" | "same" | ExplicitPadding,
          dataFormat?: "NWC" | "NCW",
          dilation?: number,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • stride: number
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldataFormat: "NWC" | "NCW"
      • Optionaldilation: number
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2d

    • conv2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    conv2dTranspose

    • conv2dTranspose<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          outputShape: [number, number, number, number] | [number, number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • outputShape: [number, number, number, number] | [number, number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    cos

    • cos<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cosh

    • cosh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    cumprod

    • cumprod<R extends Rank>(
          axis?: number,
          exclusive?: boolean,
          reverse?: boolean,
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns tf.Tensor<R>

    cumsum

    • cumsum<R extends Rank>(
          axis?: number,
          exclusive?: boolean,
          reverse?: boolean,
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • Optionalaxis: number
      • Optionalexclusive: boolean
      • Optionalreverse: boolean

      Returns tf.Tensor<R>

    data

    • data<D extends keyof DataTypeMap = NumericDataType>(): Promise<DataTypeMap[D]>

    • Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

      -

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns Promise<DataTypeMap[D]>

      Doc

    dataSync

    • dataSync<D>(): DataTypeMap[D]

    • Synchronously downloads the values from the tf.Tensor. This blocks the +

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns Promise<DataTypeMap[D]>

      Doc

    dataSync

    • dataSync<D extends keyof DataTypeMap = NumericDataType>(): DataTypeMap[D]

    • Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

      -

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns DataTypeMap[D]

      Doc

    dataToGPU

    • dataToGPU(options?): GPUData

    • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() +

      Type Parameters

      • D extends keyof DataTypeMap = NumericDataType

      Returns DataTypeMap[D]

      Doc

    dataToGPU

    • dataToGPU(options?: DataToGPUWebGLOption): GPUData

    • Copy the tensor's data to a new GPU resource. Comparing to the dataSync() and data(), this method prevents data from being downloaded to CPU.

      For WebGL backend, the data will be stored on a densely packed texture. This means that the texture will use the RGBA channels to store value.

      @@ -216,9 +216,9 @@ texShape: [number, number] // [height, width] Remember to dispose the GPUData after it is used by `res.tensorRef.dispose()`. -

      Doc

    depthToSpace

    • depthToSpace<T>(blockSize, dataFormat): T

    • Type Parameters

      Parameters

      • blockSize: number
      • dataFormat: "NHWC" | "NCHW"

      Returns T

    depthwiseConv2d

    • depthwiseConv2d<T>(filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    dilation2d

    • dilation2d<T>(filter, strides, pad, dilations?, dataFormat?): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilations: number | [number, number]
      • OptionaldataFormat: "NHWC"

      Returns T

    dispose

    • dispose(): void

    • Disposes tf.Tensor from memory.

      -

      Returns void

      Doc

    div

    • div<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    divNoNan

    • divNoNan<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    dot

    elu

    • elu<T>(): T

    • Type Parameters

      Returns T

    equal

    • equal<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    erf

    • erf<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    euclideanNorm

    • euclideanNorm<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    exp

    • exp<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    expandDims

    • expandDims<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    expm1

    • expm1<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    fft

    • fft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    flatten

    • flatten<T>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    floor

    • floor<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    floorDiv

    • floorDiv<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    gather

    • gather<T>(this, indices, axis?, batchDims?): T

    • Type Parameters

      Parameters

      Returns T

    greater

    • greater<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    greaterEqual

    • greaterEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    ifft

    • ifft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    irfft

    • irfft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    isFinite

    • isFinite<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isInf

    • isInf<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isNaN

    • isNaN<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    leakyRelu

    • leakyRelu<T>(alpha): T

    • Type Parameters

      Parameters

      • alpha: number

      Returns T

    less

    • less<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    lessEqual

    • lessEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    localResponseNormalization

    • localResponseNormalization<T>(depthRadius?, bias?, alpha?, beta?): T

    • Type Parameters

      Parameters

      • OptionaldepthRadius: number
      • Optionalbias: number
      • Optionalalpha: number
      • Optionalbeta: number

      Returns T

    log

    • log<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    log1p

    • log1p<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logicalAnd

    • logicalAnd<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logicalNot

    • logicalNot<T>(): T

    • Type Parameters

      Returns T

    logicalOr

    • logicalOr<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logicalXor

    • logicalXor<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    logSigmoid

    • logSigmoid<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logSoftmax

    • logSoftmax<T>(this, axis?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns T

    logSumExp

    • logSumExp<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    matMul

    • matMul<T>(b, transposeA?, transposeB?): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    max

    • max<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    maximum

    • maximum<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    maxPool

    • maxPool<T>(filterSize, strides, pad, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    mean

    • mean<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    min

    • min<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    minimum

    • minimum<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    mirrorPad

    • mirrorPad<T>(paddings, mode): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • mode: "reflect" | "symmetric"

      Returns T

    mod

    • mod<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    mul

    • mul<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    neg

    • neg<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    norm

    • norm<T>(ord?, axis?, keepDims?): Tensor<Rank>

    • Type Parameters

      Parameters

      • Optionalord: number | "euclidean" | "fro"
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns Tensor<Rank>

    notEqual

    • notEqual<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    oneHot

    • oneHot(depth, onValue, offValue): Tensor<Rank>

    • Parameters

      • depth: number
      • onValue: number
      • offValue: number

      Returns Tensor<Rank>

    onesLike

    • onesLike<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    pad

    • pad<T>(paddings, constantValue?): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • OptionalconstantValue: number

      Returns T

    pool

    • pool<T>(windowShape, poolingType, padding, diationRate?, strides?, dimRoundingMode?): T

    • Type Parameters

      Parameters

      • windowShape: number | [number, number]
      • poolingType: "avg" | "max"
      • padding:
            | number
            | "valid"
            | "same"
            | ExplicitPadding
      • OptionaldiationRate: number | [number, number]
      • Optionalstrides: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    pow

    • pow<T>(exp): T

    • Type Parameters

      Parameters

      Returns T

    prelu

    • prelu<T>(alpha): T

    • Type Parameters

      Parameters

      Returns T

    print

    • print(verbose?): void

    • Prints the tf.Tensor. See tf.print for details.

      +

      Doc

    depthToSpace

    • depthToSpace<T extends Tensor4D>(
          blockSize: number,
          dataFormat: "NHWC" | "NCHW",
      ): T

    • Type Parameters

      Parameters

      • blockSize: number
      • dataFormat: "NHWC" | "NCHW"

      Returns T

    depthwiseConv2d

    • depthwiseConv2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor4D | TensorLike4D,
          strides: number | [number, number],
          pad: number | "valid" | "same",
          dataFormat?: "NHWC" | "NCHW",
          dilations?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor4D | TensorLike4D
      • strides: number | [number, number]
      • pad: number | "valid" | "same"
      • OptionaldataFormat: "NHWC" | "NCHW"
      • Optionaldilations: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    dilation2d

    • dilation2d<T extends Tensor4D | Tensor3D>(
          filter: Tensor3D | TensorLike3D,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilations?: number | [number, number],
          dataFormat?: "NHWC",
      ): T

    • Type Parameters

      Parameters

      • filter: Tensor3D | TensorLike3D
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilations: number | [number, number]
      • OptionaldataFormat: "NHWC"

      Returns T

    dispose

    • dispose(): void

    • Disposes tf.Tensor from memory.

      +

      Returns void

      Doc

    div

    divNoNan

    dot

    elu

    • elu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    equal

    erf

    • erf<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    euclideanNorm

    • euclideanNorm<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    exp

    • exp<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    expandDims

    • expandDims<T extends tf.Tensor>(axis?: number): T

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T

    expm1

    • expm1<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    fft

    flatten

    • flatten<T extends tf.Tensor>(): Tensor1D

    • Type Parameters

      Returns Tensor1D

    floor

    • floor<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    floorDiv

    gather

    • gather<T extends tf.Tensor>(
          this: T,
          indices: TensorLike | tf.Tensor,
          axis?: number,
          batchDims?: number,
      ): T

    • Type Parameters

      Parameters

      Returns T

    greater

    greaterEqual

    ifft

    irfft

    isFinite

    • isFinite<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isInf

    • isInf<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    isNaN

    • isNaN<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    leakyRelu

    • leakyRelu<T extends tf.Tensor>(alpha: number): T

    • Type Parameters

      Parameters

      • alpha: number

      Returns T

    less

    lessEqual

    localResponseNormalization

    • localResponseNormalization<T extends tf.Tensor>(
          depthRadius?: number,
          bias?: number,
          alpha?: number,
          beta?: number,
      ): T

    • Type Parameters

      Parameters

      • OptionaldepthRadius: number
      • Optionalbias: number
      • Optionalalpha: number
      • Optionalbeta: number

      Returns T

    log

    • log<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    log1p

    • log1p<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logicalAnd

    logicalNot

    • logicalNot<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    logicalOr

    logicalXor

    logSigmoid

    • logSigmoid<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    logSoftmax

    • logSoftmax<T extends tf.Tensor>(this: T, axis?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns T

    logSumExp

    • logSumExp<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    matMul

    • matMul<T extends tf.Tensor>(
          b: TensorLike | tf.Tensor,
          transposeA?: boolean,
          transposeB?: boolean,
      ): tf.Tensor

    • Type Parameters

      Parameters

      Returns tf.Tensor

    max

    • max<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    maximum

    maxPool

    • maxPool<T extends Tensor4D | Tensor3D>(
          filterSize: number | [number, number],
          strides: number | [number, number],
          pad: number | "valid" | "same" | ExplicitPadding,
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • filterSize: number | [number, number]
      • strides: number | [number, number]
      • pad: number | "valid" | "same" | ExplicitPadding
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    mean

    • mean<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    min

    • min<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    minimum

    mirrorPad

    • mirrorPad<T extends tf.Tensor>(
          paddings: [number, number][],
          mode: "reflect" | "symmetric",
      ): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • mode: "reflect" | "symmetric"

      Returns T

    mod

    mul

    neg

    • neg<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    norm

    • norm<T extends tf.Tensor>(
          ord?: number | "euclidean" | "fro",
          axis?: number | number[],
          keepDims?: boolean,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • Optionalord: number | "euclidean" | "fro"
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns tf.Tensor

    notEqual

    oneHot

    • oneHot(depth: number, onValue: number, offValue: number): tf.Tensor

    • Parameters

      • depth: number
      • onValue: number
      • offValue: number

      Returns tf.Tensor

    onesLike

    • onesLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    pad

    • pad<T extends tf.Tensor>(
          paddings: [number, number][],
          constantValue?: number,
      ): T

    • Type Parameters

      Parameters

      • paddings: [number, number][]
      • OptionalconstantValue: number

      Returns T

    pool

    • pool<T extends Tensor4D | Tensor3D>(
          windowShape: number | [number, number],
          poolingType: "avg" | "max",
          padding: number | "valid" | "same" | ExplicitPadding,
          diationRate?: number | [number, number],
          strides?: number | [number, number],
          dimRoundingMode?: "floor" | "round" | "ceil",
      ): T

    • Type Parameters

      Parameters

      • windowShape: number | [number, number]
      • poolingType: "avg" | "max"
      • padding: number | "valid" | "same" | ExplicitPadding
      • OptionaldiationRate: number | [number, number]
      • Optionalstrides: number | [number, number]
      • OptionaldimRoundingMode: "floor" | "round" | "ceil"

      Returns T

    pow

    prelu

    • prelu<T extends tf.Tensor>(alpha: TensorLike | T): T

    • Type Parameters

      Parameters

      Returns T

    print

    • print(verbose?: boolean): void

    • Prints the tf.Tensor. See tf.print for details.

      Parameters

      • Optionalverbose: boolean

        Whether to print verbose information about the tensor, including dtype and size.

        -

      Returns void

      Doc

    prod

    • prod<T>(this, axis?, keepDims?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    reciprocal

    • reciprocal<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T>(shape): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T>(x): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfPixelCenters: boolean

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfFloatCenters: boolean

      Returns T

    reverse

    • reverse<T>(this, axis?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]

      Returns T

    rfft

    • rfft<T>(this): Tensor<Rank>

    • Type Parameters

      Parameters

      Returns Tensor<Rank>

    round

    • round<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

    • Type Parameters

      Parameters

      • depthwiseFilter: Tensor4D | TensorLike4D
      • pointwiseFilter: Tensor4D | TensorLike
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilation: number | [number, number]
      • OptionaldataFormat: "NHWC" | "NCHW"

      Returns T

    sigmoid

    • sigmoid<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T>(this, begin, size?): T

    • Type Parameters

      Parameters

      • this: T
      • begin: number | number[]
      • Optionalsize: number | number[]

      Returns T

    softmax

    • softmax<T>(this, dim?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • paddings: number[][]

      Returns Tensor<R>

    split

    • split<T>(numOrSizeSplits, axis?): T[]

    • Type Parameters

      Parameters

      • numOrSizeSplits: number | number[]
      • Optionalaxis: number

      Returns T[]

    sqrt

    • sqrt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    • squaredDifference<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    squeeze

    • squeeze<T>(axis?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T>(x, axis?): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T>(this, alpha?): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

    • Type Parameters

      Parameters

      • this: Tensor<Rank>
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns Tensor<Rank>

    sub

    • sub<T>(b): T

    • Type Parameters

      Parameters

      Returns T

    sum

    • sum<T>(axis?, keepDims?): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    tan

    • tan<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T>(b): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T>(this, k?, sorted?): {
          indices: T;
          values: T;
      }

    • Type Parameters

      Parameters

      • this: T
      • Optionalk: number
      • Optionalsorted: boolean

      Returns {
          indices: T;
          values: T;
      }

      • indices: T
      • values: T

    toString

    • toString(verbose?): string

    • Returns a human-readable description of the tensor. Useful for logging.

      -

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T>(perm?): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T>(this, axis?): {
          indices: T;
          values: T;
      }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns {
          indices: T;
          values: T;
      }

      • indices: T
      • values: T

    unsortedSegmentSum

    • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

    • Type Parameters

      Parameters

      • this: T
      • segmentIds: Tensor1D | TensorLike1D
      • numSegments: number

      Returns T

    unstack

    • unstack<T>(axis?): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(trainable?, name?, dtype?): Variable<R>

    • Parameters

      • Optionaltrainable: boolean
      • Optionalname: string
      • Optionaldtype: keyof DataTypeMap

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T>(this): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    Settings

    Member Visibility

    On This Page

    Constructors
    Properties
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    Methods
    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarassignasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    +

    Returns void

    Doc

    prod

    • prod<T extends tf.Tensor>(
          this: T,
          axis?: number | number[],
          keepDims?: boolean,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    reciprocal

    • reciprocal<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    relu

    • relu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    relu6

    • relu6<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    reshape

    • reshape<T extends tf.Tensor>(shape: number[]): T

    • Type Parameters

      Parameters

      • shape: number[]

      Returns T

    reshapeAs

    • reshapeAs<T extends tf.Tensor>(x: T): T

    • Type Parameters

      Parameters

      Returns T

    resizeBilinear

    • resizeBilinear<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfPixelCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfPixelCenters: boolean

      Returns T

    resizeNearestNeighbor

    • resizeNearestNeighbor<T extends Tensor4D | Tensor3D>(
          newShape2D: [number, number],
          alignCorners?: boolean,
          halfFloatCenters?: boolean,
      ): T

    • Type Parameters

      Parameters

      • newShape2D: [number, number]
      • OptionalalignCorners: boolean
      • OptionalhalfFloatCenters: boolean

      Returns T

    reverse

    • reverse<T extends tf.Tensor>(this: T, axis?: number | number[]): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number | number[]

      Returns T

    rfft

    round

    • round<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    rsqrt

    • rsqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    selu

    • selu<T extends tf.Tensor>(): T

    • Type Parameters

      Returns T

    separableConv2d

    • separableConv2d<T extends Tensor4D | Tensor3D>(
          depthwiseFilter: Tensor4D | TensorLike4D,
          pointwiseFilter: Tensor4D | TensorLike,
          strides: number | [number, number],
          pad: "valid" | "same",
          dilation?: number | [number, number],
          dataFormat?: "NHWC" | "NCHW",
      ): T

    • Type Parameters

      Parameters

      • depthwiseFilter: Tensor4D | TensorLike4D
      • pointwiseFilter: Tensor4D | TensorLike
      • strides: number | [number, number]
      • pad: "valid" | "same"
      • Optionaldilation: number | [number, number]
      • OptionaldataFormat: "NHWC" | "NCHW"

      Returns T

    sigmoid

    • sigmoid<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sign

    • sign<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sin

    • sin<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    sinh

    • sinh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    slice

    • slice<T extends tf.Tensor>(
          this: T,
          begin: number | number[],
          size?: number | number[],
      ): T

    • Type Parameters

      Parameters

      • this: T
      • begin: number | number[]
      • Optionalsize: number | number[]

      Returns T

    softmax

    • softmax<T extends tf.Tensor>(this: T, dim?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionaldim: number

      Returns T

    softplus

    • softplus<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    spaceToBatchND

    • spaceToBatchND<R extends Rank>(
          blockShape: number[],
          paddings: number[][],
      ): tf.Tensor<R>

    • Type Parameters

      Parameters

      • blockShape: number[]
      • paddings: number[][]

      Returns tf.Tensor<R>

    split

    • split<T extends tf.Tensor>(
          numOrSizeSplits: number | number[],
          axis?: number,
      ): T[]

    • Type Parameters

      Parameters

      • numOrSizeSplits: number | number[]
      • Optionalaxis: number

      Returns T[]

    sqrt

    • sqrt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    square

    • square<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    squaredDifference

    squeeze

    • squeeze<T extends tf.Tensor>(axis?: number[]): T

    • Type Parameters

      Parameters

      • Optionalaxis: number[]

      Returns T

    stack

    • stack<T extends tf.Tensor>(x: tf.Tensor | tf.Tensor[], axis?: number): T

    • Type Parameters

      Parameters

      Returns T

    step

    • step<T extends tf.Tensor>(this: T, alpha?: number): T

    • Type Parameters

      Parameters

      • this: T
      • Optionalalpha: number

      Returns T

    stridedSlice

    • stridedSlice<T extends tf.Tensor>(
          this: tf.Tensor,
          begin: number[],
          end: number[],
          strides: number[],
          beginMask?: number,
          endMask?: number,
          ellipsisMask?: number,
          newAxisMask?: number,
          shrinkAxisMask?: number,
      ): tf.Tensor

    • Type Parameters

      Parameters

      • this: tf.Tensor
      • begin: number[]
      • end: number[]
      • strides: number[]
      • OptionalbeginMask: number
      • OptionalendMask: number
      • OptionalellipsisMask: number
      • OptionalnewAxisMask: number
      • OptionalshrinkAxisMask: number

      Returns tf.Tensor

    sub

    sum

    • sum<T extends tf.Tensor>(axis?: number | number[], keepDims?: boolean): T

    • Type Parameters

      Parameters

      • Optionalaxis: number | number[]
      • OptionalkeepDims: boolean

      Returns T

    tan

    • tan<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    tanh

    • tanh<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    throwIfDisposed

    • throwIfDisposed(): void

    • Returns void

    tile

    • tile<T extends tf.Tensor>(b: number[]): T

    • Type Parameters

      Parameters

      • b: number[]

      Returns T

    toBool

    • toBool<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toFloat

    • toFloat<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    toInt

    • toInt<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

    topk

    • topk<T extends tf.Tensor>(
          this: T,
          k?: number,
          sorted?: boolean,
      ): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalk: number
      • Optionalsorted: boolean

      Returns { indices: T; values: T }

    toString

    • toString(verbose?: boolean): string

    • Returns a human-readable description of the tensor. Useful for logging.

      +

      Parameters

      • Optionalverbose: boolean

      Returns string

      Doc

    transpose

    • transpose<T extends tf.Tensor>(perm?: number[]): T

    • Type Parameters

      Parameters

      • Optionalperm: number[]

      Returns T

    unique

    • unique<T extends tf.Tensor>(this: T, axis?: number): { indices: T; values: T }

    • Type Parameters

      Parameters

      • this: T
      • Optionalaxis: number

      Returns { indices: T; values: T }

    unsortedSegmentSum

    • unsortedSegmentSum<T extends tf.Tensor>(
          this: T,
          segmentIds: Tensor1D | TensorLike1D,
          numSegments: number,
      ): T

    • Type Parameters

      Parameters

      • this: T
      • segmentIds: Tensor1D | TensorLike1D
      • numSegments: number

      Returns T

    unstack

    • unstack<T extends tf.Tensor>(axis?: number): T[]

    • Type Parameters

      Parameters

      • Optionalaxis: number

      Returns T[]

    variable

    • variable(
          trainable?: boolean,
          name?: string,
          dtype?: keyof DataTypeMap,
      ): Variable<R>

    • Parameters

      • Optionaltrainable: boolean
      • Optionalname: string
      • Optionaldtype: keyof DataTypeMap

      Returns Variable<R>

    where

    zerosLike

    • zerosLike<T extends tf.Tensor>(this: T): T

    • Type Parameters

      Parameters

      • this: T

      Returns T

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    absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarassignasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
    diff --git a/typedoc/classes/tf.io.CompositeArrayBuffer.html b/typedoc/classes/tf.io.CompositeArrayBuffer.html index 113138e..c302c7c 100644 --- a/typedoc/classes/tf.io.CompositeArrayBuffer.html +++ b/typedoc/classes/tf.io.CompositeArrayBuffer.html @@ -1,16 +1,16 @@ -CompositeArrayBuffer | @vladmandic/face-api - v1.7.14

    Class CompositeArrayBuffer

    Wraps a list of ArrayBuffers into a slice()-able object without allocating +CompositeArrayBuffer | @vladmandic/face-api - v1.7.14

    Class CompositeArrayBuffer

    Wraps a list of ArrayBuffers into a slice()-able object without allocating a large ArrayBuffer.

    Allocating large ArrayBuffers (~2GB) can be unstable on Chrome. TFJS loads its weights as a list of (usually) 4MB ArrayBuffers and then slices the weight tensors out of them. For small models, it's safe to concatenate all the weight buffers into a single ArrayBuffer and then slice the weight tensors out of it, but for large models, a different approach is needed.

    -

    Constructors

    constructor -

    Properties

    byteLength +

    Constructors

    constructor +

    Properties

    byteLength

    Methods

    slice join -

    Constructors

    constructor

    • new CompositeArrayBuffer(buffers?): CompositeArrayBuffer

    • Parameters

      • Optionalbuffers:
            | TypedArray
            | ArrayBuffer
            | ArrayBuffer[]
            | TypedArray[]

      Returns CompositeArrayBuffer

    Properties

    ReadonlybyteLength

    byteLength: number

    Methods

    slice

    • slice(start?, end?): ArrayBuffer

    • Parameters

      • Optionalstart: number
      • Optionalend: number

      Returns ArrayBuffer

    Staticjoin

    • join(buffers?): ArrayBuffer

    • Concatenate a number of ArrayBuffers into one.

      -

      Parameters

      • Optionalbuffers: ArrayBuffer | ArrayBuffer[]

        An array of ArrayBuffers to concatenate, or a single +

    Constructors

    constructor

    • new CompositeArrayBuffer(
          buffers?: ArrayBuffer | TypedArray | ArrayBuffer[] | TypedArray[],
      ): CompositeArrayBuffer

    • Parameters

      • Optionalbuffers: ArrayBuffer | TypedArray | ArrayBuffer[] | TypedArray[]

      Returns CompositeArrayBuffer

    Properties

    ReadonlybyteLength

    byteLength: number

    Methods

    slice

    • slice(start?: number, end?: number): ArrayBuffer

    • Parameters

      • Optionalstart: number
      • Optionalend: number

      Returns ArrayBuffer

    Staticjoin

    • join(buffers?: ArrayBuffer | ArrayBuffer[]): ArrayBuffer

    • Concatenate a number of ArrayBuffers into one.

      +

      Parameters

      • Optionalbuffers: ArrayBuffer | ArrayBuffer[]

        An array of ArrayBuffers to concatenate, or a single ArrayBuffer.

      Returns ArrayBuffer

      Result of concatenating buffers in order.

      -

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    +

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    diff --git a/typedoc/enums/Gender.html b/typedoc/enums/Gender.html index b353db2..e666bb4 100644 --- a/typedoc/enums/Gender.html +++ b/typedoc/enums/Gender.html @@ -1,3 +1,3 @@ -Gender | @vladmandic/face-api - v1.7.14

    Enumeration Gender

    Enumeration Members

    FEMALE -MALE -

    Enumeration Members

    FEMALE

    FEMALE: "female"

    MALE

    MALE: "male"

    Settings

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    On This Page

    Enumeration Members
    +Gender | @vladmandic/face-api - v1.7.14

    Enumeration Gender

    Enumeration Members

    FEMALE +MALE +

    Enumeration Members

    FEMALE

    FEMALE: "female"

    MALE

    MALE: "male"

    Settings

    Member Visibility

    On This Page

    Enumeration Members
    diff --git a/typedoc/enums/draw.AnchorPosition.html b/typedoc/enums/draw.AnchorPosition.html index 9339bb9..1d87eba 100644 --- a/typedoc/enums/draw.AnchorPosition.html +++ b/typedoc/enums/draw.AnchorPosition.html @@ -1,5 +1,5 @@ -AnchorPosition | @vladmandic/face-api - v1.7.14

    Enumeration AnchorPosition

    Enumeration Members

    BOTTOM_LEFT -BOTTOM_RIGHT -TOP_LEFT -TOP_RIGHT -

    Enumeration Members

    BOTTOM_LEFT

    BOTTOM_LEFT: "BOTTOM_LEFT"

    BOTTOM_RIGHT

    BOTTOM_RIGHT: "BOTTOM_RIGHT"

    TOP_LEFT

    TOP_LEFT: "TOP_LEFT"

    TOP_RIGHT

    TOP_RIGHT: "TOP_RIGHT"

    Settings

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    On This Page

    Enumeration Members
    +AnchorPosition | @vladmandic/face-api - v1.7.14

    Enumeration AnchorPosition

    Enumeration Members

    BOTTOM_LEFT +BOTTOM_RIGHT +TOP_LEFT +TOP_RIGHT +

    Enumeration Members

    BOTTOM_LEFT

    BOTTOM_LEFT: "BOTTOM_LEFT"

    BOTTOM_RIGHT

    BOTTOM_RIGHT: "BOTTOM_RIGHT"

    TOP_LEFT

    TOP_LEFT: "TOP_LEFT"

    TOP_RIGHT

    TOP_RIGHT: "TOP_RIGHT"

    Settings

    Member Visibility

    On This Page

    Enumeration Members
    diff --git a/typedoc/enums/tf.Rank.html b/typedoc/enums/tf.Rank.html index f6bede1..0033ea6 100644 --- a/typedoc/enums/tf.Rank.html +++ b/typedoc/enums/tf.Rank.html @@ -1,8 +1,8 @@ -Rank | @vladmandic/face-api - v1.7.14

    Enumeration Rank

    Enumeration Members

    R0 -R1 -R2 -R3 -R4 -R5 -R6 -

    Enumeration Members

    R0

    R0: "R0"

    R1

    R1: "R1"

    R2

    R2: "R2"

    R3

    R3: "R3"

    R4

    R4: "R4"

    R5

    R5: "R5"

    R6

    R6: "R6"

    Settings

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    On This Page

    Enumeration Members
    +Rank | @vladmandic/face-api - v1.7.14

    Enumeration Rank

    Enumeration Members

    R0 +R1 +R2 +R3 +R4 +R5 +R6 +

    Enumeration Members

    R0

    R0: "R0"

    R1

    R1: "R1"

    R2

    R2: "R2"

    R3

    R3: "R3"

    R4

    R4: "R4"

    R5

    R5: "R5"

    R6

    R6: "R6"

    Settings

    Member Visibility

    On This Page

    Enumeration Members
    diff --git a/typedoc/functions/allFaces.html b/typedoc/functions/allFaces.html index 46db0df..519c362 100644 --- a/typedoc/functions/allFaces.html +++ b/typedoc/functions/allFaces.html @@ -1 +1 @@ -allFaces | @vladmandic/face-api - v1.7.14

    Function allFaces

    Settings

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    +allFaces | @vladmandic/face-api - v1.7.14

    Function allFaces

    Settings

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    diff --git a/typedoc/functions/allFacesSsdMobilenetv1.html b/typedoc/functions/allFacesSsdMobilenetv1.html index 14cf1f4..0938a20 100644 --- a/typedoc/functions/allFacesSsdMobilenetv1.html +++ b/typedoc/functions/allFacesSsdMobilenetv1.html @@ -1 +1 @@ -allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function allFacesSsdMobilenetv1

    Settings

    Member Visibility
    +allFacesSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function allFacesSsdMobilenetv1

    Settings

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    diff --git a/typedoc/functions/allFacesTinyYolov2.html b/typedoc/functions/allFacesTinyYolov2.html index ed134df..0f24d17 100644 --- a/typedoc/functions/allFacesTinyYolov2.html +++ b/typedoc/functions/allFacesTinyYolov2.html @@ -1 +1 @@ -allFacesTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function allFacesTinyYolov2

    Settings

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    +allFacesTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function allFacesTinyYolov2

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    diff --git a/typedoc/functions/awaitMediaLoaded.html b/typedoc/functions/awaitMediaLoaded.html index f1793bf..a28e43b 100644 --- a/typedoc/functions/awaitMediaLoaded.html +++ b/typedoc/functions/awaitMediaLoaded.html @@ -1 +1 @@ -awaitMediaLoaded | @vladmandic/face-api - v1.7.14

    Function awaitMediaLoaded

    • awaitMediaLoaded(media): Promise<unknown>

    • Parameters

      • media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

      Returns Promise<unknown>

    Settings

    Member Visibility
    +awaitMediaLoaded | @vladmandic/face-api - v1.7.14

    Function awaitMediaLoaded

    • awaitMediaLoaded(
          media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement,
      ): Promise<unknown>

    • Parameters

      • media: HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

      Returns Promise<unknown>

    Settings

    Member Visibility
    diff --git a/typedoc/functions/bufferToImage.html b/typedoc/functions/bufferToImage.html index 25d881f..12847f3 100644 --- a/typedoc/functions/bufferToImage.html +++ b/typedoc/functions/bufferToImage.html @@ -1 +1 @@ -bufferToImage | @vladmandic/face-api - v1.7.14

    Function bufferToImage

    • bufferToImage(buf): Promise<HTMLImageElement>

    • Parameters

      • buf: Blob

      Returns Promise<HTMLImageElement>

    Settings

    Member Visibility
    +bufferToImage | @vladmandic/face-api - v1.7.14

    Function bufferToImage

    • bufferToImage(buf: Blob): Promise<HTMLImageElement>

    • Parameters

      • buf: Blob

      Returns Promise<HTMLImageElement>

    Settings

    Member Visibility
    diff --git a/typedoc/functions/computeFaceDescriptor.html b/typedoc/functions/computeFaceDescriptor.html index 7e26c0b..2c1898d 100644 --- a/typedoc/functions/computeFaceDescriptor.html +++ b/typedoc/functions/computeFaceDescriptor.html @@ -1,6 +1,6 @@ -computeFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function computeFaceDescriptor

    Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image, +computeFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function computeFaceDescriptor

    Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image, which uniquely represents the features of that persons face. The computed face descriptor can be used to measure the similarity between faces, by computing the euclidean distance of two face descriptors.

    -
    • computeFaceDescriptor(input): Promise<Float32Array | Float32Array[]>

    • Parameters

      Returns Promise<Float32Array | Float32Array[]>

      Face descriptor with 128 entries or array thereof in case of batch input.

      -

    Settings

    Member Visibility
    +
    • computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>

    • Parameters

      Returns Promise<Float32Array | Float32Array[]>

      Face descriptor with 128 entries or array thereof in case of batch input.

      +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/createCanvas.html b/typedoc/functions/createCanvas.html index 131e373..0ae6a42 100644 --- a/typedoc/functions/createCanvas.html +++ b/typedoc/functions/createCanvas.html @@ -1 +1 @@ -createCanvas | @vladmandic/face-api - v1.7.14

    Function createCanvas

    Settings

    Member Visibility
    +createCanvas | @vladmandic/face-api - v1.7.14

    Function createCanvas

    Settings

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    diff --git a/typedoc/functions/createCanvasFromMedia.html b/typedoc/functions/createCanvasFromMedia.html index 5017d24..254199c 100644 --- a/typedoc/functions/createCanvasFromMedia.html +++ b/typedoc/functions/createCanvasFromMedia.html @@ -1 +1 @@ -createCanvasFromMedia | @vladmandic/face-api - v1.7.14

    Function createCanvasFromMedia

    • createCanvasFromMedia(media, dims?): HTMLCanvasElement

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement | ImageData
      • Optionaldims: IDimensions

      Returns HTMLCanvasElement

    Settings

    Member Visibility
    +createCanvasFromMedia | @vladmandic/face-api - v1.7.14

    Function createCanvasFromMedia

    • createCanvasFromMedia(
          media: HTMLImageElement | HTMLVideoElement | ImageData,
          dims?: IDimensions,
      ): HTMLCanvasElement

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement | ImageData
      • Optionaldims: IDimensions

      Returns HTMLCanvasElement

    Settings

    Member Visibility
    diff --git a/typedoc/functions/createFaceDetectionNet.html b/typedoc/functions/createFaceDetectionNet.html index ef637c0..f8e9b17 100644 --- a/typedoc/functions/createFaceDetectionNet.html +++ b/typedoc/functions/createFaceDetectionNet.html @@ -1 +1 @@ -createFaceDetectionNet | @vladmandic/face-api - v1.7.14

    Function createFaceDetectionNet

    Settings

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    +createFaceDetectionNet | @vladmandic/face-api - v1.7.14

    Function createFaceDetectionNet

    Settings

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    diff --git a/typedoc/functions/createFaceRecognitionNet.html b/typedoc/functions/createFaceRecognitionNet.html index 5d60d48..79838ff 100644 --- a/typedoc/functions/createFaceRecognitionNet.html +++ b/typedoc/functions/createFaceRecognitionNet.html @@ -1 +1 @@ -createFaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Function createFaceRecognitionNet

    Settings

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    +createFaceRecognitionNet | @vladmandic/face-api - v1.7.14

    Function createFaceRecognitionNet

    Settings

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    diff --git a/typedoc/functions/createSsdMobilenetv1.html b/typedoc/functions/createSsdMobilenetv1.html index 1b8b2e4..8f80f55 100644 --- a/typedoc/functions/createSsdMobilenetv1.html +++ b/typedoc/functions/createSsdMobilenetv1.html @@ -1 +1 @@ -createSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function createSsdMobilenetv1

    Settings

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    +createSsdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function createSsdMobilenetv1

    Settings

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    diff --git a/typedoc/functions/createTinyFaceDetector.html b/typedoc/functions/createTinyFaceDetector.html index aee9222..c0fb3b4 100644 --- a/typedoc/functions/createTinyFaceDetector.html +++ b/typedoc/functions/createTinyFaceDetector.html @@ -1 +1 @@ -createTinyFaceDetector | @vladmandic/face-api - v1.7.14

    Function createTinyFaceDetector

    Settings

    Member Visibility
    +createTinyFaceDetector | @vladmandic/face-api - v1.7.14

    Function createTinyFaceDetector

    Settings

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    diff --git a/typedoc/functions/createTinyYolov2.html b/typedoc/functions/createTinyYolov2.html index d7b0455..9692afe 100644 --- a/typedoc/functions/createTinyYolov2.html +++ b/typedoc/functions/createTinyYolov2.html @@ -1 +1 @@ -createTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function createTinyYolov2

    Settings

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    +createTinyYolov2 | @vladmandic/face-api - v1.7.14

    Function createTinyYolov2

    Settings

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    diff --git a/typedoc/functions/detectAllFaces.html b/typedoc/functions/detectAllFaces.html index a8aa454..3543d41 100644 --- a/typedoc/functions/detectAllFaces.html +++ b/typedoc/functions/detectAllFaces.html @@ -1 +1 @@ -detectAllFaces | @vladmandic/face-api - v1.7.14

    Function detectAllFaces

    Settings

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    +detectAllFaces | @vladmandic/face-api - v1.7.14

    Function detectAllFaces

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    diff --git a/typedoc/functions/detectFaceLandmarks.html b/typedoc/functions/detectFaceLandmarks.html index 5971831..da931b4 100644 --- a/typedoc/functions/detectFaceLandmarks.html +++ b/typedoc/functions/detectFaceLandmarks.html @@ -1,3 +1,3 @@ -detectFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarks

    Detects the 68 point face landmark positions of the face shown in an image.

    -

    Settings

    Member Visibility
    +detectFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarks

    Detects the 68 point face landmark positions of the face shown in an image.

    +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/detectFaceLandmarksTiny.html b/typedoc/functions/detectFaceLandmarksTiny.html index 57336ef..59a7fe3 100644 --- a/typedoc/functions/detectFaceLandmarksTiny.html +++ b/typedoc/functions/detectFaceLandmarksTiny.html @@ -1,5 +1,5 @@ -detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarksTiny

    Detects the 68 point face landmark positions of the face shown in an image +detectFaceLandmarksTiny | @vladmandic/face-api - v1.7.14

    Function detectFaceLandmarksTiny

    Detects the 68 point face landmark positions of the face shown in an image using a tinier version of the 68 point face landmark model, which is slightly faster at inference, but also slightly less accurate.

    -

    Settings

    Member Visibility
    +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/detectLandmarks.html b/typedoc/functions/detectLandmarks.html index 82c9bf7..d26d73f 100644 --- a/typedoc/functions/detectLandmarks.html +++ b/typedoc/functions/detectLandmarks.html @@ -1,3 +1,3 @@ -detectLandmarks | @vladmandic/face-api - v1.7.14

    Function detectLandmarks

    Settings

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    +detectLandmarks | @vladmandic/face-api - v1.7.14

    Function detectLandmarks

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    diff --git a/typedoc/functions/detectSingleFace.html b/typedoc/functions/detectSingleFace.html index 57345c9..e1564bb 100644 --- a/typedoc/functions/detectSingleFace.html +++ b/typedoc/functions/detectSingleFace.html @@ -1 +1 @@ -detectSingleFace | @vladmandic/face-api - v1.7.14

    Function detectSingleFace

    Settings

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    +detectSingleFace | @vladmandic/face-api - v1.7.14

    Function detectSingleFace

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    diff --git a/typedoc/functions/draw.drawContour.html b/typedoc/functions/draw.drawContour.html index b8d5318..f1645b9 100644 --- a/typedoc/functions/draw.drawContour.html +++ b/typedoc/functions/draw.drawContour.html @@ -1 +1 @@ -drawContour | @vladmandic/face-api - v1.7.14

    Function drawContour

    • drawContour(ctx, points, isClosed?): void

    • Parameters

      • ctx: CanvasRenderingContext2D
      • points: Point[]
      • isClosed: boolean = false

      Returns void

    Settings

    Member Visibility
    +drawContour | @vladmandic/face-api - v1.7.14

    Function drawContour

    • drawContour(
          ctx: CanvasRenderingContext2D,
          points: Point[],
          isClosed?: boolean,
      ): void

    • Parameters

      • ctx: CanvasRenderingContext2D
      • points: Point[]
      • isClosed: boolean = false

      Returns void

    Settings

    Member Visibility
    diff --git a/typedoc/functions/draw.drawDetections.html b/typedoc/functions/draw.drawDetections.html index 5af3324..2225247 100644 --- a/typedoc/functions/draw.drawDetections.html +++ b/typedoc/functions/draw.drawDetections.html @@ -1 +1 @@ -drawDetections | @vladmandic/face-api - v1.7.14

    Function drawDetections

    Settings

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    +drawDetections | @vladmandic/face-api - v1.7.14

    Function drawDetections

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    diff --git a/typedoc/functions/draw.drawFaceExpressions.html b/typedoc/functions/draw.drawFaceExpressions.html index c113946..1be370d 100644 --- a/typedoc/functions/draw.drawFaceExpressions.html +++ b/typedoc/functions/draw.drawFaceExpressions.html @@ -1 +1 @@ -drawFaceExpressions | @vladmandic/face-api - v1.7.14

    Function drawFaceExpressions

    Settings

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    +drawFaceExpressions | @vladmandic/face-api - v1.7.14

    Function drawFaceExpressions

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    diff --git a/typedoc/functions/draw.drawFaceLandmarks-1.html b/typedoc/functions/draw.drawFaceLandmarks-1.html index b0fdd84..b5de6b0 100644 --- a/typedoc/functions/draw.drawFaceLandmarks-1.html +++ b/typedoc/functions/draw.drawFaceLandmarks-1.html @@ -1 +1 @@ -drawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function drawFaceLandmarks

    Settings

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    +drawFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function drawFaceLandmarks

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    diff --git a/typedoc/functions/euclideanDistance.html b/typedoc/functions/euclideanDistance.html index 5eb514b..13e6197 100644 --- a/typedoc/functions/euclideanDistance.html +++ b/typedoc/functions/euclideanDistance.html @@ -1 +1 @@ -euclideanDistance | @vladmandic/face-api - v1.7.14

    Function euclideanDistance

    • euclideanDistance(arr1, arr2): number

    • Parameters

      • arr1: Float32Array | number[]
      • arr2: Float32Array | number[]

      Returns number

    Settings

    Member Visibility
    +euclideanDistance | @vladmandic/face-api - v1.7.14

    Function euclideanDistance

    • euclideanDistance(
          arr1: Float32Array | number[],
          arr2: Float32Array | number[],
      ): number

    • Parameters

      • arr1: Float32Array | number[]
      • arr2: Float32Array | number[]

      Returns number

    Settings

    Member Visibility
    diff --git a/typedoc/functions/extendWithAge.html b/typedoc/functions/extendWithAge.html index e6a36b9..5243d21 100644 --- a/typedoc/functions/extendWithAge.html +++ b/typedoc/functions/extendWithAge.html @@ -1 +1 @@ -extendWithAge | @vladmandic/face-api - v1.7.14

    Function extendWithAge

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    +extendWithAge | @vladmandic/face-api - v1.7.14

    Function extendWithAge

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    diff --git a/typedoc/functions/extendWithFaceDescriptor.html b/typedoc/functions/extendWithFaceDescriptor.html index d776982..d7bdda0 100644 --- a/typedoc/functions/extendWithFaceDescriptor.html +++ b/typedoc/functions/extendWithFaceDescriptor.html @@ -1 +1 @@ -extendWithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDescriptor

    Settings

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    +extendWithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDescriptor

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    diff --git a/typedoc/functions/extendWithFaceDetection.html b/typedoc/functions/extendWithFaceDetection.html index 34f280f..7519d79 100644 --- a/typedoc/functions/extendWithFaceDetection.html +++ b/typedoc/functions/extendWithFaceDetection.html @@ -1 +1 @@ -extendWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDetection

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    +extendWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function extendWithFaceDetection

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    diff --git a/typedoc/functions/extendWithFaceExpressions.html b/typedoc/functions/extendWithFaceExpressions.html index 3d211f2..eb33e9f 100644 --- a/typedoc/functions/extendWithFaceExpressions.html +++ b/typedoc/functions/extendWithFaceExpressions.html @@ -1 +1 @@ -extendWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function extendWithFaceExpressions

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    +extendWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function extendWithFaceExpressions

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    diff --git a/typedoc/functions/extendWithFaceLandmarks.html b/typedoc/functions/extendWithFaceLandmarks.html index 1ec5f89..c78ac5d 100644 --- a/typedoc/functions/extendWithFaceLandmarks.html +++ b/typedoc/functions/extendWithFaceLandmarks.html @@ -1 +1 @@ -extendWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function extendWithFaceLandmarks

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    +extendWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function extendWithFaceLandmarks

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    diff --git a/typedoc/functions/extendWithGender.html b/typedoc/functions/extendWithGender.html index b96953a..9d83e48 100644 --- a/typedoc/functions/extendWithGender.html +++ b/typedoc/functions/extendWithGender.html @@ -1 +1 @@ -extendWithGender | @vladmandic/face-api - v1.7.14

    Function extendWithGender

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    +extendWithGender | @vladmandic/face-api - v1.7.14

    Function extendWithGender

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    diff --git a/typedoc/functions/extractFaceTensors.html b/typedoc/functions/extractFaceTensors.html index 3348935..19aa9f5 100644 --- a/typedoc/functions/extractFaceTensors.html +++ b/typedoc/functions/extractFaceTensors.html @@ -1,8 +1,8 @@ -extractFaceTensors | @vladmandic/face-api - v1.7.14

    Function extractFaceTensors

    • extractFaceTensors(imageTensor, detections): Promise<Tensor3D[]>

    • Extracts the tensors of the image regions containing the detected faces. +extractFaceTensors | @vladmandic/face-api - v1.7.14

      Function extractFaceTensors

      • extractFaceTensors(
            imageTensor: Tensor4D | Tensor3D,
            detections: (Rect | FaceDetection)[],
        ): Promise<Tensor3D[]>

      • Extracts the tensors of the image regions containing the detected faces. Useful if you want to compute the face descriptors for the face images. Using this method is faster then extracting a canvas for each face and converting them to tensors individually.

        -

        Parameters

        • imageTensor: Tensor4D | Tensor3D

          The image tensor that face detection has been performed on.

          -
        • detections: (Rect | FaceDetection)[]

          The face detection results or face bounding boxes for that image.

          +

          Parameters

          • imageTensor: Tensor4D | Tensor3D

            The image tensor that face detection has been performed on.

            +
          • detections: (Rect | FaceDetection)[]

            The face detection results or face bounding boxes for that image.

          Returns Promise<Tensor3D[]>

          Tensors of the corresponding image region for each detected face.

          -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/extractFaces.html b/typedoc/functions/extractFaces.html index aa2b867..32f9b3a 100644 --- a/typedoc/functions/extractFaces.html +++ b/typedoc/functions/extractFaces.html @@ -1,5 +1,5 @@ -extractFaces | @vladmandic/face-api - v1.7.14

    Function extractFaces

    • extractFaces(input, detections): Promise<HTMLCanvasElement[]>

    • Extracts the image regions containing the detected faces.

      +extractFaces | @vladmandic/face-api - v1.7.14

      Function extractFaces

      • extractFaces(
            input: TNetInput,
            detections: (Rect | FaceDetection)[],
        ): Promise<HTMLCanvasElement[]>

      • Extracts the image regions containing the detected faces.

        Parameters

        • input: TNetInput

          The image that face detection has been performed on.

          -
        • detections: (Rect | FaceDetection)[]

          The face detection results or face bounding boxes for that image.

          +
        • detections: (Rect | FaceDetection)[]

          The face detection results or face bounding boxes for that image.

        Returns Promise<HTMLCanvasElement[]>

        The Canvases of the corresponding image region for each detected face.

        -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/fetchImage.html b/typedoc/functions/fetchImage.html index cbe874a..3c85585 100644 --- a/typedoc/functions/fetchImage.html +++ b/typedoc/functions/fetchImage.html @@ -1 +1 @@ -fetchImage | @vladmandic/face-api - v1.7.14

    Function fetchImage

    • fetchImage(uri): Promise<HTMLImageElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLImageElement>

    Settings

    Member Visibility
    +fetchImage | @vladmandic/face-api - v1.7.14

    Function fetchImage

    • fetchImage(uri: string): Promise<HTMLImageElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLImageElement>

    Settings

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    diff --git a/typedoc/functions/fetchJson.html b/typedoc/functions/fetchJson.html index a9df200..fbb05de 100644 --- a/typedoc/functions/fetchJson.html +++ b/typedoc/functions/fetchJson.html @@ -1 +1 @@ -fetchJson | @vladmandic/face-api - v1.7.14

    Function fetchJson

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    +fetchJson | @vladmandic/face-api - v1.7.14

    Function fetchJson

    • fetchJson<T>(uri: string): Promise<T>

    • Type Parameters

      • T

      Parameters

      • uri: string

      Returns Promise<T>

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    diff --git a/typedoc/functions/fetchNetWeights.html b/typedoc/functions/fetchNetWeights.html index ae05794..c4ef9a7 100644 --- a/typedoc/functions/fetchNetWeights.html +++ b/typedoc/functions/fetchNetWeights.html @@ -1 +1 @@ -fetchNetWeights | @vladmandic/face-api - v1.7.14

    Function fetchNetWeights

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    +fetchNetWeights | @vladmandic/face-api - v1.7.14

    Function fetchNetWeights

    • fetchNetWeights(uri: string): Promise<Float32Array>

    • Parameters

      • uri: string

      Returns Promise<Float32Array>

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    diff --git a/typedoc/functions/fetchOrThrow.html b/typedoc/functions/fetchOrThrow.html index 1c6256c..be4babf 100644 --- a/typedoc/functions/fetchOrThrow.html +++ b/typedoc/functions/fetchOrThrow.html @@ -1 +1 @@ -fetchOrThrow | @vladmandic/face-api - v1.7.14

    Function fetchOrThrow

    • fetchOrThrow(url, init?): Promise<Response>

    • Parameters

      • url: string
      • Optionalinit: RequestInit

      Returns Promise<Response>

    Settings

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    +fetchOrThrow | @vladmandic/face-api - v1.7.14

    Function fetchOrThrow

    • fetchOrThrow(url: string, init?: RequestInit): Promise<Response>

    • Parameters

      • url: string
      • Optionalinit: RequestInit

      Returns Promise<Response>

    Settings

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    diff --git a/typedoc/functions/fetchVideo.html b/typedoc/functions/fetchVideo.html index 291942d..cad6a3f 100644 --- a/typedoc/functions/fetchVideo.html +++ b/typedoc/functions/fetchVideo.html @@ -1 +1 @@ -fetchVideo | @vladmandic/face-api - v1.7.14

    Function fetchVideo

    • fetchVideo(uri): Promise<HTMLVideoElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLVideoElement>

    Settings

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    +fetchVideo | @vladmandic/face-api - v1.7.14

    Function fetchVideo

    • fetchVideo(uri: string): Promise<HTMLVideoElement>

    • Parameters

      • uri: string

      Returns Promise<HTMLVideoElement>

    Settings

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    diff --git a/typedoc/functions/getContext2dOrThrow.html b/typedoc/functions/getContext2dOrThrow.html index 2b81793..ff4c0a5 100644 --- a/typedoc/functions/getContext2dOrThrow.html +++ b/typedoc/functions/getContext2dOrThrow.html @@ -1 +1 @@ -getContext2dOrThrow | @vladmandic/face-api - v1.7.14

    Function getContext2dOrThrow

    • getContext2dOrThrow(canvasArg): CanvasRenderingContext2D

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns CanvasRenderingContext2D

    Settings

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    +getContext2dOrThrow | @vladmandic/face-api - v1.7.14

    Function getContext2dOrThrow

    • getContext2dOrThrow(
          canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D,
      ): CanvasRenderingContext2D

    • Parameters

      • canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D

      Returns CanvasRenderingContext2D

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    diff --git a/typedoc/functions/getMediaDimensions.html b/typedoc/functions/getMediaDimensions.html index ec13614..a1bf1e0 100644 --- a/typedoc/functions/getMediaDimensions.html +++ b/typedoc/functions/getMediaDimensions.html @@ -1 +1 @@ -getMediaDimensions | @vladmandic/face-api - v1.7.14

    Function getMediaDimensions

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    +getMediaDimensions | @vladmandic/face-api - v1.7.14

    Function getMediaDimensions

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    diff --git a/typedoc/functions/imageTensorToCanvas.html b/typedoc/functions/imageTensorToCanvas.html index 238ced8..137f746 100644 --- a/typedoc/functions/imageTensorToCanvas.html +++ b/typedoc/functions/imageTensorToCanvas.html @@ -1 +1 @@ -imageTensorToCanvas | @vladmandic/face-api - v1.7.14

    Function imageTensorToCanvas

    • imageTensorToCanvas(imgTensor, canvas?): Promise<HTMLCanvasElement>

    • Parameters

      • imgTensor: Tensor<Rank>
      • Optionalcanvas: HTMLCanvasElement

      Returns Promise<HTMLCanvasElement>

    Settings

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    +imageTensorToCanvas | @vladmandic/face-api - v1.7.14

    Function imageTensorToCanvas

    • imageTensorToCanvas(
          imgTensor: tf.Tensor,
          canvas?: HTMLCanvasElement,
      ): Promise<HTMLCanvasElement>

    • Parameters

      • imgTensor: tf.Tensor
      • Optionalcanvas: HTMLCanvasElement

      Returns Promise<HTMLCanvasElement>

    Settings

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    diff --git a/typedoc/functions/imageToSquare.html b/typedoc/functions/imageToSquare.html index 19b5aa5..102aa3f 100644 --- a/typedoc/functions/imageToSquare.html +++ b/typedoc/functions/imageToSquare.html @@ -1 +1 @@ -imageToSquare | @vladmandic/face-api - v1.7.14

    Function imageToSquare

    • imageToSquare(input, inputSize, centerImage?): HTMLCanvasElement

    • Parameters

      • input: HTMLCanvasElement | HTMLImageElement
      • inputSize: number
      • centerImage: boolean = false

      Returns HTMLCanvasElement

    Settings

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    +imageToSquare | @vladmandic/face-api - v1.7.14

    Function imageToSquare

    • imageToSquare(
          input: HTMLCanvasElement | HTMLImageElement,
          inputSize: number,
          centerImage?: boolean,
      ): HTMLCanvasElement

    • Parameters

      • input: HTMLCanvasElement | HTMLImageElement
      • inputSize: number
      • centerImage: boolean = false

      Returns HTMLCanvasElement

    Settings

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    diff --git a/typedoc/functions/inverseSigmoid.html b/typedoc/functions/inverseSigmoid.html index e51b93f..5090651 100644 --- a/typedoc/functions/inverseSigmoid.html +++ b/typedoc/functions/inverseSigmoid.html @@ -1 +1 @@ -inverseSigmoid | @vladmandic/face-api - v1.7.14

    Function inverseSigmoid

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    +inverseSigmoid | @vladmandic/face-api - v1.7.14

    Function inverseSigmoid

    • inverseSigmoid(x: number): number

    • Parameters

      • x: number

      Returns number

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    diff --git a/typedoc/functions/iou.html b/typedoc/functions/iou.html index ff48a33..6a180a6 100644 --- a/typedoc/functions/iou.html +++ b/typedoc/functions/iou.html @@ -1 +1 @@ -iou | @vladmandic/face-api - v1.7.14

    Function iou

    • iou(box1, box2, isIOU?): number

    • Parameters

      • box1: Box<any>
      • box2: Box<any>
      • isIOU: boolean = true

      Returns number

    Settings

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    +iou | @vladmandic/face-api - v1.7.14

    Function iou

    • iou(box1: Box, box2: Box, isIOU?: boolean): number

    • Parameters

      • box1: Box
      • box2: Box
      • isIOU: boolean = true

      Returns number

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    diff --git a/typedoc/functions/isMediaElement.html b/typedoc/functions/isMediaElement.html index ae4c769..c11e0b7 100644 --- a/typedoc/functions/isMediaElement.html +++ b/typedoc/functions/isMediaElement.html @@ -1 +1 @@ -isMediaElement | @vladmandic/face-api - v1.7.14

    Function isMediaElement

    • isMediaElement(input): input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    • Parameters

      • input: any

      Returns input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Settings

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    +isMediaElement | @vladmandic/face-api - v1.7.14

    Function isMediaElement

    • isMediaElement(
          input: any,
      ): input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    • Parameters

      • input: any

      Returns input is HTMLCanvasElement | HTMLImageElement | HTMLVideoElement

    Settings

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    diff --git a/typedoc/functions/isMediaLoaded.html b/typedoc/functions/isMediaLoaded.html index be507a1..5ba2ff2 100644 --- a/typedoc/functions/isMediaLoaded.html +++ b/typedoc/functions/isMediaLoaded.html @@ -1 +1 @@ -isMediaLoaded | @vladmandic/face-api - v1.7.14

    Function isMediaLoaded

    • isMediaLoaded(media): boolean

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement

      Returns boolean

    Settings

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    +isMediaLoaded | @vladmandic/face-api - v1.7.14

    Function isMediaLoaded

    • isMediaLoaded(media: HTMLImageElement | HTMLVideoElement): boolean

    • Parameters

      • media: HTMLImageElement | HTMLVideoElement

      Returns boolean

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    diff --git a/typedoc/functions/isWithAge.html b/typedoc/functions/isWithAge.html index 64f81fa..acc0308 100644 --- a/typedoc/functions/isWithAge.html +++ b/typedoc/functions/isWithAge.html @@ -1 +1 @@ -isWithAge | @vladmandic/face-api - v1.7.14

    Function isWithAge

    • isWithAge(obj): obj is {
          age: number;
      }

    • Parameters

      • obj: any

      Returns obj is {
          age: number;
      }

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    +isWithAge | @vladmandic/face-api - v1.7.14

    Function isWithAge

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    diff --git a/typedoc/functions/isWithFaceDetection.html b/typedoc/functions/isWithFaceDetection.html index d404fc6..73ff339 100644 --- a/typedoc/functions/isWithFaceDetection.html +++ b/typedoc/functions/isWithFaceDetection.html @@ -1 +1 @@ -isWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function isWithFaceDetection

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    +isWithFaceDetection | @vladmandic/face-api - v1.7.14

    Function isWithFaceDetection

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    diff --git a/typedoc/functions/isWithFaceExpressions.html b/typedoc/functions/isWithFaceExpressions.html index 7a3e166..c129b27 100644 --- a/typedoc/functions/isWithFaceExpressions.html +++ b/typedoc/functions/isWithFaceExpressions.html @@ -1 +1 @@ -isWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function isWithFaceExpressions

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    +isWithFaceExpressions | @vladmandic/face-api - v1.7.14

    Function isWithFaceExpressions

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    diff --git a/typedoc/functions/isWithFaceLandmarks.html b/typedoc/functions/isWithFaceLandmarks.html index a84bdc3..4862e3f 100644 --- a/typedoc/functions/isWithFaceLandmarks.html +++ b/typedoc/functions/isWithFaceLandmarks.html @@ -1 +1 @@ -isWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function isWithFaceLandmarks

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    +isWithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Function isWithFaceLandmarks

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    diff --git a/typedoc/functions/isWithGender.html b/typedoc/functions/isWithGender.html index 7331352..06c7374 100644 --- a/typedoc/functions/isWithGender.html +++ b/typedoc/functions/isWithGender.html @@ -1 +1 @@ -isWithGender | @vladmandic/face-api - v1.7.14

    Function isWithGender

    • isWithGender(obj): obj is {
          gender: Gender;
          genderProbability: number;
      }

    • Parameters

      • obj: any

      Returns obj is {
          gender: Gender;
          genderProbability: number;
      }

    Settings

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    +isWithGender | @vladmandic/face-api - v1.7.14

    Function isWithGender

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    diff --git a/typedoc/functions/loadAgeGenderModel.html b/typedoc/functions/loadAgeGenderModel.html index b88137d..0593f37 100644 --- a/typedoc/functions/loadAgeGenderModel.html +++ b/typedoc/functions/loadAgeGenderModel.html @@ -1 +1 @@ -loadAgeGenderModel | @vladmandic/face-api - v1.7.14

    Function loadAgeGenderModel

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    +loadAgeGenderModel | @vladmandic/face-api - v1.7.14

    Function loadAgeGenderModel

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    diff --git a/typedoc/functions/loadFaceDetectionModel.html b/typedoc/functions/loadFaceDetectionModel.html index 9d6c047..599e3de 100644 --- a/typedoc/functions/loadFaceDetectionModel.html +++ b/typedoc/functions/loadFaceDetectionModel.html @@ -1 +1 @@ -loadFaceDetectionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceDetectionModel

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    +loadFaceDetectionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceDetectionModel

    • loadFaceDetectionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceExpressionModel.html b/typedoc/functions/loadFaceExpressionModel.html index d216085..dfd3580 100644 --- a/typedoc/functions/loadFaceExpressionModel.html +++ b/typedoc/functions/loadFaceExpressionModel.html @@ -1 +1 @@ -loadFaceExpressionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceExpressionModel

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    +loadFaceExpressionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceExpressionModel

    • loadFaceExpressionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceLandmarkModel.html b/typedoc/functions/loadFaceLandmarkModel.html index 5a7a976..46612d2 100644 --- a/typedoc/functions/loadFaceLandmarkModel.html +++ b/typedoc/functions/loadFaceLandmarkModel.html @@ -1 +1 @@ -loadFaceLandmarkModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkModel

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    +loadFaceLandmarkModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkModel

    • loadFaceLandmarkModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceLandmarkTinyModel.html b/typedoc/functions/loadFaceLandmarkTinyModel.html index bf7feba..9a6ac67 100644 --- a/typedoc/functions/loadFaceLandmarkTinyModel.html +++ b/typedoc/functions/loadFaceLandmarkTinyModel.html @@ -1 +1 @@ -loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkTinyModel

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    +loadFaceLandmarkTinyModel | @vladmandic/face-api - v1.7.14

    Function loadFaceLandmarkTinyModel

    • loadFaceLandmarkTinyModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadFaceRecognitionModel.html b/typedoc/functions/loadFaceRecognitionModel.html index bad924d..0f38c1f 100644 --- a/typedoc/functions/loadFaceRecognitionModel.html +++ b/typedoc/functions/loadFaceRecognitionModel.html @@ -1 +1 @@ -loadFaceRecognitionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceRecognitionModel

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    +loadFaceRecognitionModel | @vladmandic/face-api - v1.7.14

    Function loadFaceRecognitionModel

    • loadFaceRecognitionModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadSsdMobilenetv1Model.html b/typedoc/functions/loadSsdMobilenetv1Model.html index 9e9e5bf..1e01b8d 100644 --- a/typedoc/functions/loadSsdMobilenetv1Model.html +++ b/typedoc/functions/loadSsdMobilenetv1Model.html @@ -1 +1 @@ -loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.14

    Function loadSsdMobilenetv1Model

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    +loadSsdMobilenetv1Model | @vladmandic/face-api - v1.7.14

    Function loadSsdMobilenetv1Model

    • loadSsdMobilenetv1Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadTinyFaceDetectorModel.html b/typedoc/functions/loadTinyFaceDetectorModel.html index 2272a5c..bfe2606 100644 --- a/typedoc/functions/loadTinyFaceDetectorModel.html +++ b/typedoc/functions/loadTinyFaceDetectorModel.html @@ -1 +1 @@ -loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.14

    Function loadTinyFaceDetectorModel

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    +loadTinyFaceDetectorModel | @vladmandic/face-api - v1.7.14

    Function loadTinyFaceDetectorModel

    • loadTinyFaceDetectorModel(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadTinyYolov2Model.html b/typedoc/functions/loadTinyYolov2Model.html index 8784199..f312669 100644 --- a/typedoc/functions/loadTinyYolov2Model.html +++ b/typedoc/functions/loadTinyYolov2Model.html @@ -1 +1 @@ -loadTinyYolov2Model | @vladmandic/face-api - v1.7.14

    Function loadTinyYolov2Model

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    +loadTinyYolov2Model | @vladmandic/face-api - v1.7.14

    Function loadTinyYolov2Model

    • loadTinyYolov2Model(url: string): Promise<void>

    • Parameters

      • url: string

      Returns Promise<void>

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    diff --git a/typedoc/functions/loadWeightMap.html b/typedoc/functions/loadWeightMap.html index 332a964..2c8ff00 100644 --- a/typedoc/functions/loadWeightMap.html +++ b/typedoc/functions/loadWeightMap.html @@ -1 +1 @@ -loadWeightMap | @vladmandic/face-api - v1.7.14

    Function loadWeightMap

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    +loadWeightMap | @vladmandic/face-api - v1.7.14

    Function loadWeightMap

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    diff --git a/typedoc/functions/locateFaces.html b/typedoc/functions/locateFaces.html index 2598f8d..ed8c3bc 100644 --- a/typedoc/functions/locateFaces.html +++ b/typedoc/functions/locateFaces.html @@ -1,5 +1,5 @@ -locateFaces | @vladmandic/face-api - v1.7.14

    Function locateFaces

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    diff --git a/typedoc/functions/matchDimensions.html b/typedoc/functions/matchDimensions.html index 7fb7d75..18a1839 100644 --- a/typedoc/functions/matchDimensions.html +++ b/typedoc/functions/matchDimensions.html @@ -1 +1 @@ -matchDimensions | @vladmandic/face-api - v1.7.14

    Function matchDimensions

    • matchDimensions(input, reference, useMediaDimensions?): {
          height: number;
          width: number;
      }

    • Parameters

      Returns {
          height: number;
          width: number;
      }

      • height: number
      • width: number

    Settings

    Member Visibility
    +matchDimensions | @vladmandic/face-api - v1.7.14

    Function matchDimensions

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    diff --git a/typedoc/functions/minBbox.html b/typedoc/functions/minBbox.html index a532b02..9854174 100644 --- a/typedoc/functions/minBbox.html +++ b/typedoc/functions/minBbox.html @@ -1 +1 @@ -minBbox | @vladmandic/face-api - v1.7.14

    Function minBbox

    Settings

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    +minBbox | @vladmandic/face-api - v1.7.14

    Function minBbox

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    diff --git a/typedoc/functions/nonMaxSuppression.html b/typedoc/functions/nonMaxSuppression.html index ff3038f..e169f71 100644 --- a/typedoc/functions/nonMaxSuppression.html +++ b/typedoc/functions/nonMaxSuppression.html @@ -1 +1 @@ -nonMaxSuppression | @vladmandic/face-api - v1.7.14

    Function nonMaxSuppression

    • nonMaxSuppression(boxes, scores, iouThreshold, isIOU?): number[]

    • Parameters

      • boxes: Box<any>[]
      • scores: number[]
      • iouThreshold: number
      • isIOU: boolean = true

      Returns number[]

    Settings

    Member Visibility
    +nonMaxSuppression | @vladmandic/face-api - v1.7.14

    Function nonMaxSuppression

    • nonMaxSuppression(
          boxes: Box[],
          scores: number[],
          iouThreshold: number,
          isIOU?: boolean,
      ): number[]

    • Parameters

      • boxes: Box[]
      • scores: number[]
      • iouThreshold: number
      • isIOU: boolean = true

      Returns number[]

    Settings

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    diff --git a/typedoc/functions/normalize.html b/typedoc/functions/normalize.html index 38776d9..7d3e52f 100644 --- a/typedoc/functions/normalize.html +++ b/typedoc/functions/normalize.html @@ -1 +1 @@ -normalize | @vladmandic/face-api - v1.7.14

    Function normalize

    Settings

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    +normalize | @vladmandic/face-api - v1.7.14

    Function normalize

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    diff --git a/typedoc/functions/padToSquare.html b/typedoc/functions/padToSquare.html index 5cd4c5a..01bd94d 100644 --- a/typedoc/functions/padToSquare.html +++ b/typedoc/functions/padToSquare.html @@ -1,6 +1,6 @@ -padToSquare | @vladmandic/face-api - v1.7.14

    Function padToSquare

    • padToSquare(imgTensor, isCenterImage?): Tensor4D

    • Pads the smaller dimension of an image tensor with zeros, such that width === height.

      +padToSquare | @vladmandic/face-api - v1.7.14

      Function padToSquare

      • padToSquare(imgTensor: Tensor4D, isCenterImage?: boolean): Tensor4D

      • Pads the smaller dimension of an image tensor with zeros, such that width === height.

        Parameters

        • imgTensor: Tensor4D

          The image tensor.

        • isCenterImage: boolean = false

          (optional, default: false) If true, add an equal amount of padding on both sides of the minor dimension oof the image.

        Returns Tensor4D

        The padded tensor with width === height.

        -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/predictAgeAndGender.html b/typedoc/functions/predictAgeAndGender.html index dd32ecb..09290ad 100644 --- a/typedoc/functions/predictAgeAndGender.html +++ b/typedoc/functions/predictAgeAndGender.html @@ -1,3 +1,3 @@ -predictAgeAndGender | @vladmandic/face-api - v1.7.14

    Function predictAgeAndGender

    Predicts age and gender from a face image.

    -

    Settings

    Member Visibility
    +predictAgeAndGender | @vladmandic/face-api - v1.7.14

    Function predictAgeAndGender

    Predicts age and gender from a face image.

    +

    Settings

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    diff --git a/typedoc/functions/recognizeFaceExpressions.html b/typedoc/functions/recognizeFaceExpressions.html index 4e15b94..1afcd61 100644 --- a/typedoc/functions/recognizeFaceExpressions.html +++ b/typedoc/functions/recognizeFaceExpressions.html @@ -1,3 +1,3 @@ -recognizeFaceExpressions | @vladmandic/face-api - v1.7.14

    Function recognizeFaceExpressions

    Recognizes the facial expressions from a face image.

    -

    Settings

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    +recognizeFaceExpressions | @vladmandic/face-api - v1.7.14

    Function recognizeFaceExpressions

    Recognizes the facial expressions from a face image.

    +

    Settings

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    diff --git a/typedoc/functions/resizeResults.html b/typedoc/functions/resizeResults.html index 30ceaba..7b2afb3 100644 --- a/typedoc/functions/resizeResults.html +++ b/typedoc/functions/resizeResults.html @@ -1 +1 @@ -resizeResults | @vladmandic/face-api - v1.7.14

    Function resizeResults

    Settings

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    +resizeResults | @vladmandic/face-api - v1.7.14

    Function resizeResults

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    diff --git a/typedoc/functions/resolveInput.html b/typedoc/functions/resolveInput.html index 929bd06..e56c98f 100644 --- a/typedoc/functions/resolveInput.html +++ b/typedoc/functions/resolveInput.html @@ -1 +1 @@ -resolveInput | @vladmandic/face-api - v1.7.14

    Function resolveInput

    Settings

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    +resolveInput | @vladmandic/face-api - v1.7.14

    Function resolveInput

    Settings

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    diff --git a/typedoc/functions/shuffleArray.html b/typedoc/functions/shuffleArray.html index 0464e67..70c58e6 100644 --- a/typedoc/functions/shuffleArray.html +++ b/typedoc/functions/shuffleArray.html @@ -1 +1 @@ -shuffleArray | @vladmandic/face-api - v1.7.14

    Function shuffleArray

    Settings

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    +shuffleArray | @vladmandic/face-api - v1.7.14

    Function shuffleArray

    Settings

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    diff --git a/typedoc/functions/sigmoid.html b/typedoc/functions/sigmoid.html index 7e85717..b045c7b 100644 --- a/typedoc/functions/sigmoid.html +++ b/typedoc/functions/sigmoid.html @@ -1 +1 @@ -sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

    Settings

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    +sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

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    diff --git a/typedoc/functions/ssdMobilenetv1-1.html b/typedoc/functions/ssdMobilenetv1-1.html index 7ca2153..639efe8 100644 --- a/typedoc/functions/ssdMobilenetv1-1.html +++ b/typedoc/functions/ssdMobilenetv1-1.html @@ -1,5 +1,5 @@ -ssdMobilenetv1 | @vladmandic/face-api - v1.7.14

    Function ssdMobilenetv1

    Attempts to detect all faces in an image using SSD Mobilenetv1 Network.

    -

    Settings

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    diff --git a/typedoc/functions/tf.add.html b/typedoc/functions/tf.add.html index dac498a..0ae97ac 100644 --- a/typedoc/functions/tf.add.html +++ b/typedoc/functions/tf.add.html @@ -1,10 +1,10 @@ -add | @vladmandic/face-api - v1.7.14

    Function add

    • add<T>(a, b): T

    • Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

      +add | @vladmandic/face-api - v1.7.14

      Function add

      • add<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

        const a = tf.tensor1d([1, 2, 3, 4]);
        const b = tf.tensor1d([10, 20, 30, 40]);

        a.add(b).print();  // or tf.add(a, b)
 
        // Broadcast add a with b.
        const a = tf.scalar(5);
        const b = tf.tensor1d([10, 20, 30, 40]);

        a.add(b).print();  // or tf.add(a, b)
        -

        Type Parameters

        Parameters

        Returns T

        Doc

      Settings

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      +

      Type Parameters

      Parameters

      Returns T

      Doc

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    diff --git a/typedoc/functions/tf.avgPool.html b/typedoc/functions/tf.avgPool.html index 413310b..fc9cb8f 100644 --- a/typedoc/functions/tf.avgPool.html +++ b/typedoc/functions/tf.avgPool.html @@ -1,19 +1,19 @@ -avgPool | @vladmandic/face-api - v1.7.14

    Function avgPool

    • avgPool<T>(x, filterSize, strides, pad, dimRoundingMode?): T

    • Computes the 2D average pooling of an image.

      -

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tensor, of rank 4 or rank 3 of shape +avgPool | @vladmandic/face-api - v1.7.14

        Function avgPool

        • avgPool<T extends Tensor4D | Tensor3D>(
              x: TensorLike | T,
              filterSize: number | [number, number],
              strides: number | [number, number],
              pad: number | "valid" | "same" | ExplicitPadding,
              dimRoundingMode?: "floor" | "round" | "ceil",
          ): T

        • Computes the 2D average pooling of an image.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor, of rank 4 or rank 3 of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

            -
          • filterSize: number | [number, number]

            The filter size: [filterHeight, filterWidth]. If +

          • filterSize: number | [number, number]

            The filter size: [filterHeight, filterWidth]. If filterSize is a single number, then filterHeight == filterWidth.

            -
          • strides: number | [number, number]

            The strides of the pooling: [strideHeight, strideWidth]. If +

          • strides: number | [number, number]

            The strides of the pooling: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            -
          • pad:
                | number
                | "valid"
                | "same"
                | ExplicitPadding

            The type of padding algorithm:

            +
          • pad: number | "valid" | "same" | ExplicitPadding

            The type of padding algorithm:

            -
          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is +

          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is provided, it will default to truncate.

            -

          Returns T

          Doc

        Settings

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        +

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.batchNorm.html b/typedoc/functions/tf.batchNorm.html index f5accde..40cc5ba 100644 --- a/typedoc/functions/tf.batchNorm.html +++ b/typedoc/functions/tf.batchNorm.html @@ -1,6 +1,6 @@ -batchNorm | @vladmandic/face-api - v1.7.14

    Function batchNorm

    Returns tf.Tensor<R>

    Doc

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    diff --git a/typedoc/functions/tf.browser.draw.html b/typedoc/functions/tf.browser.draw.html index 7c110de..6a68d7f 100644 --- a/typedoc/functions/tf.browser.draw.html +++ b/typedoc/functions/tf.browser.draw.html @@ -1,8 +1,8 @@ -draw | @vladmandic/face-api - v1.7.14

    Function draw

    • draw(image, canvas, options?): void

    • Draws a tf.Tensor to a canvas.

      +draw | @vladmandic/face-api - v1.7.14

      Function draw

      • draw(
            image: Tensor2D | TensorLike | Tensor3D,
            canvas: HTMLCanvasElement,
            options?: DrawOptions,
        ): void

      • Draws a tf.Tensor to a canvas.

        When the dtype of the input is 'float32', we assume values in the range [0-1]. Otherwise, when input is 'int32', we assume values in the range [0-255].

        -

        Parameters

        • image: Tensor2D | TensorLike | Tensor3D

          The tensor to draw on the canvas. Must match one of +

          Parameters

          • image: Tensor2D | TensorLike | Tensor3D

            The tensor to draw on the canvas. Must match one of these shapes:

            • Rank-2 with shape [height, width]: Drawn as grayscale.
              • @@ -14,4 +14,4 @@ these shapes:

          • canvas: HTMLCanvasElement

            The canvas to draw to.

          • Optionaloptions: DrawOptions

            The configuration arguments for image to be drawn and the canvas to draw to.

            -

          Returns void

          Doc

      Settings

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      +

    Returns void

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.browser.fromPixels.html b/typedoc/functions/tf.browser.fromPixels.html index 0ff76cd..341e635 100644 --- a/typedoc/functions/tf.browser.fromPixels.html +++ b/typedoc/functions/tf.browser.fromPixels.html @@ -1,8 +1,8 @@ -fromPixels | @vladmandic/face-api - v1.7.14

    Function fromPixels

    • fromPixels(pixels, numChannels?): Tensor3D

    • Creates a tf.Tensor from an image.

      +fromPixels | @vladmandic/face-api - v1.7.14

      Function fromPixels

      • fromPixels(
            pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData,
            numChannels?: number,
        ): Tensor3D

      • Creates a tf.Tensor from an image.

        const image = new ImageData(1, 1);
        image.data[0] = 100;
        image.data[1] = 150;
        image.data[2] = 200;
        image.data[3] = 255;

        tf.browser.fromPixels(image).print();
        -

        Parameters

        • pixels:
              | HTMLCanvasElement
              | HTMLImageElement
              | HTMLVideoElement
              | ImageData
              | ImageBitmap
              | PixelData

          The input image to construct the tensor from. The +

          Parameters

          • pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData

            The input image to construct the tensor from. The supported image types are all 4-channel. You can also pass in an image object with following attributes: {data: Uint8Array; width: number; height: number}

            @@ -15,5 +15,5 @@ slightly different tensor values, if rendered by different rendering engines. This means that results from different browsers, or even same browser with CPU and GPU rendering engines can be different. See discussion in details: -https://github.com/tensorflow/tfjs/issues/5482

            -

            Doc

      Settings

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      +https://github.com/tensorflow/tfjs/issues/5482

      +

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.browser.fromPixelsAsync.html b/typedoc/functions/tf.browser.fromPixelsAsync.html index 70838bd..aaaf6ef 100644 --- a/typedoc/functions/tf.browser.fromPixelsAsync.html +++ b/typedoc/functions/tf.browser.fromPixelsAsync.html @@ -1,15 +1,15 @@ -fromPixelsAsync | @vladmandic/face-api - v1.7.14

    Function fromPixelsAsync

    • fromPixelsAsync(pixels, numChannels?): Promise<Tensor3D>

    • Creates a tf.Tensor from an image in async way.

      +fromPixelsAsync | @vladmandic/face-api - v1.7.14

      Function fromPixelsAsync

      • fromPixelsAsync(
            pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData,
            numChannels?: number,
        ): Promise<Tensor3D>

      • Creates a tf.Tensor from an image in async way.

        const image = new ImageData(1, 1);
        image.data[0] = 100;
        image.data[1] = 150;
        image.data[2] = 200;
        image.data[3] = 255;

        (await tf.browser.fromPixelsAsync(image)).print();

        This API is the async version of fromPixels. The API will first check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to imageBitmap if the flag is set to true.

        -

        Parameters

        • pixels:
              | HTMLCanvasElement
              | HTMLImageElement
              | HTMLVideoElement
              | ImageData
              | ImageBitmap
              | PixelData

          The input image to construct the tensor from. The +

          Parameters

          • pixels:
                | HTMLCanvasElement
                | HTMLImageElement
                | HTMLVideoElement
                | ImageData
                | ImageBitmap
                | PixelData

            The input image to construct the tensor from. The supported image types are all 4-channel. You can also pass in an image object with following attributes: {data: Uint8Array; width: number; height: number}

          • OptionalnumChannels: number

            The number of channels of the output tensor. A numChannels value less than 4 allows you to ignore channels. Defaults to 3 (ignores alpha channel of input image).

            -

          Returns Promise<Tensor3D>

          Doc

      Settings

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      +

    Returns Promise<Tensor3D>

    Doc

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.browser.toPixels.html b/typedoc/functions/tf.browser.toPixels.html index 8b94f79..2b4c82d 100644 --- a/typedoc/functions/tf.browser.toPixels.html +++ b/typedoc/functions/tf.browser.toPixels.html @@ -1,14 +1,14 @@ -toPixels | @vladmandic/face-api - v1.7.14

    Function toPixels

    • toPixels(img, canvas?): Promise<Uint8ClampedArray>

    • Draws a tf.Tensor of pixel values to a byte array or optionally a +toPixels | @vladmandic/face-api - v1.7.14

      Function toPixels

      • toPixels(
            img: Tensor2D | TensorLike | Tensor3D,
            canvas?: HTMLCanvasElement,
        ): Promise<Uint8ClampedArray>

      • Draws a tf.Tensor of pixel values to a byte array or optionally a canvas.

        When the dtype of the input is 'float32', we assume values in the range [0-1]. Otherwise, when input is 'int32', we assume values in the range [0-255].

        Returns a promise that resolves when the canvas has been drawn to.

        -

        Parameters

        • img: Tensor2D | TensorLike | Tensor3D

          A rank-2 tensor with shape [height, width], or a rank-3 tensor +

          Parameters

          • img: Tensor2D | TensorLike | Tensor3D

            A rank-2 tensor with shape [height, width], or a rank-3 tensor of shape [height, width, numChannels]. If rank-2, draws grayscale. If rank-3, must have depth of 1, 3 or 4. When depth of 1, draws grayscale. When depth of 3, we draw with the first three components of the depth dimension corresponding to r, g, b and alpha = 1. When depth of 4, all four components of the depth dimension correspond to r, g, b, a.

          • Optionalcanvas: HTMLCanvasElement

            The canvas to draw to.

            -

          Returns Promise<Uint8ClampedArray>

          Doc

      Settings

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      +

    Returns Promise<Uint8ClampedArray>

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.cast.html b/typedoc/functions/tf.cast.html index c3ca94d..3cb3898 100644 --- a/typedoc/functions/tf.cast.html +++ b/typedoc/functions/tf.cast.html @@ -1,7 +1,7 @@ -cast | @vladmandic/face-api - v1.7.14

    Function cast

    • cast<T>(x, dtype): T

    • Casts a tf.Tensor to a new dtype.

      +cast | @vladmandic/face-api - v1.7.14

      Function cast

      • cast<T extends tf.Tensor>(x: TensorLike | T, dtype: keyof DataTypeMap): T

      • Casts a tf.Tensor to a new dtype.

        const x = tf.tensor1d([1.5, 2.5, 3]);
        tf.cast(x, 'int32').print();
        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor to be casted.

          -
        • dtype: keyof DataTypeMap

          The dtype to cast the input tensor to.

          -

        Returns T

        Doc

      Settings

      Member Visibility
      +

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tensor to be casted.

        +
      • dtype: keyof DataTypeMap

        The dtype to cast the input tensor to.

        +

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.clipByValue.html b/typedoc/functions/tf.clipByValue.html index 40eba22..d288b1d 100644 --- a/typedoc/functions/tf.clipByValue.html +++ b/typedoc/functions/tf.clipByValue.html @@ -1,8 +1,8 @@ -clipByValue | @vladmandic/face-api - v1.7.14

    Function clipByValue

    • clipByValue<T>(x, clipValueMin, clipValueMax): T

    • Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

      +clipByValue | @vladmandic/face-api - v1.7.14

      Function clipByValue

      • clipByValue<T extends tf.Tensor>(
            x: TensorLike | T,
            clipValueMin: number,
            clipValueMax: number,
        ): T

      • Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

        const x = tf.tensor1d([-1, 2, -3, 4]);

        x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor.

          • clipValueMin: number

            Lower bound of range to be clipped to.

          • clipValueMax: number

            Upper bound of range to be clipped to.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.concat.html b/typedoc/functions/tf.concat.html index f47820c..6adea10 100644 --- a/typedoc/functions/tf.concat.html +++ b/typedoc/functions/tf.concat.html @@ -1,4 +1,4 @@ -concat | @vladmandic/face-api - v1.7.14

    Function concat

    • concat<T>(tensors, axis?): T

    • Concatenates a list of tf.Tensors along a given axis.

      +concat | @vladmandic/face-api - v1.7.14

      Function concat

      • concat<T extends tf.Tensor>(tensors: (TensorLike | T)[], axis?: number): T

      • Concatenates a list of tf.Tensors along a given axis.

        The tensors ranks and types must match, and their sizes must match in all dimensions except axis.

        Also available are stricter rank-specific methods that assert that @@ -20,6 +20,6 @@ same signature as this method.

        const a = tf.tensor2d([[1, 2], [10, 20]]);
        const b = tf.tensor2d([[3, 4], [30, 40]]);
        const axis = 1;
        tf.concat([a, b], axis).print();
        -

        Type Parameters

        Parameters

        • tensors: (TensorLike | T)[]

          A list of tensors to concatenate.

          +

          Type Parameters

          Parameters

          • tensors: (TensorLike | T)[]

            A list of tensors to concatenate.

          • Optionalaxis: number

            The axis to concatenate along. Defaults to 0 (the first dim).

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.conv2d.html b/typedoc/functions/tf.conv2d.html index 8174a79..cf8dba4 100644 --- a/typedoc/functions/tf.conv2d.html +++ b/typedoc/functions/tf.conv2d.html @@ -1,24 +1,24 @@ -conv2d | @vladmandic/face-api - v1.7.14

    Function conv2d

    • conv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Computes a 2D convolution over the input x.

      -

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tensor, of rank 4 or rank 3, of shape +conv2d | @vladmandic/face-api - v1.7.14

        Function conv2d

        • conv2d<T extends Tensor4D | Tensor3D>(
              x: TensorLike | T,
              filter: Tensor4D | TensorLike,
              strides: number | [number, number],
              pad: number | "valid" | "same" | ExplicitPadding,
              dataFormat?: "NHWC" | "NCHW",
              dilations?: number | [number, number],
              dimRoundingMode?: "floor" | "round" | "ceil",
          ): T

        • Computes a 2D convolution over the input x.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

            -
          • filter: Tensor4D | TensorLike

            The filter, rank 4, of shape +

          • filter: Tensor4D | TensorLike

            The filter, rank 4, of shape [filterHeight, filterWidth, inDepth, outDepth].

            -
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth].

            -
          • pad:
                | number
                | "valid"
                | "same"
                | ExplicitPadding

            The type of padding algorithm.

            +
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth].

            +
          • pad: number | "valid" | "same" | ExplicitPadding

            The type of padding algorithm.

            -
          • OptionaldataFormat: "NHWC" | "NCHW"
          • Optionaldilations: number | [number, number]

            The dilation rates: [dilationHeight, dilationWidth] +

          • OptionaldataFormat: "NHWC" | "NCHW"
          • Optionaldilations: number | [number, number]

            The dilation rates: [dilationHeight, dilationWidth] in which we sample input values across the height and width dimensions in atrous convolution. Defaults to [1, 1]. If dilations is a single number, then dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1.

            -
          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is +

          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is provided, it will default to truncate.

            -

          Returns T

          Doc

        Settings

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        +

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.depthwiseConv2d.html b/typedoc/functions/tf.depthwiseConv2d.html index 15f67af..5cd1182 100644 --- a/typedoc/functions/tf.depthwiseConv2d.html +++ b/typedoc/functions/tf.depthwiseConv2d.html @@ -1,4 +1,4 @@ -depthwiseConv2d | @vladmandic/face-api - v1.7.14

    Function depthwiseConv2d

    • depthwiseConv2d<T>(x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?): T

    • Depthwise 2D convolution.

      +depthwiseConv2d | @vladmandic/face-api - v1.7.14

      Function depthwiseConv2d

      • depthwiseConv2d<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            filter: Tensor4D | TensorLike,
            strides: number | [number, number],
            pad: number | "valid" | "same" | ExplicitPadding,
            dataFormat?: "NHWC" | "NCHW",
            dilations?: number | [number, number],
            dimRoundingMode?: "floor" | "round" | "ceil",
        ): T

      • Depthwise 2D convolution.

        Given a 4D input array and a filter array of shape [filterHeight, filterWidth, inChannels, channelMultiplier] containing inChannels convolutional filters of depth 1, this op applies a @@ -6,28 +6,28 @@ different filter to each input channel (expanding from 1 channel to channelMultiplier channels for each), then concatenates the results together. The output has inChannels * channelMultiplier channels.

        See -https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d +https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d for more details.

        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor, of rank 4 or rank 3, of shape +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

            -
          • filter: Tensor4D | TensorLike

            The filter tensor, rank 4, of shape +

          • filter: Tensor4D | TensorLike

            The filter tensor, rank 4, of shape [filterHeight, filterWidth, inChannels, channelMultiplier].

            -
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            -
          • pad:
                | number
                | "valid"
                | "same"
                | ExplicitPadding

            The type of padding algorithm.

            +
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            +
          • pad: number | "valid" | "same" | ExplicitPadding

            The type of padding algorithm.

            -
          • OptionaldataFormat: "NHWC" | "NCHW"
          • Optionaldilations: number | [number, number]

            The dilation rates: [dilationHeight, dilationWidth] +

          • OptionaldataFormat: "NHWC" | "NCHW"
          • Optionaldilations: number | [number, number]

            The dilation rates: [dilationHeight, dilationWidth] in which we sample input values across the height and width dimensions in atrous convolution. Defaults to [1, 1]. If rate is a single number, then dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1.

            -
          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is +

          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is provided, it will default to truncate.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.div.html b/typedoc/functions/tf.div.html index 3744661..8d77343 100644 --- a/typedoc/functions/tf.div.html +++ b/typedoc/functions/tf.div.html @@ -1,11 +1,11 @@ -div | @vladmandic/face-api - v1.7.14

    Function div

    • div<T>(a, b): T

    • Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

      +div | @vladmandic/face-api - v1.7.14

      Function div

      • div<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

        const a = tf.tensor1d([1, 4, 9, 16]);
        const b = tf.tensor1d([1, 2, 3, 4]);

        a.div(b).print();  // or tf.div(a, b)
 
        // Broadcast div a with b.
        const a = tf.tensor1d([2, 4, 6, 8]);
        const b = tf.scalar(2);

        a.div(b).print();  // or tf.div(a, b)
        -

        Type Parameters

        Parameters

        • a: TensorLike | Tensor<Rank>

          The first tensor as the numerator.

          -
        • b: TensorLike | Tensor<Rank>

          The second tensor as the denominator. Must have the same dtype as +

          Type Parameters

          Parameters

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.exp.html b/typedoc/functions/tf.exp.html index cc044b8..a0c97d8 100644 --- a/typedoc/functions/tf.exp.html +++ b/typedoc/functions/tf.exp.html @@ -1,6 +1,6 @@ -exp | @vladmandic/face-api - v1.7.14

    Function exp

    • exp<T>(x): T

    • Computes exponential of the input tf.Tensor element-wise. e ^ x

      +exp | @vladmandic/face-api - v1.7.14

      Function exp

      • exp<T extends tf.Tensor>(x: TensorLike | T): T

      • Computes exponential of the input tf.Tensor element-wise. e ^ x

        const x = tf.tensor1d([1, 2, -3]);

        x.exp().print();  // or tf.exp(x)
        -

        Type Parameters

        Parameters

        Returns T

        Doc

      Settings

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      +

      Type Parameters

      Parameters

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.expandDims.html b/typedoc/functions/tf.expandDims.html index 1cf1c95..1ccd12d 100644 --- a/typedoc/functions/tf.expandDims.html +++ b/typedoc/functions/tf.expandDims.html @@ -1,9 +1,9 @@ -expandDims | @vladmandic/face-api - v1.7.14

    Function expandDims

    • expandDims<T>(x, axis?): T

    • Returns a tf.Tensor that has expanded rank, by inserting a dimension +expandDims | @vladmandic/face-api - v1.7.14

      Function expandDims

      • expandDims<T extends tf.Tensor>(x: TensorLike | tf.Tensor, axis?: number): T

      • Returns a tf.Tensor that has expanded rank, by inserting a dimension into the tensor's shape.

        const x = tf.tensor1d([1, 2, 3, 4]);
        const axis = 1;
        x.expandDims(axis).print();
        -

        Type Parameters

        Parameters

        • x: TensorLike | Tensor<Rank>

          The input tensor whose dimensions are to be expanded.

          +

          Type Parameters

          Parameters

          • x: TensorLike | tf.Tensor

            The input tensor whose dimensions are to be expanded.

          • Optionalaxis: number

            The dimension index at which to insert shape of 1. Defaults to 0 (the first dimension).

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.fill.html b/typedoc/functions/tf.fill.html index 91ddea1..d5b34ca 100644 --- a/typedoc/functions/tf.fill.html +++ b/typedoc/functions/tf.fill.html @@ -1,9 +1,9 @@ -fill | @vladmandic/face-api - v1.7.14

    Function fill

    • fill<R>(shape, value, dtype?): Tensor<R>

    • Creates a tf.Tensor filled with a scalar value.

      +fill | @vladmandic/face-api - v1.7.14

      Function fill

      • fill<R extends Rank>(
            shape: ShapeMap[R],
            value: string | number,
            dtype?: keyof DataTypeMap,
        ): tf.Tensor<R>

      • Creates a tf.Tensor filled with a scalar value.

        tf.fill([2, 2], 4).print();
        -

        Type Parameters

        Parameters

        • shape: ShapeMap[R]

          An array of integers defining the output tensor shape.

          -
        • value: string | number

          The scalar value to fill the tensor with.

          +

          Type Parameters

          Parameters

          • shape: ShapeMap[R]

            An array of integers defining the output tensor shape.

            +
          • value: string | number

            The scalar value to fill the tensor with.

          • Optionaldtype: keyof DataTypeMap

            The type of an element in the resulting tensor. Defaults to 'float32' if the given param value is a number, otherwise 'string'.

            -

          Returns Tensor<R>

          Doc

      Settings

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      +

    Returns tf.Tensor<R>

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.io.browserFiles.html b/typedoc/functions/tf.io.browserFiles.html index b407086..5214314 100644 --- a/typedoc/functions/tf.io.browserFiles.html +++ b/typedoc/functions/tf.io.browserFiles.html @@ -1,4 +1,4 @@ -browserFiles | @vladmandic/face-api - v1.7.14

    Function browserFiles

    • browserFiles(files): IOHandler

    • Creates an IOHandler that loads model artifacts from user-selected files.

      +browserFiles | @vladmandic/face-api - v1.7.14

      Function browserFiles

      • browserFiles(files: File[]): IOHandler

      • Creates an IOHandler that loads model artifacts from user-selected files.

        This method can be used for loading from files such as user-selected files in the browser. When used in conjunction with tf.loadLayersModel, an instance of @@ -20,4 +20,4 @@ Python PIP package. If no weights files are provided, only the model topology will be loaded from the JSON file above.

    Returns IOHandler

    An instance of Files IOHandler.

    -

    Doc

    Settings

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    +

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.io.browserHTTPRequest.html b/typedoc/functions/tf.io.browserHTTPRequest.html index 2003b47..03ec45e 100644 --- a/typedoc/functions/tf.io.browserHTTPRequest.html +++ b/typedoc/functions/tf.io.browserHTTPRequest.html @@ -1,2 +1,2 @@ -browserHTTPRequest | @vladmandic/face-api - v1.7.14

    Function browserHTTPRequest

    • browserHTTPRequest(path, loadOptions?): IOHandler

    • Deprecated. Use tf.io.http.

      -

      Parameters

      Returns IOHandler

    Settings

    Member Visibility
    +browserHTTPRequest | @vladmandic/face-api - v1.7.14

    Function browserHTTPRequest

    • browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler

    • Deprecated. Use tf.io.http.

      +

      Parameters

      Returns IOHandler

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.concatenateArrayBuffers.html b/typedoc/functions/tf.io.concatenateArrayBuffers.html index c345b6f..09a0ce0 100644 --- a/typedoc/functions/tf.io.concatenateArrayBuffers.html +++ b/typedoc/functions/tf.io.concatenateArrayBuffers.html @@ -1,6 +1,6 @@ -concatenateArrayBuffers | @vladmandic/face-api - v1.7.14

    Function concatenateArrayBuffers

    • concatenateArrayBuffers(buffers): ArrayBuffer

    • Concatenate a number of ArrayBuffers into one.

      -

      Parameters

      • buffers: ArrayBuffer | ArrayBuffer[]

        An array of ArrayBuffers to concatenate, or a single +concatenateArrayBuffers | @vladmandic/face-api - v1.7.14

        Function concatenateArrayBuffers

        • concatenateArrayBuffers(buffers: ArrayBuffer | ArrayBuffer[]): ArrayBuffer

        • Concatenate a number of ArrayBuffers into one.

          +

          Parameters

          • buffers: ArrayBuffer | ArrayBuffer[]

            An array of ArrayBuffers to concatenate, or a single ArrayBuffer.

          Returns ArrayBuffer

          Result of concatenating buffers in order.

          -

          Deprecated

          Use tf.io.CompositeArrayBuffer.join() instead.

          -

        Settings

        Member Visibility
        +

        Deprecated

        Use tf.io.CompositeArrayBuffer.join() instead.

        +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.copyModel.html b/typedoc/functions/tf.io.copyModel.html index 66fc871..af5c060 100644 --- a/typedoc/functions/tf.io.copyModel.html +++ b/typedoc/functions/tf.io.copyModel.html @@ -1,4 +1,4 @@ -copyModel | @vladmandic/face-api - v1.7.14

    Function copyModel

    • copyModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

    • Copy a model from one URL to another.

      +copyModel | @vladmandic/face-api - v1.7.14

      Function copyModel

      • copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

      • Copy a model from one URL to another.

        This function supports:

        1. Copying within a storage medium, e.g., @@ -13,6 +13,6 @@
      • destURL: string

        Destination URL of copying.

      Returns Promise<ModelArtifactsInfo>

      ModelArtifactsInfo of the copied model (if and only if copying is successful).

      -

      Throws

      Error if copying fails, e.g., if no model exists at sourceURL, or +

      Throws

      Error if copying fails, e.g., if no model exists at sourceURL, or if oldPath and newPath are identical.

      -

      Doc

    Settings

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    +

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.io.decodeWeights.html b/typedoc/functions/tf.io.decodeWeights.html index 210b1c7..9e3e353 100644 --- a/typedoc/functions/tf.io.decodeWeights.html +++ b/typedoc/functions/tf.io.decodeWeights.html @@ -1,4 +1,4 @@ -decodeWeights | @vladmandic/face-api - v1.7.14

    Function decodeWeights

    • decodeWeights(weightData, specs): NamedTensorMap

    • Decode flat ArrayBuffer as weights.

      +decodeWeights | @vladmandic/face-api - v1.7.14

      Function decodeWeights

      • decodeWeights(
            weightData: WeightData,
            specs: WeightsManifestEntry[],
        ): NamedTensorMap

      • Decode flat ArrayBuffer as weights.

        This function does not handle sharding.

        This function is the reverse of encodeWeights.

        Parameters

        • weightData: WeightData

          A flat ArrayBuffer or an array of ArrayBuffers carrying the @@ -8,5 +8,5 @@ binary values of the tensors concatenated in the order specified in whose value are encoded by buffer.

        Returns NamedTensorMap

        A map from tensor name to tensor value, with the names corresponding to names in specs.

        -

        Throws

        Error, if any of the tensors has unsupported dtype.

        -

      Settings

      Member Visibility
      +

      Throws

      Error, if any of the tensors has unsupported dtype.

      +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.decodeWeightsStream.html b/typedoc/functions/tf.io.decodeWeightsStream.html index e9502a7..0a95168 100644 --- a/typedoc/functions/tf.io.decodeWeightsStream.html +++ b/typedoc/functions/tf.io.decodeWeightsStream.html @@ -1 +1 @@ -decodeWeightsStream | @vladmandic/face-api - v1.7.14

    Function decodeWeightsStream

    • decodeWeightsStream(weightStream, specs): Promise<NamedTensorMap>

    • Parameters

      Returns Promise<NamedTensorMap>

    Settings

    Member Visibility
    +decodeWeightsStream | @vladmandic/face-api - v1.7.14

    Function decodeWeightsStream

    • decodeWeightsStream(
          weightStream: ReadableStream<ArrayBuffer>,
          specs: WeightsManifestEntry[],
      ): Promise<NamedTensorMap>

    • Parameters

      Returns Promise<NamedTensorMap>

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.encodeWeights.html b/typedoc/functions/tf.io.encodeWeights.html index 0f499bd..87b402b 100644 --- a/typedoc/functions/tf.io.encodeWeights.html +++ b/typedoc/functions/tf.io.encodeWeights.html @@ -1,15 +1,15 @@ -encodeWeights | @vladmandic/face-api - v1.7.14

    Function encodeWeights

    • encodeWeights(tensors, group?): Promise<{
          data: ArrayBuffer;
          specs: WeightsManifestEntry[];
      }>

    • Encode a map from names to weight values as an ArrayBuffer, along with an +encodeWeights | @vladmandic/face-api - v1.7.14

      Function encodeWeights

      • encodeWeights(
            tensors: NamedTensorMap | NamedTensor[],
            group?: WeightGroup,
        ): Promise<{ data: ArrayBuffer; specs: WeightsManifestEntry[] }>

      • Encode a map from names to weight values as an ArrayBuffer, along with an Array of WeightsManifestEntry as specification of the encoded weights.

        This function does not perform sharding.

        This function is the reverse of decodeWeights.

        -

        Parameters

        • tensors: NamedTensorMap | NamedTensor[]

          A map ("dict") from names to tensors.

          +

          Parameters

          • tensors: NamedTensorMap | NamedTensor[]

            A map ("dict") from names to tensors.

          • Optionalgroup: WeightGroup

            Group to which the weights belong (optional).

            -

          Returns Promise<{
              data: ArrayBuffer;
              specs: WeightsManifestEntry[];
          }>

          A Promise of

          +

      Returns Promise<{ data: ArrayBuffer; specs: WeightsManifestEntry[] }>

      A Promise of

      • A flat ArrayBuffer with all the binary values of the Tensors concatenated.
      • An Array of WeightManifestEntrys, carrying information including tensor names, dtypes and shapes.
      -

      Throws

      Error: on unsupported tensor dtype.

      -

    Settings

    Member Visibility
    +

    Throws

    Error: on unsupported tensor dtype.

    +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.fromMemory.html b/typedoc/functions/tf.io.fromMemory.html index 8b98375..20ac761 100644 --- a/typedoc/functions/tf.io.fromMemory.html +++ b/typedoc/functions/tf.io.fromMemory.html @@ -1,10 +1,10 @@ -fromMemory | @vladmandic/face-api - v1.7.14

    Function fromMemory

    • fromMemory(modelArtifacts, weightSpecs?, weightData?, trainingConfig?): IOHandler

    • Creates an IOHandler that loads model artifacts from memory.

      +fromMemory | @vladmandic/face-api - v1.7.14

      Function fromMemory

      • fromMemory(
            modelArtifacts: {} | ModelArtifacts,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
            trainingConfig?: TrainingConfig,
        ): IOHandler

      • Creates an IOHandler that loads model artifacts from memory.

        When used in conjunction with tf.loadLayersModel, an instance of tf.LayersModel (Keras-style) can be constructed from the loaded artifacts.

        const model = await tf.loadLayersModel(tf.io.fromMemory(
            modelTopology, weightSpecs, weightData));
        -

        Parameters

        • modelArtifacts: {} | ModelArtifacts

          a object containing model topology (i.e., parsed from +

          Parameters

          • modelArtifacts: {} | ModelArtifacts

            a object containing model topology (i.e., parsed from the JSON format).

          • OptionalweightSpecs: WeightsManifestEntry[]

            An array of WeightsManifestEntry objects describing the names, shapes, types, and quantization of the weight data. Optional.

            @@ -12,4 +12,4 @@ names, shapes, types, and quantization of the weight data. Optional.

            concatenated in the order described by the weightSpecs. Optional.

          • OptionaltrainingConfig: TrainingConfig

            Model training configuration. Optional.

          Returns IOHandler

          A passthrough IOHandler that simply loads the provided data.

          -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/tf.io.fromMemorySync.html b/typedoc/functions/tf.io.fromMemorySync.html index ab3ec5d..53916ce 100644 --- a/typedoc/functions/tf.io.fromMemorySync.html +++ b/typedoc/functions/tf.io.fromMemorySync.html @@ -1,10 +1,10 @@ -fromMemorySync | @vladmandic/face-api - v1.7.14

    Function fromMemorySync

    • fromMemorySync(modelArtifacts, weightSpecs?, weightData?, trainingConfig?): IOHandlerSync

    • Creates an IOHandler that loads model artifacts from memory.

      +fromMemorySync | @vladmandic/face-api - v1.7.14

      Function fromMemorySync

      • fromMemorySync(
            modelArtifacts: {} | ModelArtifacts,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
            trainingConfig?: TrainingConfig,
        ): IOHandlerSync

      • Creates an IOHandler that loads model artifacts from memory.

        When used in conjunction with tf.loadLayersModel, an instance of tf.LayersModel (Keras-style) can be constructed from the loaded artifacts.

        const model = await tf.loadLayersModel(tf.io.fromMemory(
            modelTopology, weightSpecs, weightData));
        -

        Parameters

        • modelArtifacts: {} | ModelArtifacts

          a object containing model topology (i.e., parsed from +

          Parameters

          • modelArtifacts: {} | ModelArtifacts

            a object containing model topology (i.e., parsed from the JSON format).

          • OptionalweightSpecs: WeightsManifestEntry[]

            An array of WeightsManifestEntry objects describing the names, shapes, types, and quantization of the weight data. Optional.

            @@ -12,4 +12,4 @@ names, shapes, types, and quantization of the weight data. Optional.

            concatenated in the order described by the weightSpecs. Optional.

          • OptionaltrainingConfig: TrainingConfig

            Model training configuration. Optional.

          Returns IOHandlerSync

          A passthrough IOHandlerSync that simply loads the provided data.

          -

      Settings

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      +

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    diff --git a/typedoc/functions/tf.io.getLoadHandlers.html b/typedoc/functions/tf.io.getLoadHandlers.html index 2fcd518..3733cf6 100644 --- a/typedoc/functions/tf.io.getLoadHandlers.html +++ b/typedoc/functions/tf.io.getLoadHandlers.html @@ -1 +1 @@ -getLoadHandlers | @vladmandic/face-api - v1.7.14

    Function getLoadHandlers

    • getLoadHandlers(url, loadOptions?): IOHandler[]

    • Parameters

      Returns IOHandler[]

    Settings

    Member Visibility
    +getLoadHandlers | @vladmandic/face-api - v1.7.14

    Function getLoadHandlers

    • getLoadHandlers(url: string | string[], loadOptions?: LoadOptions): IOHandler[]

    • Parameters

      Returns IOHandler[]

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.getModelArtifactsForJSON.html b/typedoc/functions/tf.io.getModelArtifactsForJSON.html index 301ef92..1f6aa42 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSON.html @@ -1,7 +1,7 @@ -getModelArtifactsForJSON | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsForJSON

    Returns Promise<ModelArtifacts>

    A Promise of the ModelArtifacts, as described by the JSON file.

    +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html b/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html index 641738a..05e681e 100644 --- a/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html +++ b/typedoc/functions/tf.io.getModelArtifactsForJSONSync.html @@ -1,4 +1,4 @@ -getModelArtifactsForJSONSync | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsForJSONSync

    • getModelArtifactsForJSONSync(modelJSON, weightSpecs?, weightData?): ModelArtifacts

    • Create ModelArtifacts from a JSON file and weights.

      +getModelArtifactsForJSONSync | @vladmandic/face-api - v1.7.14

      Function getModelArtifactsForJSONSync

      • getModelArtifactsForJSONSync(
            modelJSON: ModelJSON,
            weightSpecs?: WeightsManifestEntry[],
            weightData?: WeightData,
        ): ModelArtifacts

      • Create ModelArtifacts from a JSON file and weights.

        Parameters

        • modelJSON: ModelJSON

          Object containing the parsed JSON of model.json

        • OptionalweightSpecs: WeightsManifestEntry[]

          The list of WeightsManifestEntry for the model. Must be passed if the modelJSON has a weightsManifest.

          @@ -6,4 +6,4 @@ passed if the modelJSON has a weightsManifest.

          the model corresponding to the weights in weightSpecs. Must be passed if the modelJSON has a weightsManifest.

        Returns ModelArtifacts

        A Promise of the ModelArtifacts, as described by the JSON file.

        -

      Settings

      Member Visibility
      +

    Settings

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    diff --git a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html index 83f7050..9223641 100644 --- a/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html +++ b/typedoc/functions/tf.io.getModelArtifactsInfoForJSON.html @@ -1,3 +1,3 @@ -getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.14

    Function getModelArtifactsInfoForJSON

    • getModelArtifactsInfoForJSON(modelArtifacts): ModelArtifactsInfo

    • Populate ModelArtifactsInfo fields for a model with JSON topology.

      +getModelArtifactsInfoForJSON | @vladmandic/face-api - v1.7.14

      Function getModelArtifactsInfoForJSON

      • getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo

      • Populate ModelArtifactsInfo fields for a model with JSON topology.

        Parameters

        Returns ModelArtifactsInfo

        A ModelArtifactsInfo object.

        -

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      +

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    diff --git a/typedoc/functions/tf.io.getSaveHandlers.html b/typedoc/functions/tf.io.getSaveHandlers.html index 034b94e..3aefc42 100644 --- a/typedoc/functions/tf.io.getSaveHandlers.html +++ b/typedoc/functions/tf.io.getSaveHandlers.html @@ -1 +1 @@ -getSaveHandlers | @vladmandic/face-api - v1.7.14

    Function getSaveHandlers

    • getSaveHandlers(url): IOHandler[]

    • Parameters

      • url: string | string[]

      Returns IOHandler[]

    Settings

    Member Visibility
    +getSaveHandlers | @vladmandic/face-api - v1.7.14

    Function getSaveHandlers

    • getSaveHandlers(url: string | string[]): IOHandler[]

    • Parameters

      • url: string | string[]

      Returns IOHandler[]

    Settings

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    diff --git a/typedoc/functions/tf.io.getWeightSpecs.html b/typedoc/functions/tf.io.getWeightSpecs.html index 79380fc..54a1830 100644 --- a/typedoc/functions/tf.io.getWeightSpecs.html +++ b/typedoc/functions/tf.io.getWeightSpecs.html @@ -1,5 +1,5 @@ -getWeightSpecs | @vladmandic/face-api - v1.7.14

    Function getWeightSpecs

    • getWeightSpecs(weightsManifest): WeightsManifestEntry[]

    • Concatenate the weights stored in a WeightsManifestConfig into a list of +getWeightSpecs | @vladmandic/face-api - v1.7.14

      Function getWeightSpecs

      • getWeightSpecs(weightsManifest: WeightsManifestConfig): WeightsManifestEntry[]

      • Concatenate the weights stored in a WeightsManifestConfig into a list of WeightsManifestEntry

        Parameters

        Returns WeightsManifestEntry[]

        A list of WeightsManifestEntry of the weights in the weightsManifest

        -

      Settings

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      +

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    diff --git a/typedoc/functions/tf.io.http.html b/typedoc/functions/tf.io.http.html index 173423e..75fd027 100644 --- a/typedoc/functions/tf.io.http.html +++ b/typedoc/functions/tf.io.http.html @@ -1,4 +1,4 @@ -http | @vladmandic/face-api - v1.7.14

    Function http

    • http(path, loadOptions?): IOHandler

    • Creates an IOHandler subtype that sends model artifacts to HTTP server.

      +http | @vladmandic/face-api - v1.7.14

      Function http

      • http(path: string, loadOptions?: LoadOptions): IOHandler

      • Creates an IOHandler subtype that sends model artifacts to HTTP server.

        An HTTP request of the multipart/form-data mime type will be sent to the path URL. The form data includes artifacts that represent the topology and/or weights of the model. In the case of Keras-style tf.Model, two @@ -7,7 +7,7 @@ blobs (files) exist in form-data:

      • A JSON file consisting of modelTopology and weightsManifest.
      • A binary weights file consisting of the concatenated weight values. These files are in the same format as the one generated by -tfjs_converter.
        • +tfjs_converter.

      The following code snippet exemplifies the client-side code that uses this function:

      @@ -20,14 +20,14 @@ such as headers, you can simply pass an HTTP or HTTPS URL to model.save

      The following GitHub Gist -https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 -implements a server based on flask that -can receive the request. Upon receiving the model artifacts via the requst, -this particular server reconstitutes instances of Keras +https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 +implements a server based on flask that +can receive the request. Upon receiving the model artifacts via the request, +this particular server reconstitutes instances of Keras Models in memory.

      Parameters

      • path: string

        A URL path to the model. Can be an absolute HTTP path (e.g., -'http://localhost:8000/model-upload)') or a relative path (e.g., +'http://localhost:8000/model-upload)') or a relative path (e.g., './model-upload').

      • OptionalloadOptions: LoadOptions

        Optional configuration for the loading. It includes the following fields:

        @@ -40,4 +40,4 @@ the fetch from node-fetch can be used here.
        • before the load is completed.

    Returns IOHandler

    An instance of IOHandler.

    -

    Doc

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    +

    Doc

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    diff --git a/typedoc/functions/tf.io.isHTTPScheme.html b/typedoc/functions/tf.io.isHTTPScheme.html index 82861a7..13cc8c8 100644 --- a/typedoc/functions/tf.io.isHTTPScheme.html +++ b/typedoc/functions/tf.io.isHTTPScheme.html @@ -1 +1 @@ -isHTTPScheme | @vladmandic/face-api - v1.7.14

    Function isHTTPScheme

    • isHTTPScheme(url): boolean

    • Parameters

      • url: string

      Returns boolean

    Settings

    Member Visibility
    +isHTTPScheme | @vladmandic/face-api - v1.7.14

    Function isHTTPScheme

    • isHTTPScheme(url: string): boolean

    • Parameters

      • url: string

      Returns boolean

    Settings

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    diff --git a/typedoc/functions/tf.io.listModels.html b/typedoc/functions/tf.io.listModels.html index def2bce..8a78dd0 100644 --- a/typedoc/functions/tf.io.listModels.html +++ b/typedoc/functions/tf.io.listModels.html @@ -1,11 +1,11 @@ -listModels | @vladmandic/face-api - v1.7.14

    Function listModels

    • listModels(): Promise<{
          [url: string]: ModelArtifactsInfo;
      }>

    • List all models stored in registered storage mediums.

      +listModels | @vladmandic/face-api - v1.7.14

      Function listModels

      • listModels(): Promise<{ [url: string]: ModelArtifactsInfo }>

      • List all models stored in registered storage mediums.

        For a web browser environment, the registered mediums are Local Storage and IndexedDB.

        // First create and save a model.
        const model = tf.sequential();
        model.add(tf.layers.dense(
            {units: 1, inputShape: [10], activation: 'sigmoid'}));
        await model.save('localstorage://demo/management/model1');

        // Then list existing models.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Delete the model.
        await tf.io.removeModel('localstorage://demo/management/model1');

        // List models again.
        console.log(JSON.stringify(await tf.io.listModels()));
        -

        Returns Promise<{
            [url: string]: ModelArtifactsInfo;
        }>

        A Promise of a dictionary mapping URLs of existing models to +

      Returns Promise<{ [url: string]: ModelArtifactsInfo }>

      A Promise of a dictionary mapping URLs of existing models to their model artifacts info. URLs include medium-specific schemes, e.g., 'indexeddb://my/model/1'. Model artifacts info include type of the model's topology, byte sizes of the topology, weights, etc.

      -

      Doc

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    +

    Doc

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    diff --git a/typedoc/functions/tf.io.loadWeights.html b/typedoc/functions/tf.io.loadWeights.html index 227f71b..1fb95ce 100644 --- a/typedoc/functions/tf.io.loadWeights.html +++ b/typedoc/functions/tf.io.loadWeights.html @@ -1,7 +1,7 @@ -loadWeights | @vladmandic/face-api - v1.7.14

    Function loadWeights

    • loadWeights(manifest, filePathPrefix?, weightNames?, requestInit?): Promise<NamedTensorMap>

    • Reads a weights manifest JSON configuration, fetches the weights and +loadWeights | @vladmandic/face-api - v1.7.14

      Function loadWeights

      • loadWeights(
            manifest: WeightsManifestConfig,
            filePathPrefix?: string,
            weightNames?: string[],
            requestInit?: RequestInit,
        ): Promise<NamedTensorMap>

      • Reads a weights manifest JSON configuration, fetches the weights and returns them as Tensors.

        Parameters

        • manifest: WeightsManifestConfig

          The weights manifest JSON.

        • OptionalfilePathPrefix: string

          The path prefix for filenames given in the manifest. Defaults to the empty string.

        • OptionalweightNames: string[]

          The names of the weights to be fetched.

          -
        • OptionalrequestInit: RequestInit

        Returns Promise<NamedTensorMap>

      Settings

      Member Visibility
      +
    • OptionalrequestInit: RequestInit

    Returns Promise<NamedTensorMap>

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.moveModel.html b/typedoc/functions/tf.io.moveModel.html index ba18c8d..f59e26d 100644 --- a/typedoc/functions/tf.io.moveModel.html +++ b/typedoc/functions/tf.io.moveModel.html @@ -1,4 +1,4 @@ -moveModel | @vladmandic/face-api - v1.7.14

    Function moveModel

    • moveModel(sourceURL, destURL): Promise<ModelArtifactsInfo>

    • Move a model from one URL to another.

      +moveModel | @vladmandic/face-api - v1.7.14

      Function moveModel

      • moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>

      • Move a model from one URL to another.

        This function supports:

        1. Moving within a storage medium, e.g., @@ -13,6 +13,6 @@
      • destURL: string

        Destination URL of moving.

      Returns Promise<ModelArtifactsInfo>

      ModelArtifactsInfo of the copied model (if and only if copying is successful).

      -

      Throws

      Error if moving fails, e.g., if no model exists at sourceURL, or +

      Throws

      Error if moving fails, e.g., if no model exists at sourceURL, or if oldPath and newPath are identical.

      -

      Doc

    Settings

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    +

    Doc

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    diff --git a/typedoc/functions/tf.io.registerLoadRouter.html b/typedoc/functions/tf.io.registerLoadRouter.html index 172a023..9bc0ad0 100644 --- a/typedoc/functions/tf.io.registerLoadRouter.html +++ b/typedoc/functions/tf.io.registerLoadRouter.html @@ -1 +1 @@ -registerLoadRouter | @vladmandic/face-api - v1.7.14

    Function registerLoadRouter

    • registerLoadRouter(loudRouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

    Member Visibility
    +registerLoadRouter | @vladmandic/face-api - v1.7.14

    Function registerLoadRouter

    • registerLoadRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

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    diff --git a/typedoc/functions/tf.io.registerSaveRouter.html b/typedoc/functions/tf.io.registerSaveRouter.html index afdbad3..566a950 100644 --- a/typedoc/functions/tf.io.registerSaveRouter.html +++ b/typedoc/functions/tf.io.registerSaveRouter.html @@ -1 +1 @@ -registerSaveRouter | @vladmandic/face-api - v1.7.14

    Function registerSaveRouter

    • registerSaveRouter(loudRouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

    Member Visibility
    +registerSaveRouter | @vladmandic/face-api - v1.7.14

    Function registerSaveRouter

    • registerSaveRouter(loudRouter: IORouter): void

    • Parameters

      • loudRouter: IORouter

      Returns void

    Settings

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    diff --git a/typedoc/functions/tf.io.removeModel.html b/typedoc/functions/tf.io.removeModel.html index bcf4fff..aaa6d48 100644 --- a/typedoc/functions/tf.io.removeModel.html +++ b/typedoc/functions/tf.io.removeModel.html @@ -1,4 +1,4 @@ -removeModel | @vladmandic/face-api - v1.7.14

    Function removeModel

    • removeModel(url): Promise<ModelArtifactsInfo>

    • Remove a model specified by URL from a registered storage medium.

      +removeModel | @vladmandic/face-api - v1.7.14

      Function removeModel

      • removeModel(url: string): Promise<ModelArtifactsInfo>

      • Remove a model specified by URL from a registered storage medium.

        // First create and save a model.
        const model = tf.sequential();
        model.add(tf.layers.dense(
            {units: 1, inputShape: [10], activation: 'sigmoid'}));
        await model.save('localstorage://demo/management/model1');

        // Then list existing models.
        console.log(JSON.stringify(await tf.io.listModels()));

        // Delete the model.
        await tf.io.removeModel('localstorage://demo/management/model1');

        // List models again.
        console.log(JSON.stringify(await tf.io.listModels()));
        @@ -6,5 +6,5 @@ 'localstorage://my-model-1', 'indexeddb://my/model/2'.

      Returns Promise<ModelArtifactsInfo>

      ModelArtifactsInfo of the deleted model (if and only if deletion is successful).

      -

      Throws

      Error if deletion fails, e.g., if no model exists at path.

      -

      Doc

    Settings

    Member Visibility
    +

    Throws

    Error if deletion fails, e.g., if no model exists at path.

    +

    Doc

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.weightsLoaderFactory.html b/typedoc/functions/tf.io.weightsLoaderFactory.html index 58f74ce..e0b628d 100644 --- a/typedoc/functions/tf.io.weightsLoaderFactory.html +++ b/typedoc/functions/tf.io.weightsLoaderFactory.html @@ -1,9 +1,9 @@ -weightsLoaderFactory | @vladmandic/face-api - v1.7.14

    Function weightsLoaderFactory

    • weightsLoaderFactory(fetchWeightsFunction): ((manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>)

    • Creates a function, which reads a weights manifest JSON configuration, +weightsLoaderFactory | @vladmandic/face-api - v1.7.14

      Function weightsLoaderFactory

      • weightsLoaderFactory(
            fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>,
        ): (
            manifest: WeightsManifestConfig,
            filePathPrefix?: string,
            weightNames?: string[],
        ) => Promise<NamedTensorMap>

      • Creates a function, which reads a weights manifest JSON configuration, fetches the weight files using the specified function and returns them as Tensors.

        // example for creating a nodejs weight loader, which reads the weight files
        // from disk using fs.readFileSync

        import * as fs from 'fs'

        const fetchWeightsFromDisk = (filePaths: string[]) =>
          filePaths.map(filePath => fs.readFileSync(filePath).buffer)

        const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)

        const manifest = JSON.parse(
          fs.readFileSync('./my_model-weights_manifest').toString()
        )
        const weightMap = await loadWeights(manifest, './')
        -

        Parameters

        • fetchWeightsFunction: ((fetchUrls: string[]) => Promise<ArrayBuffer[]>)

          The function used for fetching the weight files.

          -
            • (fetchUrls): Promise<ArrayBuffer[]>

            • Parameters

              • fetchUrls: string[]

              Returns Promise<ArrayBuffer[]>

        Returns ((manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>)

        Weight loading function.

        -

      Settings

      Member Visibility
      +

      Parameters

      • fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>

        The function used for fetching the weight files.

        +

      Returns (
          manifest: WeightsManifestConfig,
          filePathPrefix?: string,
          weightNames?: string[],
      ) => Promise<NamedTensorMap>

      Weight loading function.

      +

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.withSaveHandler.html b/typedoc/functions/tf.io.withSaveHandler.html index 18a8b7d..d7045d2 100644 --- a/typedoc/functions/tf.io.withSaveHandler.html +++ b/typedoc/functions/tf.io.withSaveHandler.html @@ -1,7 +1,7 @@ -withSaveHandler | @vladmandic/face-api - v1.7.14

    Function withSaveHandler

    • withSaveHandler(saveHandler): IOHandler

    • Creates an IOHandler that passes saved model artifacts to a callback.

      +withSaveHandler | @vladmandic/face-api - v1.7.14

      Function withSaveHandler

      • withSaveHandler(
            saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>,
        ): IOHandler

      • Creates an IOHandler that passes saved model artifacts to a callback.

        function handleSave(artifacts) {
          // ... do something with the artifacts ...
          return {modelArtifactsInfo: {...}, ...};
        }

        const saveResult = model.save(tf.io.withSaveHandler(handleSave));
        -

        Parameters

        • saveHandler: ((artifacts: ModelArtifacts) => Promise<SaveResult>)

          A function that accepts a ModelArtifacts and returns a +

          Parameters

          Returns IOHandler

      Settings

      Member Visibility
      +

    Returns IOHandler

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.io.withSaveHandlerSync.html b/typedoc/functions/tf.io.withSaveHandlerSync.html index 4f2029c..63a5c52 100644 --- a/typedoc/functions/tf.io.withSaveHandlerSync.html +++ b/typedoc/functions/tf.io.withSaveHandlerSync.html @@ -1,7 +1,7 @@ -withSaveHandlerSync | @vladmandic/face-api - v1.7.14

    Function withSaveHandlerSync

    • withSaveHandlerSync(saveHandler): IOHandlerSync

    • Creates an IOHandlerSync that passes saved model artifacts to a callback.

      +withSaveHandlerSync | @vladmandic/face-api - v1.7.14

      Function withSaveHandlerSync

      • withSaveHandlerSync(
            saveHandler: (artifacts: ModelArtifacts) => SaveResult,
        ): IOHandlerSync

      • Creates an IOHandlerSync that passes saved model artifacts to a callback.

        function handleSave(artifacts) {
          // ... do something with the artifacts ...
          return {modelArtifactsInfo: {...}, ...};
        }

        const saveResult = model.save(tf.io.withSaveHandler(handleSave));
        -

        Parameters

      Settings

      Member Visibility
      +

    Returns IOHandlerSync

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.matMul.html b/typedoc/functions/tf.matMul.html index 11615e2..f211232 100644 --- a/typedoc/functions/tf.matMul.html +++ b/typedoc/functions/tf.matMul.html @@ -1,9 +1,9 @@ -matMul | @vladmandic/face-api - v1.7.14

    Function matMul

    • matMul<T>(a, b, transposeA?, transposeB?): T

    • Computes the dot product of two matrices, A * B. These must be matrices.

      +matMul | @vladmandic/face-api - v1.7.14

      Function matMul

      • matMul<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
            transposeA?: boolean,
            transposeB?: boolean,
        ): T

      • Computes the dot product of two matrices, A * B. These must be matrices.

        const a = tf.tensor2d([1, 2], [1, 2]);
        const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);

        a.matMul(b).print();  // or tf.matMul(a, b)
        -

        Type Parameters

        Parameters

        • a: TensorLike | Tensor<Rank>

          First matrix in dot product operation.

          -
        • b: TensorLike | Tensor<Rank>

          Second matrix in dot product operation.

          +

          Type Parameters

          Parameters

          • a: TensorLike | tf.Tensor

            First matrix in dot product operation.

            +
          • b: TensorLike | tf.Tensor

            Second matrix in dot product operation.

          • OptionaltransposeA: boolean

            If true, a is transposed before multiplication.

          • OptionaltransposeB: boolean

            If true, b is transposed before multiplication.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

    Settings

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    diff --git a/typedoc/functions/tf.maxPool.html b/typedoc/functions/tf.maxPool.html index 79cb403..aaabb30 100644 --- a/typedoc/functions/tf.maxPool.html +++ b/typedoc/functions/tf.maxPool.html @@ -1,19 +1,19 @@ -maxPool | @vladmandic/face-api - v1.7.14

    Function maxPool

    • maxPool<T>(x, filterSize, strides, pad, dimRoundingMode?): T

    • Computes the 2D max pooling of an image.

      -

      Type Parameters

      Parameters

      • x: TensorLike | T

        The input tensor, of rank 4 or rank 3 of shape +maxPool | @vladmandic/face-api - v1.7.14

        Function maxPool

        • maxPool<T extends Tensor4D | Tensor3D>(
              x: TensorLike | T,
              filterSize: number | [number, number],
              strides: number | [number, number],
              pad: number | "valid" | "same" | ExplicitPadding,
              dimRoundingMode?: "floor" | "round" | "ceil",
          ): T

        • Computes the 2D max pooling of an image.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor, of rank 4 or rank 3 of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

            -
          • filterSize: number | [number, number]

            The filter size: [filterHeight, filterWidth]. If +

          • filterSize: number | [number, number]

            The filter size: [filterHeight, filterWidth]. If filterSize is a single number, then filterHeight == filterWidth.

            -
          • strides: number | [number, number]

            The strides of the pooling: [strideHeight, strideWidth]. If +

          • strides: number | [number, number]

            The strides of the pooling: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            -
          • pad:
                | number
                | "valid"
                | "same"
                | ExplicitPadding

            The type of padding algorithm.

            +
          • pad: number | "valid" | "same" | ExplicitPadding

            The type of padding algorithm.

            -
          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is +

          • OptionaldimRoundingMode: "floor" | "round" | "ceil"

            A string from: 'ceil', 'round', 'floor'. If none is provided, it will default to truncate.

            -

          Returns T

        Settings

        Member Visibility
        +

      Returns T

    Settings

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    diff --git a/typedoc/functions/tf.mul.html b/typedoc/functions/tf.mul.html index 36e25e5..4f1685c 100644 --- a/typedoc/functions/tf.mul.html +++ b/typedoc/functions/tf.mul.html @@ -1,4 +1,4 @@ -mul | @vladmandic/face-api - v1.7.14

    Function mul

    • mul<T>(a, b): T

    • Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

      +mul | @vladmandic/face-api - v1.7.14

      Function mul

      • mul<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

        We also expose tf.mulStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

        const a = tf.tensor1d([1, 2, 3, 4]);
        const b = tf.tensor1d([2, 3, 4, 5]);

        a.mul(b).print();  // or tf.mul(a, b)
@@ -7,6 +7,6 @@ asserts that a and b are the same shape (does not broa
 
        // Broadcast mul a with b.
        const a = tf.tensor1d([1, 2, 3, 4]);
        const b = tf.scalar(5);

        a.mul(b).print();  // or tf.mul(a, b)
 
        
 
-

        Type Parameters

        Parameters

        Returns T

        Doc

      Settings

      Member Visibility
      +

      Type Parameters

      Parameters

      Returns T

      Doc

    Settings

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    diff --git a/typedoc/functions/tf.pad.html b/typedoc/functions/tf.pad.html index 349f759..eabe551 100644 --- a/typedoc/functions/tf.pad.html +++ b/typedoc/functions/tf.pad.html @@ -1,4 +1,4 @@ -pad | @vladmandic/face-api - v1.7.14

    Function pad

    • pad<T>(x, paddings, constantValue?): T

    • Pads a tf.Tensor with a given value and paddings.

      +pad | @vladmandic/face-api - v1.7.14

      Function pad

      • pad<T extends tf.Tensor>(
            x: TensorLike | T,
            paddings: [number, number][],
            constantValue?: number,
        ): T

      • Pads a tf.Tensor with a given value and paddings.

        This operation implements CONSTANT mode. For REFLECT and SYMMETRIC, refer to tf.mirrorPad.

        Also available are stricter rank-specific methods with the same signature @@ -12,9 +12,9 @@ as this method that assert that paddings is of given length.

        const x = tf.tensor1d([1, 2, 3, 4]);
        x.pad([[1, 2]]).print();
        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The tensor to pad.

          -
        • paddings: [number, number][]

          An array of length R (the rank of the tensor), where +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The tensor to pad.

            +
          • paddings: [number, number][]

            An array of length R (the rank of the tensor), where each element is a length-2 tuple of ints [padBefore, padAfter], specifying how much to pad along each dimension of the tensor.

          • OptionalconstantValue: number

            The pad value to use. Defaults to 0.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.relu.html b/typedoc/functions/tf.relu.html index 580d2ef..546ffb9 100644 --- a/typedoc/functions/tf.relu.html +++ b/typedoc/functions/tf.relu.html @@ -1,7 +1,7 @@ -relu | @vladmandic/face-api - v1.7.14

    Function relu

    • relu<T>(x): T

    • Computes rectified linear element-wise: max(x, 0).

      +relu | @vladmandic/face-api - v1.7.14

      Function relu

      • relu<T extends tf.Tensor>(x: T | TensorLike): T

      • Computes rectified linear element-wise: max(x, 0).

        const x = tf.tensor1d([-1, 2, -3, 4]);

        x.relu().print();  // or tf.relu(x)
        -

        Type Parameters

        Parameters

        • x: T | TensorLike

          The input tensor. If the dtype is bool, the output dtype will be +

          Type Parameters

          Parameters

          • x: T | TensorLike

            The input tensor. If the dtype is bool, the output dtype will be int32.

            -

          Returns T

          Doc

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.reshape.html b/typedoc/functions/tf.reshape.html index 7f9a0da..c140c86 100644 --- a/typedoc/functions/tf.reshape.html +++ b/typedoc/functions/tf.reshape.html @@ -1,4 +1,4 @@ -reshape | @vladmandic/face-api - v1.7.14

    Function reshape

    • reshape<R>(x, shape): Tensor<R>

    • Reshapes a tf.Tensor to a given shape.

      +reshape | @vladmandic/face-api - v1.7.14

      Function reshape

      • reshape<R extends Rank>(
            x: TensorLike | tf.Tensor,
            shape: ShapeMap[R],
        ): tf.Tensor<R>

      • Reshapes a tf.Tensor to a given shape.

        Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape.

        If one component of shape is the special value -1, the size of that @@ -12,6 +12,6 @@ tensor.

        const x = tf.tensor1d([1, 2, 3, 4]);
        x.reshape([2, 2]).print();
        -

        Type Parameters

        Parameters

        • x: TensorLike | Tensor<Rank>

          The input tensor to be reshaped.

          -
        • shape: ShapeMap[R]

          An array of integers defining the output tensor shape.

          -

        Returns Tensor<R>

        Doc

      Settings

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      +

      Type Parameters

      Parameters

      • x: TensorLike | tf.Tensor

        The input tensor to be reshaped.

        +
      • shape: ShapeMap[R]

        An array of integers defining the output tensor shape.

        +

      Returns tf.Tensor<R>

      Doc

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    diff --git a/typedoc/functions/tf.scalar.html b/typedoc/functions/tf.scalar.html index 9b28660..361fabd 100644 --- a/typedoc/functions/tf.scalar.html +++ b/typedoc/functions/tf.scalar.html @@ -1,9 +1,9 @@ -scalar | @vladmandic/face-api - v1.7.14

    Function scalar

    • scalar(value, dtype?): Scalar

    • Creates rank-0 tf.Tensor (scalar) with the provided value and dtype.

      +scalar | @vladmandic/face-api - v1.7.14

      Function scalar

      • scalar(
            value: string | number | boolean | Uint8Array,
            dtype?: keyof DataTypeMap,
        ): Scalar

      • Creates rank-0 tf.Tensor (scalar) with the provided value and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.scalar as it makes the code more readable.

        tf.scalar(3.14).print();
        -

        Parameters

        • value:
              | string
              | number
              | boolean
              | Uint8Array

          The value of the scalar.

          +

          Parameters

          • value: string | number | boolean | Uint8Array

            The value of the scalar.

          • Optionaldtype: keyof DataTypeMap

            The data type.

            -

          Returns Scalar

          Doc

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      +

    Returns Scalar

    Doc

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    diff --git a/typedoc/functions/tf.separableConv2d.html b/typedoc/functions/tf.separableConv2d.html index ad49790..1faf6da 100644 --- a/typedoc/functions/tf.separableConv2d.html +++ b/typedoc/functions/tf.separableConv2d.html @@ -1,28 +1,28 @@ -separableConv2d | @vladmandic/face-api - v1.7.14

    Function separableConv2d

    • separableConv2d<T>(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

    • 2-D convolution with separable filters.

      +separableConv2d | @vladmandic/face-api - v1.7.14

      Function separableConv2d

      • separableConv2d<T extends Tensor4D | Tensor3D>(
            x: TensorLike | T,
            depthwiseFilter: Tensor4D | TensorLike,
            pointwiseFilter: Tensor4D | TensorLike,
            strides: number | [number, number],
            pad: "valid" | "same",
            dilation?: number | [number, number],
            dataFormat?: "NHWC" | "NCHW",
        ): T

      • 2-D convolution with separable filters.

        Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions [1, 2] and 3, not spatial separability between dimensions 1 and 2.

        See -https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d +https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d for more details.

        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tensor, of rank 4 or rank 3, of shape +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.

            -
          • depthwiseFilter: Tensor4D | TensorLike

            The depthwise filter tensor, rank 4, of shape +

          • depthwiseFilter: Tensor4D | TensorLike

            The depthwise filter tensor, rank 4, of shape [filterHeight, filterWidth, inChannels, channelMultiplier]. This is the filter used in the first step.

            -
          • pointwiseFilter: Tensor4D | TensorLike

            The pointwise filter tensor, rank 4, of shape +

          • pointwiseFilter: Tensor4D | TensorLike

            The pointwise filter tensor, rank 4, of shape [1, 1, inChannels * channelMultiplier, outChannels]. This is the filter used in the second step.

            -
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            -
          • pad: "valid" | "same"

            The type of padding algorithm.

            +
          • strides: number | [number, number]

            The strides of the convolution: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.

            +
          • pad: "valid" | "same"

            The type of padding algorithm.

            -
          • Optionaldilation: number | [number, number]
          • OptionaldataFormat: "NHWC" | "NCHW"

          Returns T

          Doc

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      +
    • Optionaldilation: number | [number, number]
    • OptionaldataFormat: "NHWC" | "NCHW"

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.sigmoid.html b/typedoc/functions/tf.sigmoid.html index d7f7949..103cb39 100644 --- a/typedoc/functions/tf.sigmoid.html +++ b/typedoc/functions/tf.sigmoid.html @@ -1,6 +1,6 @@ -sigmoid | @vladmandic/face-api - v1.7.14

    Function sigmoid

    • sigmoid<T>(x): T

    • Computes sigmoid element-wise, 1 / (1 + exp(-x))

      +sigmoid | @vladmandic/face-api - v1.7.14

      Function sigmoid

      • sigmoid<T extends tf.Tensor>(x: TensorLike | T): T

      • Computes sigmoid element-wise, 1 / (1 + exp(-x))

        const x = tf.tensor1d([0, -1, 2, -3]);

        x.sigmoid().print();  // or tf.sigmoid(x)
        -

        Type Parameters

        Parameters

        Returns T

        Doc

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      +

      Type Parameters

      Parameters

      Returns T

      Doc

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    diff --git a/typedoc/functions/tf.slice.html b/typedoc/functions/tf.slice.html index 064adb7..28be138 100644 --- a/typedoc/functions/tf.slice.html +++ b/typedoc/functions/tf.slice.html @@ -1,4 +1,4 @@ -slice | @vladmandic/face-api - v1.7.14

    Function slice

    • slice<R, T>(x, begin, size?): T

    • Extracts a slice from a tf.Tensor starting at coordinates begin +slice | @vladmandic/face-api - v1.7.14

      Function slice

      • slice<R extends Rank, T extends tf.Tensor<R>>(
            x: TensorLike | T,
            begin: number | number[],
            size?: number | number[],
        ): T

      • Extracts a slice from a tf.Tensor starting at coordinates begin and is of size size.

        Also available are stricter rank-specific methods with the same signature as this method that assert that x is of the given rank:

        @@ -14,13 +14,13 @@ as this method that assert that x is of the given rank:

        const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

        x.slice([1, 0], [1, 2]).print();
        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The input tf.Tensor to slice from.

          -
        • begin: number | number[]

          The coordinates to start the slice from. The length can be +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The input tf.Tensor to slice from.

            +
          • begin: number | number[]

            The coordinates to start the slice from. The length can be less than the rank of x - the rest of the axes will have implicit 0 as start. Can also be a single number, in which case it specifies the first axis.

            -
          • Optionalsize: number | number[]

            The size of the slice. The length can be less than the rank of +

          • Optionalsize: number | number[]

            The size of the slice. The length can be less than the rank of x - the rest of the axes will have implicit -1. A value of -1 requests the rest of the dimensions in the axis. Can also be a single number, in which case it specifies the size of the first axis.

            -

          Returns T

          Doc

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.slice3d.html b/typedoc/functions/tf.slice3d.html index fb08b27..f3c686d 100644 --- a/typedoc/functions/tf.slice3d.html +++ b/typedoc/functions/tf.slice3d.html @@ -1,3 +1,3 @@ -slice3d | @vladmandic/face-api - v1.7.14

    Function slice3d

    • slice3d(x, begin, size): Tensor3D

    • Extracts a 3D slice from a 3D array starting at coordinates begin and +slice3d | @vladmandic/face-api - v1.7.14

      Function slice3d

      • slice3d(
            x: TensorLike | Tensor3D,
            begin: [number, number, number],
            size: [number, number, number],
        ): Tensor3D

      • Extracts a 3D slice from a 3D array starting at coordinates begin and is of size size. See slice for details.

        -

        Parameters

        Returns Tensor3D

      Settings

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      +

      Parameters

      Returns Tensor3D

    Settings

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    diff --git a/typedoc/functions/tf.softmax.html b/typedoc/functions/tf.softmax.html index 950d2cf..87c00ca 100644 --- a/typedoc/functions/tf.softmax.html +++ b/typedoc/functions/tf.softmax.html @@ -1,11 +1,11 @@ -softmax | @vladmandic/face-api - v1.7.14

    Function softmax

    • softmax<T>(logits, dim?): T

    • Computes the softmax normalized vector given the logits.

      +softmax | @vladmandic/face-api - v1.7.14

      Function softmax

      • softmax<T extends tf.Tensor>(logits: TensorLike | T, dim?: number): T

      • Computes the softmax normalized vector given the logits.

        const a = tf.tensor1d([1, 2, 3]);

        a.softmax().print();  // or tf.softmax(a)
 
        const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);

        a.softmax().print();  // or tf.softmax(a)
        -

        Type Parameters

        Parameters

        • logits: TensorLike | T

          The logits array.

          +

          Type Parameters

          Parameters

          • logits: TensorLike | T

            The logits array.

          • Optionaldim: number

            The dimension softmax would be performed on. Defaults to -1 which indicates the last dimension.

            -

          Returns T

          Doc

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.stack.html b/typedoc/functions/tf.stack.html index f0ff3a5..3dca08b 100644 --- a/typedoc/functions/tf.stack.html +++ b/typedoc/functions/tf.stack.html @@ -1,7 +1,7 @@ -stack | @vladmandic/face-api - v1.7.14

    Function stack

    • stack<T>(tensors, axis?): Tensor

    • Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

      +stack | @vladmandic/face-api - v1.7.14

      Function stack

      • stack<T extends tf.Tensor>(
            tensors: (TensorLike | T)[],
            axis?: number,
        ): tf.Tensor

      • Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

        const a = tf.tensor1d([1, 2]);
        const b = tf.tensor1d([3, 4]);
        const c = tf.tensor1d([5, 6]);
        tf.stack([a, b, c]).print();
        -

        Type Parameters

        Parameters

        • tensors: (TensorLike | T)[]

          A list of tensor objects with the same shape and dtype.

          +

          Type Parameters

          Parameters

          • tensors: (TensorLike | T)[]

            A list of tensor objects with the same shape and dtype.

          • Optionalaxis: number

            The axis to stack along. Defaults to 0 (the first dim).

            -

          Returns Tensor

          Doc

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      +

    Returns tf.Tensor

    Doc

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    diff --git a/typedoc/functions/tf.sub.html b/typedoc/functions/tf.sub.html index b233e2d..4ee0f7e 100644 --- a/typedoc/functions/tf.sub.html +++ b/typedoc/functions/tf.sub.html @@ -1,11 +1,11 @@ -sub | @vladmandic/face-api - v1.7.14

    Function sub

    • sub<T>(a, b): T

    • Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

      +sub | @vladmandic/face-api - v1.7.14

      Function sub

      • sub<T extends tf.Tensor>(
            a: TensorLike | tf.Tensor,
            b: TensorLike | tf.Tensor,
        ): T

      • Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

        const a = tf.tensor1d([10, 20, 30, 40]);
        const b = tf.tensor1d([1, 2, 3, 4]);

        a.sub(b).print();  // or tf.sub(a, b)
 
        // Broadcast subtract a with b.
        const a = tf.tensor1d([10, 20, 30, 40]);
        const b = tf.scalar(5);

        a.sub(b).print();  // or tf.sub(a, b)
        -

        Type Parameters

        Parameters

        • a: TensorLike | Tensor<Rank>

          The first tf.Tensor to subtract from.

          -
        • b: TensorLike | Tensor<Rank>

          The second tf.Tensor to be subtracted. Must have the same dtype as +

          Type Parameters

          Parameters

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.tensor-2.html b/typedoc/functions/tf.tensor-2.html index 3bd2fe0..bbaea42 100644 --- a/typedoc/functions/tf.tensor-2.html +++ b/typedoc/functions/tf.tensor-2.html @@ -1,4 +1,4 @@ -tensor | @vladmandic/face-api - v1.7.14

    Function tensor

    • tensor<R>(values, shape?, dtype?): Tensor<R>

    • Creates a tf.Tensor with the provided values, shape and dtype.

      +tensor | @vladmandic/face-api - v1.7.14

      Function tensor

      • tensor<R extends Rank>(
            values: TensorLike | WebGLData | WebGPUData,
            shape?: ShapeMap[R],
            dtype?: keyof DataTypeMap,
        ): tf.Tensor<R>

      • Creates a tf.Tensor with the provided values, shape and dtype.

        // Pass an array of values to create a vector.
        tf.tensor([1, 2, 3, 4]).print();
        @@ -14,7 +14,7 @@ 
        // Pass a `WebGPUData` object and specify a shape yourself.

        // This makes it possible for TF.js applications to avoid GPU / CPU sync.
        // For example, if your application includes a preprocessing step on the GPU,
        // you could upload the GPU output directly to TF.js, rather than first
        // downloading the values. Unlike WebGL, this optionally supports zero copy
        // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this
        // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy
        // is true, this GPUBuffer is bound directly by the tensor, so do not destroy
        // this GPUBuffer until all access is done.

        // Example for WebGPU:
        function createGPUBufferFromData(device, data, dtype) {
          const bytesPerElement = 4;
          const sizeInBytes = data.length * bytesPerElement;

          const gpuWriteBuffer = device.createBuffer({
            mappedAtCreation: true,
            size: sizeInBytes,
            usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
          });
          const arrayBuffer = gpuWriteBuffer.getMappedRange();
          if (dtype === 'float32') {
            new Float32Array(arrayBuffer).set(data);
          } else if (dtype === 'int32') {
            new Int32Array(arrayBuffer).set(data);
          } else {
            throw new Error(
                `Creating tensor from GPUBuffer only supports` +
                `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
          }
          gpuWriteBuffer.unmap();

          const gpuReadBuffer = device.createBuffer({
            mappedAtCreation: false,
            size: sizeInBytes,
            usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
                GPUBufferUsage.COPY_SRC
          });

          const copyEncoder = device.createCommandEncoder();
          copyEncoder.copyBufferToBuffer(
              gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
          const copyCommands = copyEncoder.finish();
          device.queue.submit([copyCommands]);
          gpuWriteBuffer.destroy();
          return gpuReadBuffer;
        }

        const savedBackend = tf.getBackend();
        await tf.setBackend('webgpu').catch(
            () => {throw new Error(
                'Failed to use WebGPU backend. Please use Chrome Canary to run.')});
        const dtype = 'float32';
        const device = tf.backend().device;
        const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
        const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
        const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
        const aBuffer = createGPUBufferFromData(device, aData, dtype);
        const shape = [aData.length];
        // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy
        // aBuffer untill all access is done.
        const a = tf.tensor({buffer: aBuffer}, shape, dtype);
        const b = tf.tensor(bData, shape, dtype);
        const result = tf.add(a, b);
        result.print();
        a.dispose();
        b.dispose();
        result.dispose();
        aBuffer.destroy();
        await tf.setBackend(savedBackend);
        -

        Type Parameters

        Parameters

        • values: TensorLike | WebGLData | WebGPUData

          The values of the tensor. Can be nested array of numbers, +

          Type Parameters

          Parameters

          • values: TensorLike | WebGLData | WebGPUData

            The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray(At the moment it supports Uint8Array, Uint8ClampedArray, Int32Array, Float32Array) data types, or a WebGLData object, or a WebGPUData object. If the values are strings, they will be @@ -40,7 +40,7 @@ false or undefined(default),this passing GPUBuffer can be destroyed after tensor is created. When zeroCopy is true, this GPUBuffer is bound directly by the tensor, so do not destroy this GPUBuffer until all access is done.

            • -
        • Optionalshape: ShapeMap[R]

          The shape of the tensor. Optional. If not provided, +

        • Optionalshape: ShapeMap[R]

          The shape of the tensor. Optional. If not provided, it is inferred from values.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor<R>

        Doc

      Settings

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      +

    Returns tf.Tensor<R>

    Doc

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    diff --git a/typedoc/functions/tf.tensor1d-1.html b/typedoc/functions/tf.tensor1d-1.html index 80c58a3..81f7cfa 100644 --- a/typedoc/functions/tf.tensor1d-1.html +++ b/typedoc/functions/tf.tensor1d-1.html @@ -1,4 +1,4 @@ -tensor1d | @vladmandic/face-api - v1.7.14

    Function tensor1d

    • tensor1d(values, dtype?): Tensor1D

    • Creates rank-1 tf.Tensor with the provided values, shape and dtype.

      +tensor1d | @vladmandic/face-api - v1.7.14

      Function tensor1d

      • tensor1d(values: TensorLike1D, dtype?: keyof DataTypeMap): Tensor1D

      • Creates rank-1 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor1d as it makes the code more readable.

        tf.tensor1d([1, 2, 3]).print();
@@ -7,4 +7,4 @@ we recommend using tf.tensor1d as it makes the code more readable.<

        Parameters

        • values: TensorLike1D

          The values of the tensor. Can be array of numbers, or a TypedArray.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor1D

        Doc

      Settings

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      +

    Returns Tensor1D

    Doc

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    diff --git a/typedoc/functions/tf.tensor2d-1.html b/typedoc/functions/tf.tensor2d-1.html index 8ac6d2f..b824aa6 100644 --- a/typedoc/functions/tf.tensor2d-1.html +++ b/typedoc/functions/tf.tensor2d-1.html @@ -1,4 +1,4 @@ -tensor2d | @vladmandic/face-api - v1.7.14

    Function tensor2d

    • tensor2d(values, shape?, dtype?): Tensor2D

    • Creates rank-2 tf.Tensor with the provided values, shape and dtype.

      +tensor2d | @vladmandic/face-api - v1.7.14

      Function tensor2d

      • tensor2d(
            values: TensorLike2D,
            shape?: [number, number],
            dtype?: keyof DataTypeMap,
        ): Tensor2D

      • Creates rank-2 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor2d as it makes the code more readable.

        // Pass a nested array.
        tf.tensor2d([[1, 2], [3, 4]]).print();
@@ -9,7 +9,7 @@ we recommend using tf.tensor2d as it makes the code more readable.<

        Parameters

        • values: TensorLike2D

          The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.

          -
        • Optionalshape: [number, number]

          The shape of the tensor. If not provided, it is inferred from +

        • Optionalshape: [number, number]

          The shape of the tensor. If not provided, it is inferred from values.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor2D

        Doc

      Settings

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      +

    Returns Tensor2D

    Doc

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    diff --git a/typedoc/functions/tf.tensor3d-1.html b/typedoc/functions/tf.tensor3d-1.html index 3cce6a8..8280ff8 100644 --- a/typedoc/functions/tf.tensor3d-1.html +++ b/typedoc/functions/tf.tensor3d-1.html @@ -1,4 +1,4 @@ -tensor3d | @vladmandic/face-api - v1.7.14

    Function tensor3d

    • tensor3d(values, shape?, dtype?): Tensor3D

    • Creates rank-3 tf.Tensor with the provided values, shape and dtype.

      +tensor3d | @vladmandic/face-api - v1.7.14

      Function tensor3d

      • tensor3d(
            values: TensorLike3D,
            shape?: [number, number, number],
            dtype?: keyof DataTypeMap,
        ): Tensor3D

      • Creates rank-3 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor3d as it makes the code more readable.

        // Pass a nested array.
        tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
@@ -9,7 +9,7 @@ we recommend using tf.tensor3d as it makes the code more readable.<

        Parameters

        • values: TensorLike3D

          The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.

          -
        • Optionalshape: [number, number, number]

          The shape of the tensor. If not provided, it is inferred from +

        • Optionalshape: [number, number, number]

          The shape of the tensor. If not provided, it is inferred from values.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor3D

        Doc

      Settings

      Member Visibility
      +

    Returns Tensor3D

    Doc

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    diff --git a/typedoc/functions/tf.tensor4d-1.html b/typedoc/functions/tf.tensor4d-1.html index c018ad3..7b0872c 100644 --- a/typedoc/functions/tf.tensor4d-1.html +++ b/typedoc/functions/tf.tensor4d-1.html @@ -1,4 +1,4 @@ -tensor4d | @vladmandic/face-api - v1.7.14

    Function tensor4d

    • tensor4d(values, shape?, dtype?): Tensor4D

    • Creates rank-4 tf.Tensor with the provided values, shape and dtype.

      +tensor4d | @vladmandic/face-api - v1.7.14

      Function tensor4d

      • tensor4d(
            values: TensorLike4D,
            shape?: [number, number, number, number],
            dtype?: keyof DataTypeMap,
        ): Tensor4D

      • Creates rank-4 tf.Tensor with the provided values, shape and dtype.

        The same functionality can be achieved with tf.tensor, but in general we recommend using tf.tensor4d as it makes the code more readable.

        // Pass a nested array.
        tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
@@ -9,7 +9,7 @@ we recommend using tf.tensor4d as it makes the code more readable.<

        Parameters

        • values: TensorLike4D

          The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.

          -
        • Optionalshape: [number, number, number, number]

          The shape of the tensor. Optional. If not provided, +

        • Optionalshape: [number, number, number, number]

          The shape of the tensor. Optional. If not provided, it is inferred from values.

        • Optionaldtype: keyof DataTypeMap

          The data type.

          -

        Returns Tensor4D

        Doc

      Settings

      Member Visibility
      +

    Returns Tensor4D

    Doc

    Settings

    Member Visibility
    diff --git a/typedoc/functions/tf.tidy.html b/typedoc/functions/tf.tidy.html index adc7250..e923716 100644 --- a/typedoc/functions/tf.tidy.html +++ b/typedoc/functions/tf.tidy.html @@ -1,4 +1,4 @@ -tidy | @vladmandic/face-api - v1.7.14

    Function tidy

    • tidy<T>(nameOrFn, fn?): T

    • Executes the provided function fn and after it is executed, cleans up all +tidy | @vladmandic/face-api - v1.7.14

      Function tidy

      • tidy<T extends TensorContainer>(
            nameOrFn: string | ScopeFn<T>,
            fn?: ScopeFn<T>,
        ): T

      • Executes the provided function fn and after it is executed, cleans up all intermediate tensors allocated by fn except those returned by fn. fn must not return a Promise (async functions not allowed). The returned result can be a complex object.

        @@ -10,9 +10,9 @@ directly on variables.

        // y = 2 ^ 2 + 1
        const y = tf.tidy(() => {
          // a, b, and one will be cleaned up when the tidy ends.
          const one = tf.scalar(1);
          const a = tf.scalar(2);
          const b = a.square();

          console.log('numTensors (in tidy): ' + tf.memory().numTensors);

          // The value returned inside the tidy function will return
          // through the tidy, in this case to the variable y.
          return b.add(one);
        });

        console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
        y.print();
        -

        Type Parameters

        • T extends TensorContainer

        Parameters

        • nameOrFn: string | ScopeFn<T>

          The name of the closure, or the function to execute. +

          Type Parameters

          • T extends TensorContainer

          Parameters

          • nameOrFn: string | ScopeFn<T>

            The name of the closure, or the function to execute. If a name is provided, the 2nd argument should be the function. If debug mode is on, the timing and the memory usage of the function will be tracked and displayed on the console using the provided name.

            -
          • Optionalfn: ScopeFn<T>

            The function to execute.

            -

          Returns T

          Doc

      Settings

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      +
    • Optionalfn: ScopeFn<T>

      The function to execute.

      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.tile.html b/typedoc/functions/tf.tile.html index d83f0aa..a7db387 100644 --- a/typedoc/functions/tf.tile.html +++ b/typedoc/functions/tf.tile.html @@ -1,4 +1,4 @@ -tile | @vladmandic/face-api - v1.7.14

    Function tile

    • tile<T>(x, reps): T

    • Construct a tensor by repeating it the number of times given by reps.

      +tile | @vladmandic/face-api - v1.7.14

      Function tile

      • tile<T extends tf.Tensor>(x: TensorLike | T, reps: number[]): T

      • Construct a tensor by repeating it the number of times given by reps.

        This operation creates a new tensor by replicating input reps times. The output tensor's ith dimension has input.shape[i] * reps[i] elements, and the values of input are replicated reps[i] times along the ith dimension. For example, tiling @@ -9,6 +9,6 @@ times. The output tensor's ith dimension has input.shape[i] * 

        const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

        a.tile([1, 2]).print();  // or tf.tile(a, [1,2])
        -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The tensor to tile.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The tensor to tile.

          • reps: number[]

            Determines the number of replications per dimension.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.transpose.html b/typedoc/functions/tf.transpose.html index 00073c0..bf54709 100644 --- a/typedoc/functions/tf.transpose.html +++ b/typedoc/functions/tf.transpose.html @@ -1,4 +1,4 @@ -transpose | @vladmandic/face-api - v1.7.14

    Function transpose

    • transpose<T>(x, perm?, conjugate?): T

    • Transposes the tf.Tensor. Permutes the dimensions according to perm.

      +transpose | @vladmandic/face-api - v1.7.14

      Function transpose

      • transpose<T extends tf.Tensor>(
            x: TensorLike | T,
            perm?: number[],
            conjugate?: boolean,
        ): T

      • Transposes the tf.Tensor. Permutes the dimensions according to perm.

        The returned tf.Tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to [n-1...0], where n is the rank of the input tf.Tensor. Hence by default, this @@ -6,7 +6,7 @@ operation performs a regular matrix transpose on 2-D input tf.Tensorconst a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);

        a.transpose().print(); // or tf.transpose(a)         -

        Type Parameters

        Parameters

        • x: TensorLike | T

          The tensor to transpose.

          +

          Type Parameters

          Parameters

          • x: TensorLike | T

            The tensor to transpose.

          • Optionalperm: number[]

            The permutation of the dimensions of a.

          • Optionalconjugate: boolean

            Will conjugate complex input if true.

            -

          Returns T

          Doc

      Settings

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      +

    Returns T

    Doc

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    diff --git a/typedoc/functions/tf.unstack.html b/typedoc/functions/tf.unstack.html index 68bf6bf..6a22d36 100644 --- a/typedoc/functions/tf.unstack.html +++ b/typedoc/functions/tf.unstack.html @@ -1,7 +1,7 @@ -unstack | @vladmandic/face-api - v1.7.14

    Function unstack

    • unstack(x, axis?): Tensor[]

    • Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

      +unstack | @vladmandic/face-api - v1.7.14

      Function unstack

      • unstack(x: TensorLike | tf.Tensor, axis?: number): tf.Tensor[]

      • Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

        const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

        tf.unstack(a).forEach(tensor => tensor.print());
        -

        Parameters

        • x: TensorLike | Tensor<Rank>

          A tensor object.

          +

          Parameters

          • x: TensorLike | tf.Tensor

            A tensor object.

          • Optionalaxis: number

            The axis to unstack along. Defaults to 0 (the first dim).

            -

          Returns Tensor[]

          Doc

      Settings

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      +

    Returns tf.Tensor[]

    Doc

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    diff --git a/typedoc/functions/tf.zeros.html b/typedoc/functions/tf.zeros.html index 0b8ef84..c22c3f2 100644 --- a/typedoc/functions/tf.zeros.html +++ b/typedoc/functions/tf.zeros.html @@ -1,8 +1,8 @@ -zeros | @vladmandic/face-api - v1.7.14

    Function zeros

    • zeros<R>(shape, dtype?): Tensor<R>

    • Creates a tf.Tensor with all elements set to 0.

      +zeros | @vladmandic/face-api - v1.7.14

      Function zeros

      • zeros<R extends Rank>(
            shape: ShapeMap[R],
            dtype?: keyof DataTypeMap,
        ): tf.Tensor<R>

      • Creates a tf.Tensor with all elements set to 0.

        tf.zeros([2, 2]).print();
        -

        Type Parameters

        Parameters

        • shape: ShapeMap[R]

          An array of integers defining the output tensor shape.

          +

          Type Parameters

          Parameters

          • shape: ShapeMap[R]

            An array of integers defining the output tensor shape.

          • Optionaldtype: keyof DataTypeMap

            The type of an element in the resulting tensor. Can be 'float32', 'int32' or 'bool'. Defaults to 'float'.

            -

          Returns Tensor<R>

          Doc

      Settings

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      +

    Returns tf.Tensor<R>

    Doc

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    diff --git a/typedoc/functions/tinyFaceDetector-1.html b/typedoc/functions/tinyFaceDetector-1.html index 31dc1df..1516e66 100644 --- a/typedoc/functions/tinyFaceDetector-1.html +++ b/typedoc/functions/tinyFaceDetector-1.html @@ -1,5 +1,5 @@ -tinyFaceDetector | @vladmandic/face-api - v1.7.14

    Function tinyFaceDetector

    Attempts to detect all faces in an image using the Tiny Face Detector.

    -

    Settings

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    diff --git a/typedoc/functions/tinyYolov2-1.html b/typedoc/functions/tinyYolov2-1.html index 0ef703d..d0e0926 100644 --- a/typedoc/functions/tinyYolov2-1.html +++ b/typedoc/functions/tinyYolov2-1.html @@ -1,5 +1,5 @@ -tinyYolov2 | @vladmandic/face-api - v1.7.14

    Function tinyYolov2

    Attempts to detect all faces in an image using the Tiny Yolov2 Network.

    -

    Settings

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    diff --git a/typedoc/functions/toNetInput.html b/typedoc/functions/toNetInput.html index 12a1064..5506011 100644 --- a/typedoc/functions/toNetInput.html +++ b/typedoc/functions/toNetInput.html @@ -1,4 +1,4 @@ -toNetInput | @vladmandic/face-api - v1.7.14

    Function toNetInput

    • toNetInput(inputs): Promise<NetInput>

    • Validates the input to make sure, they are valid net inputs and awaits all media elements +toNetInput | @vladmandic/face-api - v1.7.14

      Function toNetInput

      • toNetInput(inputs: TNetInput): Promise<NetInput>

      • Validates the input to make sure, they are valid net inputs and awaits all media elements to be finished loading.

        Parameters

        Returns Promise<NetInput>

        A NetInput instance, which can be passed into one of the neural networks.

        -

      Settings

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      +

    Settings

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    diff --git a/typedoc/functions/utils.computeReshapedDimensions.html b/typedoc/functions/utils.computeReshapedDimensions.html index af77d42..030a5d5 100644 --- a/typedoc/functions/utils.computeReshapedDimensions.html +++ b/typedoc/functions/utils.computeReshapedDimensions.html @@ -1 +1 @@ -computeReshapedDimensions | @vladmandic/face-api - v1.7.14

    Function computeReshapedDimensions

    Settings

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    +computeReshapedDimensions | @vladmandic/face-api - v1.7.14

    Function computeReshapedDimensions

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    diff --git a/typedoc/functions/utils.getCenterPoint.html b/typedoc/functions/utils.getCenterPoint.html index 4372d5b..6eb9557 100644 --- a/typedoc/functions/utils.getCenterPoint.html +++ b/typedoc/functions/utils.getCenterPoint.html @@ -1 +1 @@ -getCenterPoint | @vladmandic/face-api - v1.7.14

    Function getCenterPoint

    Settings

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    +getCenterPoint | @vladmandic/face-api - v1.7.14

    Function getCenterPoint

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    diff --git a/typedoc/functions/utils.isDimensions.html b/typedoc/functions/utils.isDimensions.html index 2bfd97f..0548aa6 100644 --- a/typedoc/functions/utils.isDimensions.html +++ b/typedoc/functions/utils.isDimensions.html @@ -1 +1 @@ -isDimensions | @vladmandic/face-api - v1.7.14

    Function isDimensions

    Settings

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    +isDimensions | @vladmandic/face-api - v1.7.14

    Function isDimensions

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    diff --git a/typedoc/functions/utils.isEven.html b/typedoc/functions/utils.isEven.html index 41c02fb..d129d75 100644 --- a/typedoc/functions/utils.isEven.html +++ b/typedoc/functions/utils.isEven.html @@ -1 +1 @@ -isEven | @vladmandic/face-api - v1.7.14

    Function isEven

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    +isEven | @vladmandic/face-api - v1.7.14

    Function isEven

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    diff --git a/typedoc/functions/utils.isFloat.html b/typedoc/functions/utils.isFloat.html index 5c44d52..02df35e 100644 --- a/typedoc/functions/utils.isFloat.html +++ b/typedoc/functions/utils.isFloat.html @@ -1 +1 @@ -isFloat | @vladmandic/face-api - v1.7.14

    Function isFloat

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    +isFloat | @vladmandic/face-api - v1.7.14

    Function isFloat

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    diff --git a/typedoc/functions/utils.isTensor.html b/typedoc/functions/utils.isTensor.html index 803be42..dd0ffb3 100644 --- a/typedoc/functions/utils.isTensor.html +++ b/typedoc/functions/utils.isTensor.html @@ -1 +1 @@ -isTensor | @vladmandic/face-api - v1.7.14

    Function isTensor

    • isTensor(tensor, dim): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

    Settings

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    +isTensor | @vladmandic/face-api - v1.7.14

    Function isTensor

    • isTensor(tensor: any, dim: number): boolean

    • Parameters

      • tensor: any
      • dim: number

      Returns boolean

    Settings

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    diff --git a/typedoc/functions/utils.isTensor1D.html b/typedoc/functions/utils.isTensor1D.html index 4cf8f55..84462ba 100644 --- a/typedoc/functions/utils.isTensor1D.html +++ b/typedoc/functions/utils.isTensor1D.html @@ -1 +1 @@ -isTensor1D | @vladmandic/face-api - v1.7.14

    Function isTensor1D

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    +isTensor1D | @vladmandic/face-api - v1.7.14

    Function isTensor1D

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    diff --git a/typedoc/functions/utils.isTensor2D.html b/typedoc/functions/utils.isTensor2D.html index 7c8defb..596307e 100644 --- a/typedoc/functions/utils.isTensor2D.html +++ b/typedoc/functions/utils.isTensor2D.html @@ -1 +1 @@ -isTensor2D | @vladmandic/face-api - v1.7.14

    Function isTensor2D

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    +isTensor2D | @vladmandic/face-api - v1.7.14

    Function isTensor2D

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    diff --git a/typedoc/functions/utils.isTensor3D.html b/typedoc/functions/utils.isTensor3D.html index 1d85650..1899344 100644 --- a/typedoc/functions/utils.isTensor3D.html +++ b/typedoc/functions/utils.isTensor3D.html @@ -1 +1 @@ -isTensor3D | @vladmandic/face-api - v1.7.14

    Function isTensor3D

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    +isTensor3D | @vladmandic/face-api - v1.7.14

    Function isTensor3D

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    diff --git a/typedoc/functions/utils.isTensor4D.html b/typedoc/functions/utils.isTensor4D.html index 822cbd3..c869219 100644 --- a/typedoc/functions/utils.isTensor4D.html +++ b/typedoc/functions/utils.isTensor4D.html @@ -1 +1 @@ -isTensor4D | @vladmandic/face-api - v1.7.14

    Function isTensor4D

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    +isTensor4D | @vladmandic/face-api - v1.7.14

    Function isTensor4D

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    diff --git a/typedoc/functions/utils.isValidNumber.html b/typedoc/functions/utils.isValidNumber.html index 93f9b29..67cb864 100644 --- a/typedoc/functions/utils.isValidNumber.html +++ b/typedoc/functions/utils.isValidNumber.html @@ -1 +1 @@ -isValidNumber | @vladmandic/face-api - v1.7.14

    Function isValidNumber

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    +isValidNumber | @vladmandic/face-api - v1.7.14

    Function isValidNumber

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    diff --git a/typedoc/functions/utils.isValidProbablitiy.html b/typedoc/functions/utils.isValidProbablitiy.html index ff3bcb5..b0dae4d 100644 --- a/typedoc/functions/utils.isValidProbablitiy.html +++ b/typedoc/functions/utils.isValidProbablitiy.html @@ -1 +1 @@ -isValidProbablitiy | @vladmandic/face-api - v1.7.14

    Function isValidProbablitiy

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    +isValidProbablitiy | @vladmandic/face-api - v1.7.14

    Function isValidProbablitiy

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    diff --git a/typedoc/functions/utils.range.html b/typedoc/functions/utils.range.html index dd7cfa7..3e15f62 100644 --- a/typedoc/functions/utils.range.html +++ b/typedoc/functions/utils.range.html @@ -1 +1 @@ -range | @vladmandic/face-api - v1.7.14

    Function range

    • range(num, start, step): number[]

    • Parameters

      • num: number
      • start: number
      • step: number

      Returns number[]

    Settings

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    +range | @vladmandic/face-api - v1.7.14

    Function range

    • range(num: number, start: number, step: number): number[]

    • Parameters

      • num: number
      • start: number
      • step: number

      Returns number[]

    Settings

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    diff --git a/typedoc/functions/utils.round.html b/typedoc/functions/utils.round.html index 3add397..702ab71 100644 --- a/typedoc/functions/utils.round.html +++ b/typedoc/functions/utils.round.html @@ -1 +1 @@ -round | @vladmandic/face-api - v1.7.14

    Function round

    • round(num, prec?): number

    • Parameters

      • num: number
      • prec: number = 2

      Returns number

    Settings

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    +round | @vladmandic/face-api - v1.7.14

    Function round

    • round(num: number, prec?: number): number

    • Parameters

      • num: number
      • prec: number = 2

      Returns number

    Settings

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    diff --git a/typedoc/functions/validateConfig.html b/typedoc/functions/validateConfig.html index 360a3fe..af54aa6 100644 --- a/typedoc/functions/validateConfig.html +++ b/typedoc/functions/validateConfig.html @@ -1 +1 @@ -validateConfig | @vladmandic/face-api - v1.7.14

    Function validateConfig

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    +validateConfig | @vladmandic/face-api - v1.7.14

    Function validateConfig

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    diff --git a/typedoc/hierarchy.html b/typedoc/hierarchy.html index b06934e..b759f1a 100644 --- a/typedoc/hierarchy.html +++ b/typedoc/hierarchy.html @@ -1 +1 @@ -@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Class Hierarchy

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    +@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Hierarchy Summary

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    diff --git a/typedoc/index.html b/typedoc/index.html index 038acad..2c30792 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,155 +1 @@ -@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Index

    Namespaces

    draw -tf -utils -

    Enumerations

    Gender -

    Classes

    AgeGenderNet -BoundingBox -Box -ComposableTask -ComputeAllFaceDescriptorsTask -ComputeFaceDescriptorsTaskBase -ComputeSingleFaceDescriptorTask -DetectAllFaceLandmarksTask -DetectAllFacesTask -DetectFaceLandmarksTaskBase -DetectFacesTaskBase -DetectSingleFaceLandmarksTask -DetectSingleFaceTask -Dimensions -FaceDetection -FaceDetectionNet -FaceExpressionNet -FaceExpressions -FaceLandmark68Net -FaceLandmark68TinyNet -FaceLandmarkNet -FaceLandmarks -FaceLandmarks5 -FaceLandmarks68 -FaceMatch -FaceMatcher -FaceRecognitionNet -LabeledBox -LabeledFaceDescriptors -NetInput -NeuralNetwork -ObjectDetection -Point -PredictedBox -Rect -SsdMobilenetv1 -SsdMobilenetv1Options -TinyFaceDetector -TinyFaceDetectorOptions -TinyYolov2 -TinyYolov2Options -

    Interfaces

    IBoundingBox -IDimensions -IFaceDetecion -IFaceLandmarks -IFaceMatch -IPoint -IRect -ISsdMobilenetv1Options -ITinyYolov2Options -

    Type Aliases

    AgeAndGenderPrediction -BatchNorm -ConvWithBatchNorm -DefaultTinyYolov2NetParams -Environment -FaceDetectionFunction -FaceDetectionOptions -FileSystem -ITinyFaceDetectorOptions -MobilenetParams -NetOutput -NetParams -TinyYolov2Config -TinyYolov2ExtractBoxesResult -TinyYolov2NetParams -TMediaElement -TNetInput -TResolvedNetInput -WithAge -WithFaceDescriptor -WithFaceDetection -WithFaceExpressions -WithFaceLandmarks -WithGender -

    Variables

    env -FACE_EXPRESSION_LABELS -nets -version -

    Functions

    allFaces -allFacesSsdMobilenetv1 -allFacesTinyYolov2 -awaitMediaLoaded -bufferToImage -computeFaceDescriptor -createCanvas -createCanvasFromMedia -createFaceDetectionNet -createFaceRecognitionNet -createSsdMobilenetv1 -createTinyFaceDetector -createTinyYolov2 -detectAllFaces -detectFaceLandmarks -detectFaceLandmarksTiny -detectLandmarks -detectSingleFace -euclideanDistance -extendWithAge -extendWithFaceDescriptor -extendWithFaceDetection -extendWithFaceExpressions -extendWithFaceLandmarks -extendWithGender -extractFaces -extractFaceTensors -fetchImage -fetchJson -fetchNetWeights -fetchOrThrow -fetchVideo -getContext2dOrThrow -getMediaDimensions -imageTensorToCanvas -imageToSquare -inverseSigmoid -iou -isMediaElement -isMediaLoaded -isWithAge -isWithFaceDetection -isWithFaceExpressions -isWithFaceLandmarks -isWithGender -loadAgeGenderModel -loadFaceDetectionModel -loadFaceExpressionModel -loadFaceLandmarkModel -loadFaceLandmarkTinyModel -loadFaceRecognitionModel -loadSsdMobilenetv1Model -loadTinyFaceDetectorModel -loadTinyYolov2Model -loadWeightMap -locateFaces -matchDimensions -minBbox -nonMaxSuppression -normalize -padToSquare -predictAgeAndGender -recognizeFaceExpressions -resizeResults -resolveInput -shuffleArray -sigmoid -ssdMobilenetv1 -tinyFaceDetector -tinyYolov2 -toNetInput -validateConfig -

    Settings

    Member Visibility
    +@vladmandic/face-api - v1.7.14

    @vladmandic/face-api - v1.7.14

    Namespaces

    draw
    tf
    utils

    Enumerations

    Gender

    Classes

    AgeGenderNet
    BoundingBox
    Box
    ComposableTask
    ComputeAllFaceDescriptorsTask
    ComputeFaceDescriptorsTaskBase
    ComputeSingleFaceDescriptorTask
    DetectAllFaceLandmarksTask
    DetectAllFacesTask
    DetectFaceLandmarksTaskBase
    DetectFacesTaskBase
    DetectSingleFaceLandmarksTask
    DetectSingleFaceTask
    Dimensions
    FaceDetection
    FaceDetectionNet
    FaceExpressionNet
    FaceExpressions
    FaceLandmark68Net
    FaceLandmark68TinyNet
    FaceLandmarkNet
    FaceLandmarks
    FaceLandmarks5
    FaceLandmarks68
    FaceMatch
    FaceMatcher
    FaceRecognitionNet
    LabeledBox
    LabeledFaceDescriptors
    NetInput
    NeuralNetwork
    ObjectDetection
    Point
    PredictedBox
    Rect
    SsdMobilenetv1
    SsdMobilenetv1Options
    TinyFaceDetector
    TinyFaceDetectorOptions
    TinyYolov2
    TinyYolov2Options

    Interfaces

    IBoundingBox
    IDimensions
    IFaceDetecion
    IFaceLandmarks
    IFaceMatch
    IPoint
    IRect
    ISsdMobilenetv1Options
    ITinyYolov2Options

    Type Aliases

    AgeAndGenderPrediction
    BatchNorm
    ConvWithBatchNorm
    DefaultTinyYolov2NetParams
    Environment
    FaceDetectionFunction
    FaceDetectionOptions
    FileSystem
    ITinyFaceDetectorOptions
    MobilenetParams
    NetOutput
    NetParams
    TinyYolov2Config
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    TinyYolov2NetParams
    TMediaElement
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    WithFaceLandmarks
    WithGender

    Variables

    env
    FACE_EXPRESSION_LABELS
    nets
    version

    Functions

    allFaces
    allFacesSsdMobilenetv1
    allFacesTinyYolov2
    awaitMediaLoaded
    bufferToImage
    computeFaceDescriptor
    createCanvas
    createCanvasFromMedia
    createFaceDetectionNet
    createFaceRecognitionNet
    createSsdMobilenetv1
    createTinyFaceDetector
    createTinyYolov2
    detectAllFaces
    detectFaceLandmarks
    detectFaceLandmarksTiny
    detectLandmarks
    detectSingleFace
    euclideanDistance
    extendWithAge
    extendWithFaceDescriptor
    extendWithFaceDetection
    extendWithFaceExpressions
    extendWithFaceLandmarks
    extendWithGender
    extractFaces
    extractFaceTensors
    fetchImage
    fetchJson
    fetchNetWeights
    fetchOrThrow
    fetchVideo
    getContext2dOrThrow
    getMediaDimensions
    imageTensorToCanvas
    imageToSquare
    inverseSigmoid
    iou
    isMediaElement
    isMediaLoaded
    isWithAge
    isWithFaceDetection
    isWithFaceExpressions
    isWithFaceLandmarks
    isWithGender
    loadAgeGenderModel
    loadFaceDetectionModel
    loadFaceExpressionModel
    loadFaceLandmarkModel
    loadFaceLandmarkTinyModel
    loadFaceRecognitionModel
    loadSsdMobilenetv1Model
    loadTinyFaceDetectorModel
    loadTinyYolov2Model
    loadWeightMap
    locateFaces
    matchDimensions
    minBbox
    nonMaxSuppression
    normalize
    padToSquare
    predictAgeAndGender
    recognizeFaceExpressions
    resizeResults
    resolveInput
    shuffleArray
    sigmoid
    ssdMobilenetv1
    tinyFaceDetector
    tinyYolov2
    toNetInput
    validateConfig

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    allFacesallFacesSsdMobilenetv1allFacesTinyYolov2awaitMediaLoadedbufferToImagecomputeFaceDescriptorcreateCanvascreateCanvasFromMediacreateFaceDetectionNetcreateFaceRecognitionNetcreateSsdMobilenetv1createTinyFaceDetectorcreateTinyYolov2detectAllFacesdetectFaceLandmarksdetectFaceLandmarksTinydetectLandmarksdetectSingleFaceeuclideanDistanceextendWithAgeextendWithFaceDescriptorextendWithFaceDetectionextendWithFaceExpressionsextendWithFaceLandmarksextendWithGenderextractFacesextractFaceTensorsfetchImagefetchJsonfetchNetWeightsfetchOrThrowfetchVideogetContext2dOrThrowgetMediaDimensionsimageTensorToCanvasimageToSquareinverseSigmoidiouisMediaElementisMediaLoadedisWithAgeisWithFaceDetectionisWithFaceExpressionsisWithFaceLandmarksisWithGenderloadAgeGenderModelloadFaceDetectionModelloadFaceExpressionModelloadFaceLandmarkModelloadFaceLandmarkTinyModelloadFaceRecognitionModelloadSsdMobilenetv1ModelloadTinyFaceDetectorModelloadTinyYolov2ModelloadWeightMaplocateFacesmatchDimensionsminBboxnonMaxSuppressionnormalizepadToSquarepredictAgeAndGenderrecognizeFaceExpressionsresizeResultsresolveInputshuffleArraysigmoidssdMobilenetv1tinyFaceDetectortinyYolov2toNetInputvalidateConfig
    diff --git a/typedoc/interfaces/IBoundingBox.html b/typedoc/interfaces/IBoundingBox.html index 607b8db..5a2e233 100644 --- a/typedoc/interfaces/IBoundingBox.html +++ b/typedoc/interfaces/IBoundingBox.html @@ -1,5 +1,5 @@ -IBoundingBox | @vladmandic/face-api - v1.7.14

    Interface IBoundingBox

    interface IBoundingBox {
        bottom: number;
        left: number;
        right: number;
        top: number;
    }

    Implemented by

    Properties

    bottom +IBoundingBox | @vladmandic/face-api - v1.7.14

    Interface IBoundingBox

    interface IBoundingBox {
        bottom: number;
        left: number;
        right: number;
        top: number;
    }

    Implemented by

    Properties

    bottom left right top -

    Properties

    bottom

    bottom: number

    left

    left: number

    right

    right: number

    top

    top: number

    Settings

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    +

    Properties

    bottom

    bottom: number

    left

    left: number

    right

    right: number

    top

    top: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IDimensions.html b/typedoc/interfaces/IDimensions.html index ef1b49e..a4c2897 100644 --- a/typedoc/interfaces/IDimensions.html +++ b/typedoc/interfaces/IDimensions.html @@ -1,3 +1,3 @@ -IDimensions | @vladmandic/face-api - v1.7.14

    Interface IDimensions

    interface IDimensions {
        height: number;
        width: number;
    }

    Implemented by

    Properties

    height +IDimensions | @vladmandic/face-api - v1.7.14

    Interface IDimensions

    interface IDimensions {
        height: number;
        width: number;
    }

    Implemented by

    Properties

    height width -

    Properties

    height

    height: number

    width

    width: number

    Settings

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    Properties
    +

    Properties

    height

    height: number

    width

    width: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceDetecion.html b/typedoc/interfaces/IFaceDetecion.html index eb4c087..9e9b2cb 100644 --- a/typedoc/interfaces/IFaceDetecion.html +++ b/typedoc/interfaces/IFaceDetecion.html @@ -1,3 +1,3 @@ -IFaceDetecion | @vladmandic/face-api - v1.7.14

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box<any>;
        score: number;
    }

    Implemented by

    Properties

    box +IFaceDetecion | @vladmandic/face-api - v1.7.14

    Interface IFaceDetecion

    interface IFaceDetecion {
        box: Box;
        score: number;
    }

    Implemented by

    Properties

    box score -

    Properties

    box

    box: Box<any>

    score

    score: number

    Settings

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    +

    Properties

    box

    box: Box

    score

    score: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceLandmarks.html b/typedoc/interfaces/IFaceLandmarks.html index 0749a19..15d21d1 100644 --- a/typedoc/interfaces/IFaceLandmarks.html +++ b/typedoc/interfaces/IFaceLandmarks.html @@ -1,3 +1,3 @@ -IFaceLandmarks | @vladmandic/face-api - v1.7.14

    Interface IFaceLandmarks

    interface IFaceLandmarks {
        positions: Point[];
        shift: Point;
    }

    Implemented by

    Properties

    positions +IFaceLandmarks | @vladmandic/face-api - v1.7.14

    Interface IFaceLandmarks

    interface IFaceLandmarks {
        positions: Point[];
        shift: Point;
    }

    Implemented by

    Properties

    positions shift -

    Properties

    positions

    positions: Point[]

    shift

    shift: Point

    Settings

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    +

    Properties

    positions

    positions: Point[]

    shift

    shift: Point

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IFaceMatch.html b/typedoc/interfaces/IFaceMatch.html index e5001c4..12b7ac4 100644 --- a/typedoc/interfaces/IFaceMatch.html +++ b/typedoc/interfaces/IFaceMatch.html @@ -1,3 +1,3 @@ -IFaceMatch | @vladmandic/face-api - v1.7.14

    Interface IFaceMatch

    interface IFaceMatch {
        distance: number;
        label: string;
    }

    Implemented by

    Properties

    distance +IFaceMatch | @vladmandic/face-api - v1.7.14

    Interface IFaceMatch

    interface IFaceMatch {
        distance: number;
        label: string;
    }

    Implemented by

    Properties

    distance label -

    Properties

    distance

    distance: number

    label

    label: string

    Settings

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    +

    Properties

    distance

    distance: number

    label

    label: string

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IPoint.html b/typedoc/interfaces/IPoint.html index 1fdb763..37d0ea1 100644 --- a/typedoc/interfaces/IPoint.html +++ b/typedoc/interfaces/IPoint.html @@ -1,3 +1,3 @@ -IPoint | @vladmandic/face-api - v1.7.14

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x +IPoint | @vladmandic/face-api - v1.7.14

    Interface IPoint

    interface IPoint {
        x: number;
        y: number;
    }

    Implemented by

    Properties

    x y -

    Properties

    x

    x: number

    y

    y: number

    Settings

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    On This Page

    Properties
    +

    Properties

    x

    x: number

    y

    y: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/IRect.html b/typedoc/interfaces/IRect.html index f238e42..116ac00 100644 --- a/typedoc/interfaces/IRect.html +++ b/typedoc/interfaces/IRect.html @@ -1,5 +1,5 @@ -IRect | @vladmandic/face-api - v1.7.14

    Interface IRect

    interface IRect {
        height: number;
        width: number;
        x: number;
        y: number;
    }

    Implemented by

    Properties

    height +IRect | @vladmandic/face-api - v1.7.14

    Interface IRect

    interface IRect {
        height: number;
        width: number;
        x: number;
        y: number;
    }

    Implemented by

    Properties

    height width x y -

    Properties

    height

    height: number

    width

    width: number

    x

    x: number

    y

    y: number

    Settings

    Member Visibility

    On This Page

    Properties
    +

    Properties

    height

    height: number

    width

    width: number

    x

    x: number

    y

    y: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/ISsdMobilenetv1Options.html b/typedoc/interfaces/ISsdMobilenetv1Options.html index 5fc4337..a6e57ed 100644 --- a/typedoc/interfaces/ISsdMobilenetv1Options.html +++ b/typedoc/interfaces/ISsdMobilenetv1Options.html @@ -1,3 +1,3 @@ -ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? -minConfidence? -

    Properties

    OptionalmaxResults

    maxResults?: number

    OptionalminConfidence

    minConfidence?: number

    Settings

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    Properties
    +ISsdMobilenetv1Options | @vladmandic/face-api - v1.7.14

    Interface ISsdMobilenetv1Options

    interface ISsdMobilenetv1Options {
        maxResults?: number;
        minConfidence?: number;
    }

    Properties

    maxResults? +minConfidence? +

    Properties

    OptionalmaxResults

    maxResults?: number

    OptionalminConfidence

    minConfidence?: number

    Settings

    Member Visibility

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    Properties
    diff --git a/typedoc/interfaces/ITinyYolov2Options.html b/typedoc/interfaces/ITinyYolov2Options.html index 776e136..782fb3e 100644 --- a/typedoc/interfaces/ITinyYolov2Options.html +++ b/typedoc/interfaces/ITinyYolov2Options.html @@ -1,3 +1,3 @@ -ITinyYolov2Options | @vladmandic/face-api - v1.7.14

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? -scoreThreshold? -

    Properties

    OptionalinputSize

    inputSize?: number

    OptionalscoreThreshold

    scoreThreshold?: number

    Settings

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    Properties
    +ITinyYolov2Options | @vladmandic/face-api - v1.7.14

    Interface ITinyYolov2Options

    interface ITinyYolov2Options {
        inputSize?: number;
        scoreThreshold?: number;
    }

    Properties

    inputSize? +scoreThreshold? +

    Properties

    OptionalinputSize

    inputSize?: number

    OptionalscoreThreshold

    scoreThreshold?: number

    Settings

    Member Visibility

    On This Page

    Properties
    diff --git a/typedoc/interfaces/draw.IDrawBoxOptions.html b/typedoc/interfaces/draw.IDrawBoxOptions.html index 4e1b7a0..4498e5c 100644 --- a/typedoc/interfaces/draw.IDrawBoxOptions.html +++ b/typedoc/interfaces/draw.IDrawBoxOptions.html @@ -1,5 +1,5 @@ -IDrawBoxOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawBoxOptions

    interface IDrawBoxOptions {
        boxColor?: string;
        drawLabelOptions?: IDrawTextFieldOptions;
        label?: string;
        lineWidth?: number;
    }

    Properties

    boxColor? -drawLabelOptions? +IDrawBoxOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawBoxOptions

    interface IDrawBoxOptions {
        boxColor?: string;
        drawLabelOptions?: IDrawTextFieldOptions;
        label?: string;
        lineWidth?: number;
    }

    Properties

    boxColor? +drawLabelOptions? label? -lineWidth? -

    Properties

    OptionalboxColor

    boxColor?: string

    OptionaldrawLabelOptions

    drawLabelOptions?: IDrawTextFieldOptions

    Optionallabel

    label?: string

    OptionallineWidth

    lineWidth?: number

    Settings

    Member Visibility

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    Properties
    +lineWidth? +

    Properties

    OptionalboxColor

    boxColor?: string

    OptionaldrawLabelOptions

    drawLabelOptions?: IDrawTextFieldOptions

    Optionallabel

    label?: string

    OptionallineWidth

    lineWidth?: number

    Settings

    Member Visibility

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    Properties
    diff --git a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html index 33929ac..2116d63 100644 --- a/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html +++ b/typedoc/interfaces/draw.IDrawFaceLandmarksOptions.html @@ -1,7 +1,7 @@ -IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawFaceLandmarksOptions

    interface IDrawFaceLandmarksOptions {
        drawLines?: boolean;
        drawPoints?: boolean;
        lineColor?: string;
        lineWidth?: number;
        pointColor?: string;
        pointSize?: number;
    }

    Properties

    drawLines? -drawPoints? -lineColor? -lineWidth? -pointColor? -pointSize? -

    Properties

    OptionaldrawLines

    drawLines?: boolean

    OptionaldrawPoints

    drawPoints?: boolean

    OptionallineColor

    lineColor?: string

    OptionallineWidth

    lineWidth?: number

    OptionalpointColor

    pointColor?: string

    OptionalpointSize

    pointSize?: number

    Settings

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    +IDrawFaceLandmarksOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawFaceLandmarksOptions

    interface IDrawFaceLandmarksOptions {
        drawLines?: boolean;
        drawPoints?: boolean;
        lineColor?: string;
        lineWidth?: number;
        pointColor?: string;
        pointSize?: number;
    }

    Properties

    drawLines? +drawPoints? +lineColor? +lineWidth? +pointColor? +pointSize? +

    Properties

    OptionaldrawLines

    drawLines?: boolean

    OptionaldrawPoints

    drawPoints?: boolean

    OptionallineColor

    lineColor?: string

    OptionallineWidth

    lineWidth?: number

    OptionalpointColor

    pointColor?: string

    OptionalpointSize

    pointSize?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/draw.IDrawTextFieldOptions.html b/typedoc/interfaces/draw.IDrawTextFieldOptions.html index 5f093eb..d35fee6 100644 --- a/typedoc/interfaces/draw.IDrawTextFieldOptions.html +++ b/typedoc/interfaces/draw.IDrawTextFieldOptions.html @@ -1,7 +1,7 @@ -IDrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawTextFieldOptions

    interface IDrawTextFieldOptions {
        anchorPosition?: AnchorPosition;
        backgroundColor?: string;
        fontColor?: string;
        fontSize?: number;
        fontStyle?: string;
        padding?: number;
    }

    Implemented by

    Properties

    anchorPosition? -backgroundColor? -fontColor? -fontSize? -fontStyle? +IDrawTextFieldOptions | @vladmandic/face-api - v1.7.14

    Interface IDrawTextFieldOptions

    interface IDrawTextFieldOptions {
        anchorPosition?: AnchorPosition;
        backgroundColor?: string;
        fontColor?: string;
        fontSize?: number;
        fontStyle?: string;
        padding?: number;
    }

    Implemented by

    Properties

    anchorPosition? +backgroundColor? +fontColor? +fontSize? +fontStyle? padding? -

    Properties

    OptionalanchorPosition

    anchorPosition?: AnchorPosition

    OptionalbackgroundColor

    backgroundColor?: string

    OptionalfontColor

    fontColor?: string

    OptionalfontSize

    fontSize?: number

    OptionalfontStyle

    fontStyle?: string

    Optionalpadding

    padding?: number

    Settings

    Member Visibility

    On This Page

    Properties
    +

    Properties

    OptionalanchorPosition

    anchorPosition?: AnchorPosition

    OptionalbackgroundColor

    backgroundColor?: string

    OptionalfontColor

    fontColor?: string

    OptionalfontSize

    fontSize?: number

    OptionalfontStyle

    fontStyle?: string

    Optionalpadding

    padding?: number

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.IOHandler.html b/typedoc/interfaces/tf.io.IOHandler.html index ee10757..c1a705b 100644 --- a/typedoc/interfaces/tf.io.IOHandler.html +++ b/typedoc/interfaces/tf.io.IOHandler.html @@ -1,6 +1,6 @@ -IOHandler | @vladmandic/face-api - v1.7.14

    Interface IOHandler

    Interface for a model import/export handler.

    +IOHandler | @vladmandic/face-api - v1.7.14

    Interface IOHandler

    Interface for a model import/export handler.

    The save and load handlers are both optional, in order to allow handlers that support only saving or loading.

    -
    interface IOHandler {
        load?: LoadHandler;
        save?: SaveHandler;
    }

    Properties

    load? +
    interface IOHandler {
        load?: LoadHandler;
        save?: SaveHandler;
    }

    Properties

    load? save? -

    Properties

    Optionalload

    load?: LoadHandler

    Optionalsave

    save?: SaveHandler

    Settings

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    Properties
    +

    Properties

    Optionalload

    load?: LoadHandler

    Optionalsave

    save?: SaveHandler

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.LoadOptions.html b/typedoc/interfaces/tf.io.LoadOptions.html index a65aac4..87b4dfc 100644 --- a/typedoc/interfaces/tf.io.LoadOptions.html +++ b/typedoc/interfaces/tf.io.LoadOptions.html @@ -1,32 +1,33 @@ -LoadOptions | @vladmandic/face-api - v1.7.14

    Interface LoadOptions

    Innamespace

    io

    -
    interface LoadOptions {
        fetchFunc?: {
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: string | Request | URL, init?: RequestInit): Promise<Response>;
        };
        fromTFHub?: boolean;
        onProgress?: OnProgressCallback;
        requestInit?: RequestInit;
        streamWeights?: boolean;
        strict?: boolean;
        weightPathPrefix?: string;
        weightUrlConverter?: ((weightFileName: string) => Promise<string>);
    }

    Properties

    fetchFunc? -fromTFHub? -onProgress? -requestInit? -streamWeights? +LoadOptions | @vladmandic/face-api - v1.7.14

    Interface LoadOptions

    Innamespace

    io

    +
    interface LoadOptions {
        fetchFunc?: {
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
            (input: string | Request | URL, init?: RequestInit): Promise<Response>;
        };
        fromTFHub?: boolean;
        onProgress?: OnProgressCallback;
        requestInit?: RequestInit;
        streamWeights?: boolean;
        strict?: boolean;
        weightPathPrefix?: string;
        weightUrlConverter?: (weightFileName: string) => Promise<string>;
    }

    Properties

    fetchFunc? +fromTFHub? +onProgress? +requestInit? +streamWeights? strict? -weightPathPrefix? -weightUrlConverter? -

    Properties

    OptionalfetchFunc

    fetchFunc?: {
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: string | Request | URL, init?: RequestInit): Promise<Response>;
    }

    A function used to override the window.fetch function.

    -

    Type declaration

      • (input, init?): Promise<Response>

      • MDN Reference

        -

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • MDN Reference

        -

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input, init?): Promise<Response>

      • Parameters

        • input: string | Request | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

    OptionalfromTFHub

    fromTFHub?: boolean

    Whether the module or model is to be loaded from TF Hub.

    +weightPathPrefix? +weightUrlConverter? +

    Properties

    OptionalfetchFunc

    fetchFunc?: {
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: RequestInfo | URL, init?: RequestInit): Promise<Response>;
        (input: string | Request | URL, init?: RequestInit): Promise<Response>;
    }

    A function used to override the window.fetch function.

    +

    Type declaration

      • (input: RequestInfo | URL, init?: RequestInit): Promise<Response>

      • MDN Reference

        +

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input: RequestInfo | URL, init?: RequestInit): Promise<Response>

      • MDN Reference

        +

        Parameters

        • input: RequestInfo | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

      • (input: string | Request | URL, init?: RequestInit): Promise<Response>

      • MDN Reference

        +

        Parameters

        • input: string | Request | URL
        • Optionalinit: RequestInit

        Returns Promise<Response>

    OptionalfromTFHub

    fromTFHub?: boolean

    Whether the module or model is to be loaded from TF Hub.

    Setting this to true allows passing a TF-Hub module URL, omitting the standard model file name and the query parameters.

    Default: false.

    -

    OptionalonProgress

    onProgress?: OnProgressCallback

    Progress callback.

    -

    OptionalrequestInit

    requestInit?: RequestInit

    RequestInit (options) for HTTP requests.

    +

    OptionalonProgress

    onProgress?: OnProgressCallback

    Progress callback.

    +

    OptionalrequestInit

    requestInit?: RequestInit

    RequestInit (options) for HTTP requests.

    For detailed information on the supported fields, see -https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

    -

    OptionalstreamWeights

    streamWeights?: boolean

    Whether to stream the model directly to the backend or cache all its +https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

    +

    OptionalstreamWeights

    streamWeights?: boolean

    Whether to stream the model directly to the backend or cache all its weights on CPU first. Useful for large models.

    -

    Optionalstrict

    strict?: boolean

    Strict loading model: whether extraneous weights or missing +

    Optionalstrict

    strict?: boolean

    Strict loading model: whether extraneous weights or missing weights should trigger an Error.

    If true, require that the provided weights exactly match those required by the layers. false means that both extra weights and missing weights will be silently ignored.

    Default: true.

    -

    OptionalweightPathPrefix

    weightPathPrefix?: string

    Path prefix for weight files, by default this is calculated from the +

    OptionalweightPathPrefix

    weightPathPrefix?: string

    Path prefix for weight files, by default this is calculated from the path of the model JSON file.

    For instance, if the path to the model JSON file is http://localhost/foo/model.json, then the default path prefix will be @@ -36,11 +37,11 @@ loaded from http://localhost/foo/group1-shard1of2 by default. Howev if you provide a weightPathPrefix value of http://localhost/foo/alt-weights, then the weight file will be loaded from the path http://localhost/foo/alt-weights/group1-shard1of2 instead.

    -

    OptionalweightUrlConverter

    weightUrlConverter?: ((weightFileName: string) => Promise<string>)

    An async function to convert weight file name to URL. The weight file +

    OptionalweightUrlConverter

    weightUrlConverter?: (weightFileName: string) => Promise<string>

    An async function to convert weight file name to URL. The weight file names are stored in model.json's weightsManifest.paths field. By default we consider weight files are colocated with the model.json file. For example: -model.json URL: https://www.google.com/models/1/model.json +model.json URL: https://www.google.com/models/1/model.json group1-shard1of1.bin url: -https://www.google.com/models/1/group1-shard1of1.bin

    +https://www.google.com/models/1/group1-shard1of1.bin

    With this func you can convert the weight file name to any URL.

    -

    Settings

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    +

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.ModelArtifacts.html b/typedoc/interfaces/tf.io.ModelArtifacts.html index f8aa529..0cb4d60 100644 --- a/typedoc/interfaces/tf.io.ModelArtifacts.html +++ b/typedoc/interfaces/tf.io.ModelArtifacts.html @@ -1,47 +1,47 @@ -ModelArtifacts | @vladmandic/face-api - v1.7.14

    Interface ModelArtifacts

    The serialized artifacts of a model, including topology and weights.

    +ModelArtifacts | @vladmandic/face-api - v1.7.14

    Interface ModelArtifacts

    The serialized artifacts of a model, including topology and weights.

    The modelTopology, trainingConfig, weightSpecs and weightData fields of this interface are optional, in order to support topology- or weights-only saving and loading.

    Note this interface is used internally in IOHandlers. For the file format written to disk as model.json, see ModelJSON.

    -
    interface ModelArtifacts {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        getWeightStream?: (() => ReadableStream<ArrayBuffer>);
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology?: {} | ArrayBuffer;
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: {
            [key: string]: {};
        };
        weightData?: WeightData;
        weightSpecs?: WeightsManifestEntry[];
    }

    Properties

    convertedBy? +
    interface ModelArtifacts {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        getWeightStream?: () => ReadableStream<ArrayBuffer>;
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology?: {} | ArrayBuffer;
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: { [key: string]: {} };
        weightData?: WeightData;
        weightSpecs?: WeightsManifestEntry[];
    }

    Properties

    convertedBy? format? -generatedBy? -getWeightStream? -initializerSignature? -modelInitializer? -modelTopology? +generatedBy? +getWeightStream? +initializerSignature? +modelInitializer? +modelTopology? signature? -trainingConfig? -userDefinedMetadata? -weightData? -weightSpecs? -

    Properties

    OptionalconvertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model +trainingConfig? +userDefinedMetadata? +weightData? +weightSpecs? +

    Properties

    OptionalconvertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model to this format, applicable only if the model is output by a converter.

    Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

    A value of null means the model artifacts are generated without any conversion process (e.g., saved directly from a TensorFlow.js tf.LayersModel instance.)

    -

    Optionalformat

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted +

    Optionalformat

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted by TensorFlow.js Converter.

    -

    OptionalgeneratedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    +

    OptionalgeneratedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

    -

    OptionalgetWeightStream

    getWeightStream?: (() => ReadableStream<ArrayBuffer>)

    Returns a stream of the weights. Some models are too large to fit in +

    OptionalgetWeightStream

    getWeightStream?: () => ReadableStream<ArrayBuffer>

    Returns a stream of the weights. Some models are too large to fit in V8's memory heap, and getWeightStream loads their weights without storing them all in memory at the same time.

    -

    OptionalinitializerSignature

    initializerSignature?: {}

    Inputs and outputs signature for model initializer.

    -

    OptionalmodelInitializer

    modelInitializer?: {}

    Initializer for the model.

    -

    OptionalmodelTopology

    modelTopology?: {} | ArrayBuffer

    Model topology.

    +

    OptionalinitializerSignature

    initializerSignature?: {}

    Inputs and outputs signature for model initializer.

    +

    OptionalmodelInitializer

    modelInitializer?: {}

    Initializer for the model.

    +

    OptionalmodelTopology

    modelTopology?: {} | ArrayBuffer

    Model topology.

    For Keras-style tf.Models, this is a JSON object. For TensorFlow-style models (e.g., SavedModel), this is the JSON encoding of the GraphDef protocol buffer.

    -

    Optionalsignature

    signature?: {}

    Inputs and outputs signature for saved model.

    -

    OptionaltrainingConfig

    trainingConfig?: TrainingConfig

    Serialized configuration for the model's training.

    -

    OptionaluserDefinedMetadata

    userDefinedMetadata?: {
        [key: string]: {};
    }

    User-defined metadata about the model.

    -

    OptionalweightData

    weightData?: WeightData

    Binary buffer(s) for all weight values in the order specified by +

    Optionalsignature

    signature?: {}

    Inputs and outputs signature for saved model.

    +

    OptionaltrainingConfig

    trainingConfig?: TrainingConfig

    Serialized configuration for the model's training.

    +

    OptionaluserDefinedMetadata

    userDefinedMetadata?: { [key: string]: {} }

    User-defined metadata about the model.

    +

    OptionalweightData

    weightData?: WeightData

    Binary buffer(s) for all weight values in the order specified by weightSpecs. This may be a single ArrayBuffer of all the weights concatenated together or an Array of ArrayBuffers containing the weights (weights may be sharded across multiple ArrayBuffers).

    -

    OptionalweightSpecs

    weightSpecs?: WeightsManifestEntry[]

    Weight specifications.

    +

    OptionalweightSpecs

    weightSpecs?: WeightsManifestEntry[]

    Weight specifications.

    This corresponds to the weightsData below.

    -

    Settings

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    +

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html index 4015255..8012155 100644 --- a/typedoc/interfaces/tf.io.ModelArtifactsInfo.html +++ b/typedoc/interfaces/tf.io.ModelArtifactsInfo.html @@ -1,11 +1,11 @@ -ModelArtifactsInfo | @vladmandic/face-api - v1.7.14

    Interface ModelArtifactsInfo

    interface ModelArtifactsInfo {
        dateSaved: Date;
        modelTopologyBytes?: number;
        modelTopologyType: "JSON" | "GraphDef";
        weightDataBytes?: number;
        weightSpecsBytes?: number;
    }

    Properties

    dateSaved -modelTopologyBytes? -modelTopologyType -weightDataBytes? -weightSpecsBytes? -

    Properties

    dateSaved

    dateSaved: Date

    Timestamp for when the model is saved.

    -

    OptionalmodelTopologyBytes

    modelTopologyBytes?: number

    Size of model topology (Keras JSON or GraphDef), in bytes.

    -

    modelTopologyType

    modelTopologyType: "JSON" | "GraphDef"

    TODO (cais,yassogba) consider removing GraphDef as GraphDefs now +ModelArtifactsInfo | @vladmandic/face-api - v1.7.14

    Interface ModelArtifactsInfo

    interface ModelArtifactsInfo {
        dateSaved: Date;
        modelTopologyBytes?: number;
        modelTopologyType: "JSON" | "GraphDef";
        weightDataBytes?: number;
        weightSpecsBytes?: number;
    }

    Properties

    dateSaved +modelTopologyBytes? +modelTopologyType +weightDataBytes? +weightSpecsBytes? +

    Properties

    dateSaved

    dateSaved: Date

    Timestamp for when the model is saved.

    +

    OptionalmodelTopologyBytes

    modelTopologyBytes?: number

    Size of model topology (Keras JSON or GraphDef), in bytes.

    +

    modelTopologyType

    modelTopologyType: "JSON" | "GraphDef"

    TODO (cais,yassogba) consider removing GraphDef as GraphDefs now come in a JSON format and none of our IOHandlers support a non json format. We could conder replacing this with 'Binary' if we want to allow future handlers to save to non json formats (though they will @@ -16,9 +16,9 @@ Type of the model topology

    • JSON: JSON config (human-readable, e.g., Keras JSON).
    • GraphDef: TensorFlow -GraphDef +GraphDef protocol buffer (binary).
    -

    OptionalweightDataBytes

    weightDataBytes?: number

    Size of weight value data, in bytes.

    -

    OptionalweightSpecsBytes

    weightSpecsBytes?: number

    Size of weight specification or manifest, in bytes.

    -

    Settings

    Member Visibility

    On This Page

    Properties
    +

    OptionalweightDataBytes

    weightDataBytes?: number

    Size of weight value data, in bytes.

    +

    OptionalweightSpecsBytes

    weightSpecsBytes?: number

    Size of weight specification or manifest, in bytes.

    +

    Settings

    Member Visibility

    On This Page

    Properties
    diff --git a/typedoc/interfaces/tf.io.ModelJSON.html b/typedoc/interfaces/tf.io.ModelJSON.html index 1a37b6d..0fd3ead 100644 --- a/typedoc/interfaces/tf.io.ModelJSON.html +++ b/typedoc/interfaces/tf.io.ModelJSON.html @@ -1,38 +1,38 @@ -ModelJSON | @vladmandic/face-api - v1.7.14

    Interface ModelJSON

    The on-disk format of the model.json file.

    +ModelJSON | @vladmandic/face-api - v1.7.14

    Interface ModelJSON

    The on-disk format of the model.json file.

    TF.js 1.0 always populates the optional fields when writing model.json. Prior versions did not provide those fields.

    -
    interface ModelJSON {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology: {};
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: {
            [key: string]: {};
        };
        weightsManifest: WeightsManifestConfig;
    }

    Properties

    convertedBy? +
    interface ModelJSON {
        convertedBy?: null | string;
        format?: string;
        generatedBy?: string;
        initializerSignature?: {};
        modelInitializer?: {};
        modelTopology: {};
        signature?: {};
        trainingConfig?: TrainingConfig;
        userDefinedMetadata?: { [key: string]: {} };
        weightsManifest: WeightsManifestConfig;
    }

    Properties

    convertedBy? format? -generatedBy? -initializerSignature? -modelInitializer? -modelTopology +generatedBy? +initializerSignature? +modelInitializer? +modelTopology signature? -trainingConfig? -userDefinedMetadata? -weightsManifest -

    Properties

    OptionalconvertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model +trainingConfig? +userDefinedMetadata? +weightsManifest +

    Properties

    OptionalconvertedBy

    convertedBy?: null | string

    What library or tool is responsible for converting the original model to this format, applicable only if the model is output by a converter.

    Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.

    A value of null means the model artifacts are generated without any conversion process (e.g., saved directly from a TensorFlow.js tf.LayersModel instance.)

    -

    Optionalformat

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted +

    Optionalformat

    format?: string

    Hard-coded format name for models saved from TensorFlow.js or converted by TensorFlow.js Converter.

    -

    OptionalgeneratedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    +

    OptionalgeneratedBy

    generatedBy?: string

    What library is responsible for originally generating this artifact.

    Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.

    -

    OptionalinitializerSignature

    initializerSignature?: {}

    Inputs and outputs signature for model initializer.

    -

    OptionalmodelInitializer

    modelInitializer?: {}

    Initializer for the model.

    -

    modelTopology

    modelTopology: {}

    Model topology.

    +

    OptionalinitializerSignature

    initializerSignature?: {}

    Inputs and outputs signature for model initializer.

    +

    OptionalmodelInitializer

    modelInitializer?: {}

    Initializer for the model.

    +

    modelTopology

    modelTopology: {}

    Model topology.

    For Keras-style tf.Models, this is a JSON object. For TensorFlow-style models (e.g., SavedModel), this is the JSON encoding of the GraphDef protocol buffer.

    -

    Optionalsignature

    signature?: {}

    Inputs and outputs signature for saved model.

    -

    OptionaltrainingConfig

    trainingConfig?: TrainingConfig

    Model training configuration.

    -

    OptionaluserDefinedMetadata

    userDefinedMetadata?: {
        [key: string]: {};
    }

    User-defined metadata about the model.

    -

    weightsManifest

    weightsManifest: WeightsManifestConfig

    Weights manifest.

    +

    Optionalsignature

    signature?: {}

    Inputs and outputs signature for saved model.

    +

    OptionaltrainingConfig

    trainingConfig?: TrainingConfig

    Model training configuration.

    +

    OptionaluserDefinedMetadata

    userDefinedMetadata?: { [key: string]: {} }

    User-defined metadata about the model.

    +

    weightsManifest

    weightsManifest: WeightsManifestConfig

    Weights manifest.

    The weights manifest consists of an ordered list of weight-manifest groups. Each weight-manifest group consists of a number of weight values stored in a number of paths. See the documentation of WeightsManifestConfig for more details.

    -

    Settings

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    +

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.ModelStoreManager.html b/typedoc/interfaces/tf.io.ModelStoreManager.html index 6188cfa..698afc7 100644 --- a/typedoc/interfaces/tf.io.ModelStoreManager.html +++ b/typedoc/interfaces/tf.io.ModelStoreManager.html @@ -1,4 +1,4 @@ -ModelStoreManager | @vladmandic/face-api - v1.7.14

    Interface ModelStoreManager

    An interface for the manager of a model store.

    +ModelStoreManager | @vladmandic/face-api - v1.7.14

    Interface ModelStoreManager

    An interface for the manager of a model store.

    A model store is defined as a storage medium on which multiple models can be stored. Each stored model has a unique path as its identifier. A ModelStoreManager for the store allows actions including

    @@ -6,14 +6,14 @@ A ModelStoreManager for the store allows actions including

  • Listing the models stored in the store.
  • Deleting a model from the store.
    • -
    interface ModelStoreManager {
        listModels(): Promise<{
            [path: string]: ModelArtifactsInfo;
        }>;
        removeModel(path: string): Promise<ModelArtifactsInfo>;
    }

    Methods

    listModels -removeModel -

    Methods

    listModels

    • listModels(): Promise<{
          [path: string]: ModelArtifactsInfo;
      }>

    • List all models in the model store.

      -

      Returns Promise<{
          [path: string]: ModelArtifactsInfo;
      }>

      A dictionary mapping paths of existing models to their +

    interface ModelStoreManager {
        listModels(): Promise<{ [path: string]: ModelArtifactsInfo }>;
        removeModel(path: string): Promise<ModelArtifactsInfo>;
    }

    Methods

    listModels +removeModel +

    Methods

    listModels

    • listModels(): Promise<{ [path: string]: ModelArtifactsInfo }>

    • List all models in the model store.

      +

      Returns Promise<{ [path: string]: ModelArtifactsInfo }>

      A dictionary mapping paths of existing models to their model artifacts info. Model artifacts info include type of the model's topology, byte sizes of the topology, weights, etc.

      -

    removeModel

    • removeModel(path): Promise<ModelArtifactsInfo>

    • Remove a model specified by path.

      +

    removeModel

    • removeModel(path: string): Promise<ModelArtifactsInfo>

    • Remove a model specified by path.

      Parameters

      • path: string

      Returns Promise<ModelArtifactsInfo>

      ModelArtifactsInfo of the deleted model (if and only if deletion is successful).

      -

      Throws

      Error if deletion fails, e.g., if no model exists at path.

      -

    Settings

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    Methods
    +

    Throws

    Error if deletion fails, e.g., if no model exists at path.

    +

    Settings

    Member Visibility

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    Methods
    diff --git a/typedoc/interfaces/tf.io.RequestDetails.html b/typedoc/interfaces/tf.io.RequestDetails.html index b8fddf4..76eece8 100644 --- a/typedoc/interfaces/tf.io.RequestDetails.html +++ b/typedoc/interfaces/tf.io.RequestDetails.html @@ -1,4 +1,4 @@ -RequestDetails | @vladmandic/face-api - v1.7.14

    Interface RequestDetails

    Additional options for Platform.fetch

    -
    interface RequestDetails {
        isBinary?: boolean;
    }

    Properties

    isBinary? -

    Properties

    OptionalisBinary

    isBinary?: boolean

    Is this request for a binary file (as opposed to a json file)

    -

    Settings

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    Properties
    +RequestDetails | @vladmandic/face-api - v1.7.14

    Interface RequestDetails

    Additional options for Platform.fetch

    +
    interface RequestDetails {
        isBinary?: boolean;
    }

    Properties

    isBinary? +

    Properties

    OptionalisBinary

    isBinary?: boolean

    Is this request for a binary file (as opposed to a json file)

    +

    Settings

    Member Visibility

    On This Page

    Properties
    diff --git a/typedoc/interfaces/tf.io.SaveConfig.html b/typedoc/interfaces/tf.io.SaveConfig.html index 14cba3b..a6aa632 100644 --- a/typedoc/interfaces/tf.io.SaveConfig.html +++ b/typedoc/interfaces/tf.io.SaveConfig.html @@ -1,9 +1,9 @@ -SaveConfig | @vladmandic/face-api - v1.7.14

    Interface SaveConfig

    Options for saving a model.

    -

    Innamespace

    io

    -
    interface SaveConfig {
        includeOptimizer?: boolean;
        trainableOnly?: boolean;
    }

    Properties

    includeOptimizer? -trainableOnly? -

    Properties

    OptionalincludeOptimizer

    includeOptimizer?: boolean

    Whether the optimizer will be saved (if exists).

    +SaveConfig | @vladmandic/face-api - v1.7.14

    Interface SaveConfig

    Options for saving a model.

    +

    Innamespace

    io

    +
    interface SaveConfig {
        includeOptimizer?: boolean;
        trainableOnly?: boolean;
    }

    Properties

    includeOptimizer? +trainableOnly? +

    Properties

    OptionalincludeOptimizer

    includeOptimizer?: boolean

    Whether the optimizer will be saved (if exists).

    Default: false.

    -

    OptionaltrainableOnly

    trainableOnly?: boolean

    Whether to save only the trainable weights of the model, ignoring the +

    OptionaltrainableOnly

    trainableOnly?: boolean

    Whether to save only the trainable weights of the model, ignoring the non-trainable ones.

    -

    Settings

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    +

    Settings

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    Properties
    diff --git a/typedoc/interfaces/tf.io.SaveResult.html b/typedoc/interfaces/tf.io.SaveResult.html index bb406b2..311fb75 100644 --- a/typedoc/interfaces/tf.io.SaveResult.html +++ b/typedoc/interfaces/tf.io.SaveResult.html @@ -1,9 +1,9 @@ -SaveResult | @vladmandic/face-api - v1.7.14

    Interface SaveResult

    Result of a saving operation.

    -
    interface SaveResult {
        errors?: (string | {})[];
        modelArtifactsInfo: ModelArtifactsInfo;
        responses?: Response[];
    }

    Properties

    errors? -modelArtifactsInfo +SaveResult | @vladmandic/face-api - v1.7.14

    Interface SaveResult

    Result of a saving operation.

    +
    interface SaveResult {
        errors?: (string | {})[];
        modelArtifactsInfo: ModelArtifactsInfo;
        responses?: Response[];
    }

    Properties

    errors? +modelArtifactsInfo responses? -

    Properties

    Optionalerrors

    errors?: (string | {})[]

    Error messages and related data (if any).

    -

    modelArtifactsInfo

    modelArtifactsInfo: ModelArtifactsInfo

    Information about the model artifacts saved.

    -

    Optionalresponses

    responses?: Response[]

    HTTP responses from the server that handled the model-saving request (if +

    Properties

    Optionalerrors

    errors?: (string | {})[]

    Error messages and related data (if any).

    +

    modelArtifactsInfo

    modelArtifactsInfo: ModelArtifactsInfo

    Information about the model artifacts saved.

    +

    Optionalresponses

    responses?: Response[]

    HTTP responses from the server that handled the model-saving request (if any). This is applicable only to server-based saving routes.

    -

    Settings

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    Properties
    +

    Settings

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    On This Page

    Properties
    diff --git a/typedoc/interfaces/tf.io.TrainingConfig.html b/typedoc/interfaces/tf.io.TrainingConfig.html index 9713663..4729df2 100644 --- a/typedoc/interfaces/tf.io.TrainingConfig.html +++ b/typedoc/interfaces/tf.io.TrainingConfig.html @@ -1,11 +1,11 @@ -TrainingConfig | @vladmandic/face-api - v1.7.14

    Interface TrainingConfig

    Model training configuration.

    -
    interface TrainingConfig {
        loss: string | string[] | {
            [key: string]: string;
        };
        loss_weights?: number[] | {
            [key: string]: number;
        };
        metrics?: string[] | {
            [key: string]: string;
        };
        optimizer_config: {};
        sample_weight_mode?: string;
        weighted_metrics?: string[];
    }

    Properties

    loss +TrainingConfig | @vladmandic/face-api - v1.7.14

    Interface TrainingConfig

    Model training configuration.

    +
    interface TrainingConfig {
        loss: string | string[] | { [key: string]: string };
        loss_weights?: number[] | { [key: string]: number };
        metrics?: string[] | { [key: string]: string };
        optimizer_config: {};
        sample_weight_mode?: string;
        weighted_metrics?: string[];
    }

    Properties

    loss loss_weights? metrics? optimizer_config sample_weight_mode? weighted_metrics? -

    Properties

    loss

    loss: string | string[] | {
        [key: string]: string;
    }

    Loss function(s) for the model's output(s).

    -

    Optionalloss_weights

    loss_weights?: number[] | {
        [key: string]: number;
    }

    Optionalmetrics

    metrics?: string[] | {
        [key: string]: string;
    }

    Metric function(s) for the model's output(s).

    -

    optimizer_config

    optimizer_config: {}

    Optimizer used for the model training.

    -

    Optionalsample_weight_mode

    sample_weight_mode?: string

    Optionalweighted_metrics

    weighted_metrics?: string[]

    Settings

    Member Visibility

    On This Page

    Properties
    +

    Properties

    loss

    loss: string | string[] | { [key: string]: string }

    Loss function(s) for the model's output(s).

    +

    Optionalloss_weights

    loss_weights?: number[] | { [key: string]: number }

    Optionalmetrics

    metrics?: string[] | { [key: string]: string }

    Metric function(s) for the model's output(s).

    +

    optimizer_config

    optimizer_config: {}

    Optimizer used for the model training.

    +

    Optionalsample_weight_mode

    sample_weight_mode?: string

    Optionalweighted_metrics

    weighted_metrics?: string[]

    Settings

    Member Visibility

    On This Page

    Properties
    diff --git a/typedoc/interfaces/tf.io.WeightsManifestEntry.html b/typedoc/interfaces/tf.io.WeightsManifestEntry.html index 1a1e580..97f99aa 100644 --- a/typedoc/interfaces/tf.io.WeightsManifestEntry.html +++ b/typedoc/interfaces/tf.io.WeightsManifestEntry.html @@ -1,16 +1,16 @@ -WeightsManifestEntry | @vladmandic/face-api - v1.7.14

    Interface WeightsManifestEntry

    An entry in the weight manifest.

    +WeightsManifestEntry | @vladmandic/face-api - v1.7.14

    Interface WeightsManifestEntry

    An entry in the weight manifest.

    The entry contains specification of a weight.

    -
    interface WeightsManifestEntry {
        dtype:
            | "string"
            | "float32"
            | "int32"
            | "bool"
            | "complex64";
        group?: WeightGroup;
        name: string;
        quantization?: {
            dtype: "uint16" | "uint8" | "float16";
            min?: number;
            scale?: number;
        };
        shape: number[];
    }

    Properties

    dtype +
    interface WeightsManifestEntry {
        dtype: "string" | "float32" | "int32" | "bool" | "complex64";
        group?: WeightGroup;
        name: string;
        quantization?: {
            dtype: "uint16" | "uint8" | "float16";
            min?: number;
            scale?: number;
        };
        shape: number[];
    }

    Properties

    dtype group? name quantization? shape -

    Properties

    dtype

    dtype:
        | "string"
        | "float32"
        | "int32"
        | "bool"
        | "complex64"

    Data type of the weight.

    -

    Optionalgroup

    group?: WeightGroup

    Type of the weight.

    +

    Properties

    dtype

    dtype: "string" | "float32" | "int32" | "bool" | "complex64"

    Data type of the weight.

    +

    Optionalgroup

    group?: WeightGroup

    Type of the weight.

    Optional.

    The value 'optimizer' indicates the weight belongs to an optimizer (i.e., used only during model training and not during inference).

    -

    name

    name: string

    Name of the weight, e.g., 'Dense_1/bias'

    -

    Optionalquantization

    quantization?: {
        dtype: "uint16" | "uint8" | "float16";
        min?: number;
        scale?: number;
    }

    Information for dequantization of the weight.

    -

    shape

    shape: number[]

    Shape of the weight.

    -

    Settings

    Member Visibility

    On This Page

    Properties
    +

    name

    name: string

    Name of the weight, e.g., 'Dense_1/bias'

    +

    Optionalquantization

    quantization?: {
        dtype: "uint16" | "uint8" | "float16";
        min?: number;
        scale?: number;
    }

    Information for dequantization of the weight.

    +

    shape

    shape: number[]

    Shape of the weight.

    +

    Settings

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    On This Page

    Properties
    diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index 4c1ca81..7f46d9a 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,18 +1 @@ -draw | @vladmandic/face-api - v1.7.14

    Namespace draw

    Index

    Enumerations

    AnchorPosition -

    Classes

    DrawBox -DrawBoxOptions -DrawFaceLandmarks -DrawFaceLandmarksOptions -DrawTextField -DrawTextFieldOptions -

    Interfaces

    IDrawBoxOptions -IDrawFaceLandmarksOptions -IDrawTextFieldOptions -

    Type Aliases

    DrawFaceExpressionsInput -DrawFaceLandmarksInput -TDrawDetectionsInput -

    Functions

    drawContour -drawDetections -drawFaceExpressions -drawFaceLandmarks -

    Settings

    Member Visibility
    +draw | @vladmandic/face-api - v1.7.14

    Namespace draw

    Enumerations

    AnchorPosition

    Classes

    DrawBox
    DrawBoxOptions
    DrawFaceLandmarks
    DrawFaceLandmarksOptions
    DrawTextField
    DrawTextFieldOptions

    Interfaces

    IDrawBoxOptions
    IDrawFaceLandmarksOptions
    IDrawTextFieldOptions

    Type Aliases

    DrawFaceExpressionsInput
    DrawFaceLandmarksInput
    TDrawDetectionsInput

    Functions

    drawContour
    drawDetections
    drawFaceExpressions
    drawFaceLandmarks

    Settings

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    On This Page

    Enumerations
    Classes
    Interfaces
    Type Aliases
    Functions
    diff --git a/typedoc/modules/tf.Tensor.html b/typedoc/modules/tf.Tensor.html index b3f2ceb..9ce3ad7 100644 --- a/typedoc/modules/tf.Tensor.html +++ b/typedoc/modules/tf.Tensor.html @@ -1 +1 @@ -Tensor | @vladmandic/face-api - v1.7.14

    Namespace Tensor

    Settings

    Member Visibility
    +Tensor | @vladmandic/face-api - v1.7.14

    Namespace Tensor

    Settings

    Member Visibility
    diff --git a/typedoc/modules/tf.browser.html b/typedoc/modules/tf.browser.html index 5c01cf8..965a26e 100644 --- a/typedoc/modules/tf.browser.html +++ b/typedoc/modules/tf.browser.html @@ -1,5 +1 @@ -browser | @vladmandic/face-api - v1.7.14

    Namespace browser

    Index

    Functions

    draw -fromPixels -fromPixelsAsync -toPixels -

    Settings

    Member Visibility
    +browser | @vladmandic/face-api - v1.7.14

    Namespace browser

    Functions

    draw
    fromPixels
    fromPixelsAsync
    toPixels

    Settings

    Member Visibility

    On This Page

    Functions
    diff --git a/typedoc/modules/tf.html b/typedoc/modules/tf.html index e964d7d..d947f87 100644 --- a/typedoc/modules/tf.html +++ b/typedoc/modules/tf.html @@ -1,53 +1 @@ -tf | @vladmandic/face-api - v1.7.14

    Namespace tf

    Index

    Namespaces

    browser -io -Tensor -

    Enumerations

    Rank -

    Classes

    Tensor -Variable -

    Type Aliases

    NamedTensorMap -Tensor1D -Tensor2D -Tensor3D -Tensor4D -Tensor5D -TensorLike -

    Variables

    ENV -image -version -

    Functions

    add -avgPool -batchNorm -cast -clipByValue -concat -conv2d -depthwiseConv2d -div -exp -expandDims -fill -matMul -maxPool -mul -pad -relu -reshape -scalar -separableConv2d -sigmoid -slice -slice3d -softmax -stack -sub -tensor -tensor1d -tensor2d -tensor3d -tensor4d -tidy -tile -transpose -unstack -zeros -

    Settings

    Member Visibility
    +tf | @vladmandic/face-api - v1.7.14

    Namespace tf

    Namespaces

    browser
    io
    Tensor

    Enumerations

    Rank

    Classes

    Tensor
    Variable

    Type Aliases

    NamedTensorMap
    Tensor1D
    Tensor2D
    Tensor3D
    Tensor4D
    Tensor5D
    TensorLike

    Variables

    ENV
    image
    version

    Functions

    add
    avgPool
    batchNorm
    cast
    clipByValue
    concat
    conv2d
    depthwiseConv2d
    div
    exp
    expandDims
    fill
    matMul
    maxPool
    mul
    pad
    relu
    reshape
    scalar
    separableConv2d
    sigmoid
    slice
    slice3d
    softmax
    stack
    sub
    tensor
    tensor1d
    tensor2d
    tensor3d
    tensor4d
    tidy
    tile
    transpose
    unstack
    zeros

    Settings

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    Namespaces
    Enumerations
    Classes
    Type Aliases
    Variables
    Functions
    diff --git a/typedoc/modules/tf.io.html b/typedoc/modules/tf.io.html index 2daeb82..b6e8ad9 100644 --- a/typedoc/modules/tf.io.html +++ b/typedoc/modules/tf.io.html @@ -1,46 +1 @@ -io | @vladmandic/face-api - v1.7.14

    Namespace io

    Index

    Classes

    CompositeArrayBuffer -

    Interfaces

    IOHandler -LoadOptions -ModelArtifacts -ModelArtifactsInfo -ModelJSON -ModelStoreManager -RequestDetails -SaveConfig -SaveResult -TrainingConfig -WeightsManifestEntry -

    Type Aliases

    IOHandlerSync -LoadHandler -OnProgressCallback -SaveHandler -WeightData -WeightGroup -WeightsManifestConfig -

    Functions

    browserFiles -browserHTTPRequest -concatenateArrayBuffers -copyModel -decodeWeights -decodeWeightsStream -encodeWeights -fromMemory -fromMemorySync -getLoadHandlers -getModelArtifactsForJSON -getModelArtifactsForJSONSync -getModelArtifactsInfoForJSON -getSaveHandlers -getWeightSpecs -http -isHTTPScheme -listModels -loadWeights -moveModel -registerLoadRouter -registerSaveRouter -removeModel -weightsLoaderFactory -withSaveHandler -withSaveHandlerSync -

    Settings

    Member Visibility
    +io | @vladmandic/face-api - v1.7.14

    Namespace io

    Classes

    CompositeArrayBuffer

    Interfaces

    IOHandler
    LoadOptions
    ModelArtifacts
    ModelArtifactsInfo
    ModelJSON
    ModelStoreManager
    RequestDetails
    SaveConfig
    SaveResult
    TrainingConfig
    WeightsManifestEntry

    Type Aliases

    IOHandlerSync
    LoadHandler
    OnProgressCallback
    SaveHandler
    WeightData
    WeightGroup
    WeightsManifestConfig

    Functions

    browserFiles
    browserHTTPRequest
    concatenateArrayBuffers
    copyModel
    decodeWeights
    decodeWeightsStream
    encodeWeights
    fromMemory
    fromMemorySync
    getLoadHandlers
    getModelArtifactsForJSON
    getModelArtifactsForJSONSync
    getModelArtifactsInfoForJSON
    getSaveHandlers
    getWeightSpecs
    http
    isHTTPScheme
    listModels
    loadWeights
    moveModel
    registerLoadRouter
    registerSaveRouter
    removeModel
    weightsLoaderFactory
    withSaveHandler
    withSaveHandlerSync

    Settings

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    On This Page

    Classes
    Interfaces
    Type Aliases
    Functions
    diff --git a/typedoc/modules/utils.html b/typedoc/modules/utils.html index 47a3eff..04520db 100644 --- a/typedoc/modules/utils.html +++ b/typedoc/modules/utils.html @@ -1,15 +1 @@ -utils | @vladmandic/face-api - v1.7.14

    Namespace utils

    Index

    Functions

    computeReshapedDimensions -getCenterPoint -isDimensions -isEven -isFloat -isTensor -isTensor1D -isTensor2D -isTensor3D -isTensor4D -isValidNumber -isValidProbablitiy -range -round -

    Settings

    Member Visibility
    +utils | @vladmandic/face-api - v1.7.14

    Namespace utils

    Functions

    computeReshapedDimensions
    getCenterPoint
    isDimensions
    isEven
    isFloat
    isTensor
    isTensor1D
    isTensor2D
    isTensor3D
    isTensor4D
    isValidNumber
    isValidProbablitiy
    range
    round

    Settings

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    On This Page

    Functions
    diff --git a/typedoc/types/AgeAndGenderPrediction.html b/typedoc/types/AgeAndGenderPrediction.html index 0f323cb..13a63a8 100644 --- a/typedoc/types/AgeAndGenderPrediction.html +++ b/typedoc/types/AgeAndGenderPrediction.html @@ -1 +1 @@ -AgeAndGenderPrediction | @vladmandic/face-api - v1.7.14

    Type Alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }

    Settings

    Member Visibility
    +AgeAndGenderPrediction | @vladmandic/face-api - v1.7.14

    Type Alias AgeAndGenderPrediction

    AgeAndGenderPrediction: {
        age: number;
        gender: Gender;
        genderProbability: number;
    }

    Type declaration

    • age: number
    • gender: Gender
    • genderProbability: number

    Settings

    Member Visibility
    diff --git a/typedoc/types/BatchNorm.html b/typedoc/types/BatchNorm.html index 86097f9..f910d86 100644 --- a/typedoc/types/BatchNorm.html +++ b/typedoc/types/BatchNorm.html @@ -1 +1 @@ -BatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias BatchNorm

    BatchNorm: {
        sub: Tensor1D;
        truediv: Tensor1D;
    }

    Settings

    Member Visibility
    +BatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias BatchNorm

    BatchNorm: { sub: Tensor1D; truediv: Tensor1D }

    Type declaration

    Settings

    Member Visibility
    diff --git a/typedoc/types/ConvWithBatchNorm.html b/typedoc/types/ConvWithBatchNorm.html index 0c13435..9c3c52b 100644 --- a/typedoc/types/ConvWithBatchNorm.html +++ b/typedoc/types/ConvWithBatchNorm.html @@ -1 +1 @@ -ConvWithBatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias ConvWithBatchNorm

    ConvWithBatchNorm: {
        bn: BatchNorm;
        conv: ConvParams;
    }

    Settings

    Member Visibility
    +ConvWithBatchNorm | @vladmandic/face-api - v1.7.14

    Type Alias ConvWithBatchNorm

    ConvWithBatchNorm: { bn: BatchNorm; conv: ConvParams }

    Type declaration

    Settings

    Member Visibility
    diff --git a/typedoc/types/DefaultTinyYolov2NetParams.html b/typedoc/types/DefaultTinyYolov2NetParams.html index b18d5ab..3b35a22 100644 --- a/typedoc/types/DefaultTinyYolov2NetParams.html +++ b/typedoc/types/DefaultTinyYolov2NetParams.html @@ -1 +1 @@ -DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.14

    Type Alias DefaultTinyYolov2NetParams

    DefaultTinyYolov2NetParams: {
        conv0: ConvWithBatchNorm;
        conv1: ConvWithBatchNorm;
        conv2: ConvWithBatchNorm;
        conv3: ConvWithBatchNorm;
        conv4: ConvWithBatchNorm;
        conv5: ConvWithBatchNorm;
        conv6: ConvWithBatchNorm;
        conv7: ConvWithBatchNorm;
        conv8: ConvParams;
    }

    Settings

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    +DefaultTinyYolov2NetParams | @vladmandic/face-api - v1.7.14

    Type Alias DefaultTinyYolov2NetParams

    DefaultTinyYolov2NetParams: {
        conv0: ConvWithBatchNorm;
        conv1: ConvWithBatchNorm;
        conv2: ConvWithBatchNorm;
        conv3: ConvWithBatchNorm;
        conv4: ConvWithBatchNorm;
        conv5: ConvWithBatchNorm;
        conv6: ConvWithBatchNorm;
        conv7: ConvWithBatchNorm;
        conv8: ConvParams;
    }

    Type declaration

    Settings

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    diff --git a/typedoc/types/Environment.html b/typedoc/types/Environment.html index a8ac0d0..98f94a3 100644 --- a/typedoc/types/Environment.html +++ b/typedoc/types/Environment.html @@ -1 +1 @@ -Environment | @vladmandic/face-api - v1.7.14

    Type Alias Environment

    Environment: FileSystem & {
        Canvas: typeof HTMLCanvasElement;
        CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
        createCanvasElement: (() => HTMLCanvasElement);
        createImageElement: (() => HTMLImageElement);
        createVideoElement: (() => HTMLVideoElement);
        fetch: ((url: string, init?: RequestInit) => Promise<Response>);
        Image: typeof HTMLImageElement;
        ImageData: typeof ImageData;
        Video: typeof HTMLVideoElement;
    }

    Settings

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    +Environment | @vladmandic/face-api - v1.7.14

    Type Alias Environment

    Environment: FileSystem & {
        Canvas: typeof HTMLCanvasElement;
        CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
        createCanvasElement: () => HTMLCanvasElement;
        createImageElement: () => HTMLImageElement;
        createVideoElement: () => HTMLVideoElement;
        fetch: (url: string, init?: RequestInit) => Promise<Response>;
        Image: typeof HTMLImageElement;
        ImageData: typeof ImageData;
        Video: typeof HTMLVideoElement;
    }

    Settings

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    diff --git a/typedoc/types/FaceDetectionFunction.html b/typedoc/types/FaceDetectionFunction.html index 3ebfe99..bbff0ba 100644 --- a/typedoc/types/FaceDetectionFunction.html +++ b/typedoc/types/FaceDetectionFunction.html @@ -1 +1 @@ -FaceDetectionFunction | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionFunction

    FaceDetectionFunction: ((input: TNetInput) => Promise<FaceDetection[]>)

    Settings

    Member Visibility
    +FaceDetectionFunction | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionFunction

    FaceDetectionFunction: (input: TNetInput) => Promise<FaceDetection[]>

    Type declaration

    Settings

    Member Visibility
    diff --git a/typedoc/types/FaceDetectionOptions.html b/typedoc/types/FaceDetectionOptions.html index d2e6096..046167c 100644 --- a/typedoc/types/FaceDetectionOptions.html +++ b/typedoc/types/FaceDetectionOptions.html @@ -1 +1 @@ -FaceDetectionOptions | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionOptions

    FaceDetectionOptions: TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options

    Settings

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    +FaceDetectionOptions | @vladmandic/face-api - v1.7.14

    Type Alias FaceDetectionOptions

    FaceDetectionOptions:
        | TinyFaceDetectorOptions
        | SsdMobilenetv1Options
        | TinyYolov2Options

    Settings

    Member Visibility
    diff --git a/typedoc/types/FileSystem.html b/typedoc/types/FileSystem.html index 178bb7d..a5fa931 100644 --- a/typedoc/types/FileSystem.html +++ b/typedoc/types/FileSystem.html @@ -1 +1 @@ -FileSystem | @vladmandic/face-api - v1.7.14

    Type Alias FileSystem

    FileSystem: {
        readFile: ((filePath: string) => Promise<string | Buffer>);
    }

    Settings

    Member Visibility
    +FileSystem | @vladmandic/face-api - v1.7.14

    Type Alias FileSystem

    FileSystem: { readFile: (filePath: string) => Promise<string | Buffer> }

    Type declaration

    • readFile: (filePath: string) => Promise<string | Buffer>

    Settings

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    diff --git a/typedoc/types/ITinyFaceDetectorOptions.html b/typedoc/types/ITinyFaceDetectorOptions.html index 67be6e8..41c63ab 100644 --- a/typedoc/types/ITinyFaceDetectorOptions.html +++ b/typedoc/types/ITinyFaceDetectorOptions.html @@ -1 +1 @@ -ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Type Alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options

    Settings

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    +ITinyFaceDetectorOptions | @vladmandic/face-api - v1.7.14

    Type Alias ITinyFaceDetectorOptions

    ITinyFaceDetectorOptions: ITinyYolov2Options

    Settings

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    diff --git a/typedoc/types/MobilenetParams.html b/typedoc/types/MobilenetParams.html index 6eead84..1a4b60c 100644 --- a/typedoc/types/MobilenetParams.html +++ b/typedoc/types/MobilenetParams.html @@ -1 +1 @@ -MobilenetParams | @vladmandic/face-api - v1.7.14

    Type Alias MobilenetParams

    MobilenetParams: {
        conv0: SeparableConvParams | ConvParams;
        conv1: SeparableConvParams;
        conv2: SeparableConvParams;
        conv3: SeparableConvParams;
        conv4: SeparableConvParams;
        conv5: SeparableConvParams;
        conv6?: SeparableConvParams;
        conv7?: SeparableConvParams;
        conv8: ConvParams;
    }

    Settings

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    +MobilenetParams | @vladmandic/face-api - v1.7.14

    Type Alias MobilenetParams

    MobilenetParams: {
        conv0: SeparableConvParams | ConvParams;
        conv1: SeparableConvParams;
        conv2: SeparableConvParams;
        conv3: SeparableConvParams;
        conv4: SeparableConvParams;
        conv5: SeparableConvParams;
        conv6?: SeparableConvParams;
        conv7?: SeparableConvParams;
        conv8: ConvParams;
    }

    Type declaration

    • conv0: SeparableConvParams | ConvParams
    • conv1: SeparableConvParams
    • conv2: SeparableConvParams
    • conv3: SeparableConvParams
    • conv4: SeparableConvParams
    • conv5: SeparableConvParams
    • Optionalconv6?: SeparableConvParams
    • Optionalconv7?: SeparableConvParams
    • conv8: ConvParams

    Settings

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    diff --git a/typedoc/types/NetOutput.html b/typedoc/types/NetOutput.html index 9c10807..f77a960 100644 --- a/typedoc/types/NetOutput.html +++ b/typedoc/types/NetOutput.html @@ -1 +1 @@ -NetOutput | @vladmandic/face-api - v1.7.14

    Type Alias NetOutput

    NetOutput: {
        age: Tensor1D;
        gender: Tensor2D;
    }

    Settings

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    +NetOutput | @vladmandic/face-api - v1.7.14

    Type Alias NetOutput

    NetOutput: { age: Tensor1D; gender: Tensor2D }

    Type declaration

    Settings

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    diff --git a/typedoc/types/NetParams.html b/typedoc/types/NetParams.html index 5ca428f..c125ae9 100644 --- a/typedoc/types/NetParams.html +++ b/typedoc/types/NetParams.html @@ -1 +1 @@ -NetParams | @vladmandic/face-api - v1.7.14

    Type Alias NetParams

    NetParams: {
        fc: {
            age: FCParams;
            gender: FCParams;
        };
    }

    Settings

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    +NetParams | @vladmandic/face-api - v1.7.14

    Type Alias NetParams

    NetParams: { fc: { age: FCParams; gender: FCParams } }

    Type declaration

    • fc: { age: FCParams; gender: FCParams }

    Settings

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    diff --git a/typedoc/types/TMediaElement.html b/typedoc/types/TMediaElement.html index 15809dd..b068185 100644 --- a/typedoc/types/TMediaElement.html +++ b/typedoc/types/TMediaElement.html @@ -1 +1 @@ -TMediaElement | @vladmandic/face-api - v1.7.14

    Type Alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

    Settings

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    +TMediaElement | @vladmandic/face-api - v1.7.14

    Type Alias TMediaElement

    TMediaElement: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement

    Settings

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    diff --git a/typedoc/types/TNetInput.html b/typedoc/types/TNetInput.html index 5623127..de67683 100644 --- a/typedoc/types/TNetInput.html +++ b/typedoc/types/TNetInput.html @@ -1 +1 @@ -TNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TNetInput

    TNetInput:
        | string
        | TResolvedNetInput
        | (string | TResolvedNetInput)[]
        | NetInput

    Settings

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    +TNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TNetInput

    TNetInput:
        | string
        | TResolvedNetInput
        | (string | TResolvedNetInput)[]
        | NetInput

    Settings

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    diff --git a/typedoc/types/TResolvedNetInput.html b/typedoc/types/TResolvedNetInput.html index 9f26c6f..c0b7d23 100644 --- a/typedoc/types/TResolvedNetInput.html +++ b/typedoc/types/TResolvedNetInput.html @@ -1 +1 @@ -TResolvedNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

    Settings

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    +TResolvedNetInput | @vladmandic/face-api - v1.7.14

    Type Alias TResolvedNetInput

    TResolvedNetInput: TMediaElement | Tensor3D | Tensor4D

    Settings

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    diff --git a/typedoc/types/TinyYolov2Config.html b/typedoc/types/TinyYolov2Config.html index 7760e8d..099f1ba 100644 --- a/typedoc/types/TinyYolov2Config.html +++ b/typedoc/types/TinyYolov2Config.html @@ -1 +1 @@ -TinyYolov2Config | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2Config

    TinyYolov2Config: {
        anchors: Point[];
        classes: string[];
        filterSizes?: number[];
        iouThreshold: number;
        isFirstLayerConv2d?: boolean;
        meanRgb?: [number, number, number];
        withClassScores?: boolean;
        withSeparableConvs: boolean;
    }

    Settings

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    +TinyYolov2Config | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2Config

    TinyYolov2Config: {
        anchors: Point[];
        classes: string[];
        filterSizes?: number[];
        iouThreshold: number;
        isFirstLayerConv2d?: boolean;
        meanRgb?: [number, number, number];
        withClassScores?: boolean;
        withSeparableConvs: boolean;
    }

    Type declaration

    • anchors: Point[]
    • classes: string[]
    • OptionalfilterSizes?: number[]
    • iouThreshold: number
    • OptionalisFirstLayerConv2d?: boolean
    • OptionalmeanRgb?: [number, number, number]
    • OptionalwithClassScores?: boolean
    • withSeparableConvs: boolean

    Settings

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    diff --git a/typedoc/types/TinyYolov2ExtractBoxesResult.html b/typedoc/types/TinyYolov2ExtractBoxesResult.html index 230d365..ac695cf 100644 --- a/typedoc/types/TinyYolov2ExtractBoxesResult.html +++ b/typedoc/types/TinyYolov2ExtractBoxesResult.html @@ -1 +1 @@ -TinyYolov2ExtractBoxesResult | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2ExtractBoxesResult

    TinyYolov2ExtractBoxesResult: {
        anchor: number;
        box: BoundingBox;
        classScore: number;
        col: number;
        label: number;
        row: number;
        score: number;
    }

    Settings

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    +TinyYolov2ExtractBoxesResult | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2ExtractBoxesResult

    TinyYolov2ExtractBoxesResult: {
        anchor: number;
        box: BoundingBox;
        classScore: number;
        col: number;
        label: number;
        row: number;
        score: number;
    }

    Type declaration

    • anchor: number
    • box: BoundingBox
    • classScore: number
    • col: number
    • label: number
    • row: number
    • score: number

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    diff --git a/typedoc/types/TinyYolov2NetParams.html b/typedoc/types/TinyYolov2NetParams.html index 75e81d7..d79ff82 100644 --- a/typedoc/types/TinyYolov2NetParams.html +++ b/typedoc/types/TinyYolov2NetParams.html @@ -1 +1 @@ -TinyYolov2NetParams | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

    Settings

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    +TinyYolov2NetParams | @vladmandic/face-api - v1.7.14

    Type Alias TinyYolov2NetParams

    TinyYolov2NetParams: DefaultTinyYolov2NetParams | MobilenetParams

    Settings

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    diff --git a/typedoc/types/WithAge.html b/typedoc/types/WithAge.html index 080556d..cf200ce 100644 --- a/typedoc/types/WithAge.html +++ b/typedoc/types/WithAge.html @@ -1 +1 @@ -WithAge | @vladmandic/face-api - v1.7.14

    Type Alias WithAge<TSource>

    WithAge<TSource>: TSource & {
        age: number;
    }

    Type Parameters

    • TSource

    Settings

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    +WithAge | @vladmandic/face-api - v1.7.14

    Type Alias WithAge<TSource>

    WithAge: TSource & { age: number }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceDescriptor.html b/typedoc/types/WithFaceDescriptor.html index 47ea5ff..a1c0523 100644 --- a/typedoc/types/WithFaceDescriptor.html +++ b/typedoc/types/WithFaceDescriptor.html @@ -1 +1 @@ -WithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDescriptor<TSource>

    WithFaceDescriptor<TSource>: TSource & {
        descriptor: Float32Array;
    }

    Type Parameters

    • TSource

    Settings

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    +WithFaceDescriptor | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDescriptor<TSource>

    WithFaceDescriptor: TSource & { descriptor: Float32Array }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceDetection.html b/typedoc/types/WithFaceDetection.html index 8f1871a..24122c3 100644 --- a/typedoc/types/WithFaceDetection.html +++ b/typedoc/types/WithFaceDetection.html @@ -1 +1 @@ -WithFaceDetection | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDetection<TSource>

    WithFaceDetection<TSource>: TSource & {
        detection: FaceDetection;
    }

    Type Parameters

    • TSource

    Settings

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    +WithFaceDetection | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceDetection<TSource>

    WithFaceDetection: TSource & { detection: FaceDetection }

    Type Parameters

    • TSource

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    diff --git a/typedoc/types/WithFaceExpressions.html b/typedoc/types/WithFaceExpressions.html index 1460e62..3dc446f 100644 --- a/typedoc/types/WithFaceExpressions.html +++ b/typedoc/types/WithFaceExpressions.html @@ -1 +1 @@ -WithFaceExpressions | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceExpressions<TSource>

    WithFaceExpressions<TSource>: TSource & {
        expressions: FaceExpressions;
    }

    Type Parameters

    • TSource

    Settings

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    +WithFaceExpressions | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceExpressions<TSource>

    WithFaceExpressions: TSource & { expressions: FaceExpressions }

    Type Parameters

    • TSource

    Settings

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    diff --git a/typedoc/types/WithFaceLandmarks.html b/typedoc/types/WithFaceLandmarks.html index 97bb709..300cc35 100644 --- a/typedoc/types/WithFaceLandmarks.html +++ b/typedoc/types/WithFaceLandmarks.html @@ -1 +1 @@ -WithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks<TSource, TFaceLandmarks>: TSource & {
        alignedRect: FaceDetection;
        angle: {
            pitch: number | undefined;
            roll: number | undefined;
            yaw: number | undefined;
        };
        landmarks: TFaceLandmarks;
        unshiftedLandmarks: TFaceLandmarks;
    }

    Type Parameters

    Settings

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    +WithFaceLandmarks | @vladmandic/face-api - v1.7.14

    Type Alias WithFaceLandmarks<TSource, TFaceLandmarks>

    WithFaceLandmarks: TSource & {
        alignedRect: FaceDetection;
        angle: {
            pitch: number | undefined;
            roll: number | undefined;
            yaw: number | undefined;
        };
        landmarks: TFaceLandmarks;
        unshiftedLandmarks: TFaceLandmarks;
    }

    Type Parameters

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    diff --git a/typedoc/types/WithGender.html b/typedoc/types/WithGender.html index 5d374de..6c72b5c 100644 --- a/typedoc/types/WithGender.html +++ b/typedoc/types/WithGender.html @@ -1 +1 @@ -WithGender | @vladmandic/face-api - v1.7.14

    Type Alias WithGender<TSource>

    WithGender<TSource>: TSource & {
        gender: Gender;
        genderProbability: number;
    }

    Type Parameters

    • TSource

    Settings

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    +WithGender | @vladmandic/face-api - v1.7.14

    Type Alias WithGender<TSource>

    WithGender: TSource & { gender: Gender; genderProbability: number }

    Type Parameters

    • TSource

    Settings

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    diff --git a/typedoc/types/draw.DrawFaceExpressionsInput.html b/typedoc/types/draw.DrawFaceExpressionsInput.html index fdeef04..55aae0f 100644 --- a/typedoc/types/draw.DrawFaceExpressionsInput.html +++ b/typedoc/types/draw.DrawFaceExpressionsInput.html @@ -1 +1 @@ -DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceExpressionsInput

    DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

    Settings

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    +DrawFaceExpressionsInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceExpressionsInput

    DrawFaceExpressionsInput: FaceExpressions | WithFaceExpressions<{}>

    Settings

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    diff --git a/typedoc/types/draw.DrawFaceLandmarksInput.html b/typedoc/types/draw.DrawFaceLandmarksInput.html index c2a3f1f..22f4016 100644 --- a/typedoc/types/draw.DrawFaceLandmarksInput.html +++ b/typedoc/types/draw.DrawFaceLandmarksInput.html @@ -1 +1 @@ -DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceLandmarksInput

    DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

    Settings

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    +DrawFaceLandmarksInput | @vladmandic/face-api - v1.7.14

    Type Alias DrawFaceLandmarksInput

    DrawFaceLandmarksInput: FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>

    Settings

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    diff --git a/typedoc/types/draw.TDrawDetectionsInput.html b/typedoc/types/draw.TDrawDetectionsInput.html index 2656fc6..bd953c9 100644 --- a/typedoc/types/draw.TDrawDetectionsInput.html +++ b/typedoc/types/draw.TDrawDetectionsInput.html @@ -1 +1 @@ -TDrawDetectionsInput | @vladmandic/face-api - v1.7.14

    Type Alias TDrawDetectionsInput

    TDrawDetectionsInput:
        | IRect
        | IBoundingBox
        | FaceDetection
        | WithFaceDetection<{}>

    Settings

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    +TDrawDetectionsInput | @vladmandic/face-api - v1.7.14

    Type Alias TDrawDetectionsInput

    TDrawDetectionsInput:
        | IRect
        | IBoundingBox
        | FaceDetection
        | WithFaceDetection<{}>

    Settings

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    diff --git a/typedoc/types/tf.NamedTensorMap.html b/typedoc/types/tf.NamedTensorMap.html index ed23634..0d01f90 100644 --- a/typedoc/types/tf.NamedTensorMap.html +++ b/typedoc/types/tf.NamedTensorMap.html @@ -1 +1 @@ -NamedTensorMap | @vladmandic/face-api - v1.7.14

    Type Alias NamedTensorMap

    NamedTensorMap: {
        [name: string]: Tensor;
    }

    Docalias

    Settings

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    +NamedTensorMap | @vladmandic/face-api - v1.7.14

    Type Alias NamedTensorMap

    NamedTensorMap: { [name: string]: tf.Tensor }

    Type declaration

    Docalias

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    diff --git a/typedoc/types/tf.Tensor1D.html b/typedoc/types/tf.Tensor1D.html index 126cb9b..f0e3ad3 100644 --- a/typedoc/types/tf.Tensor1D.html +++ b/typedoc/types/tf.Tensor1D.html @@ -1,2 +1,2 @@ -Tensor1D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor1D

    Tensor1D: Tensor<R1>

    Doclink

    Tensor

    -

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    +Tensor1D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor1D

    Tensor1D: tf.Tensor<R1>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor2D.html b/typedoc/types/tf.Tensor2D.html index 55b0f93..920d086 100644 --- a/typedoc/types/tf.Tensor2D.html +++ b/typedoc/types/tf.Tensor2D.html @@ -1,2 +1,2 @@ -Tensor2D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor2D

    Tensor2D: Tensor<R2>

    Doclink

    Tensor

    -

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    +Tensor2D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor2D

    Tensor2D: tf.Tensor<R2>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor3D.html b/typedoc/types/tf.Tensor3D.html index 9274bae..301e07b 100644 --- a/typedoc/types/tf.Tensor3D.html +++ b/typedoc/types/tf.Tensor3D.html @@ -1,2 +1,2 @@ -Tensor3D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor3D

    Tensor3D: Tensor<R3>

    Doclink

    Tensor

    -

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    +Tensor3D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor3D

    Tensor3D: tf.Tensor<R3>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor4D.html b/typedoc/types/tf.Tensor4D.html index a219d78..4c1382d 100644 --- a/typedoc/types/tf.Tensor4D.html +++ b/typedoc/types/tf.Tensor4D.html @@ -1,2 +1,2 @@ -Tensor4D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor4D

    Tensor4D: Tensor<R4>

    Doclink

    Tensor

    -

    Settings

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    +Tensor4D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor4D

    Tensor4D: tf.Tensor<R4>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.Tensor5D.html b/typedoc/types/tf.Tensor5D.html index 2ab3025..43a7aba 100644 --- a/typedoc/types/tf.Tensor5D.html +++ b/typedoc/types/tf.Tensor5D.html @@ -1,2 +1,2 @@ -Tensor5D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor5D

    Tensor5D: Tensor<R5>

    Doclink

    Tensor

    -

    Settings

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    +Tensor5D | @vladmandic/face-api - v1.7.14

    Type Alias Tensor5D

    Tensor5D: tf.Tensor<R5>

    Doclink

    Tensor

    +

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    diff --git a/typedoc/types/tf.TensorLike.html b/typedoc/types/tf.TensorLike.html index 340cfb3..acb4611 100644 --- a/typedoc/types/tf.TensorLike.html +++ b/typedoc/types/tf.TensorLike.html @@ -1,2 +1,2 @@ -TensorLike | @vladmandic/face-api - v1.7.14

    Type Alias TensorLike

    TensorLike:
        | TypedArray
        | number
        | boolean
        | string
        | RecursiveArray<number | number[] | TypedArray>
        | RecursiveArray<boolean>
        | RecursiveArray<string>
        | Uint8Array[]

    Docalias

    TypedArray|Array

    -

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    +TensorLike | @vladmandic/face-api - v1.7.14

    Type Alias TensorLike

    TensorLike:
        | TypedArray
        | number
        | boolean
        | string
        | RecursiveArray<number | number[] | TypedArray>
        | RecursiveArray<boolean>
        | RecursiveArray<string>
        | Uint8Array[]

    Docalias

    TypedArray|Array

    +

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    diff --git a/typedoc/types/tf.io.IOHandlerSync.html b/typedoc/types/tf.io.IOHandlerSync.html index 9fe09f7..63a3a6c 100644 --- a/typedoc/types/tf.io.IOHandlerSync.html +++ b/typedoc/types/tf.io.IOHandlerSync.html @@ -1,4 +1,4 @@ -IOHandlerSync | @vladmandic/face-api - v1.7.14

    Type Alias IOHandlerSync

    IOHandlerSync: {
        load?: LoadHandlerSync;
        save?: SaveHandlerSync;
    }

    Interface for a synchronous model import/export handler.

    +IOHandlerSync | @vladmandic/face-api - v1.7.14

    Type Alias IOHandlerSync

    IOHandlerSync: { load?: LoadHandlerSync; save?: SaveHandlerSync }

    Interface for a synchronous model import/export handler.

    The save and load handlers are both optional, in order to allow handlers that support only saving or loading.

    -

    Settings

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    +

    Type declaration

    • Optionalload?: LoadHandlerSync
    • Optionalsave?: SaveHandlerSync

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    diff --git a/typedoc/types/tf.io.LoadHandler.html b/typedoc/types/tf.io.LoadHandler.html index a255a7a..9b8752c 100644 --- a/typedoc/types/tf.io.LoadHandler.html +++ b/typedoc/types/tf.io.LoadHandler.html @@ -1,2 +1,2 @@ -LoadHandler | @vladmandic/face-api - v1.7.14

    Type Alias LoadHandler

    LoadHandler: (() => Promise<ModelArtifacts>)

    Type definition for handlers of loading operations.

    -

    Settings

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    +LoadHandler | @vladmandic/face-api - v1.7.14

    Type Alias LoadHandler

    LoadHandler: () => Promise<ModelArtifacts>

    Type definition for handlers of loading operations.

    +

    Type declaration

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    diff --git a/typedoc/types/tf.io.OnProgressCallback.html b/typedoc/types/tf.io.OnProgressCallback.html index 75b84ea..cfdc64b 100644 --- a/typedoc/types/tf.io.OnProgressCallback.html +++ b/typedoc/types/tf.io.OnProgressCallback.html @@ -1,5 +1,5 @@ -OnProgressCallback | @vladmandic/face-api - v1.7.14

    Type Alias OnProgressCallback

    OnProgressCallback: ((fraction: number) => void)

    Callback for the progress of a long-running action such as an HTTP +OnProgressCallback | @vladmandic/face-api - v1.7.14

    Type Alias OnProgressCallback

    OnProgressCallback: (fraction: number) => void

    Callback for the progress of a long-running action such as an HTTP request for a large binary object.

    fraction should be a number in the [0, 1] interval, indicating how much of the action has completed.

    -

    Settings

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    +

    Type declaration

      • (fraction: number): void

      • Parameters

        • fraction: number

        Returns void

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    diff --git a/typedoc/types/tf.io.SaveHandler.html b/typedoc/types/tf.io.SaveHandler.html index e5f169b..ed5ed3d 100644 --- a/typedoc/types/tf.io.SaveHandler.html +++ b/typedoc/types/tf.io.SaveHandler.html @@ -1,2 +1,2 @@ -SaveHandler | @vladmandic/face-api - v1.7.14

    Type Alias SaveHandler

    SaveHandler: ((modelArtifact: ModelArtifacts) => Promise<SaveResult>)

    Type definition for handlers of saving operations.

    -

    Settings

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    +SaveHandler | @vladmandic/face-api - v1.7.14

    Type Alias SaveHandler

    SaveHandler: (modelArtifact: ModelArtifacts) => Promise<SaveResult>

    Type definition for handlers of saving operations.

    +

    Type declaration

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    diff --git a/typedoc/types/tf.io.WeightData.html b/typedoc/types/tf.io.WeightData.html index 9da676e..9a414d6 100644 --- a/typedoc/types/tf.io.WeightData.html +++ b/typedoc/types/tf.io.WeightData.html @@ -1 +1 @@ -WeightData | @vladmandic/face-api - v1.7.14

    Type Alias WeightData

    WeightData: ArrayBuffer | ArrayBuffer[]

    Settings

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    +WeightData | @vladmandic/face-api - v1.7.14

    Type Alias WeightData

    WeightData: ArrayBuffer | ArrayBuffer[]

    Settings

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    diff --git a/typedoc/types/tf.io.WeightGroup.html b/typedoc/types/tf.io.WeightGroup.html index 8eda845..e2d5fc2 100644 --- a/typedoc/types/tf.io.WeightGroup.html +++ b/typedoc/types/tf.io.WeightGroup.html @@ -1,5 +1,5 @@ -WeightGroup | @vladmandic/face-api - v1.7.14

    Type Alias WeightGroup

    WeightGroup: "model" | "optimizer"

    Group to which the weight belongs.

    +WeightGroup | @vladmandic/face-api - v1.7.14

    Type Alias WeightGroup

    WeightGroup: "model" | "optimizer"

    Group to which the weight belongs.

    • 'optimizer': Weight from a stateful optimizer.
    -

    Settings

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    +

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    diff --git a/typedoc/types/tf.io.WeightsManifestConfig.html b/typedoc/types/tf.io.WeightsManifestConfig.html index e0c42d4..ee22c4f 100644 --- a/typedoc/types/tf.io.WeightsManifestConfig.html +++ b/typedoc/types/tf.io.WeightsManifestConfig.html @@ -1,6 +1,6 @@ -WeightsManifestConfig | @vladmandic/face-api - v1.7.14

    Type Alias WeightsManifestConfig

    WeightsManifestConfig: WeightsManifestGroupConfig[]

    A weight manifest.

    +WeightsManifestConfig | @vladmandic/face-api - v1.7.14

    Type Alias WeightsManifestConfig

    WeightsManifestConfig: WeightsManifestGroupConfig[]

    A weight manifest.

    The weight manifest consists of an ordered list of weight-manifest groups. Each weight-manifest group ("group" for short hereafter) consists of a number of weight values stored in a number of paths. See the documentation of WeightManifestGroupConfig below for more details.

    -

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    diff --git a/typedoc/variables/FACE_EXPRESSION_LABELS.html b/typedoc/variables/FACE_EXPRESSION_LABELS.html index fec5ac6..573a630 100644 --- a/typedoc/variables/FACE_EXPRESSION_LABELS.html +++ b/typedoc/variables/FACE_EXPRESSION_LABELS.html @@ -1 +1 @@ -FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.14

    Variable FACE_EXPRESSION_LABELSConst

    FACE_EXPRESSION_LABELS: readonly ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"] = ...

    Settings

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    +FACE_EXPRESSION_LABELS | @vladmandic/face-api - v1.7.14

    Variable FACE_EXPRESSION_LABELSConst

    FACE_EXPRESSION_LABELS: readonly [
        "neutral",
        "happy",
        "sad",
        "angry",
        "fearful",
        "disgusted",
        "surprised",
    ] = ...

    Settings

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    diff --git a/typedoc/variables/env.html b/typedoc/variables/env.html index af3cfea..c3c51f6 100644 --- a/typedoc/variables/env.html +++ b/typedoc/variables/env.html @@ -1 +1 @@ -env | @vladmandic/face-api - v1.7.14

    Variable envConst

    env: {
        createBrowserEnv: (() => Environment);
        createFileSystem: ((fs?: any) => FileSystem);
        createNodejsEnv: (() => Environment);
        getEnv: (() => Environment);
        initialize: (() => null | void);
        isBrowser: (() => boolean);
        isNodejs: (() => boolean);
        monkeyPatch: ((env: Partial<Environment>) => void);
        setEnv: ((env: Environment) => void);
    } = ...

    Settings

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    +env | @vladmandic/face-api - v1.7.14

    Variable envConst

    env: {
        createBrowserEnv: () => Environment;
        createFileSystem: (fs?: any) => FileSystem;
        createNodejsEnv: () => Environment;
        getEnv: () => Environment;
        initialize: () => null | void;
        isBrowser: () => boolean;
        isNodejs: () => boolean;
        monkeyPatch: (env: Partial<Environment>) => void;
        setEnv: (env: Environment) => void;
    } = ...

    Type declaration

    Settings

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    diff --git a/typedoc/variables/nets.html b/typedoc/variables/nets.html index 367646c..a0ec603 100644 --- a/typedoc/variables/nets.html +++ b/typedoc/variables/nets.html @@ -1 +1 @@ -nets | @vladmandic/face-api - v1.7.14

    Variable netsConst

    nets: {
        ageGenderNet: AgeGenderNet;
        faceExpressionNet: FaceExpressionNet;
        faceLandmark68Net: FaceLandmark68Net;
        faceLandmark68TinyNet: FaceLandmark68TinyNet;
        faceRecognitionNet: FaceRecognitionNet;
        ssdMobilenetv1: SsdMobilenetv1;
        tinyFaceDetector: TinyFaceDetector;
        tinyYolov2: TinyYolov2;
    } = ...

    Settings

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    +nets | @vladmandic/face-api - v1.7.14

    Variable netsConst

    nets: {
        ageGenderNet: AgeGenderNet;
        faceExpressionNet: FaceExpressionNet;
        faceLandmark68Net: FaceLandmark68Net;
        faceLandmark68TinyNet: FaceLandmark68TinyNet;
        faceRecognitionNet: FaceRecognitionNet;
        ssdMobilenetv1: SsdMobilenetv1;
        tinyFaceDetector: TinyFaceDetector;
        tinyYolov2: TinyYolov2;
    } = ...

    Type declaration

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    diff --git a/typedoc/variables/tf.ENV.html b/typedoc/variables/tf.ENV.html index 5c35ffe..616c842 100644 --- a/typedoc/variables/tf.ENV.html +++ b/typedoc/variables/tf.ENV.html @@ -1 +1 @@ -ENV | @vladmandic/face-api - v1.7.14

    Variable ENV

    ENV: Environment

    Settings

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    +ENV | @vladmandic/face-api - v1.7.14

    Variable ENV

    ENV: Environment

    Settings

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    diff --git a/typedoc/variables/tf.image.html b/typedoc/variables/tf.image.html index b577cb9..c81716d 100644 --- a/typedoc/variables/tf.image.html +++ b/typedoc/variables/tf.image.html @@ -1 +1 @@ -image | @vladmandic/face-api - v1.7.14

    Variable imageConst

    image: {
        cropAndResize: ((image: TensorLike | Tensor4D, boxes: TensorLike | Tensor2D, boxInd: TensorLike | Tensor1D, cropSize: [number, number], method?: "bilinear" | "nearest", extrapolationValue?: number) => Tensor4D);
        flipLeftRight: ((image: TensorLike | Tensor4D) => Tensor4D);
        grayscaleToRGB: (<T>(image: TensorLike | T) => T);
        nonMaxSuppression: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number) => Tensor1D);
        nonMaxSuppressionAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number) => Promise<Tensor1D>);
        nonMaxSuppressionPadded: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, padToMaxOutputSize?: boolean) => NamedTensorMap);
        nonMaxSuppressionPaddedAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, padToMaxOutputSize?: boolean) => Promise<NamedTensorMap>);
        nonMaxSuppressionWithScore: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number) => NamedTensorMap);
        nonMaxSuppressionWithScoreAsync: ((boxes: TensorLike | Tensor2D, scores: TensorLike | Tensor1D, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number, softNmsSigma?: number) => Promise<NamedTensorMap>);
        resizeBilinear: (<T_2>(images: TensorLike | T_2, size: [number, number], alignCorners?: boolean, halfPixelCenters?: boolean) => T_2);
        resizeNearestNeighbor: (<T_1>(images: TensorLike | T_1, size: [number, number], alignCorners?: boolean, halfPixelCenters?: boolean) => T_1);
        rgbToGrayscale: (<T_3>(image: TensorLike | T_3) => T_3);
        rotateWithOffset: ((image: TensorLike | Tensor4D, radians: number, fillValue?: number | [number, number, number], center?: number | [number, number]) => Tensor4D);
        threshold: ((image: TensorLike | Tensor3D, method?: string, inverted?: boolean, threshValue?: number) => Tensor3D);
        transform: ((image: TensorLike | Tensor4D, transforms: TensorLike | Tensor2D, interpolation?: "bilinear" | "nearest", fillMode?:
            | "reflect"
            | "nearest"
            | "constant"
            | "wrap", fillValue?: number, outputShape?: [number, number]) => Tensor4D);
    }

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    +image | @vladmandic/face-api - v1.7.14

    Variable imageConst

    image: {
        cropAndResize: (
            image: TensorLike | Tensor4D,
            boxes: TensorLike | Tensor2D,
            boxInd: TensorLike | Tensor1D,
            cropSize: [number, number],
            method?: "bilinear" | "nearest",
            extrapolationValue?: number,
        ) => Tensor4D;
        flipLeftRight: (image: TensorLike | Tensor4D) => Tensor4D;
        grayscaleToRGB: <
            T extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            image: TensorLike | T,
        ) => T;
        nonMaxSuppression: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
        ) => Tensor1D;
        nonMaxSuppressionAsync: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
        ) => Promise<Tensor1D>;
        nonMaxSuppressionPadded: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
            padToMaxOutputSize?: boolean,
        ) => NamedTensorMap;
        nonMaxSuppressionPaddedAsync: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
            padToMaxOutputSize?: boolean,
        ) => Promise<NamedTensorMap>;
        nonMaxSuppressionWithScore: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
            softNmsSigma?: number,
        ) => NamedTensorMap;
        nonMaxSuppressionWithScoreAsync: (
            boxes: TensorLike | Tensor2D,
            scores: TensorLike | Tensor1D,
            maxOutputSize: number,
            iouThreshold?: number,
            scoreThreshold?: number,
            softNmsSigma?: number,
        ) => Promise<NamedTensorMap>;
        resizeBilinear: <T_2 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_2,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_2;
        resizeNearestNeighbor: <T_1 extends Tensor3D | Tensor4D>(
            images: TensorLike | T_1,
            size: [number, number],
            alignCorners?: boolean,
            halfPixelCenters?: boolean,
        ) => T_1;
        rgbToGrayscale: <
            T_3 extends Tensor2D
            | Tensor3D
            | Tensor4D
            | Tensor5D
            | Tensor6D,
        >(
            image: TensorLike | T_3,
        ) => T_3;
        rotateWithOffset: (
            image: TensorLike | Tensor4D,
            radians: number,
            fillValue?: number | [number, number, number],
            center?: number | [number, number],
        ) => Tensor4D;
        threshold: (
            image: TensorLike | Tensor3D,
            method?: string,
            inverted?: boolean,
            threshValue?: number,
        ) => Tensor3D;
        transform: (
            image: TensorLike | Tensor4D,
            transforms: TensorLike | Tensor2D,
            interpolation?: "bilinear" | "nearest",
            fillMode?: "reflect" | "nearest" | "constant" | "wrap",
            fillValue?: number,
            outputShape?: [number, number],
        ) => Tensor4D;
    }

    Type declaration

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    diff --git a/typedoc/variables/tf.version.html b/typedoc/variables/tf.version.html index 2763662..804c439 100644 --- a/typedoc/variables/tf.version.html +++ b/typedoc/variables/tf.version.html @@ -1 +1 @@ -version | @vladmandic/face-api - v1.7.14

    Variable versionConst

    version: {
        tfjs: string;
        tfjs-backend-cpu: string;
        tfjs-backend-webgl: string;
        tfjs-converter: string;
        tfjs-core: string;
        tfjs-data: string;
        tfjs-layers: string;
    }

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    +version | @vladmandic/face-api - v1.7.14

    Variable versionConst

    version: {
        tfjs: string;
        "tfjs-backend-cpu": string;
        "tfjs-backend-webgl": string;
        "tfjs-converter": string;
        "tfjs-core": string;
        "tfjs-data": string;
        "tfjs-layers": string;
    }

    Type declaration

    • tfjs: string
    • tfjs-backend-cpu: string
    • tfjs-backend-webgl: string
    • tfjs-converter: string
    • tfjs-core: string
    • tfjs-data: string
    • tfjs-layers: string

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    diff --git a/typedoc/variables/version.html b/typedoc/variables/version.html index 96a65db..ce01574 100644 --- a/typedoc/variables/version.html +++ b/typedoc/variables/version.html @@ -1 +1 @@ -version | @vladmandic/face-api - v1.7.14

    Variable versionConst

    version: string = ...

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    +version | @vladmandic/face-api - v1.7.14

    Variable versionConst

    version: string = ...

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    diff --git a/types/face-api.d.ts b/types/face-api.d.ts index df827c6..1ca55c2 100644 --- a/types/face-api.d.ts +++ b/types/face-api.d.ts @@ -1675,7 +1675,7 @@ declare interface GPUData { * The following GitHub Gist * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 * implements a server based on [flask](https://github.com/pallets/flask) that - * can receive the request. Upon receiving the model artifacts via the requst, + * can receive the request. Upon receiving the model artifacts via the request, * this particular server reconstitutes instances of [Keras * Models](https://keras.io/models/model/) in memory. *