From e70d9bb18b5ed6158a2841acc440c1c1f63740e8 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 1 Dec 2021 15:37:52 -0500 Subject: [PATCH] switch to custom tfjs and new typedefs --- build.json => .build.json | 28 +- .npmignore | 1 + CHANGELOG.md | 6 +- api-extractor.json | 38 + build.js | 49 + dist/face-api.esm-nobundle.js | 1 - dist/face-api.esm-nobundle.js.map | 7 - dist/face-api.esm.js | 13101 ++++++++++--- dist/face-api.esm.js.map | 6 +- dist/face-api.js | 3375 +++- dist/tfjs.esm.d.ts | 23 + dist/tfjs.esm.js | 15687 ++++++++++------ dist/tfjs.esm.js.map | 7 - package.json | 10 +- src/ageGenderNet/AgeGenderNet.ts | 3 +- src/tfjs/tf-custom.ts | 4 + typedoc/classes/AgeGenderNet.html | 2 +- types/dist/tfjs.esm.d.ts | 1 - types/face-api.d.ts | 1784 ++ types/lib/dist/tfjs.esm.d.ts | 23 + types/{ => lib}/src/NeuralNetwork.d.ts | 0 .../src/ageGenderNet/AgeGenderNet.d.ts | 8 +- .../src/ageGenderNet/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 types/{ => lib}/src/ageGenderNet/index.d.ts | 0 types/{ => lib}/src/ageGenderNet/types.d.ts | 0 types/{ => lib}/src/classes/BoundingBox.d.ts | 0 types/{ => lib}/src/classes/Box.d.ts | 0 types/{ => lib}/src/classes/Dimensions.d.ts | 0 .../{ => lib}/src/classes/FaceDetection.d.ts | 0 .../{ => lib}/src/classes/FaceLandmarks.d.ts | 0 .../{ => lib}/src/classes/FaceLandmarks5.d.ts | 0 .../src/classes/FaceLandmarks68.d.ts | 0 types/{ => lib}/src/classes/FaceMatch.d.ts | 0 types/{ => lib}/src/classes/LabeledBox.d.ts | 0 .../src/classes/LabeledFaceDescriptors.d.ts | 0 .../src/classes/ObjectDetection.d.ts | 0 types/{ => lib}/src/classes/Point.d.ts | 0 types/{ => lib}/src/classes/PredictedBox.d.ts | 0 types/{ => lib}/src/classes/Rect.d.ts | 0 types/{ => lib}/src/classes/index.d.ts | 0 types/{ => lib}/src/common/convLayer.d.ts | 0 .../src/common/depthwiseSeparableConv.d.ts | 0 .../common/disposeUnusedWeightTensors.d.ts | 0 .../src/common/extractConvParamsFactory.d.ts | 0 .../src/common/extractFCParamsFactory.d.ts | 0 .../extractSeparableConvParamsFactory.d.ts | 0 .../src/common/extractWeightEntryFactory.d.ts | 0 .../src/common/extractWeightsFactory.d.ts | 0 .../src/common/fullyConnectedLayer.d.ts | 0 types/{ => lib}/src/common/getModelUris.d.ts | 0 types/{ => lib}/src/common/index.d.ts | 0 .../src/common/loadConvParamsFactory.d.ts | 0 types/{ => lib}/src/common/types.d.ts | 0 types/{ => lib}/src/dom/NetInput.d.ts | 0 types/{ => lib}/src/dom/awaitMediaLoaded.d.ts | 0 types/{ => lib}/src/dom/bufferToImage.d.ts | 0 types/{ => lib}/src/dom/bufferToVideo.d.ts | 0 types/{ => lib}/src/dom/createCanvas.d.ts | 0 .../{ => lib}/src/dom/extractFaceTensors.d.ts | 0 types/{ => lib}/src/dom/extractFaces.d.ts | 0 types/{ => lib}/src/dom/fetchImage.d.ts | 0 types/{ => lib}/src/dom/fetchJson.d.ts | 0 types/{ => lib}/src/dom/fetchNetWeights.d.ts | 0 types/{ => lib}/src/dom/fetchOrThrow.d.ts | 0 types/{ => lib}/src/dom/fetchVideo.d.ts | 0 .../src/dom/getContext2dOrThrow.d.ts | 0 .../{ => lib}/src/dom/getMediaDimensions.d.ts | 0 .../src/dom/imageTensorToCanvas.d.ts | 0 types/{ => lib}/src/dom/imageToSquare.d.ts | 0 types/{ => lib}/src/dom/index.d.ts | 0 types/{ => lib}/src/dom/isMediaElement.d.ts | 0 types/{ => lib}/src/dom/isMediaLoaded.d.ts | 0 types/{ => lib}/src/dom/loadWeightMap.d.ts | 0 types/{ => lib}/src/dom/matchDimensions.d.ts | 0 types/{ => lib}/src/dom/resolveInput.d.ts | 0 types/{ => lib}/src/dom/toNetInput.d.ts | 0 types/{ => lib}/src/dom/types.d.ts | 0 types/{ => lib}/src/draw/DrawBox.d.ts | 0 .../{ => lib}/src/draw/DrawFaceLandmarks.d.ts | 0 types/{ => lib}/src/draw/DrawTextField.d.ts | 0 types/{ => lib}/src/draw/drawContour.d.ts | 0 types/{ => lib}/src/draw/drawDetections.d.ts | 0 .../src/draw/drawFaceExpressions.d.ts | 0 types/{ => lib}/src/draw/index.d.ts | 0 types/{ => lib}/src/env/createBrowserEnv.d.ts | 0 types/{ => lib}/src/env/createFileSystem.d.ts | 0 types/{ => lib}/src/env/createNodejsEnv.d.ts | 0 types/{ => lib}/src/env/index.d.ts | 0 types/{ => lib}/src/env/isBrowser.d.ts | 0 types/{ => lib}/src/env/isNodejs.d.ts | 0 types/{ => lib}/src/env/types.d.ts | 0 types/{ => lib}/src/euclideanDistance.d.ts | 0 .../faceExpressionNet/FaceExpressionNet.d.ts | 0 .../faceExpressionNet/FaceExpressions.d.ts | 0 .../src/faceExpressionNet/index.d.ts | 0 .../FaceFeatureExtractor.d.ts | 0 .../TinyFaceFeatureExtractor.d.ts | 0 .../src/faceFeatureExtractor/denseBlock.d.ts | 0 .../faceFeatureExtractor/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 .../extractParamsFromWeightMapTiny.d.ts | 0 .../extractParamsTiny.d.ts | 0 .../extractorsFactory.d.ts | 0 .../loadParamsFactory.d.ts | 0 .../src/faceFeatureExtractor/types.d.ts | 0 .../faceLandmarkNet/FaceLandmark68Net.d.ts | 0 .../FaceLandmark68NetBase.d.ts | 0 .../FaceLandmark68TinyNet.d.ts | 0 .../{ => lib}/src/faceLandmarkNet/index.d.ts | 0 .../src/faceProcessor/FaceProcessor.d.ts | 0 .../src/faceProcessor/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 types/{ => lib}/src/faceProcessor/types.d.ts | 0 types/{ => lib}/src/faceProcessor/util.d.ts | 0 .../FaceRecognitionNet.d.ts | 0 .../src/faceRecognitionNet/convLayer.d.ts | 0 .../src/faceRecognitionNet/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 .../src/faceRecognitionNet/index.d.ts | 0 .../src/faceRecognitionNet/residualLayer.d.ts | 0 .../src/faceRecognitionNet/scaleLayer.d.ts | 0 .../src/faceRecognitionNet/types.d.ts | 0 types/{ => lib}/src/factories/WithAge.d.ts | 0 .../src/factories/WithFaceDescriptor.d.ts | 0 .../src/factories/WithFaceDetection.d.ts | 0 .../src/factories/WithFaceExpressions.d.ts | 0 .../src/factories/WithFaceLandmarks.d.ts | 0 types/{ => lib}/src/factories/WithGender.d.ts | 0 types/{ => lib}/src/factories/index.d.ts | 0 .../src/globalApi/ComposableTask.d.ts | 0 .../ComputeFaceDescriptorsTasks.d.ts | 0 .../globalApi/DetectFaceLandmarksTasks.d.ts | 0 .../src/globalApi/DetectFacesTasks.d.ts | 0 .../{ => lib}/src/globalApi/FaceMatcher.d.ts | 0 .../globalApi/PredictAgeAndGenderTask.d.ts | 0 .../globalApi/PredictFaceExpressionsTask.d.ts | 0 types/{ => lib}/src/globalApi/allFaces.d.ts | 0 .../{ => lib}/src/globalApi/detectFaces.d.ts | 0 .../extractFacesAndComputeResults.d.ts | 0 types/{ => lib}/src/globalApi/index.d.ts | 0 types/{ => lib}/src/globalApi/nets.d.ts | 0 types/{ => lib}/src/globalApi/types.d.ts | 0 types/{ => lib}/src/index.d.ts | 0 types/{ => lib}/src/ops/index.d.ts | 0 types/{ => lib}/src/ops/iou.d.ts | 0 types/{ => lib}/src/ops/minBbox.d.ts | 0 .../{ => lib}/src/ops/nonMaxSuppression.d.ts | 0 types/{ => lib}/src/ops/normalize.d.ts | 0 types/{ => lib}/src/ops/padToSquare.d.ts | 0 types/{ => lib}/src/ops/shuffleArray.d.ts | 0 types/{ => lib}/src/resizeResults.d.ts | 0 .../src/ssdMobilenetv1/SsdMobilenetv1.d.ts | 0 .../ssdMobilenetv1/SsdMobilenetv1Options.d.ts | 0 .../ssdMobilenetv1/boxPredictionLayer.d.ts | 0 .../src/ssdMobilenetv1/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 types/{ => lib}/src/ssdMobilenetv1/index.d.ts | 0 .../src/ssdMobilenetv1/mobileNetV1.d.ts | 0 .../src/ssdMobilenetv1/nonMaxSuppression.d.ts | 0 .../src/ssdMobilenetv1/outputLayer.d.ts | 0 .../ssdMobilenetv1/pointwiseConvLayer.d.ts | 0 .../src/ssdMobilenetv1/predictionLayer.d.ts | 0 types/{ => lib}/src/ssdMobilenetv1/types.d.ts | 0 .../tinyFaceDetector/TinyFaceDetector.d.ts | 0 .../TinyFaceDetectorOptions.d.ts | 0 .../{ => lib}/src/tinyFaceDetector/const.d.ts | 0 .../{ => lib}/src/tinyFaceDetector/index.d.ts | 0 .../{ => lib}/src/tinyYolov2/TinyYolov2.d.ts | 0 .../src/tinyYolov2/TinyYolov2Base.d.ts | 0 .../src/tinyYolov2/TinyYolov2Options.d.ts | 0 types/{ => lib}/src/tinyYolov2/config.d.ts | 0 types/{ => lib}/src/tinyYolov2/const.d.ts | 0 .../src/tinyYolov2/convWithBatchNorm.d.ts | 0 .../tinyYolov2/depthwiseSeparableConv.d.ts | 0 .../src/tinyYolov2/extractParams.d.ts | 0 .../extractParamsFromWeightMap.d.ts | 0 types/{ => lib}/src/tinyYolov2/index.d.ts | 0 types/{ => lib}/src/tinyYolov2/leaky.d.ts | 0 types/{ => lib}/src/tinyYolov2/types.d.ts | 0 types/{ => lib}/src/utils/index.d.ts | 0 .../{ => lib}/src/xception/TinyXception.d.ts | 0 .../{ => lib}/src/xception/extractParams.d.ts | 0 .../xception/extractParamsFromWeightMap.d.ts | 0 types/{ => lib}/src/xception/types.d.ts | 0 185 files changed, 25394 insertions(+), 8770 deletions(-) rename build.json => .build.json (81%) create mode 100644 api-extractor.json create mode 100644 build.js delete mode 100644 dist/face-api.esm-nobundle.js.map create mode 100644 dist/tfjs.esm.d.ts delete mode 100644 dist/tfjs.esm.js.map create mode 100644 src/tfjs/tf-custom.ts delete mode 100644 types/dist/tfjs.esm.d.ts create mode 100644 types/face-api.d.ts create mode 100644 types/lib/dist/tfjs.esm.d.ts rename types/{ => lib}/src/NeuralNetwork.d.ts (100%) rename types/{ => lib}/src/ageGenderNet/AgeGenderNet.d.ts (81%) rename types/{ => lib}/src/ageGenderNet/extractParams.d.ts (100%) rename types/{ => lib}/src/ageGenderNet/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/ageGenderNet/index.d.ts (100%) rename types/{ => lib}/src/ageGenderNet/types.d.ts (100%) rename types/{ => lib}/src/classes/BoundingBox.d.ts (100%) rename types/{ => lib}/src/classes/Box.d.ts (100%) rename types/{ => lib}/src/classes/Dimensions.d.ts (100%) rename types/{ => lib}/src/classes/FaceDetection.d.ts (100%) rename types/{ => lib}/src/classes/FaceLandmarks.d.ts (100%) rename types/{ => lib}/src/classes/FaceLandmarks5.d.ts (100%) rename types/{ => lib}/src/classes/FaceLandmarks68.d.ts (100%) rename types/{ => lib}/src/classes/FaceMatch.d.ts (100%) rename types/{ => lib}/src/classes/LabeledBox.d.ts (100%) rename types/{ => lib}/src/classes/LabeledFaceDescriptors.d.ts (100%) rename types/{ => lib}/src/classes/ObjectDetection.d.ts (100%) rename types/{ => lib}/src/classes/Point.d.ts (100%) rename types/{ => lib}/src/classes/PredictedBox.d.ts (100%) rename types/{ => lib}/src/classes/Rect.d.ts (100%) rename types/{ => lib}/src/classes/index.d.ts (100%) rename types/{ => lib}/src/common/convLayer.d.ts (100%) rename types/{ => lib}/src/common/depthwiseSeparableConv.d.ts (100%) rename types/{ => lib}/src/common/disposeUnusedWeightTensors.d.ts (100%) rename types/{ => lib}/src/common/extractConvParamsFactory.d.ts (100%) rename types/{ => lib}/src/common/extractFCParamsFactory.d.ts (100%) rename types/{ => lib}/src/common/extractSeparableConvParamsFactory.d.ts (100%) rename types/{ => lib}/src/common/extractWeightEntryFactory.d.ts (100%) rename types/{ => lib}/src/common/extractWeightsFactory.d.ts (100%) rename types/{ => lib}/src/common/fullyConnectedLayer.d.ts (100%) rename types/{ => lib}/src/common/getModelUris.d.ts (100%) rename types/{ => lib}/src/common/index.d.ts (100%) rename types/{ => lib}/src/common/loadConvParamsFactory.d.ts (100%) rename types/{ => lib}/src/common/types.d.ts (100%) rename types/{ => lib}/src/dom/NetInput.d.ts (100%) rename types/{ => lib}/src/dom/awaitMediaLoaded.d.ts (100%) rename types/{ => lib}/src/dom/bufferToImage.d.ts (100%) rename types/{ => lib}/src/dom/bufferToVideo.d.ts (100%) rename types/{ => lib}/src/dom/createCanvas.d.ts (100%) rename types/{ => lib}/src/dom/extractFaceTensors.d.ts (100%) rename types/{ => lib}/src/dom/extractFaces.d.ts (100%) rename types/{ => lib}/src/dom/fetchImage.d.ts (100%) rename types/{ => lib}/src/dom/fetchJson.d.ts (100%) rename types/{ => lib}/src/dom/fetchNetWeights.d.ts (100%) rename types/{ => lib}/src/dom/fetchOrThrow.d.ts (100%) rename types/{ => lib}/src/dom/fetchVideo.d.ts (100%) rename types/{ => lib}/src/dom/getContext2dOrThrow.d.ts (100%) rename types/{ => lib}/src/dom/getMediaDimensions.d.ts (100%) rename types/{ => lib}/src/dom/imageTensorToCanvas.d.ts (100%) rename types/{ => lib}/src/dom/imageToSquare.d.ts (100%) rename types/{ => lib}/src/dom/index.d.ts (100%) rename types/{ => lib}/src/dom/isMediaElement.d.ts (100%) rename types/{ => lib}/src/dom/isMediaLoaded.d.ts (100%) rename types/{ => lib}/src/dom/loadWeightMap.d.ts (100%) rename types/{ => lib}/src/dom/matchDimensions.d.ts (100%) rename types/{ => lib}/src/dom/resolveInput.d.ts (100%) rename types/{ => lib}/src/dom/toNetInput.d.ts (100%) rename types/{ => lib}/src/dom/types.d.ts (100%) rename types/{ => lib}/src/draw/DrawBox.d.ts (100%) rename types/{ => lib}/src/draw/DrawFaceLandmarks.d.ts (100%) rename types/{ => lib}/src/draw/DrawTextField.d.ts (100%) rename types/{ => lib}/src/draw/drawContour.d.ts (100%) rename types/{ => lib}/src/draw/drawDetections.d.ts (100%) rename types/{ => lib}/src/draw/drawFaceExpressions.d.ts (100%) rename types/{ => lib}/src/draw/index.d.ts (100%) rename types/{ => lib}/src/env/createBrowserEnv.d.ts (100%) rename types/{ => lib}/src/env/createFileSystem.d.ts (100%) rename types/{ => lib}/src/env/createNodejsEnv.d.ts (100%) rename types/{ => lib}/src/env/index.d.ts (100%) rename types/{ => lib}/src/env/isBrowser.d.ts (100%) rename types/{ => lib}/src/env/isNodejs.d.ts (100%) rename types/{ => lib}/src/env/types.d.ts (100%) rename types/{ => lib}/src/euclideanDistance.d.ts (100%) rename types/{ => lib}/src/faceExpressionNet/FaceExpressionNet.d.ts (100%) rename types/{ => lib}/src/faceExpressionNet/FaceExpressions.d.ts (100%) rename types/{ => lib}/src/faceExpressionNet/index.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/denseBlock.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/extractParams.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/extractParamsFromWeightMapTiny.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/extractParamsTiny.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/extractorsFactory.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/loadParamsFactory.d.ts (100%) rename types/{ => lib}/src/faceFeatureExtractor/types.d.ts (100%) rename types/{ => lib}/src/faceLandmarkNet/FaceLandmark68Net.d.ts (100%) rename types/{ => lib}/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts (100%) rename types/{ => lib}/src/faceLandmarkNet/FaceLandmark68TinyNet.d.ts (100%) rename types/{ => lib}/src/faceLandmarkNet/index.d.ts (100%) rename types/{ => lib}/src/faceProcessor/FaceProcessor.d.ts (100%) rename types/{ => lib}/src/faceProcessor/extractParams.d.ts (100%) rename types/{ => lib}/src/faceProcessor/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/faceProcessor/types.d.ts (100%) rename types/{ => lib}/src/faceProcessor/util.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/FaceRecognitionNet.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/convLayer.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/extractParams.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/index.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/residualLayer.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/scaleLayer.d.ts (100%) rename types/{ => lib}/src/faceRecognitionNet/types.d.ts (100%) rename types/{ => lib}/src/factories/WithAge.d.ts (100%) rename types/{ => lib}/src/factories/WithFaceDescriptor.d.ts (100%) rename types/{ => lib}/src/factories/WithFaceDetection.d.ts (100%) rename types/{ => lib}/src/factories/WithFaceExpressions.d.ts (100%) rename types/{ => lib}/src/factories/WithFaceLandmarks.d.ts (100%) rename types/{ => lib}/src/factories/WithGender.d.ts (100%) rename types/{ => lib}/src/factories/index.d.ts (100%) rename types/{ => lib}/src/globalApi/ComposableTask.d.ts (100%) rename types/{ => lib}/src/globalApi/ComputeFaceDescriptorsTasks.d.ts (100%) rename types/{ => lib}/src/globalApi/DetectFaceLandmarksTasks.d.ts (100%) rename types/{ => lib}/src/globalApi/DetectFacesTasks.d.ts (100%) rename types/{ => lib}/src/globalApi/FaceMatcher.d.ts (100%) rename types/{ => lib}/src/globalApi/PredictAgeAndGenderTask.d.ts (100%) rename types/{ => lib}/src/globalApi/PredictFaceExpressionsTask.d.ts (100%) rename types/{ => lib}/src/globalApi/allFaces.d.ts (100%) rename types/{ => lib}/src/globalApi/detectFaces.d.ts (100%) rename types/{ => lib}/src/globalApi/extractFacesAndComputeResults.d.ts (100%) rename types/{ => lib}/src/globalApi/index.d.ts (100%) rename types/{ => lib}/src/globalApi/nets.d.ts (100%) rename types/{ => lib}/src/globalApi/types.d.ts (100%) rename types/{ => lib}/src/index.d.ts (100%) rename types/{ => lib}/src/ops/index.d.ts (100%) rename types/{ => lib}/src/ops/iou.d.ts (100%) rename types/{ => lib}/src/ops/minBbox.d.ts (100%) rename types/{ => lib}/src/ops/nonMaxSuppression.d.ts (100%) rename types/{ => lib}/src/ops/normalize.d.ts (100%) rename types/{ => lib}/src/ops/padToSquare.d.ts (100%) rename types/{ => lib}/src/ops/shuffleArray.d.ts (100%) rename types/{ => lib}/src/resizeResults.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/SsdMobilenetv1.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/SsdMobilenetv1Options.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/boxPredictionLayer.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/extractParams.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/index.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/mobileNetV1.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/nonMaxSuppression.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/outputLayer.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/pointwiseConvLayer.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/predictionLayer.d.ts (100%) rename types/{ => lib}/src/ssdMobilenetv1/types.d.ts (100%) rename types/{ => lib}/src/tinyFaceDetector/TinyFaceDetector.d.ts (100%) rename types/{ => lib}/src/tinyFaceDetector/TinyFaceDetectorOptions.d.ts (100%) rename types/{ => lib}/src/tinyFaceDetector/const.d.ts (100%) rename types/{ => lib}/src/tinyFaceDetector/index.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/TinyYolov2.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/TinyYolov2Base.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/TinyYolov2Options.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/config.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/const.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/convWithBatchNorm.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/depthwiseSeparableConv.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/extractParams.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/index.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/leaky.d.ts (100%) rename types/{ => lib}/src/tinyYolov2/types.d.ts (100%) rename types/{ => lib}/src/utils/index.d.ts (100%) rename types/{ => lib}/src/xception/TinyXception.d.ts (100%) rename types/{ => lib}/src/xception/extractParams.d.ts (100%) rename types/{ => lib}/src/xception/extractParamsFromWeightMap.d.ts (100%) rename types/{ => lib}/src/xception/types.d.ts (100%) diff --git a/build.json b/.build.json similarity index 81% rename from build.json rename to .build.json index 5dec32d..9760ec5 100644 --- a/build.json +++ b/.build.json @@ -10,7 +10,7 @@ "development": ["serve", "watch", "compile"] }, "clean": { - "locations": ["dist/*", "types/src/*", "typedoc/*"] + "locations": ["dist/*", "typedoc/*", "types/lib/src"] }, "lint": { "locations": [ "src/**" ], @@ -31,6 +31,8 @@ "build": { "global": { "target": "es2018", + "treeShaking": true, + "ignoreAnnotations": true, "sourcemap": false, "banner": { "js": "/*\n Face-API\n homepage: \n author: '\n*/\n" } }, @@ -72,8 +74,7 @@ "platform": "browser", "format": "esm", "input": "src/tfjs/tf-version.ts", - "output": "dist/tfjs.version.js", - "external": ["fs", "os", "buffer", "util"] + "output": "dist/tfjs.version.js" }, { "name": "tfjs/browser/esm/nobundle", @@ -81,25 +82,22 @@ "format": "esm", "input": "src/tfjs/tf-browser.ts", "output": "dist/tfjs.esm.js", - "external": ["fs","buffer","util","os","@tensorflow"] + "external": ["@tensorflow"] }, { "name": "faceapi/browser/esm/nobundle", "platform": "browser", "format": "esm", - "sourcemap": true, "input": "src/index.ts", - "output": "dist/face-api.esm-nobundle.js", - "external": ["fs","buffer","util","os","@tensorflow","tfjs.esm.js"] + "external": ["@tensorflow"], + "output": "dist/face-api.esm-nobundle.js" }, { "name": "tfjs/browser/esm/bundle", "platform": "browser", "format": "esm", - "sourcemap": true, - "input": "src/tfjs/tf-browser.ts", - "output": "dist/tfjs.esm.js", - "external": ["fs","buffer","util","os"] + "input": "src/tfjs/tf-custom.ts", + "output": "dist/tfjs.esm.js" }, { "name": "faceapi/browser/iife/bundle", @@ -109,7 +107,7 @@ "minify": true, "input": "src/index.ts", "output": "dist/face-api.js", - "external": ["fs","buffer","util","os"] + "external": ["@tensorflow"] }, { "name": "faceapi/browser/esm/bundle", @@ -118,9 +116,9 @@ "sourcemap": true, "input": "src/index.ts", "output": "dist/face-api.esm.js", - "external": ["fs","buffer","util","os"], - "typings": "types", - "typedoc": "typedoc" + "typings": "types/lib", + "typedoc": "typedoc", + "external": ["@tensorflow"] } ] }, diff --git a/.npmignore b/.npmignore index b90277f..e38f325 100644 --- a/.npmignore +++ b/.npmignore @@ -2,3 +2,4 @@ node_modules pnpm-lock.yaml typedoc test +types/lib diff --git a/CHANGELOG.md b/CHANGELOG.md index 6312f01..a364908 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,12 +9,12 @@ ## Changelog +### **HEAD -> master** 2021/12/01 mandic00@live.com + + ### **1.5.8** 2021/11/30 mandic00@live.com -### **origin/master** 2021/10/28 mandic00@live.com - - ### **1.5.7** 2021/10/28 mandic00@live.com diff --git a/api-extractor.json b/api-extractor.json new file mode 100644 index 0000000..9f43ab4 --- /dev/null +++ b/api-extractor.json @@ -0,0 +1,38 @@ +{ + "$schema": "https://developer.microsoft.com/json-schemas/api-extractor/v7/api-extractor.schema.json", + "mainEntryPointFilePath": "types/lib/src/index.d.ts", + "bundledPackages": ["@tensorflow/tfjs-core", "@tensorflow/tfjs-converter", "@types/offscreencanvas"], + "compiler": { + "skipLibCheck": false + }, + "newlineKind": "lf", + "dtsRollup": { + "enabled": true, + "untrimmedFilePath": "types/face-api.d.ts" + }, + "docModel": { "enabled": false }, + "tsdocMetadata": { + "enabled": false + }, + "apiReport": { "enabled": false }, + "messages": { + "compilerMessageReporting": { + "default": { + "logLevel": "warning" + } + }, + "extractorMessageReporting": { + "default": { + "logLevel": "warning" + }, + "ae-missing-release-tag": { + "logLevel": "none" + } + }, + "tsdocMessageReporting": { + "default": { + "logLevel": "warning" + } + } + } +} diff --git a/build.js b/build.js new file mode 100644 index 0000000..7e5a228 --- /dev/null +++ b/build.js @@ -0,0 +1,49 @@ +const fs = require('fs'); +const log = require('@vladmandic/pilogger'); +const Build = require('@vladmandic/build').Build; +const APIExtractor = require('@microsoft/api-extractor'); + +// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars +function copy(src, dst) { + if (!fs.existsSync(src)) return; + const buffer = fs.readFileSync(src); + fs.writeFileSync(dst, buffer); +} + +const apiIgnoreList = ['ae-forgotten-export', 'ae-unresolved-link', 'tsdoc-param-tag-missing-hyphen']; + +async function main() { + // run production build + const build = new Build(); + await build.run('production'); + // patch tfjs typedefs + log.state('Copy:', { input: 'types/lib/dist/tfjs.esm.d.ts' }); + copy('types/lib/dist/tfjs.esm.d.ts', 'dist/tfjs.esm.d.ts'); + // run api-extractor to create typedef rollup + const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('api-extractor.json'); + const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, { + localBuild: true, + showVerboseMessages: false, + messageCallback: (msg) => { + msg.handled = true; + if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return; + if (msg.sourceFilePath?.includes('/node_modules/')) return; + if (apiIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; + log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text }); + }, + }); + log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount }); + // distribute typedefs + /* + log.state('Copy:', { input: 'types/human.d.ts' }); + copy('types/human.d.ts', 'dist/human.esm-nobundle.d.ts'); + copy('types/human.d.ts', 'dist/human.esm.d.ts'); + copy('types/human.d.ts', 'dist/human.d.ts'); + copy('types/human.d.ts', 'dist/human.node-gpu.d.ts'); + copy('types/human.d.ts', 'dist/human.node.d.ts'); + copy('types/human.d.ts', 'dist/human.node-wasm.d.ts'); + */ + log.info('FaceAPI Build complete...'); +} + +main(); diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js index d5da74e..ef9c607 100644 --- a/dist/face-api.esm-nobundle.js +++ b/dist/face-api.esm-nobundle.js @@ -4496,4 +4496,3 @@ export { validateConfig, version11 as version }; -//# sourceMappingURL=face-api.esm-nobundle.js.map diff --git a/dist/face-api.esm-nobundle.js.map b/dist/face-api.esm-nobundle.js.map deleted file mode 100644 index d61a7ad..0000000 --- a/dist/face-api.esm-nobundle.js.map +++ /dev/null @@ -1,7 +0,0 @@ -{ - "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/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/isNodejs.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", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeightMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeightMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeightMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeightMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeightMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeightMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"], - "sourcesContent": ["/*\n Face-API\n homepage: \n author: '\n*/\n\n// src/tfjs/tf-browser.ts\nexport * from \"@tensorflow/tfjs/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-webgl/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-wasm/dist/index.js\";\n\n// dist/tfjs.version.js\nvar version = \"3.11.0\";\nvar version2 = \"3.11.0\";\nvar version3 = \"3.11.0\";\nvar version4 = \"3.11.0\";\nvar version5 = \"3.11.0\";\nvar version6 = \"3.11.0\";\nvar version7 = \"3.11.0\";\nvar version8 = \"3.11.0\";\nvar version9 = {\n tfjs: version,\n \"tfjs-core\": version2,\n \"tfjs-data\": version3,\n \"tfjs-layers\": version4,\n \"tfjs-converter\": version5,\n \"tfjs-backend-cpu\": version6,\n \"tfjs-backend-webgl\": version7,\n \"tfjs-backend-wasm\": version8\n};\nexport {\n version9 as version\n};\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({\n x, y, width, height,\n });\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({\n x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight,\n })).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({\n x, y, width, height,\n });\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 {\n dy, edy, dx, edx, y, ey, x, ex, w, h,\n };\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.Tensor) => tf.cast(t, '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", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n let requireFsError = '';\n\n if (!fs) {\n try {\n // eslint-disable-next-line global-require\n fs = require('fs');\n } catch (err) {\n requireFsError = err.toString();\n }\n }\n\n const readFile = fs\n ? (filePath: string) => new Promise((resolve, reject) => {\n fs.readFile(filePath, (err: any, buffer: Buffer) => (err ? reject(err) : resolve(buffer)));\n })\n : () => {\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`);\n };\n\n return {\n readFile,\n };\n}\n", "/* eslint-disable max-classes-per-file */\nimport { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n // eslint-disable-next-line dot-notation\n const Canvas = global['Canvas'] || global.HTMLCanvasElement;\n const Image = global.Image || global.HTMLImageElement;\n // eslint-disable-next-line dot-notation\n const Video = global['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", "export function isNodejs(): boolean {\n return typeof global === 'object'\n && typeof require === 'function'\n && typeof module !== 'undefined'\n && typeof process !== 'undefined' && !!process.version;\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\n if (canvasArg instanceof CanvasRenderingContext2D) {\n return canvasArg;\n }\n\n const canvas = resolveInput(canvasArg);\n\n if (!(canvas instanceof Canvas)) {\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\n }\n\n const ctx = canvas.getContext('2d');\n if (!ctx) {\n throw new Error('resolveContext2d - canvas 2d context is null');\n }\n\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)) resolve(null);\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 const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\n this._canvases[idx] = canvas;\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, isCenterInputs);\n\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n imgTensor = tf.image.resizeBilinear(imgTensor, [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\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\n const boxes = detections\n .map((det) => (det instanceof FaceDetection\n ? det.forSize(imgWidth, imgHeight).box\n : det))\n .map((box) => box.clipAtImageBorders(imgWidth, imgHeight));\n\n const faceTensors = boxes.map(({\n x, y, width, height,\n }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));\n\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 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) => 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'];\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 { 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 > = TSource & {\n landmarks: TFaceLandmarks,\n unshiftedLandmarks: TFaceLandmarks,\n alignedRect: FaceDetection,\n angle: { roll: number | undefined, pitch: number | undefined, yaw: number | undefined },\n }\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks, FaceLandmarks> {\n return isWithFaceDetection(obj)\n // eslint-disable-next-line dot-notation\n && obj['landmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n // eslint-disable-next-line dot-notation\n && obj['alignedRect'] instanceof FaceDetection;\n}\n\nfunction calculateFaceAngle(mesh) {\n // returns the angle in the plane (in radians) between the positive x-axis and the ray from (0,0) to the point (x,y)\n const radians = (a1, a2, b1, b2) => (Math.atan2(b2 - a2, b1 - a1) % Math.PI);\n // convert radians to degrees\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const degrees = (theta) => (theta * 180) / Math.PI;\n\n const angle = { roll: undefined, pitch: undefined, yaw: undefined };\n\n if (!mesh || !mesh._positions || mesh._positions.length !== 68) return angle;\n const pt = mesh._positions;\n\n // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n // value of 0 means center\n\n // roll is face lean from left to right\n // comparing x,y of outside corners of leftEye and rightEye\n angle.roll = -radians(pt[36]._x, pt[36]._y, pt[45]._x, pt[45]._y);\n\n // pitch is face turn from left right\n // comparing x distance of top of nose to left and right edge of face\n // precision is lacking since coordinates are not precise enough\n angle.pitch = radians(0, Math.abs(pt[0]._x - pt[30]._x) / pt[30]._x, Math.PI, Math.abs(pt[16]._x - pt[30]._x) / pt[30]._x);\n\n // yaw is face move from up to down\n // comparing size of the box around the face with top and bottom of detected landmarks\n // silly hack, but this gives us face compression on y-axis\n // e.g., tilting head up hides the forehead that doesn't have any landmarks so ratio drops\n const bottom = pt.reduce((prev, cur) => (prev < cur._y ? prev : cur._y), +Infinity);\n const top = pt.reduce((prev, cur) => (prev > cur._y ? prev : cur._y), -Infinity);\n angle.yaw = Math.PI * (mesh._imgDims._height / (top - bottom) / 1.40 - 1);\n\n return angle;\n}\n\nexport function extendWithFaceLandmarks<\n TSource extends WithFaceDetection<{}>,\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68 >(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks {\n const { box: shift } = sourceObj.detection;\n const landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\n\n const rect = landmarks.align();\n const { imageDims } = sourceObj.detection;\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\n const angle = calculateFaceAngle(unshiftedLandmarks);\n\n const extension = {\n landmarks,\n unshiftedLandmarks,\n alignedRect,\n angle,\n };\n\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", "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, `${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractWeights,\n getRemainingWeights,\n } = extractWeightsFactory(weights);\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = extractorsFactory(extractWeights, paramMappings);\n\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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(`${mappedPrefix}/separable_conv2`);\n\n return { separable_conv0, separable_conv1, separable_conv2 };\n }\n\n return {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n };\n}\n\nexport function extractParamsFromWeightMap(\n weightMap: tf.NamedTensorMap,\n numMainBlocks: number,\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n const paramMappings: ParamMapping[] = [];\n\n const {\n extractConvParams,\n extractSeparableConvParams,\n extractReductionBlockParams,\n extractMainBlockParams,\n } = loadParamsFactory(weightMap, paramMappings);\n\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\n\n const entry_flow = {\n conv_in: entry_flow_conv_in,\n reduction_block_0: entry_flow_reduction_block_0,\n reduction_block_1: entry_flow_reduction_block_1,\n };\n\n const middle_flow = {};\n range(numMainBlocks, 0, 1).forEach((idx) => {\n middle_flow[`main_block_${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(`${prefix}/bias`, 1);\n return { weights, bias };\n }\n\n const params = {\n fc: {\n age: extractFcParams('fc/age'),\n gender: extractFcParams('fc/gender'),\n },\n };\n\n disposeUnusedWeightTensors(weightMap, paramMappings);\n\n return { params, paramMappings };\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { FCParams } from '../common/index';\n\n// eslint-disable-next-line no-shadow\nexport enum Gender {\n // eslint-disable-next-line no-unused-vars\n FEMALE = 'female',\n // eslint-disable-next-line no-unused-vars\n MALE = 'male'\n}\n\nexport type AgeAndGenderPrediction = {\n age: number\n gender: Gender\n genderProbability: number\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n fc: {\n age: FCParams\n gender: FCParams\n }\n}\n", "import * as tf from '../../dist/tfjs.esm';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeightMap } from './extractParamsFromWeightMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom/index';\n\nexport class AgeGenderNet extends NeuralNetwork {\n private _faceFeatureExtractor: TinyXception;\n\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n super('AgeGenderNet');\n this._faceFeatureExtractor = faceFeatureExtractor;\n }\n\n public get faceFeatureExtractor(): TinyXception {\n return this._faceFeatureExtractor;\n }\n\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n const { params } = this;\n\n if (!params) {\n throw new Error(`${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;\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 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, 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 = [] as any;\n const scoresData = await scoresTensor.array();\n const boxesData = await boxesTensor.array();\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(\n faces.map((face) => this.landmarkNet.detectLandmarks(face)),\n ) as FaceLandmarks68[];\n faces.forEach((f) => f instanceof tf.Tensor && f.dispose());\n return parentResults.map((parentResult, i) => extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]));\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\n const desc1 = Array.from(arr1);\n const desc2 = Array.from(arr2);\n\n return Math.sqrt(\n desc1\n .map((val, i) => val - desc2[i])\n .reduce((res, diff) => res + (diff ** 2), 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\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version as string, node, browser };\n\n// set webgl defaults\nif (browser) {\n tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n tf.ENV.set('WEBGL_CPU_FORWARD', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n}\n"], - "mappings": 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- "names": [] -} diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js index c8227cb..0a6190a 100644 --- a/dist/face-api.esm.js +++ b/dist/face-api.esm.js @@ -128,6 +128,7 @@ __export(tfjs_esm_exports, { LogicalAnd: () => LogicalAnd, LogicalNot: () => LogicalNot, LogicalOr: () => LogicalOr, + MathBackendCPU: () => MathBackendCPU, MathBackendWebGL: () => MathBackendWebGL, Max: () => Max, MaxPool: () => MaxPool, @@ -251,6 +252,7 @@ __export(tfjs_esm_exports, { booleanMaskAsync: () => booleanMaskAsync, broadcastArgs: () => broadcastArgs, broadcastTo: () => broadcastTo, + broadcast_util: () => broadcast_util_exports, browser: () => browser_exports, buffer: () => buffer, callbacks: () => callbacks, @@ -276,7 +278,7 @@ __export(tfjs_esm_exports, { cosineWindow: () => cosineWindow, cumsum: () => cumsum, customGrad: () => customGrad, - data: () => dist_exports, + data: () => src_exports, denseBincount: () => denseBincount, deprecationWarn: () => deprecationWarn, depthToSpace: () => depthToSpace, @@ -348,7 +350,7 @@ __export(tfjs_esm_exports, { loadGraphModel: () => loadGraphModel, loadLayersModel: () => loadLayersModel, localResponseNormalization: () => localResponseNormalization, - log: () => log5, + log: () => log4, log1p: () => log1p, logSigmoid: () => logSigmoid, logSoftmax: () => logSoftmax, @@ -440,6 +442,7 @@ __export(tfjs_esm_exports, { setWasmPaths: () => setWasmPaths, setWebGLContext: () => setWebGLContext, setdiff1dAsync: () => setdiff1dAsync, + shared: () => shared_exports, sigmoid: () => sigmoid, sign: () => sign, signal: () => signal, @@ -498,14 +501,16 @@ __export(tfjs_esm_exports, { valueAndGrads: () => valueAndGrads, variable: () => variable, variableGrads: () => variableGrads, - version: () => version92, + version: () => version8, version_converter: () => version3, version_core: () => version, + version_cpu: () => version5, version_layers: () => version2, - version_wasm: () => version8, - version_webgl: () => version5, + version_wasm: () => version7, + version_webgl: () => version6, webgl: () => webgl, webgl_util: () => webgl_util_exports, + webgpu: () => webgpu_exports, where: () => where, whereAsync: () => whereAsync, zeros: () => zeros, @@ -518,14 +523,7 @@ var __getOwnPropNames = Object.getOwnPropertyNames; var __getProtoOf = Object.getPrototypeOf; var __hasOwnProp = Object.prototype.hasOwnProperty; var __markAsModule2 = (target) => __defProp2(target, "__esModule", { value: true }); -var __require2 = /* @__PURE__ */ ((x) => typeof __require !== "undefined" ? __require : typeof Proxy !== "undefined" ? new Proxy(x, { - get: (a, b) => (typeof __require !== "undefined" ? __require : a)[b] -}) : x)(function(x) { - if (typeof __require !== "undefined") - return __require.apply(this, arguments); - throw new Error('Dynamic require of "' + x + '" is not supported'); -}); -var __commonJS = (cb, mod4) => function __require22() { +var __commonJS = (cb, mod4) => function __require2() { return mod4 || (0, cb[Object.keys(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports; }; var __export2 = (target, all5) => { @@ -545,7 +543,7 @@ var __toModule = (module2) => { return __reExport(__markAsModule2(__defProp2(module2 != null ? __create(__getProtoOf(module2)) : {}, "default", module2 && module2.__esModule && "default" in module2 ? { get: () => module2.default, enumerable: true } : { value: module2, enumerable: true })), module2); }; var require_long = __commonJS({ - "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module2) { + "src/node_modules/long/src/long.js"(exports, module2) { module2.exports = Long2; var wasm = null; try { @@ -1368,11 +1366,15 @@ var require_long = __commonJS({ } }); var require_browser = __commonJS({ - "(disabled):node_modules/.pnpm/node-fetch@2.6.6/node_modules/node-fetch/browser.js"() { + "(disabled):src/node_modules/node-fetch/browser.js"() { + } +}); +var require_util = __commonJS({ + "(disabled):util"() { } }); var require_alea = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(exports, module2) { + "src/node_modules/seedrandom/lib/alea.js"(exports, module2) { (function(global2, module22, define2) { function Alea(seed) { var me = this, mash = Mash(); @@ -1428,7 +1430,7 @@ var require_alea = __commonJS({ function Mash() { var n = 4022871197; var mash = function(data) { - data = data.toString(); + data = String(data); for (var i = 0; i < data.length; i++) { n += data.charCodeAt(i); var h = 0.02519603282416938 * n; @@ -1456,7 +1458,7 @@ var require_alea = __commonJS({ } }); var require_xor128 = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(exports, module2) { + "src/node_modules/seedrandom/lib/xor128.js"(exports, module2) { (function(global2, module22, define2) { function XorGen(seed) { var me = this, strseed = ""; @@ -1522,7 +1524,7 @@ var require_xor128 = __commonJS({ } }); var require_xorwow = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(exports, module2) { + "src/node_modules/seedrandom/lib/xorwow.js"(exports, module2) { (function(global2, module22, define2) { function XorGen(seed) { var me = this, strseed = ""; @@ -1595,7 +1597,7 @@ var require_xorwow = __commonJS({ } }); var require_xorshift7 = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(exports, module2) { + "src/node_modules/seedrandom/lib/xorshift7.js"(exports, module2) { (function(global2, module22, define2) { function XorGen(seed) { var me = this; @@ -1684,7 +1686,7 @@ var require_xorshift7 = __commonJS({ } }); var require_xor4096 = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(exports, module2) { + "src/node_modules/seedrandom/lib/xor4096.js"(exports, module2) { (function(global2, module22, define2) { function XorGen(seed) { var me = this; @@ -1788,7 +1790,7 @@ var require_xor4096 = __commonJS({ } }); var require_tychei = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(exports, module2) { + "src/node_modules/seedrandom/lib/tychei.js"(exports, module2) { (function(global2, module22, define2) { function XorGen(seed) { var me = this, strseed = ""; @@ -1864,643 +1866,7 @@ var require_crypto = __commonJS({ } }); var require_seedrandom = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(exports, module2) { - (function(pool3, math) { - var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; - function seedrandom5(seed, options, callback) { - var key = []; - options = options == true ? { entropy: true } : options || {}; - var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); - var arc4 = new ARC4(key); - var prng = function() { - var n = arc4.g(chunks), d = startdenom, x = 0; - while (n < significance) { - n = (n + x) * width; - d *= width; - x = arc4.g(1); - } - while (n >= overflow) { - n /= 2; - d /= 2; - x >>>= 1; - } - return (n + x) / d; - }; - prng.int32 = function() { - return arc4.g(4) | 0; - }; - prng.quick = function() { - return arc4.g(4) / 4294967296; - }; - prng.double = prng; - mixkey(tostring(arc4.S), pool3); - return (options.pass || callback || function(prng2, seed2, is_math_call, state) { - if (state) { - if (state.S) { - copy(state, arc4); - } - prng2.state = function() { - return copy(arc4, {}); - }; - } - if (is_math_call) { - math[rngname] = prng2; - return seed2; - } else - return prng2; - })(prng, shortseed, "global" in options ? options.global : this == math, options.state); - } - math["seed" + rngname] = seedrandom5; - function ARC4(key) { - var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; - if (!keylen) { - key = [keylen++]; - } - while (i < width) { - s[i] = i++; - } - for (i = 0; i < width; i++) { - s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])]; - s[j] = t; - } - (me.g = function(count2) { - var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; - while (count2--) { - t2 = s2[i2 = mask & i2 + 1]; - r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)]; - } - me.i = i2; - me.j = j2; - return r; - })(width); - } - function copy(f, t) { - t.i = f.i; - t.j = f.j; - t.S = f.S.slice(); - return t; - } - ; - function flatten4(obj, depth) { - var result = [], typ = typeof obj, prop; - if (depth && typ == "object") { - for (prop in obj) { - try { - result.push(flatten4(obj[prop], depth - 1)); - } catch (e) { - } - } - } - return result.length ? result : typ == "string" ? obj : obj + "\0"; - } - function mixkey(seed, key) { - var stringseed = seed + "", smear, j = 0; - while (j < stringseed.length) { - key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++); - } - return tostring(key); - } - function autoseed() { - try { - var out; - if (nodecrypto && (out = nodecrypto.randomBytes)) { - out = out(width); - } else { - out = new Uint8Array(width); - (global2.crypto || global2.msCrypto).getRandomValues(out); - } - return tostring(out); - } catch (e) { - var browser2 = global2.navigator, plugins = browser2 && browser2.plugins; - return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; - } - } - function tostring(a) { - return String.fromCharCode.apply(0, a); - } - mixkey(math.random(), pool3); - if (typeof module2 == "object" && module2.exports) { - module2.exports = seedrandom5; - try { - nodecrypto = require_crypto(); - } catch (ex) { - } - } else if (typeof define == "function" && define.amd) { - define(function() { - return seedrandom5; - }); - } - })([], Math); - } -}); -var require_seedrandom2 = __commonJS({ - "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(exports, module2) { - var alea5 = require_alea(); - var xor128 = require_xor128(); - var xorwow = require_xorwow(); - var xorshift7 = require_xorshift7(); - var xor4096 = require_xor4096(); - var tychei = require_tychei(); - var sr = require_seedrandom(); - sr.alea = alea5; - sr.xor128 = xor128; - sr.xorwow = xorwow; - sr.xorshift7 = xorshift7; - sr.xor4096 = xor4096; - sr.tychei = tychei; - module2.exports = sr; - } -}); -var require_alea2 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module2) { - (function(global2, module22, define2) { - function Alea(seed) { - var me = this, mash = Mash(); - me.next = function() { - var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; - me.s0 = me.s1; - me.s1 = me.s2; - return me.s2 = t - (me.c = t | 0); - }; - me.c = 1; - me.s0 = mash(" "); - me.s1 = mash(" "); - me.s2 = mash(" "); - me.s0 -= mash(seed); - if (me.s0 < 0) { - me.s0 += 1; - } - me.s1 -= mash(seed); - if (me.s1 < 0) { - me.s1 += 1; - } - me.s2 -= mash(seed); - if (me.s2 < 0) { - me.s2 += 1; - } - mash = null; - } - function copy(f, t) { - t.c = f.c; - t.s0 = f.s0; - t.s1 = f.s1; - t.s2 = f.s2; - return t; - } - function impl(seed, opts) { - var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; - prng.int32 = function() { - return xg.next() * 4294967296 | 0; - }; - prng.double = function() { - return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; - }; - prng.quick = prng; - if (state) { - if (typeof state == "object") - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - function Mash() { - var n = 4022871197; - var mash = function(data) { - data = String(data); - for (var i = 0; i < data.length; i++) { - n += data.charCodeAt(i); - var h = 0.02519603282416938 * n; - n = h >>> 0; - h -= n; - h *= n; - n = h >>> 0; - h -= n; - n += h * 4294967296; - } - return (n >>> 0) * 23283064365386963e-26; - }; - return mash; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.alea = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_xor1282 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module2) { - (function(global2, module22, define2) { - function XorGen(seed) { - var me = this, strseed = ""; - me.x = 0; - me.y = 0; - me.z = 0; - me.w = 0; - me.next = function() { - var t = me.x ^ me.x << 11; - me.x = me.y; - me.y = me.z; - me.z = me.w; - return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; - }; - if (seed === (seed | 0)) { - me.x = seed; - } else { - strseed += seed; - } - for (var k = 0; k < strseed.length + 64; k++) { - me.x ^= strseed.charCodeAt(k) | 0; - me.next(); - } - } - function copy(f, t) { - t.x = f.x; - t.y = f.y; - t.z = f.z; - t.w = f.w; - return t; - } - function impl(seed, opts) { - var xg = new XorGen(seed), state = opts && opts.state, prng = function() { - return (xg.next() >>> 0) / 4294967296; - }; - prng.double = function() { - do { - var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); - } while (result === 0); - return result; - }; - prng.int32 = xg.next; - prng.quick = prng; - if (state) { - if (typeof state == "object") - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.xor128 = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_xorwow2 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module2) { - (function(global2, module22, define2) { - function XorGen(seed) { - var me = this, strseed = ""; - me.next = function() { - var t = me.x ^ me.x >>> 2; - me.x = me.y; - me.y = me.z; - me.z = me.w; - me.w = me.v; - return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; - }; - me.x = 0; - me.y = 0; - me.z = 0; - me.w = 0; - me.v = 0; - if (seed === (seed | 0)) { - me.x = seed; - } else { - strseed += seed; - } - for (var k = 0; k < strseed.length + 64; k++) { - me.x ^= strseed.charCodeAt(k) | 0; - if (k == strseed.length) { - me.d = me.x << 10 ^ me.x >>> 4; - } - me.next(); - } - } - function copy(f, t) { - t.x = f.x; - t.y = f.y; - t.z = f.z; - t.w = f.w; - t.v = f.v; - t.d = f.d; - return t; - } - function impl(seed, opts) { - var xg = new XorGen(seed), state = opts && opts.state, prng = function() { - return (xg.next() >>> 0) / 4294967296; - }; - prng.double = function() { - do { - var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); - } while (result === 0); - return result; - }; - prng.int32 = xg.next; - prng.quick = prng; - if (state) { - if (typeof state == "object") - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.xorwow = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_xorshift72 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module2) { - (function(global2, module22, define2) { - function XorGen(seed) { - var me = this; - me.next = function() { - var X = me.x, i = me.i, t, v, w; - t = X[i]; - t ^= t >>> 7; - v = t ^ t << 24; - t = X[i + 1 & 7]; - v ^= t ^ t >>> 10; - t = X[i + 3 & 7]; - v ^= t ^ t >>> 3; - t = X[i + 4 & 7]; - v ^= t ^ t << 7; - t = X[i + 7 & 7]; - t = t ^ t << 13; - v ^= t ^ t << 9; - X[i] = v; - me.i = i + 1 & 7; - return v; - }; - function init2(me2, seed2) { - var j, w, X = []; - if (seed2 === (seed2 | 0)) { - w = X[0] = seed2; - } else { - seed2 = "" + seed2; - for (j = 0; j < seed2.length; ++j) { - X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; - } - } - while (X.length < 8) - X.push(0); - for (j = 0; j < 8 && X[j] === 0; ++j) - ; - if (j == 8) - w = X[7] = -1; - else - w = X[j]; - me2.x = X; - me2.i = 0; - for (j = 256; j > 0; --j) { - me2.next(); - } - } - init2(me, seed); - } - function copy(f, t) { - t.x = f.x.slice(); - t.i = f.i; - return t; - } - function impl(seed, opts) { - if (seed == null) - seed = +new Date(); - var xg = new XorGen(seed), state = opts && opts.state, prng = function() { - return (xg.next() >>> 0) / 4294967296; - }; - prng.double = function() { - do { - var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); - } while (result === 0); - return result; - }; - prng.int32 = xg.next; - prng.quick = prng; - if (state) { - if (state.x) - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.xorshift7 = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_xor40962 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module2) { - (function(global2, module22, define2) { - function XorGen(seed) { - var me = this; - me.next = function() { - var w = me.w, X = me.X, i = me.i, t, v; - me.w = w = w + 1640531527 | 0; - v = X[i + 34 & 127]; - t = X[i = i + 1 & 127]; - v ^= v << 13; - t ^= t << 17; - v ^= v >>> 15; - t ^= t >>> 12; - v = X[i] = v ^ t; - me.i = i; - return v + (w ^ w >>> 16) | 0; - }; - function init2(me2, seed2) { - var t, v, i, j, w, X = [], limit = 128; - if (seed2 === (seed2 | 0)) { - v = seed2; - seed2 = null; - } else { - seed2 = seed2 + "\0"; - v = 0; - limit = Math.max(limit, seed2.length); - } - for (i = 0, j = -32; j < limit; ++j) { - if (seed2) - v ^= seed2.charCodeAt((j + 32) % seed2.length); - if (j === 0) - w = v; - v ^= v << 10; - v ^= v >>> 15; - v ^= v << 4; - v ^= v >>> 13; - if (j >= 0) { - w = w + 1640531527 | 0; - t = X[j & 127] ^= v + w; - i = t == 0 ? i + 1 : 0; - } - } - if (i >= 128) { - X[(seed2 && seed2.length || 0) & 127] = -1; - } - i = 127; - for (j = 4 * 128; j > 0; --j) { - v = X[i + 34 & 127]; - t = X[i = i + 1 & 127]; - v ^= v << 13; - t ^= t << 17; - v ^= v >>> 15; - t ^= t >>> 12; - X[i] = v ^ t; - } - me2.w = w; - me2.X = X; - me2.i = i; - } - init2(me, seed); - } - function copy(f, t) { - t.i = f.i; - t.w = f.w; - t.X = f.X.slice(); - return t; - } - ; - function impl(seed, opts) { - if (seed == null) - seed = +new Date(); - var xg = new XorGen(seed), state = opts && opts.state, prng = function() { - return (xg.next() >>> 0) / 4294967296; - }; - prng.double = function() { - do { - var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); - } while (result === 0); - return result; - }; - prng.int32 = xg.next; - prng.quick = prng; - if (state) { - if (state.X) - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.xor4096 = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_tychei2 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module2) { - (function(global2, module22, define2) { - function XorGen(seed) { - var me = this, strseed = ""; - me.next = function() { - var b = me.b, c = me.c, d = me.d, a = me.a; - b = b << 25 ^ b >>> 7 ^ c; - c = c - d | 0; - d = d << 24 ^ d >>> 8 ^ a; - a = a - b | 0; - me.b = b = b << 20 ^ b >>> 12 ^ c; - me.c = c = c - d | 0; - me.d = d << 16 ^ c >>> 16 ^ a; - return me.a = a - b | 0; - }; - me.a = 0; - me.b = 0; - me.c = 2654435769 | 0; - me.d = 1367130551; - if (seed === Math.floor(seed)) { - me.a = seed / 4294967296 | 0; - me.b = seed | 0; - } else { - strseed += seed; - } - for (var k = 0; k < strseed.length + 20; k++) { - me.b ^= strseed.charCodeAt(k) | 0; - me.next(); - } - } - function copy(f, t) { - t.a = f.a; - t.b = f.b; - t.c = f.c; - t.d = f.d; - return t; - } - ; - function impl(seed, opts) { - var xg = new XorGen(seed), state = opts && opts.state, prng = function() { - return (xg.next() >>> 0) / 4294967296; - }; - prng.double = function() { - do { - var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); - } while (result === 0); - return result; - }; - prng.int32 = xg.next; - prng.quick = prng; - if (state) { - if (typeof state == "object") - copy(state, xg); - prng.state = function() { - return copy(xg, {}); - }; - } - return prng; - } - if (module22 && module22.exports) { - module22.exports = impl; - } else if (define2 && define2.amd) { - define2(function() { - return impl; - }); - } else { - this.tychei = impl; - } - })(exports, typeof module2 == "object" && module2, typeof define == "function" && define); - } -}); -var require_seedrandom3 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module2) { + "src/node_modules/seedrandom/seedrandom.js"(exports, module2) { (function(global2, pool3, math) { var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; function seedrandom5(seed, options, callback) { @@ -2630,15 +1996,15 @@ var require_seedrandom3 = __commonJS({ })(typeof self !== "undefined" ? self : exports, [], Math); } }); -var require_seedrandom4 = __commonJS({ - "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module2) { - var alea5 = require_alea2(); - var xor128 = require_xor1282(); - var xorwow = require_xorwow2(); - var xorshift7 = require_xorshift72(); - var xor4096 = require_xor40962(); - var tychei = require_tychei2(); - var sr = require_seedrandom3(); +var require_seedrandom2 = __commonJS({ + "src/node_modules/seedrandom/index.js"(exports, module2) { + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); sr.alea = alea5; sr.xor128 = xor128; sr.xorwow = xorwow; @@ -2649,7 +2015,11 @@ var require_seedrandom4 = __commonJS({ } }); var require_string_decoder = __commonJS({ - "(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"() { + "(disabled):src/node_modules/string_decoder/index.js"() { + } +}); +var require_fs = __commonJS({ + "(disabled):fs"() { } }); var require_path = __commonJS({ @@ -2664,14 +2034,18 @@ var require_perf_hooks = __commonJS({ "(disabled):perf_hooks"() { } }); +var require_os = __commonJS({ + "(disabled):os"() { + } +}); var require_tfjs_backend_wasm_threaded_simd = __commonJS({ - "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.11.0_@tensorflow+tfjs-core@3.11.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module2) { - var WasmBackendModuleThreadedSimd = function() { + "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module2) { + var WasmBackendModuleThreadedSimd2 = function() { var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; if (typeof __filename !== "undefined") _scriptDir = _scriptDir || __filename; - return function(WasmBackendModuleThreadedSimd2) { - WasmBackendModuleThreadedSimd2 = WasmBackendModuleThreadedSimd2 || {}; + return function(WasmBackendModuleThreadedSimd3) { + WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {}; function GROWABLE_HEAP_I8() { if (wasmMemory.buffer != buffer2) { updateGlobalBufferAndViews(wasmMemory.buffer); @@ -2702,12 +2076,16 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ } return HEAPF64; } - var Module = typeof WasmBackendModuleThreadedSimd2 !== "undefined" ? WasmBackendModuleThreadedSimd2 : {}; + var Module = typeof WasmBackendModuleThreadedSimd3 !== "undefined" ? WasmBackendModuleThreadedSimd3 : {}; var readyPromiseResolve, readyPromiseReject; Module["ready"] = new Promise(function(resolve, reject) { readyPromiseResolve = resolve; readyPromiseReject = reject; }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } var moduleOverrides = {}; var key; for (key in Module) { @@ -2750,7 +2128,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ } read_ = function shell_read(filename, binary) { if (!nodeFS) - nodeFS = __require2("fs"); + nodeFS = require_fs(); if (!nodePath) nodePath = require_path(); filename = nodePath["normalize"](filename); @@ -2836,7 +2214,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ if (ENVIRONMENT_IS_NODE) { read_ = function shell_read(filename, binary) { if (!nodeFS) - nodeFS = __require2("fs"); + nodeFS = require_fs(); if (!nodePath) nodePath = require_path(); filename = nodePath["normalize"](filename); @@ -3289,7 +2667,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ function receiveInstance(instance, module22) { var exports3 = instance.exports; Module["asm"] = exports3; - wasmTable = Module["asm"]["I"]; + wasmTable = Module["asm"]["nb"]; wasmModule = module22; if (!ENVIRONMENT_IS_PTHREAD) { var numWorkersToLoad = PThread.unusedWorkers.length; @@ -3341,9 +2719,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ instantiateAsync().catch(readyPromiseReject); return {}; } - var ASM_CONSTS = { 10520: function() { + var ASM_CONSTS = { 10216: function() { throw "Canceled!"; - }, 10538: function($0, $1) { + }, 10234: function($0, $1) { setTimeout(function() { __emscripten_do_dispatch_to_thread($0, $1); }, 0); @@ -3729,7 +3107,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ } function _emscripten_num_logical_cores() { if (ENVIRONMENT_IS_NODE) - return __require2("os").cpus().length; + return require_os().cpus().length; return navigator["hardwareConcurrency"]; } function _emscripten_proxy_to_main_thread_js(index, sync) { @@ -4549,313 +3927,322 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ return (_dispose = Module["_dispose"] = Module["asm"]["H"]).apply(null, arguments); }; var _Abs = Module["_Abs"] = function() { - return (_Abs = Module["_Abs"] = Module["asm"]["J"]).apply(null, arguments); + return (_Abs = Module["_Abs"] = Module["asm"]["I"]).apply(null, arguments); }; var _Add = Module["_Add"] = function() { - return (_Add = Module["_Add"] = Module["asm"]["K"]).apply(null, arguments); + return (_Add = Module["_Add"] = Module["asm"]["J"]).apply(null, arguments); }; var _AddN = Module["_AddN"] = function() { - return (_AddN = Module["_AddN"] = Module["asm"]["L"]).apply(null, arguments); + return (_AddN = Module["_AddN"] = Module["asm"]["K"]).apply(null, arguments); }; var _All = Module["_All"] = function() { - return (_All = Module["_All"] = Module["asm"]["M"]).apply(null, arguments); + return (_All = Module["_All"] = Module["asm"]["L"]).apply(null, arguments); }; var _Any = Module["_Any"] = function() { - return (_Any = Module["_Any"] = Module["asm"]["N"]).apply(null, arguments); + return (_Any = Module["_Any"] = Module["asm"]["M"]).apply(null, arguments); }; var _ArgMax = Module["_ArgMax"] = function() { - return (_ArgMax = Module["_ArgMax"] = Module["asm"]["O"]).apply(null, arguments); + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["N"]).apply(null, arguments); }; var _AvgPool = Module["_AvgPool"] = function() { - return (_AvgPool = Module["_AvgPool"] = Module["asm"]["P"]).apply(null, arguments); + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["O"]).apply(null, arguments); }; var _BatchMatMul = Module["_BatchMatMul"] = function() { - return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["Q"]).apply(null, arguments); + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["P"]).apply(null, arguments); }; var _Ceil = Module["_Ceil"] = function() { - return (_Ceil = Module["_Ceil"] = Module["asm"]["R"]).apply(null, arguments); + return (_Ceil = Module["_Ceil"] = Module["asm"]["Q"]).apply(null, arguments); }; var _ClipByValue = Module["_ClipByValue"] = function() { - return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["S"]).apply(null, arguments); + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["R"]).apply(null, arguments); }; - var _Conv2D = Module["_Conv2D"] = function() { - return (_Conv2D = Module["_Conv2D"] = Module["asm"]["T"]).apply(null, arguments); + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["S"]).apply(null, arguments); }; var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { - return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["U"]).apply(null, arguments); + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["T"]).apply(null, arguments); }; var _Cos = Module["_Cos"] = function() { - return (_Cos = Module["_Cos"] = Module["asm"]["V"]).apply(null, arguments); + return (_Cos = Module["_Cos"] = Module["asm"]["U"]).apply(null, arguments); }; var _Cosh = Module["_Cosh"] = function() { - return (_Cosh = Module["_Cosh"] = Module["asm"]["W"]).apply(null, arguments); + return (_Cosh = Module["_Cosh"] = Module["asm"]["V"]).apply(null, arguments); }; var _CropAndResize = Module["_CropAndResize"] = function() { - return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["X"]).apply(null, arguments); + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["W"]).apply(null, arguments); }; var _Cumsum = Module["_Cumsum"] = function() { - return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Y"]).apply(null, arguments); + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["X"]).apply(null, arguments); }; var _DepthToSpace = Module["_DepthToSpace"] = function() { - return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["Z"]).apply(null, arguments); + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["Y"]).apply(null, arguments); }; var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { - return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["_"]).apply(null, arguments); + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["Z"]).apply(null, arguments); }; var _Elu = Module["_Elu"] = function() { - return (_Elu = Module["_Elu"] = Module["asm"]["$"]).apply(null, arguments); + return (_Elu = Module["_Elu"] = Module["asm"]["_"]).apply(null, arguments); }; var _Equal = Module["_Equal"] = function() { - return (_Equal = Module["_Equal"] = Module["asm"]["aa"]).apply(null, arguments); + return (_Equal = Module["_Equal"] = Module["asm"]["$"]).apply(null, arguments); }; var _Exp = Module["_Exp"] = function() { - return (_Exp = Module["_Exp"] = Module["asm"]["ba"]).apply(null, arguments); + return (_Exp = Module["_Exp"] = Module["asm"]["aa"]).apply(null, arguments); }; var _FlipLeftRight = Module["_FlipLeftRight"] = function() { - return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["ca"]).apply(null, arguments); + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["ba"]).apply(null, arguments); }; var _Floor = Module["_Floor"] = function() { - return (_Floor = Module["_Floor"] = Module["asm"]["da"]).apply(null, arguments); + return (_Floor = Module["_Floor"] = Module["asm"]["ca"]).apply(null, arguments); }; var _FloorDiv = Module["_FloorDiv"] = function() { - return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["ea"]).apply(null, arguments); + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["da"]).apply(null, arguments); }; var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { - return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["fa"]).apply(null, arguments); + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["ea"]).apply(null, arguments); }; var _FusedConv2D = Module["_FusedConv2D"] = function() { - return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["ga"]).apply(null, arguments); + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["fa"]).apply(null, arguments); }; var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { - return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["ha"]).apply(null, arguments); + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["ga"]).apply(null, arguments); }; var _Gather = Module["_Gather"] = function() { - return (_Gather = Module["_Gather"] = Module["asm"]["ia"]).apply(null, arguments); + return (_Gather = Module["_Gather"] = Module["asm"]["ha"]).apply(null, arguments); }; var _GatherNd = Module["_GatherNd"] = function() { - return (_GatherNd = Module["_GatherNd"] = Module["asm"]["ja"]).apply(null, arguments); + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["ia"]).apply(null, arguments); }; var _Greater = Module["_Greater"] = function() { - return (_Greater = Module["_Greater"] = Module["asm"]["ka"]).apply(null, arguments); + return (_Greater = Module["_Greater"] = Module["asm"]["ja"]).apply(null, arguments); }; var _GreaterEqual = Module["_GreaterEqual"] = function() { - return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["la"]).apply(null, arguments); + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["ka"]).apply(null, arguments); }; var _LeakyRelu = Module["_LeakyRelu"] = function() { - return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["ma"]).apply(null, arguments); + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["la"]).apply(null, arguments); }; var _Less = Module["_Less"] = function() { - return (_Less = Module["_Less"] = Module["asm"]["na"]).apply(null, arguments); + return (_Less = Module["_Less"] = Module["asm"]["ma"]).apply(null, arguments); }; var _LessEqual = Module["_LessEqual"] = function() { - return (_LessEqual = Module["_LessEqual"] = Module["asm"]["oa"]).apply(null, arguments); + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["na"]).apply(null, arguments); }; var _Log = Module["_Log"] = function() { - return (_Log = Module["_Log"] = Module["asm"]["pa"]).apply(null, arguments); + return (_Log = Module["_Log"] = Module["asm"]["oa"]).apply(null, arguments); }; var _LogicalAnd = Module["_LogicalAnd"] = function() { - return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["qa"]).apply(null, arguments); + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["pa"]).apply(null, arguments); }; var _Max = Module["_Max"] = function() { - return (_Max = Module["_Max"] = Module["asm"]["ra"]).apply(null, arguments); + return (_Max = Module["_Max"] = Module["asm"]["qa"]).apply(null, arguments); }; var _MaxPool = Module["_MaxPool"] = function() { - return (_MaxPool = Module["_MaxPool"] = Module["asm"]["sa"]).apply(null, arguments); + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["ra"]).apply(null, arguments); }; var _Maximum = Module["_Maximum"] = function() { - return (_Maximum = Module["_Maximum"] = Module["asm"]["ta"]).apply(null, arguments); + return (_Maximum = Module["_Maximum"] = Module["asm"]["sa"]).apply(null, arguments); }; var _Mean = Module["_Mean"] = function() { - return (_Mean = Module["_Mean"] = Module["asm"]["ua"]).apply(null, arguments); + return (_Mean = Module["_Mean"] = Module["asm"]["ta"]).apply(null, arguments); }; var _Min = Module["_Min"] = function() { - return (_Min = Module["_Min"] = Module["asm"]["va"]).apply(null, arguments); + return (_Min = Module["_Min"] = Module["asm"]["ua"]).apply(null, arguments); }; var _Minimum = Module["_Minimum"] = function() { - return (_Minimum = Module["_Minimum"] = Module["asm"]["wa"]).apply(null, arguments); + return (_Minimum = Module["_Minimum"] = Module["asm"]["va"]).apply(null, arguments); }; var _MirrorPad = Module["_MirrorPad"] = function() { - return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["xa"]).apply(null, arguments); + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["wa"]).apply(null, arguments); }; var _Multiply = Module["_Multiply"] = function() { - return (_Multiply = Module["_Multiply"] = Module["asm"]["ya"]).apply(null, arguments); + return (_Multiply = Module["_Multiply"] = Module["asm"]["xa"]).apply(null, arguments); }; var _Neg = Module["_Neg"] = function() { - return (_Neg = Module["_Neg"] = Module["asm"]["za"]).apply(null, arguments); + return (_Neg = Module["_Neg"] = Module["asm"]["ya"]).apply(null, arguments); }; var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { - return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["Aa"]).apply(null, arguments); + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["za"]).apply(null, arguments); }; var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { - return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["Ba"]).apply(null, arguments); + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["Aa"]).apply(null, arguments); }; var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { - return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["Ca"]).apply(null, arguments); + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["Ba"]).apply(null, arguments); }; var _NotEqual = Module["_NotEqual"] = function() { - return (_NotEqual = Module["_NotEqual"] = Module["asm"]["Da"]).apply(null, arguments); + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["Ca"]).apply(null, arguments); }; var _OneHot = Module["_OneHot"] = function() { - return (_OneHot = Module["_OneHot"] = Module["asm"]["Ea"]).apply(null, arguments); + return (_OneHot = Module["_OneHot"] = Module["asm"]["Da"]).apply(null, arguments); }; var _PadV2 = Module["_PadV2"] = function() { - return (_PadV2 = Module["_PadV2"] = Module["asm"]["Fa"]).apply(null, arguments); + return (_PadV2 = Module["_PadV2"] = Module["asm"]["Ea"]).apply(null, arguments); }; var _Pow = Module["_Pow"] = function() { - return (_Pow = Module["_Pow"] = Module["asm"]["Ga"]).apply(null, arguments); + return (_Pow = Module["_Pow"] = Module["asm"]["Fa"]).apply(null, arguments); }; var _Prelu = Module["_Prelu"] = function() { - return (_Prelu = Module["_Prelu"] = Module["asm"]["Ha"]).apply(null, arguments); + return (_Prelu = Module["_Prelu"] = Module["asm"]["Ga"]).apply(null, arguments); }; var _Prod = Module["_Prod"] = function() { - return (_Prod = Module["_Prod"] = Module["asm"]["Ia"]).apply(null, arguments); + return (_Prod = Module["_Prod"] = Module["asm"]["Ha"]).apply(null, arguments); }; var _RealDiv = Module["_RealDiv"] = function() { - return (_RealDiv = Module["_RealDiv"] = Module["asm"]["Ja"]).apply(null, arguments); + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["Ia"]).apply(null, arguments); }; var _Relu = Module["_Relu"] = function() { - return (_Relu = Module["_Relu"] = Module["asm"]["Ka"]).apply(null, arguments); + return (_Relu = Module["_Relu"] = Module["asm"]["Ja"]).apply(null, arguments); }; var _Relu6 = Module["_Relu6"] = function() { - return (_Relu6 = Module["_Relu6"] = Module["asm"]["La"]).apply(null, arguments); + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Ka"]).apply(null, arguments); }; var _ResizeBilinear = Module["_ResizeBilinear"] = function() { - return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["Ma"]).apply(null, arguments); + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["La"]).apply(null, arguments); }; var _Reverse = Module["_Reverse"] = function() { - return (_Reverse = Module["_Reverse"] = Module["asm"]["Na"]).apply(null, arguments); + return (_Reverse = Module["_Reverse"] = Module["asm"]["Ma"]).apply(null, arguments); }; var _RotateWithOffset = Module["_RotateWithOffset"] = function() { - return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Oa"]).apply(null, arguments); + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["Na"]).apply(null, arguments); }; var _Round = Module["_Round"] = function() { - return (_Round = Module["_Round"] = Module["asm"]["Pa"]).apply(null, arguments); + return (_Round = Module["_Round"] = Module["asm"]["Oa"]).apply(null, arguments); }; var _Rsqrt = Module["_Rsqrt"] = function() { - return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Qa"]).apply(null, arguments); + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Pa"]).apply(null, arguments); }; var _ScatterNd = Module["_ScatterNd"] = function() { - return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Ra"]).apply(null, arguments); + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Qa"]).apply(null, arguments); }; var _SelectV2 = Module["_SelectV2"] = function() { - return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Sa"]).apply(null, arguments); + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Ra"]).apply(null, arguments); }; var _Sigmoid = Module["_Sigmoid"] = function() { - return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Ta"]).apply(null, arguments); + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sa"]).apply(null, arguments); }; var _Sin = Module["_Sin"] = function() { - return (_Sin = Module["_Sin"] = Module["asm"]["Ua"]).apply(null, arguments); + return (_Sin = Module["_Sin"] = Module["asm"]["Ta"]).apply(null, arguments); }; var _Softmax = Module["_Softmax"] = function() { - return (_Softmax = Module["_Softmax"] = Module["asm"]["Va"]).apply(null, arguments); + return (_Softmax = Module["_Softmax"] = Module["asm"]["Ua"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["Va"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["Wa"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["Xa"]).apply(null, arguments); }; var _Sqrt = Module["_Sqrt"] = function() { - return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Wa"]).apply(null, arguments); + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Ya"]).apply(null, arguments); }; var _Square = Module["_Square"] = function() { - return (_Square = Module["_Square"] = Module["asm"]["Xa"]).apply(null, arguments); + return (_Square = Module["_Square"] = Module["asm"]["Za"]).apply(null, arguments); }; var _SquaredDifference = Module["_SquaredDifference"] = function() { - return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["Ya"]).apply(null, arguments); + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["_a"]).apply(null, arguments); }; var _Step = Module["_Step"] = function() { - return (_Step = Module["_Step"] = Module["asm"]["Za"]).apply(null, arguments); + return (_Step = Module["_Step"] = Module["asm"]["$a"]).apply(null, arguments); }; var _StridedSlice = Module["_StridedSlice"] = function() { - return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["_a"]).apply(null, arguments); + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["ab"]).apply(null, arguments); }; var _Sub = Module["_Sub"] = function() { - return (_Sub = Module["_Sub"] = Module["asm"]["$a"]).apply(null, arguments); + return (_Sub = Module["_Sub"] = Module["asm"]["bb"]).apply(null, arguments); }; var _Sum = Module["_Sum"] = function() { - return (_Sum = Module["_Sum"] = Module["asm"]["ab"]).apply(null, arguments); + return (_Sum = Module["_Sum"] = Module["asm"]["cb"]).apply(null, arguments); }; var _Tan = Module["_Tan"] = function() { - return (_Tan = Module["_Tan"] = Module["asm"]["bb"]).apply(null, arguments); + return (_Tan = Module["_Tan"] = Module["asm"]["db"]).apply(null, arguments); }; var _Tanh = Module["_Tanh"] = function() { - return (_Tanh = Module["_Tanh"] = Module["asm"]["cb"]).apply(null, arguments); + return (_Tanh = Module["_Tanh"] = Module["asm"]["eb"]).apply(null, arguments); }; var _Tile = Module["_Tile"] = function() { - return (_Tile = Module["_Tile"] = Module["asm"]["db"]).apply(null, arguments); + return (_Tile = Module["_Tile"] = Module["asm"]["fb"]).apply(null, arguments); }; var _TopK = Module["_TopK"] = function() { - return (_TopK = Module["_TopK"] = Module["asm"]["eb"]).apply(null, arguments); + return (_TopK = Module["_TopK"] = Module["asm"]["gb"]).apply(null, arguments); }; var _Transform = Module["_Transform"] = function() { - return (_Transform = Module["_Transform"] = Module["asm"]["fb"]).apply(null, arguments); + return (_Transform = Module["_Transform"] = Module["asm"]["hb"]).apply(null, arguments); }; var _Transpose = Module["_Transpose"] = function() { - return (_Transpose = Module["_Transpose"] = Module["asm"]["gb"]).apply(null, arguments); + return (_Transpose = Module["_Transpose"] = Module["asm"]["ib"]).apply(null, arguments); }; var __FusedMatMul = Module["__FusedMatMul"] = function() { - return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["hb"]).apply(null, arguments); + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["jb"]).apply(null, arguments); }; var _malloc = Module["_malloc"] = function() { - return (_malloc = Module["_malloc"] = Module["asm"]["ib"]).apply(null, arguments); + return (_malloc = Module["_malloc"] = Module["asm"]["kb"]).apply(null, arguments); }; var _free = Module["_free"] = function() { - return (_free = Module["_free"] = Module["asm"]["jb"]).apply(null, arguments); + return (_free = Module["_free"] = Module["asm"]["lb"]).apply(null, arguments); }; var ___errno_location = Module["___errno_location"] = function() { - return (___errno_location = Module["___errno_location"] = Module["asm"]["kb"]).apply(null, arguments); + return (___errno_location = Module["___errno_location"] = Module["asm"]["mb"]).apply(null, arguments); }; var _emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = function() { - return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["lb"]).apply(null, arguments); + return (_emscripten_get_global_libc = Module["_emscripten_get_global_libc"] = Module["asm"]["ob"]).apply(null, arguments); }; var _pthread_self = Module["_pthread_self"] = function() { - return (_pthread_self = Module["_pthread_self"] = Module["asm"]["mb"]).apply(null, arguments); + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pb"]).apply(null, arguments); }; var ___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = function() { - return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["nb"]).apply(null, arguments); + return (___pthread_tsd_run_dtors = Module["___pthread_tsd_run_dtors"] = Module["asm"]["qb"]).apply(null, arguments); }; var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { - return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["ob"]).apply(null, arguments); + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["rb"]).apply(null, arguments); }; var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() { - return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["pb"]).apply(null, arguments); + return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["sb"]).apply(null, arguments); }; var _emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = function() { - return (_emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = Module["asm"]["qb"]).apply(null, arguments); + return (_emscripten_register_main_browser_thread_id = Module["_emscripten_register_main_browser_thread_id"] = Module["asm"]["tb"]).apply(null, arguments); }; var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() { - return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["rb"]).apply(null, arguments); + return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["ub"]).apply(null, arguments); }; var __emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = function() { - return (__emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = Module["asm"]["sb"]).apply(null, arguments); + return (__emscripten_do_dispatch_to_thread = Module["__emscripten_do_dispatch_to_thread"] = Module["asm"]["vb"]).apply(null, arguments); }; var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() { - return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["tb"]).apply(null, arguments); + return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["wb"]).apply(null, arguments); }; var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() { - return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["ub"]).apply(null, arguments); + return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["xb"]).apply(null, arguments); }; var __emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = function() { - return (__emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = Module["asm"]["vb"]).apply(null, arguments); + return (__emscripten_call_on_thread = Module["__emscripten_call_on_thread"] = Module["asm"]["yb"]).apply(null, arguments); }; var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() { - return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["wb"]).apply(null, arguments); + return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["zb"]).apply(null, arguments); }; var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { - return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["xb"]).apply(null, arguments); + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["Ab"]).apply(null, arguments); }; var stackSave = Module["stackSave"] = function() { - return (stackSave = Module["stackSave"] = Module["asm"]["yb"]).apply(null, arguments); + return (stackSave = Module["stackSave"] = Module["asm"]["Bb"]).apply(null, arguments); }; var stackRestore = Module["stackRestore"] = function() { - return (stackRestore = Module["stackRestore"] = Module["asm"]["zb"]).apply(null, arguments); + return (stackRestore = Module["stackRestore"] = Module["asm"]["Cb"]).apply(null, arguments); }; var stackAlloc = Module["stackAlloc"] = function() { - return (stackAlloc = Module["stackAlloc"] = Module["asm"]["Ab"]).apply(null, arguments); + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["Db"]).apply(null, arguments); }; var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() { - return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["Bb"]).apply(null, arguments); + return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["Eb"]).apply(null, arguments); }; var _memalign = Module["_memalign"] = function() { - return (_memalign = Module["_memalign"] = Module["asm"]["Cb"]).apply(null, arguments); + return (_memalign = Module["_memalign"] = Module["asm"]["Fb"]).apply(null, arguments); }; - var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 10512; - var __emscripten_main_thread_futex = Module["__emscripten_main_thread_futex"] = 12148; + var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 10208; + var __emscripten_main_thread_futex = Module["__emscripten_main_thread_futex"] = 10412; Module["cwrap"] = cwrap; Module["PThread"] = PThread; Module["PThread"] = PThread; @@ -4949,33 +4336,65 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({ PThread.initWorker(); } run(); - return WasmBackendModuleThreadedSimd2.ready; + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule !== "undefined") { + actualModule = WasmBackendModule; + } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd3; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModuleThreadedSimd3.ready; }; }(); if (typeof exports === "object" && typeof module2 === "object") - module2.exports = WasmBackendModuleThreadedSimd; + module2.exports = WasmBackendModuleThreadedSimd2; else if (typeof define === "function" && define["amd"]) define([], function() { - return WasmBackendModuleThreadedSimd; + return WasmBackendModuleThreadedSimd2; }); else if (typeof exports === "object") - exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd; + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2; } }); var require_tfjs_backend_wasm = __commonJS({ - "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.11.0_@tensorflow+tfjs-core@3.11.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module2) { - var WasmBackendModule = function() { + "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module2) { + var WasmBackendModule2 = function() { var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; if (typeof __filename !== "undefined") _scriptDir = _scriptDir || __filename; - return function(WasmBackendModule2) { - WasmBackendModule2 = WasmBackendModule2 || {}; - var Module = typeof WasmBackendModule2 !== "undefined" ? WasmBackendModule2 : {}; + return function(WasmBackendModule3) { + WasmBackendModule3 = WasmBackendModule3 || {}; + var Module = typeof WasmBackendModule3 !== "undefined" ? WasmBackendModule3 : {}; var readyPromiseResolve, readyPromiseReject; Module["ready"] = new Promise(function(resolve, reject) { readyPromiseResolve = resolve; readyPromiseReject = reject; }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } var moduleOverrides = {}; var key; for (key in Module) { @@ -5014,7 +4433,7 @@ var require_tfjs_backend_wasm = __commonJS({ } read_ = function shell_read(filename, binary) { if (!nodeFS) - nodeFS = __require2("fs"); + nodeFS = require_fs(); if (!nodePath) nodePath = require_path(); filename = nodePath["normalize"](filename); @@ -5459,9 +4878,9 @@ var require_tfjs_backend_wasm = __commonJS({ function receiveInstance(instance, module22) { var exports3 = instance.exports; Module["asm"] = exports3; - wasmMemory = Module["asm"]["j"]; + wasmMemory = Module["asm"]["h"]; updateGlobalBufferAndViews(wasmMemory.buffer); - wasmTable = Module["asm"]["r"]; + wasmTable = Module["asm"]["Va"]; removeRunDependency("wasm-instantiate"); } addRunDependency("wasm-instantiate"); @@ -5591,399 +5010,241 @@ var require_tfjs_backend_wasm = __commonJS({ HEAP32[pnum >> 2] = num; return 0; } - function _pthread_create() { - return 6; - } function _pthread_join() { return 28; } - function setErrNo(value) { - HEAP32[___errno_location() >> 2] = value; - return value; - } - function _sysconf(name) { - switch (name) { - case 30: - return 16384; - case 85: - var maxHeapSize = 2147483648; - return maxHeapSize / 16384; - case 132: - case 133: - case 12: - case 137: - case 138: - case 15: - case 235: - case 16: - case 17: - case 18: - case 19: - case 20: - case 149: - case 13: - case 10: - case 236: - case 153: - case 9: - case 21: - case 22: - case 159: - case 154: - case 14: - case 77: - case 78: - case 139: - case 82: - case 68: - case 67: - case 164: - case 11: - case 29: - case 47: - case 48: - case 95: - case 52: - case 51: - case 46: - return 200809; - case 27: - case 246: - case 127: - case 128: - case 23: - case 24: - case 160: - case 161: - case 181: - case 182: - case 242: - case 183: - case 184: - case 243: - case 244: - case 245: - case 165: - case 178: - case 179: - case 49: - case 50: - case 168: - case 169: - case 175: - case 170: - case 171: - case 172: - case 97: - case 76: - case 32: - case 173: - case 35: - case 80: - case 81: - case 79: - return -1; - case 176: - case 177: - case 7: - case 155: - case 8: - case 157: - case 125: - case 126: - case 92: - case 93: - case 129: - case 130: - case 131: - case 94: - case 91: - return 1; - case 74: - case 60: - case 69: - case 70: - case 4: - return 1024; - case 31: - case 42: - case 72: - return 32; - case 87: - case 26: - case 33: - return 2147483647; - case 34: - case 1: - return 47839; - case 38: - case 36: - return 99; - case 43: - case 37: - return 2048; - case 0: - return 2097152; - case 3: - return 65536; - case 28: - return 32768; - case 44: - return 32767; - case 75: - return 16384; - case 39: - return 1e3; - case 89: - return 700; - case 71: - return 256; - case 40: - return 255; - case 2: - return 100; - case 180: - return 64; - case 25: - return 20; - case 5: - return 16; - case 6: - return 6; - case 73: - return 4; - case 84: { - if (typeof navigator === "object") - return navigator["hardwareConcurrency"] || 1; - return 1; - } - } - setErrNo(28); - return -1; - } - var asmLibraryArg = { "a": _abort, "d": _emscripten_memcpy_big, "e": _emscripten_resize_heap, "f": _fd_close, "c": _fd_seek, "b": _fd_write, "h": _pthread_create, "g": _pthread_join, "i": _sysconf }; + var asmLibraryArg = { "a": _abort, "d": _emscripten_memcpy_big, "e": _emscripten_resize_heap, "f": _fd_close, "c": _fd_seek, "b": _fd_write, "g": _pthread_join }; var asm = createWasm(); var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { - return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["k"]).apply(null, arguments); + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["i"]).apply(null, arguments); }; var _init = Module["_init"] = function() { - return (_init = Module["_init"] = Module["asm"]["l"]).apply(null, arguments); + return (_init = Module["_init"] = Module["asm"]["j"]).apply(null, arguments); }; var _init_with_threads_count = Module["_init_with_threads_count"] = function() { - return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["m"]).apply(null, arguments); + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["k"]).apply(null, arguments); }; var _get_threads_count = Module["_get_threads_count"] = function() { - return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["n"]).apply(null, arguments); + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["l"]).apply(null, arguments); }; var _register_tensor = Module["_register_tensor"] = function() { - return (_register_tensor = Module["_register_tensor"] = Module["asm"]["o"]).apply(null, arguments); + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["m"]).apply(null, arguments); }; var _dispose_data = Module["_dispose_data"] = function() { - return (_dispose_data = Module["_dispose_data"] = Module["asm"]["p"]).apply(null, arguments); + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["n"]).apply(null, arguments); }; var _dispose = Module["_dispose"] = function() { - return (_dispose = Module["_dispose"] = Module["asm"]["q"]).apply(null, arguments); + return (_dispose = Module["_dispose"] = Module["asm"]["o"]).apply(null, arguments); }; var _Abs = Module["_Abs"] = function() { - return (_Abs = Module["_Abs"] = Module["asm"]["s"]).apply(null, arguments); + return (_Abs = Module["_Abs"] = Module["asm"]["p"]).apply(null, arguments); }; var _Add = Module["_Add"] = function() { - return (_Add = Module["_Add"] = Module["asm"]["t"]).apply(null, arguments); + return (_Add = Module["_Add"] = Module["asm"]["q"]).apply(null, arguments); }; var _AddN = Module["_AddN"] = function() { - return (_AddN = Module["_AddN"] = Module["asm"]["u"]).apply(null, arguments); + return (_AddN = Module["_AddN"] = Module["asm"]["r"]).apply(null, arguments); }; var _All = Module["_All"] = function() { - return (_All = Module["_All"] = Module["asm"]["v"]).apply(null, arguments); + return (_All = Module["_All"] = Module["asm"]["s"]).apply(null, arguments); }; var _Any = Module["_Any"] = function() { - return (_Any = Module["_Any"] = Module["asm"]["w"]).apply(null, arguments); + return (_Any = Module["_Any"] = Module["asm"]["t"]).apply(null, arguments); }; var _ArgMax = Module["_ArgMax"] = function() { - return (_ArgMax = Module["_ArgMax"] = Module["asm"]["x"]).apply(null, arguments); + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["u"]).apply(null, arguments); }; var _AvgPool = Module["_AvgPool"] = function() { - return (_AvgPool = Module["_AvgPool"] = Module["asm"]["y"]).apply(null, arguments); + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["v"]).apply(null, arguments); }; var _BatchMatMul = Module["_BatchMatMul"] = function() { - return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["z"]).apply(null, arguments); + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["w"]).apply(null, arguments); }; var _Ceil = Module["_Ceil"] = function() { - return (_Ceil = Module["_Ceil"] = Module["asm"]["A"]).apply(null, arguments); + return (_Ceil = Module["_Ceil"] = Module["asm"]["x"]).apply(null, arguments); }; var _ClipByValue = Module["_ClipByValue"] = function() { - return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["B"]).apply(null, arguments); + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["y"]).apply(null, arguments); }; - var _Conv2D = Module["_Conv2D"] = function() { - return (_Conv2D = Module["_Conv2D"] = Module["asm"]["C"]).apply(null, arguments); + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["z"]).apply(null, arguments); }; var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { - return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["D"]).apply(null, arguments); + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["A"]).apply(null, arguments); }; var _Cos = Module["_Cos"] = function() { - return (_Cos = Module["_Cos"] = Module["asm"]["E"]).apply(null, arguments); + return (_Cos = Module["_Cos"] = Module["asm"]["B"]).apply(null, arguments); }; var _Cosh = Module["_Cosh"] = function() { - return (_Cosh = Module["_Cosh"] = Module["asm"]["F"]).apply(null, arguments); + return (_Cosh = Module["_Cosh"] = Module["asm"]["C"]).apply(null, arguments); }; var _CropAndResize = Module["_CropAndResize"] = function() { - return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["G"]).apply(null, arguments); + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["D"]).apply(null, arguments); }; var _Cumsum = Module["_Cumsum"] = function() { - return (_Cumsum = Module["_Cumsum"] = Module["asm"]["H"]).apply(null, arguments); + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["E"]).apply(null, arguments); }; var _DepthToSpace = Module["_DepthToSpace"] = function() { - return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["I"]).apply(null, arguments); + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["F"]).apply(null, arguments); }; var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { - return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["J"]).apply(null, arguments); + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["G"]).apply(null, arguments); }; var _Elu = Module["_Elu"] = function() { - return (_Elu = Module["_Elu"] = Module["asm"]["K"]).apply(null, arguments); + return (_Elu = Module["_Elu"] = Module["asm"]["H"]).apply(null, arguments); }; var _Equal = Module["_Equal"] = function() { - return (_Equal = Module["_Equal"] = Module["asm"]["L"]).apply(null, arguments); + return (_Equal = Module["_Equal"] = Module["asm"]["I"]).apply(null, arguments); }; var _Exp = Module["_Exp"] = function() { - return (_Exp = Module["_Exp"] = Module["asm"]["M"]).apply(null, arguments); + return (_Exp = Module["_Exp"] = Module["asm"]["J"]).apply(null, arguments); }; var _FlipLeftRight = Module["_FlipLeftRight"] = function() { - return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["N"]).apply(null, arguments); + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["K"]).apply(null, arguments); }; var _Floor = Module["_Floor"] = function() { - return (_Floor = Module["_Floor"] = Module["asm"]["O"]).apply(null, arguments); + return (_Floor = Module["_Floor"] = Module["asm"]["L"]).apply(null, arguments); }; var _FloorDiv = Module["_FloorDiv"] = function() { - return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["P"]).apply(null, arguments); + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["M"]).apply(null, arguments); }; var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { - return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["Q"]).apply(null, arguments); + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["N"]).apply(null, arguments); }; var _FusedConv2D = Module["_FusedConv2D"] = function() { - return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["R"]).apply(null, arguments); + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["O"]).apply(null, arguments); }; var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { - return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["S"]).apply(null, arguments); + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["P"]).apply(null, arguments); }; var _Gather = Module["_Gather"] = function() { - return (_Gather = Module["_Gather"] = Module["asm"]["T"]).apply(null, arguments); + return (_Gather = Module["_Gather"] = Module["asm"]["Q"]).apply(null, arguments); }; var _GatherNd = Module["_GatherNd"] = function() { - return (_GatherNd = Module["_GatherNd"] = Module["asm"]["U"]).apply(null, arguments); + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["R"]).apply(null, arguments); }; var _Greater = Module["_Greater"] = function() { - return (_Greater = Module["_Greater"] = Module["asm"]["V"]).apply(null, arguments); + return (_Greater = Module["_Greater"] = Module["asm"]["S"]).apply(null, arguments); }; var _GreaterEqual = Module["_GreaterEqual"] = function() { - return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["W"]).apply(null, arguments); + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["T"]).apply(null, arguments); }; var _LeakyRelu = Module["_LeakyRelu"] = function() { - return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["X"]).apply(null, arguments); + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["U"]).apply(null, arguments); }; var _Less = Module["_Less"] = function() { - return (_Less = Module["_Less"] = Module["asm"]["Y"]).apply(null, arguments); + return (_Less = Module["_Less"] = Module["asm"]["V"]).apply(null, arguments); }; var _LessEqual = Module["_LessEqual"] = function() { - return (_LessEqual = Module["_LessEqual"] = Module["asm"]["Z"]).apply(null, arguments); + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["W"]).apply(null, arguments); }; var _Log = Module["_Log"] = function() { - return (_Log = Module["_Log"] = Module["asm"]["_"]).apply(null, arguments); + return (_Log = Module["_Log"] = Module["asm"]["X"]).apply(null, arguments); }; var _LogicalAnd = Module["_LogicalAnd"] = function() { - return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["$"]).apply(null, arguments); + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["Y"]).apply(null, arguments); }; var _Max = Module["_Max"] = function() { - return (_Max = Module["_Max"] = Module["asm"]["aa"]).apply(null, arguments); + return (_Max = Module["_Max"] = Module["asm"]["Z"]).apply(null, arguments); }; var _MaxPool = Module["_MaxPool"] = function() { - return (_MaxPool = Module["_MaxPool"] = Module["asm"]["ba"]).apply(null, arguments); + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["_"]).apply(null, arguments); }; var _Maximum = Module["_Maximum"] = function() { - return (_Maximum = Module["_Maximum"] = Module["asm"]["ca"]).apply(null, arguments); + return (_Maximum = Module["_Maximum"] = Module["asm"]["$"]).apply(null, arguments); }; var _Mean = Module["_Mean"] = function() { - return (_Mean = Module["_Mean"] = Module["asm"]["da"]).apply(null, arguments); + return (_Mean = Module["_Mean"] = Module["asm"]["aa"]).apply(null, arguments); }; var _Min = Module["_Min"] = function() { - return (_Min = Module["_Min"] = Module["asm"]["ea"]).apply(null, arguments); + return (_Min = Module["_Min"] = Module["asm"]["ba"]).apply(null, arguments); }; var _Minimum = Module["_Minimum"] = function() { - return (_Minimum = Module["_Minimum"] = Module["asm"]["fa"]).apply(null, arguments); + return (_Minimum = Module["_Minimum"] = Module["asm"]["ca"]).apply(null, arguments); }; var _MirrorPad = Module["_MirrorPad"] = function() { - return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["ga"]).apply(null, arguments); + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["da"]).apply(null, arguments); }; var _Multiply = Module["_Multiply"] = function() { - return (_Multiply = Module["_Multiply"] = Module["asm"]["ha"]).apply(null, arguments); + return (_Multiply = Module["_Multiply"] = Module["asm"]["ea"]).apply(null, arguments); }; var _Neg = Module["_Neg"] = function() { - return (_Neg = Module["_Neg"] = Module["asm"]["ia"]).apply(null, arguments); + return (_Neg = Module["_Neg"] = Module["asm"]["fa"]).apply(null, arguments); }; var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { - return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["ja"]).apply(null, arguments); + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["ga"]).apply(null, arguments); }; var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { - return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ka"]).apply(null, arguments); + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["ha"]).apply(null, arguments); }; var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { - return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["la"]).apply(null, arguments); + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["ia"]).apply(null, arguments); }; var _NotEqual = Module["_NotEqual"] = function() { - return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ma"]).apply(null, arguments); + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["ja"]).apply(null, arguments); }; var _OneHot = Module["_OneHot"] = function() { - return (_OneHot = Module["_OneHot"] = Module["asm"]["na"]).apply(null, arguments); + return (_OneHot = Module["_OneHot"] = Module["asm"]["ka"]).apply(null, arguments); }; var _PadV2 = Module["_PadV2"] = function() { - return (_PadV2 = Module["_PadV2"] = Module["asm"]["oa"]).apply(null, arguments); + return (_PadV2 = Module["_PadV2"] = Module["asm"]["la"]).apply(null, arguments); }; var _Pow = Module["_Pow"] = function() { - return (_Pow = Module["_Pow"] = Module["asm"]["pa"]).apply(null, arguments); + return (_Pow = Module["_Pow"] = Module["asm"]["ma"]).apply(null, arguments); }; var _Prelu = Module["_Prelu"] = function() { - return (_Prelu = Module["_Prelu"] = Module["asm"]["qa"]).apply(null, arguments); + return (_Prelu = Module["_Prelu"] = Module["asm"]["na"]).apply(null, arguments); }; var _Prod = Module["_Prod"] = function() { - return (_Prod = Module["_Prod"] = Module["asm"]["ra"]).apply(null, arguments); + return (_Prod = Module["_Prod"] = Module["asm"]["oa"]).apply(null, arguments); }; var _RealDiv = Module["_RealDiv"] = function() { - return (_RealDiv = Module["_RealDiv"] = Module["asm"]["sa"]).apply(null, arguments); + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["pa"]).apply(null, arguments); }; var _Relu = Module["_Relu"] = function() { - return (_Relu = Module["_Relu"] = Module["asm"]["ta"]).apply(null, arguments); + return (_Relu = Module["_Relu"] = Module["asm"]["qa"]).apply(null, arguments); }; var _Relu6 = Module["_Relu6"] = function() { - return (_Relu6 = Module["_Relu6"] = Module["asm"]["ua"]).apply(null, arguments); + return (_Relu6 = Module["_Relu6"] = Module["asm"]["ra"]).apply(null, arguments); }; var _ResizeBilinear = Module["_ResizeBilinear"] = function() { - return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["va"]).apply(null, arguments); + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["sa"]).apply(null, arguments); }; var _Reverse = Module["_Reverse"] = function() { - return (_Reverse = Module["_Reverse"] = Module["asm"]["wa"]).apply(null, arguments); + return (_Reverse = Module["_Reverse"] = Module["asm"]["ta"]).apply(null, arguments); }; var _RotateWithOffset = Module["_RotateWithOffset"] = function() { - return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["xa"]).apply(null, arguments); + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["ua"]).apply(null, arguments); }; var _Round = Module["_Round"] = function() { - return (_Round = Module["_Round"] = Module["asm"]["ya"]).apply(null, arguments); + return (_Round = Module["_Round"] = Module["asm"]["va"]).apply(null, arguments); }; var _Rsqrt = Module["_Rsqrt"] = function() { - return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["za"]).apply(null, arguments); + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["wa"]).apply(null, arguments); }; var _ScatterNd = Module["_ScatterNd"] = function() { - return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["Aa"]).apply(null, arguments); + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["xa"]).apply(null, arguments); }; var _SelectV2 = Module["_SelectV2"] = function() { - return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["Ba"]).apply(null, arguments); + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["ya"]).apply(null, arguments); }; var _Sigmoid = Module["_Sigmoid"] = function() { - return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Ca"]).apply(null, arguments); + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["za"]).apply(null, arguments); }; var _Sin = Module["_Sin"] = function() { - return (_Sin = Module["_Sin"] = Module["asm"]["Da"]).apply(null, arguments); + return (_Sin = Module["_Sin"] = Module["asm"]["Aa"]).apply(null, arguments); }; var _Softmax = Module["_Softmax"] = function() { - return (_Softmax = Module["_Softmax"] = Module["asm"]["Ea"]).apply(null, arguments); + return (_Softmax = Module["_Softmax"] = Module["asm"]["Ba"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["Ca"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["Da"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["Ea"]).apply(null, arguments); }; var _Sqrt = Module["_Sqrt"] = function() { return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Fa"]).apply(null, arguments); @@ -6033,9 +5294,6 @@ var require_tfjs_backend_wasm = __commonJS({ var _free = Module["_free"] = function() { return (_free = Module["_free"] = Module["asm"]["Ua"]).apply(null, arguments); }; - var ___errno_location = Module["___errno_location"] = function() { - return (___errno_location = Module["___errno_location"] = Module["asm"]["Va"]).apply(null, arguments); - }; var stackSave = Module["stackSave"] = function() { return (stackSave = Module["stackSave"] = Module["asm"]["Wa"]).apply(null, arguments); }; @@ -6102,17 +5360,45 @@ var require_tfjs_backend_wasm = __commonJS({ } } run(); - return WasmBackendModule2.ready; + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule3 !== "undefined") { + actualModule = WasmBackendModule3; + } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModule3.ready; }; }(); if (typeof exports === "object" && typeof module2 === "object") - module2.exports = WasmBackendModule; + module2.exports = WasmBackendModule2; else if (typeof define === "function" && define["amd"]) define([], function() { - return WasmBackendModule; + return WasmBackendModule2; }); else if (typeof exports === "object") - exports["WasmBackendModule"] = WasmBackendModule; + exports["WasmBackendModule"] = WasmBackendModule2; } }); var EPSILON_FLOAT32 = 1e-7; @@ -6214,8 +5500,8 @@ function shuffleCombo(array2, array22) { swap(array22, counter, index); } } -function clamp(min6, x, max6) { - return Math.max(min6, Math.min(x, max6)); +function clamp(min7, x, max7) { + return Math.max(min7, Math.min(x, max7)); } function nearestLargerEven(val) { return val % 2 === 0 ? val : val + 1; @@ -6226,11 +5512,11 @@ function swap(object, left, right) { object[right] = temp; } function sum(arr) { - let sum6 = 0; + let sum7 = 0; for (let i = 0; i < arr.length; i++) { - sum6 += arr[i]; + sum7 += arr[i]; } - return sum6; + return sum7; } function randUniform(a, b) { const r = Math.random(); @@ -6634,16 +5920,6 @@ function indexToLoc(index, rank, strides) { function isPromise(object) { return object && object.then && typeof object.then === "function"; } -function warn(...msg) { - if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { - console.warn(...msg); - } -} -function log(...msg) { - if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { - console.log(...msg); - } -} var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; var Environment = class { constructor(global2) { @@ -6656,7 +5932,9 @@ var Environment = class { } setPlatform(platformName, platform) { if (this.platform != null) { - warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`); + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`); + } } this.platformName = platformName; this.platform = platform; @@ -6665,7 +5943,9 @@ var Environment = class { this.flagRegistry[flagName] = { evaluationFn, setHook }; if (this.urlFlags[flagName] != null) { const flagValue = this.urlFlags[flagName]; - warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + } this.set(flagName, flagValue); } } @@ -6966,6 +6246,16 @@ var RotateWithOffset = "RotateWithOffset"; var _FusedMatMul = "_FusedMatMul"; var FusedConv2D = "FusedConv2D"; var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; +function warn(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(...msg); + } +} +function log(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.log(...msg); + } +} var kernelRegistry = getGlobal("kernelRegistry", () => new Map()); var gradRegistry = getGlobal("gradRegistry", () => new Map()); function getKernel(kernelName, backendName) { @@ -7703,7 +6993,7 @@ var Tensor = class { const bytes = await data; try { return bytes.map((b) => decodeString(b)); - } catch (_a) { + } catch (e) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } } @@ -7715,7 +7005,7 @@ var Tensor = class { if (this.dtype === "string") { try { return data.map((b) => decodeString(b)); - } catch (_a) { + } catch (e) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } } @@ -7811,14 +7101,14 @@ __export2(tensor_util_exports, { makeTypesMatch: () => makeTypesMatch }); var Rank; -(function(Rank2) { - Rank2["R0"] = "R0"; - Rank2["R1"] = "R1"; - Rank2["R2"] = "R2"; - Rank2["R3"] = "R3"; - Rank2["R4"] = "R4"; - Rank2["R5"] = "R5"; - Rank2["R6"] = "R6"; +(function(Rank18) { + Rank18["R0"] = "R0"; + Rank18["R1"] = "R1"; + Rank18["R2"] = "R2"; + Rank18["R3"] = "R3"; + Rank18["R4"] = "R4"; + Rank18["R5"] = "R5"; + Rank18["R6"] = "R6"; })(Rank || (Rank = {})); var UpcastInt32AndMap; (function(UpcastInt32AndMap2) { @@ -7943,9 +7233,9 @@ var EngineState = class { } } }; -var Engine = class { - constructor(ENV5) { - this.ENV = ENV5; +var _Engine = class { + constructor(ENV7) { + this.ENV = ENV7; this.registry = {}; this.registryFactory = {}; this.pendingBackendInitId = 0; @@ -8171,10 +7461,10 @@ var Engine = class { } } nextTensorId() { - return Engine.nextTensorId++; + return _Engine.nextTensorId++; } nextVariableId() { - return Engine.nextVariableId++; + return _Engine.nextVariableId++; } clone(x) { const y = ENGINE.runKernel(Identity, { x }); @@ -8627,6 +7917,7 @@ var Engine = class { this.pendingBackendInit = null; } }; +var Engine = _Engine; Engine.nextTensorId = 0; Engine.nextVariableId = 0; function ones(shape) { @@ -8799,9 +8090,9 @@ function op(f) { Object.defineProperty(f2, "name", { value: opName, configurable: true }); return f2; } -function complex_(real4, imag4) { - const $real = convertToTensor(real4, "real", "complex"); - const $imag = convertToTensor(imag4, "imag", "complex"); +function complex_(real5, imag5) { + const $real = convertToTensor(real5, "real", "complex"); + const $imag = convertToTensor(imag5, "imag", "complex"); assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); const inputs = { real: $real, imag: $imag }; return ENGINE.runKernel(Complex, inputs); @@ -8963,13 +8254,13 @@ function decodeWeights(buffer2, specs) { values = new Uint8Array(byteBuffer); } else if (dtype === "complex64") { values = new Float32Array(byteBuffer); - const real4 = new Float32Array(values.length / 2); + const real5 = new Float32Array(values.length / 2); const image3 = new Float32Array(values.length / 2); - for (let i = 0; i < real4.length; i++) { - real4[i] = values[i * 2]; + for (let i = 0; i < real5.length; i++) { + real5[i] = values[i * 2]; image3[i] = values[i * 2 + 1]; } - const realTensor = tensor(real4, shape, "float32"); + const realTensor = tensor(real5, shape, "float32"); const imageTensor = tensor(image3, shape, "float32"); out[name] = complex(realTensor, imageTensor); realTensor.dispose(); @@ -9712,7 +9003,7 @@ var getNodeFetch = { var systemFetch; var PlatformNode = class { constructor() { - this.util = __require2("util"); + this.util = require_util(); this.textEncoder = new this.util.TextEncoder(); } fetch(path, requestInits) { @@ -9809,13 +9100,13 @@ var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; function defer(f) { return new Promise((resolve) => setTimeout(resolve)).then(f); } -var BrowserDownloads = class { +var _BrowserDownloads = class { constructor(fileNamePrefix) { if (!env().getBool("IS_BROWSER")) { throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); } - if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) { - fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length); + if (fileNamePrefix.startsWith(_BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(_BrowserDownloads.URL_SCHEME.length); } if (fileNamePrefix == null || fileNamePrefix.length === 0) { fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; @@ -9851,6 +9142,7 @@ var BrowserDownloads = class { } } }; +var BrowserDownloads = _BrowserDownloads; BrowserDownloads.URL_SCHEME = "downloads://"; var BrowserFiles = class { constructor(files) { @@ -10312,6 +9604,62 @@ function confusionMatrix_(labels, predictions, numClasses) { return cast(product, "int32"); } var confusionMatrix = op({ confusionMatrix_ }); +var broadcast_util_exports = {}; +__export2(broadcast_util_exports, { + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + getBroadcastDims: () => getBroadcastDims, + getReductionAxes: () => getReductionAxes +}); +function getBroadcastDims(inShape, outShape) { + const inRank = inShape.length; + const dims = []; + for (let i = 0; i < inRank; i++) { + const dim = inRank - 1 - i; + const a = inShape[dim] || 1; + const b = outShape[outShape.length - 1 - i] || 1; + if (b > 1 && a === 1) { + dims.unshift(dim); + } + } + return dims; +} +function getReductionAxes(inShape, outShape) { + const result = []; + for (let i = 0; i < outShape.length; i++) { + const inDim = inShape[inShape.length - i - 1]; + const outAxis = outShape.length - i - 1; + const outDim = outShape[outAxis]; + if (inDim == null || inDim === 1 && outDim > 1) { + result.unshift(outAxis); + } + } + return result; +} +function assertAndGetBroadcastShape(shapeA, shapeB) { + const result = []; + const l = Math.max(shapeA.length, shapeB.length); + for (let i = 0; i < l; i++) { + let a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + let b = shapeB[shapeB.length - i - 1]; + if (b == null) { + b = 1; + } + if (a === 1) { + result.unshift(b); + } else if (b === 1) { + result.unshift(a); + } else if (a !== b) { + const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`; + throw Error(errMsg); + } else { + result.unshift(a); + } + } + return result; +} var browser_exports = {}; __export2(browser_exports, { fromPixels: () => fromPixels, @@ -11157,9 +10505,9 @@ function expectPromiseToFail(fn, done) { fn().then(() => done.fail(), () => done()); } function expectArraysEqual(actual, expected) { - const exp4 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected; + const exp5 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected; if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) { - return expectArraysPredicate(actual, exp4, (a, b) => a == b); + return expectArraysPredicate(actual, exp5, (a, b) => a == b); } return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0)); } @@ -11201,7 +10549,7 @@ function encodeStrings(a) { } return a; } -var version = "3.11.0"; +var version = "0.0.0"; function enableProdMode() { env().set("PROD", true); } @@ -11703,6 +11051,23 @@ function convertConv2DDataFormat(dataFormat) { throw new Error(`Unknown dataFormat ${dataFormat}`); } } +function checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) { + if (dimRoundingMode != null) { + if (typeof pad3 === "string") { + throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "number") { + assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); + } else if (typeof pad3 === "object") { + pad3.forEach((p2) => { + p2.forEach((v) => { + assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`); + }); + }); + } else { + throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`); + } + } +} function reshape_(x, shape) { const $x = convertToTensor(x, "x", "reshape", "string_or_numeric"); const inputs = { x: $x }; @@ -11721,9 +11086,7 @@ function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) { x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); } assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("avgPool", pad3, dimRoundingMode); const inputs = { x: x4D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; let res = ENGINE.runKernel(AvgPool, inputs, attrs); @@ -11744,9 +11107,7 @@ function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = } assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`); assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("avgPool3d", pad3, dimRoundingMode); const inputs = { x: x5D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; let res = ENGINE.runKernel(AvgPool3D, inputs, attrs); @@ -11822,10 +11183,10 @@ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) { var basicLSTMCell = op({ basicLSTMCell_ }); function batchToSpaceND_(x, blockShape, crops) { const $x = convertToTensor(x, "x", "batchToSpaceND"); - const prod5 = blockShape.reduce((a, b) => a * b); + const prod6 = blockShape.reduce((a, b) => a * b); assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`); assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length ${blockShape.length}`); - assert($x.shape[0] % prod5 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`); + assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod6}`); const inputs = { x: $x }; const attrs = { blockShape, crops }; return ENGINE.runKernel(BatchToSpaceND, inputs, attrs); @@ -11844,12 +11205,12 @@ function xAs4D(x) { } return x4D; } -function batchNorm_(x, mean4, variance, offset, scale22, varianceEpsilon) { +function batchNorm_(x, mean5, variance, offset, scale22, varianceEpsilon) { if (varianceEpsilon == null) { varianceEpsilon = 1e-3; } const $x = convertToTensor(x, "x", "batchNorm"); - const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $mean = convertToTensor(mean5, "mean", "batchNorm"); const $variance = convertToTensor(variance, "variance", "batchNorm"); let $scale; if (scale22 != null) { @@ -11875,9 +11236,9 @@ function batchNorm_(x, mean4, variance, offset, scale22, varianceEpsilon) { return reshape(res, $x.shape); } var batchNorm = op({ batchNorm_ }); -function batchNorm2d_(x, mean4, variance, offset, scale22, varianceEpsilon) { +function batchNorm2d_(x, mean5, variance, offset, scale22, varianceEpsilon) { const $x = convertToTensor(x, "x", "batchNorm"); - const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $mean = convertToTensor(mean5, "mean", "batchNorm"); const $variance = convertToTensor(variance, "variance", "batchNorm"); let $scale; if (scale22 != null) { @@ -11899,9 +11260,9 @@ function batchNorm2d_(x, mean4, variance, offset, scale22, varianceEpsilon) { return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); } var batchNorm2d = op({ batchNorm2d_ }); -function batchNorm3d_(x, mean4, variance, offset, scale22, varianceEpsilon) { +function batchNorm3d_(x, mean5, variance, offset, scale22, varianceEpsilon) { const $x = convertToTensor(x, "x", "batchNorm"); - const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $mean = convertToTensor(mean5, "mean", "batchNorm"); const $variance = convertToTensor(variance, "variance", "batchNorm"); let $scale; if (scale22 != null) { @@ -11923,9 +11284,9 @@ function batchNorm3d_(x, mean4, variance, offset, scale22, varianceEpsilon) { return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon); } var batchNorm3d = op({ batchNorm3d_ }); -function batchNorm4d_(x, mean4, variance, offset, scale22, varianceEpsilon) { +function batchNorm4d_(x, mean5, variance, offset, scale22, varianceEpsilon) { const $x = convertToTensor(x, "x", "batchNorm"); - const $mean = convertToTensor(mean4, "mean", "batchNorm"); + const $mean = convertToTensor(mean5, "mean", "batchNorm"); const $variance = convertToTensor(variance, "variance", "batchNorm"); let $scale; if (scale22 != null) { @@ -12046,9 +11407,7 @@ function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, } assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("conv2d", pad3, dimRoundingMode); const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1]; assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); @@ -12072,9 +11431,7 @@ function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimR } assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`); assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("conv1d", pad3, dimRoundingMode); assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`); assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`); assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`); @@ -12107,9 +11464,7 @@ function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "N const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`); assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("conv2dDerInput", pad3, dimRoundingMode); const inputs = { dy: dy4D, filter }; const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D }; const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs); @@ -12241,9 +11596,7 @@ function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilatio assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`); assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); const inputs = { x: x4D, filter: $filter }; const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }; const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs); @@ -12280,56 +11633,6 @@ function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = return res; } var dilation2d = op({ dilation2d_ }); -function getBroadcastDims(inShape, outShape) { - const inRank = inShape.length; - const dims = []; - for (let i = 0; i < inRank; i++) { - const dim = inRank - 1 - i; - const a = inShape[dim] || 1; - const b = outShape[outShape.length - 1 - i] || 1; - if (b > 1 && a === 1) { - dims.unshift(dim); - } - } - return dims; -} -function getReductionAxes(inShape, outShape) { - const result = []; - for (let i = 0; i < outShape.length; i++) { - const inDim = inShape[inShape.length - i - 1]; - const outAxis = outShape.length - i - 1; - const outDim = outShape[outAxis]; - if (inDim == null || inDim === 1 && outDim > 1) { - result.unshift(outAxis); - } - } - return result; -} -function assertAndGetBroadcastShape(shapeA, shapeB) { - const result = []; - const l = Math.max(shapeA.length, shapeB.length); - for (let i = 0; i < l; i++) { - let a = shapeA[shapeA.length - i - 1]; - if (a == null) { - a = 1; - } - let b = shapeB[shapeB.length - i - 1]; - if (b == null) { - b = 1; - } - if (a === 1) { - result.unshift(b); - } else if (b === 1) { - result.unshift(a); - } else if (a !== b) { - const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`; - throw Error(errMsg); - } else { - result.unshift(a); - } - } - return result; -} function equal_(a, b) { let $a = convertToTensor(a, "a", "equal", "string_or_numeric"); let $b = convertToTensor(b, "b", "equal", "string_or_numeric"); @@ -12598,7 +11901,7 @@ function log_(x) { const inputs = { x: $x }; return ENGINE.runKernel(Log, inputs); } -var log5 = op({ log_ }); +var log4 = op({ log_ }); function log1p_(x) { const $x = convertToTensor(x, "x", "log1p"); const inputs = { x: $x }; @@ -12760,13 +12063,13 @@ function logSoftmax_(logits, axis = -1) { const keepDims = true; const xMax = max(logits2, axis, true); const shifted = sub(logits2, xMax); - const value = sub(cast(shifted, "float32"), log5(sum2(exp(shifted), axis, keepDims))); + const value = sub(cast(shifted, "float32"), log4(sum2(exp(shifted), axis, keepDims))); save([value]); const gradFunc = (dy, saved) => { const [value2] = saved; const keepDims2 = true; - const softmax6 = exp(value2); - return sub(dy, mul(sum2(dy, axis, keepDims2), softmax6)); + const softmax7 = exp(value2); + return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7)); }; return { value, gradFunc }; }); @@ -12843,7 +12146,7 @@ function logSumExp_(x, axis = null, keepDims = false) { const a = sub($x, xMax); const b = exp(a); const c = sum2(b, axes); - const d = log5(c); + const d = log4(c); const res = add2(reshape(xMax, d.shape), d); if (keepDims) { const newShape = expandShapeToKeepDim(res.shape, axes); @@ -12892,9 +12195,7 @@ function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) { } assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("maxPool", pad3, dimRoundingMode); const inputs = { x: x4D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; const res = ENGINE.runKernel(MaxPool, inputs, attrs); @@ -12914,9 +12215,7 @@ function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, d } assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`); assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("maxPool3d", pad3, dimRoundingMode); const inputs = { x: x5D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat }; const res = ENGINE.runKernel(MaxPool3D, inputs, attrs); @@ -12956,18 +12255,18 @@ function mean_(x, axis = null, keepDims = false) { var mean = op({ mean_ }); function zeros(shape, dtype = "float32") { if (dtype === "complex64") { - const real4 = zeros(shape, "float32"); - const imag4 = zeros(shape, "float32"); - return complex(real4, imag4); + const real5 = zeros(shape, "float32"); + const imag5 = zeros(shape, "float32"); + return complex(real5, imag5); } const values = makeZerosTypedArray(sizeFromShape(shape), dtype); return ENGINE.makeTensor(values, shape, dtype); } function ones2(shape, dtype = "float32") { if (dtype === "complex64") { - const real4 = ones2(shape, "float32"); - const imag4 = zeros(shape, "float32"); - return complex(real4, imag4); + const real5 = ones2(shape, "float32"); + const imag5 = zeros(shape, "float32"); + return complex(real5, imag5); } const values = makeOnesTypedArray(sizeFromShape(shape), dtype); return ENGINE.makeTensor(values, shape, dtype); @@ -13173,7 +12472,7 @@ function spaceToBatchND_(x, blockShape, paddings) { return ENGINE.runKernel(SpaceToBatchND, inputs, attrs); } var spaceToBatchND = op({ spaceToBatchND_ }); -function pool_(input2, windowShape, poolingType, pad3, dilations, strides) { +function pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) { if (dilations == null) { dilations = [1, 1]; } @@ -13203,7 +12502,7 @@ function pool_(input2, windowShape, poolingType, pad3, dilations, strides) { const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding); const convertedPad = isDilationOne ? pad3 : "valid"; const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding); - const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad) : () => maxPool(convertedX, windowShape, strides, convertedPad); + const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); const y = forwardOp(); const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops); if (reshapedTo4D) { @@ -13233,9 +12532,9 @@ function withSpaceToBatchBasePaddings(filterShape, dilation) { }); } var pool = op({ pool_ }); -function pow_(base2, exp4) { +function pow_(base2, exp5) { let $base = convertToTensor(base2, "base", "pow"); - let $exp = convertToTensor(exp4, "exp", "pow"); + let $exp = convertToTensor(exp5, "exp", "pow"); [$base, $exp] = makeTypesMatch($base, $exp); const inputs = { a: $base, b: $exp }; return ENGINE.runKernel(Pow, inputs); @@ -13278,8 +12577,8 @@ function rand_(shape, randFunction, dtype) { var rand = op({ rand_ }); var seedrandom = __toModule(require_seedrandom2()); var MPRandGauss = class { - constructor(mean4, stdDeviation, dtype, truncated, seed) { - this.mean = mean4; + constructor(mean5, stdDeviation, dtype, truncated, seed) { + this.mean = mean5; this.stdDev = stdDeviation; this.dtype = dtype; this.nextVal = NaN; @@ -13373,10 +12672,10 @@ var RandGamma = class { } }; var UniformRandom = class { - constructor(min6 = 0, max6 = 1, dtype, seed) { + constructor(min7 = 0, max7 = 1, dtype, seed) { this.canReturnFloat = () => this.dtype == null || this.dtype === "float32"; - this.min = min6; - this.range = max6 - min6; + this.min = min7; + this.range = max7 - min7; this.dtype = dtype; if (seed == null) { seed = Math.random(); @@ -13385,7 +12684,7 @@ var UniformRandom = class { seed = seed.toString(); } if (!this.canReturnFloat() && this.range <= 1) { - throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`); + throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`); } this.random = seedrandom.alea(seed); } @@ -13417,11 +12716,11 @@ function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) { return res.toTensor(); } var randomGamma = op({ randomGamma_ }); -function randomNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { +function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) { if (dtype != null && dtype === "bool") { throw new Error(`Unsupported data type ${dtype}`); } - const randGauss = new MPRandGauss(mean4, stdDev, dtype, false, seed); + const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed); const res = buffer(shape, dtype); for (let i = 0; i < res.values.length; i++) { res.values[i] = randGauss.nextValue(); @@ -13865,11 +13164,11 @@ function topk_(x, k = 1, sorted = true) { return { values, indices }; } var topk = op({ topk_ }); -function truncatedNormal_(shape, mean4 = 0, stdDev = 1, dtype, seed) { +function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) { if (dtype != null && dtype === "bool") { throw new Error(`Unsupported data type $ { dtype }`); } - const randGauss = new MPRandGauss(mean4, stdDev, dtype, true, seed); + const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed); const res = buffer(shape, dtype); for (let i = 0; i < res.values.length; i++) { res.values[i] = randGauss.nextValue(); @@ -14184,9 +13483,7 @@ function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = " const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1]; assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`); assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("conv2dDerFilter", pad3, dimRoundingMode); const inputs = { x: x4D, dy: dy4D }; const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape }; return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs); @@ -14231,7 +13528,19 @@ var shouldFuse = (gradientDepth, activation2) => { const gradientMode = gradientDepth > 0; return !gradientMode || activation2 === "linear"; }; -function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { +function fusedConv2d_({ + x, + filter, + strides, + pad: pad3, + dataFormat = "NHWC", + dilations = [1, 1], + dimRoundingMode, + bias, + activation: activation2 = "linear", + preluActivationWeights, + leakyreluAlpha +}) { activation2 = activation2 || "linear"; if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); @@ -14250,9 +13559,7 @@ function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dila } assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`); assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("fused conv2d", pad3, dimRoundingMode); assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`); assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); assert(dataFormat === "NHWC", () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`); @@ -14348,7 +13655,19 @@ function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, return res; } var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ }); -function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { +function fusedDepthwiseConv2d_({ + x, + filter, + strides, + pad: pad3, + dataFormat = "NHWC", + dilations = [1, 1], + dimRoundingMode, + bias, + activation: activation2 = "linear", + preluActivationWeights, + leakyreluAlpha +}) { if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); if (bias != null) { @@ -14371,9 +13690,7 @@ function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NH dilations = [1, 1]; } assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("fused depthwiseConv2d", pad3, dimRoundingMode); const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true); let $bias; if (bias != null) { @@ -14435,7 +13752,16 @@ function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NH } } var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ }); -function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) { +function fusedMatMul_({ + a, + b, + transposeA = false, + transposeB = false, + bias, + activation: activation2 = "linear", + preluActivationWeights, + leakyreluAlpha +}) { if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) { let result = matMul(a, b, transposeA, transposeB); if (bias != null) { @@ -14454,10 +13780,9 @@ function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, acti const outerDimsB = $b.shape.slice(0, -2); const batchDimA = sizeFromShape(outerDimsA); const batchDimB = sizeFromShape(outerDimsB); - assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`); - assert(arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`); assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); - const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]); + const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]); const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]); let $bias; @@ -15215,8 +14540,8 @@ function logLoss_(labels, predictions, weights, epsilon32 = 1e-7, reduction2 = R assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: "); const one = scalar(1); const epsilonScalar = scalar(epsilon32); - const l13 = neg(mul($labels, log5(add2($predictions, epsilonScalar)))); - const l23 = mul(sub(one, $labels), log5(add2(sub(one, $predictions), epsilonScalar))); + const l13 = neg(mul($labels, log4(add2($predictions, epsilonScalar)))); + const l23 = mul(sub(one, $labels), log4(add2(sub(one, $predictions), epsilonScalar))); const losses4 = sub(l13, l23); return computeWeightedLoss(losses4, $weights, reduction2); } @@ -15305,9 +14630,9 @@ function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, } var softmaxCrossEntropy = op({ softmaxCrossEntropy_ }); function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { - const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows"); + const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows", "int32"); const $values = convertToTensor(values, "values", "sparseFillEmptyRows"); - const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows"); + const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows", "int32"); const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype); if ($indices.rank !== 2) { throw new Error(`Indices should be Tensor2D but received shape @@ -15338,9 +14663,9 @@ function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { } var sparseFillEmptyRows = op({ sparseFillEmptyRows_ }); function sparseReshape_(inputIndices, inputShape, newShape) { - const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape"); - const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape"); - const $newShape = convertToTensor(newShape, "newShape", "sparseReshape"); + const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape", "int32"); + const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape", "int32"); + const $newShape = convertToTensor(newShape, "newShape", "sparseReshape", "int32"); if ($inputIndices.rank !== 2) { throw new Error(`Input indices should be Tensor2D but received shape ${$inputIndices.shape}`); @@ -15362,8 +14687,8 @@ function sparseReshape_(inputIndices, inputShape, newShape) { var sparseReshape = op({ sparseReshape_ }); function sparseSegmentMean_(data, indices, segmentIds) { const $data = convertToTensor(data, "data", "sparseSegmentMean"); - const $indices = convertToTensor(indices, "indices", "sparseSegmentMean"); - const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentMean", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean", "int32"); if ($data.rank < 1) { throw new Error(`Data should be at least 1 dimensional but received scalar`); } @@ -15385,8 +14710,8 @@ function sparseSegmentMean_(data, indices, segmentIds) { var sparseSegmentMean = op({ sparseSegmentMean_ }); function sparseSegmentSum_(data, indices, segmentIds) { const $data = convertToTensor(data, "data", "sparseSegmentSum"); - const $indices = convertToTensor(indices, "indices", "sparseSegmentSum"); - const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum"); + const $indices = convertToTensor(indices, "indices", "sparseSegmentSum", "int32"); + const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum", "int32"); if ($data.rank < 1) { throw new Error(`Data should be at least 1 dimensional but received scalar`); } @@ -16204,6 +15529,7 @@ __export2(backend_util_exports, { axesAreInnerMostDims: () => axesAreInnerMostDims, calculateShapes: () => calculateShapes, checkEinsumDimSizes: () => checkEinsumDimSizes, + checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode, combineLocations: () => combineLocations, complexWithEvenIndex: () => complexWithEvenIndex, complexWithOddIndex: () => complexWithOddIndex, @@ -16239,6 +15565,18 @@ __export2(backend_util_exports, { getReshapedPermuted: () => getReshapedPermuted, getSliceBeginCoords: () => getSliceBeginCoords, getSliceSize: () => getSliceSize, + getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch, + getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage, + getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage, + getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage, + getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage, + getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage, + getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage, + getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage, + getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage, + getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage, + getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage, + getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage, getUndoAxesPermutation: () => getUndoAxesPermutation, isIdentityPermutation: () => isIdentityPermutation, log: () => log, @@ -16287,11 +15625,11 @@ function getImageCenter(center, imageHeight, imageWidth) { const centerY = imageHeight * (typeof center === "number" ? center : center[1]); return [centerX, centerY]; } -function getReshaped(inputShape, blockShape, prod5, batchToSpace = true) { +function getReshaped(inputShape, blockShape, prod6, batchToSpace = true) { let reshaped = []; if (batchToSpace) { reshaped = reshaped.concat(blockShape.slice(0)); - reshaped.push(inputShape[0] / prod5); + reshaped.push(inputShape[0] / prod6); reshaped = reshaped.concat(inputShape.slice(1)); } else { reshaped = reshaped.concat(inputShape[0]); @@ -16331,12 +15669,12 @@ function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) { } return permuted; } -function getReshapedPermuted(inputShape, blockShape, prod5, batchToSpace = true) { +function getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) { const reshapedPermuted = []; if (batchToSpace) { - reshapedPermuted.push(inputShape[0] / prod5); + reshapedPermuted.push(inputShape[0] / prod6); } else { - reshapedPermuted.push(inputShape[0] * prod5); + reshapedPermuted.push(inputShape[0] * prod6); } for (let i = 1; i < inputShape.length; ++i) { if (i <= blockShape.length) { @@ -16373,70 +15711,70 @@ var ERF_A2 = -0.284496736; var ERF_A3 = 1.421413741; var ERF_A4 = -1.453152027; var ERF_A5 = 1.061405429; -function mergeRealAndImagArrays(real4, imag4) { - if (real4.length !== imag4.length) { - throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`); +function mergeRealAndImagArrays(real5, imag5) { + if (real5.length !== imag5.length) { + throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`); } - const result = new Float32Array(real4.length * 2); + const result = new Float32Array(real5.length * 2); for (let i = 0; i < result.length; i += 2) { - result[i] = real4[i / 2]; - result[i + 1] = imag4[i / 2]; + result[i] = real5[i / 2]; + result[i + 1] = imag5[i / 2]; } return result; } -function splitRealAndImagArrays(complex4) { - const real4 = new Float32Array(complex4.length / 2); - const imag4 = new Float32Array(complex4.length / 2); - for (let i = 0; i < complex4.length; i += 2) { - real4[i / 2] = complex4[i]; - imag4[i / 2] = complex4[i + 1]; +function splitRealAndImagArrays(complex5) { + const real5 = new Float32Array(complex5.length / 2); + const imag5 = new Float32Array(complex5.length / 2); + for (let i = 0; i < complex5.length; i += 2) { + real5[i / 2] = complex5[i]; + imag5[i / 2] = complex5[i + 1]; } - return { real: real4, imag: imag4 }; + return { real: real5, imag: imag5 }; } -function complexWithEvenIndex(complex4) { - const len = Math.ceil(complex4.length / 4); - const real4 = new Float32Array(len); - const imag4 = new Float32Array(len); - for (let i = 0; i < complex4.length; i += 4) { - real4[Math.floor(i / 4)] = complex4[i]; - imag4[Math.floor(i / 4)] = complex4[i + 1]; +function complexWithEvenIndex(complex5) { + const len = Math.ceil(complex5.length / 4); + const real5 = new Float32Array(len); + const imag5 = new Float32Array(len); + for (let i = 0; i < complex5.length; i += 4) { + real5[Math.floor(i / 4)] = complex5[i]; + imag5[Math.floor(i / 4)] = complex5[i + 1]; } - return { real: real4, imag: imag4 }; + return { real: real5, imag: imag5 }; } -function complexWithOddIndex(complex4) { - const len = Math.floor(complex4.length / 4); - const real4 = new Float32Array(len); - const imag4 = new Float32Array(len); - for (let i = 2; i < complex4.length; i += 4) { - real4[Math.floor(i / 4)] = complex4[i]; - imag4[Math.floor(i / 4)] = complex4[i + 1]; +function complexWithOddIndex(complex5) { + const len = Math.floor(complex5.length / 4); + const real5 = new Float32Array(len); + const imag5 = new Float32Array(len); + for (let i = 2; i < complex5.length; i += 4) { + real5[Math.floor(i / 4)] = complex5[i]; + imag5[Math.floor(i / 4)] = complex5[i + 1]; } - return { real: real4, imag: imag4 }; + return { real: real5, imag: imag5 }; } -function getComplexWithIndex(complex4, index) { - const real4 = complex4[index * 2]; - const imag4 = complex4[index * 2 + 1]; - return { real: real4, imag: imag4 }; +function getComplexWithIndex(complex5, index) { + const real5 = complex5[index * 2]; + const imag5 = complex5[index * 2 + 1]; + return { real: real5, imag: imag5 }; } -function assignToTypedArray(data, real4, imag4, index) { - data[index * 2] = real4; - data[index * 2 + 1] = imag4; +function assignToTypedArray(data, real5, imag5, index) { + data[index * 2] = real5; + data[index * 2 + 1] = imag5; } function exponents(n, inverse) { - const real4 = new Float32Array(n / 2); - const imag4 = new Float32Array(n / 2); + const real5 = new Float32Array(n / 2); + const imag5 = new Float32Array(n / 2); for (let i = 0; i < Math.ceil(n / 2); i++) { const x = (inverse ? 2 : -2) * Math.PI * (i / n); - real4[i] = Math.cos(x); - imag4[i] = Math.sin(x); + real5[i] = Math.cos(x); + imag5[i] = Math.sin(x); } - return { real: real4, imag: imag4 }; + return { real: real5, imag: imag5 }; } function exponent(k, n, inverse) { const x = (inverse ? 2 : -2) * Math.PI * (k / n); - const real4 = Math.cos(x); - const imag4 = Math.sin(x); - return { real: real4, imag: imag4 }; + const real5 = Math.cos(x); + const imag5 = Math.sin(x); + return { real: real5, imag: imag5 }; } var ARROW = "->"; var ARROW_REGEX = /->/g; @@ -16500,14 +15838,14 @@ function getEinsumPermutation(nDims, idDims) { for (let i = 0; i < idDims.length; ++i) { permutationIndices[idDims[i]] = i; } - const expandDims6 = []; + const expandDims7 = []; for (let i = 0; i < nDims; ++i) { if (permutationIndices[i] === -1) { - expandDims6.push(i); + expandDims7.push(i); } } permutationIndices = permutationIndices.filter((d) => d !== -1); - return { permutationIndices, expandDims: expandDims6 }; + return { permutationIndices, expandDims: expandDims7 }; } function checkEinsumDimSizes(nDims, idDims, tensors) { const dimSizes = new Array(nDims); @@ -16581,6 +15919,48 @@ function prepareSplitSize(x, numOrSizeSplits, axis = 0) { } return splitSizes; } +function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) { + return `Received SparseTensor with denseShape[0] = 0 but + indices.shape[0] = ${indicesLength}`; +} +function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) { + return `indices(${index}, 0) is invalid: ${value} < 0`; +} +function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) { + return `indices(${index}, 0) is invalid: ${value} >= ${limit}`; +} +function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) { + return `only one output dimension may be -1, not both ${dim1} and ${dim2}`; +} +function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) { + return `size ${dim} must be non-negative, not ${value}`; +} +function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() { + return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"; +} +function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a SparseTensor with ${inputSize} + dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`; +} +function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) { + const inputSize = sizeFromShape(inputShape); + const outputSize = sizeFromShape(outputShape); + return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`; +} +function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() { + return `segment ids must be >= 0`; +} +function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() { + return `segment ids are not increasing`; +} +function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) { + return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`; +} +function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) { + return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`; +} var segment_util_exports = {}; __export2(segment_util_exports, { collectGatherOpShapeInfo: () => collectGatherOpShapeInfo, @@ -16849,9 +16229,7 @@ function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) } assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("avgPool3dGrad", pad3, dimRoundingMode); const inputs = { dy: dy5D, input: input5D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs); @@ -17106,9 +16484,7 @@ var depthwiseConv2dNativeGradConfig = { assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`); assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`); assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode); return { x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode), filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode) @@ -17207,55 +16583,55 @@ var fusedBatchNormGradConfig = { inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (dy, saved, attrs) => { const { varianceEpsilon } = attrs; - const [x, mean4, variance, scale22] = saved; + const [x, mean5, variance, scale22] = saved; const scaleValue = scale22 == null ? scalar(1) : scale22; - const reductionAxes = getReductionAxes(mean4.shape, x.shape); + const reductionAxes = getReductionAxes(mean5.shape, x.shape); const tileShape = []; - if (mean4.rank === 1) { + if (mean5.rank === 1) { for (let i = 0; i < x.shape.length - 1; ++i) { tileShape.push(x.shape[i]); } tileShape.push(1); } - const xMinusMean = sub(x, mean4); + const xMinusMean = sub(x, mean5); const dyTimesScaleValue = mul(dy, scaleValue); const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon))); const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5)); const derX = () => { - if (mean4.rank === 1) { - return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean4.shape[0]]), tileShape)), scaleValue), x.shape); + if (mean5.rank === 1) { + return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape); } else { return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape); } }; const derMean = () => { let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue); - if (mean4.rank === 1) { + if (mean5.rank === 1) { meanDer = sum2(meanDer, reductionAxes); } - return reshape(meanDer, mean4.shape); + return reshape(meanDer, mean5.shape); }; const derVariance = () => { let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue); - if (mean4.rank === 1) { + if (mean5.rank === 1) { varianceDer = sum2(varianceDer, reductionAxes); } - return reshape(varianceDer, mean4.shape); + return reshape(varianceDer, mean5.shape); }; const derScale = () => { const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance); let scaleDer = mul(dy, xMinusMean2TimesRsqrt); - if (mean4.rank === 1) { + if (mean5.rank === 1) { scaleDer = sum2(scaleDer, reductionAxes); } - return reshape(scaleDer, mean4.shape); + return reshape(scaleDer, mean5.shape); }; const derOffset = () => { let offsetDer = dy; - if (mean4.rank === 1) { + if (mean5.rank === 1) { offsetDer = sum2(offsetDer, reductionAxes); } - return reshape(offsetDer, mean4.shape); + return reshape(offsetDer, mean5.shape); }; return { x: derX, @@ -17379,8 +16755,8 @@ var logSoftmaxGradConfig = { return { logits: () => { const keepDims = true; - const softmax6 = exp(value); - return sub(dy, mul(sum2(dy, axis, keepDims), softmax6)); + const softmax7 = exp(value); + return sub(dy, mul(sum2(dy, axis, keepDims), softmax7)); } }; } @@ -17474,9 +16850,7 @@ function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundi assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`); assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`); assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("maxPool3dGrad", pad3, dimRoundingMode); const inputs = { dy: dy5D, input: input5D, output: output5D }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs); @@ -17505,9 +16879,7 @@ function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRounding assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`); assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`); assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`); - if (dimRoundingMode != null) { - assert(isInt(pad3), () => `Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`); - } + checkPadOnDimRoundingMode("maxPoolGrad", pad3, dimRoundingMode); const inputs = { dy: $dy, input: $input, output: $output }; const attrs = { filterSize, strides, pad: pad3, dimRoundingMode }; return ENGINE.runKernel(MaxPoolGrad, inputs, attrs); @@ -17679,10 +17051,10 @@ var powGradConfig = { gradFunc: (dy, saved) => { const [a, b, y] = saved; const base2 = a; - const exp4 = b; - const outShape = assertAndGetBroadcastShape(base2.shape, exp4.shape); + const exp5 = b; + const outShape = assertAndGetBroadcastShape(base2.shape, exp5.shape); const derBase = () => { - const expFloat = cast(exp4, "float32"); + const expFloat = cast(exp5, "float32"); let res = mul(dy, mul(expFloat, pow(base2, sub(expFloat, scalar(1))))); const reduceAxes = getReductionAxes(base2.shape, outShape); if (reduceAxes.length > 0) { @@ -17692,13 +17064,13 @@ var powGradConfig = { }; const derExp = () => { const condition = greater(base2, 0); - const logBase = where(condition, log5(base2), zerosLike(base2)); + const logBase = where(condition, log4(base2), zerosLike(base2)); let res = mul(dy, mul(y, logBase)); - const reduceAxes = getReductionAxes(exp4.shape, outShape); + const reduceAxes = getReductionAxes(exp5.shape, outShape); if (reduceAxes.length > 0) { res = sum2(res, reduceAxes); } - return reshape(res, exp4.shape); + return reshape(res, exp5.shape); }; return { a: derBase, b: derExp }; } @@ -18229,551 +17601,6 @@ var gradConfigs = [ for (const gradientConfig of gradConfigs) { registerGradient(gradientConfig); } -getGlobalTensorClass().prototype.abs = function() { - this.throwIfDisposed(); - return abs(this); -}; -getGlobalTensorClass().prototype.acos = function() { - this.throwIfDisposed(); - return acos(this); -}; -getGlobalTensorClass().prototype.acosh = function() { - this.throwIfDisposed(); - return acosh(this); -}; -getGlobalTensorClass().prototype.add = function(b) { - this.throwIfDisposed(); - return add2(this, b); -}; -getGlobalTensorClass().prototype.all = function(axis, keepDims) { - this.throwIfDisposed(); - return all(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.any = function(axis, keepDims) { - this.throwIfDisposed(); - return any(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.argMax = function(axis) { - this.throwIfDisposed(); - return argMax(this, axis); -}; -getGlobalTensorClass().prototype.argMin = function(axis) { - this.throwIfDisposed(); - return argMin(this, axis); -}; -getGlobalTensorClass().prototype.asScalar = function() { - this.throwIfDisposed(); - assert(this.size === 1, () => "The array must have only 1 element."); - return reshape(this, []); -}; -getGlobalTensorClass().prototype.asType = function(dtype) { - this.throwIfDisposed(); - return cast(this, dtype); -}; -getGlobalTensorClass().prototype.as1D = function() { - this.throwIfDisposed(); - return reshape(this, [this.size]); -}; -getGlobalTensorClass().prototype.as2D = function(rows, columns) { - this.throwIfDisposed(); - return reshape(this, [rows, columns]); -}; -getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) { - this.throwIfDisposed(); - return reshape(this, [rows, columns, depth]); -}; -getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) { - this.throwIfDisposed(); - return reshape(this, [rows, columns, depth, depth2]); -}; -getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) { - this.throwIfDisposed(); - return reshape(this, [rows, columns, depth, depth2, depth3]); -}; -getGlobalTensorClass().prototype.asin = function() { - this.throwIfDisposed(); - return asin(this); -}; -getGlobalTensorClass().prototype.asinh = function() { - this.throwIfDisposed(); - return asinh(this); -}; -getGlobalTensorClass().prototype.atan = function() { - this.throwIfDisposed(); - return atan(this); -}; -getGlobalTensorClass().prototype.atan2 = function(b) { - this.throwIfDisposed(); - return atan2(this, b); -}; -getGlobalTensorClass().prototype.atanh = function() { - this.throwIfDisposed(); - return atanh(this); -}; -getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) { - this.throwIfDisposed(); - return avgPool(this, filterSize, strides, pad3, dimRoundingMode); -}; -getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) { - this.throwIfDisposed(); - return batchToSpaceND(this, blockShape, crops); -}; -getGlobalTensorClass().prototype.batchNorm = function(mean4, variance, offset, scale22, varianceEpsilon) { - this.throwIfDisposed(); - return batchNorm(this, mean4, variance, offset, scale22, varianceEpsilon); -}; -getGlobalTensorClass().prototype.broadcastTo = function(shape) { - this.throwIfDisposed(); - return broadcastTo(this, shape); -}; -getGlobalTensorClass().prototype.cast = function(dtype) { - this.throwIfDisposed(); - return cast(this, dtype); -}; -getGlobalTensorClass().prototype.ceil = function() { - this.throwIfDisposed(); - return ceil(this); -}; -getGlobalTensorClass().prototype.clipByValue = function(min6, max6) { - this.throwIfDisposed(); - return clipByValue(this, min6, max6); -}; -getGlobalTensorClass().prototype.concat = function(x, axis) { - this.throwIfDisposed(); - if (x instanceof Tensor) { - x = [x]; - } - return concat([this, ...x], axis); -}; -getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) { - this.throwIfDisposed(); - return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode); -}; -getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) { - this.throwIfDisposed(); - return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode); -}; -getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { - this.throwIfDisposed(); - return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); -}; -getGlobalTensorClass().prototype.cos = function() { - this.throwIfDisposed(); - return cos(this); -}; -getGlobalTensorClass().prototype.cosh = function() { - this.throwIfDisposed(); - return cosh(this); -}; -getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) { - this.throwIfDisposed(); - return cumsum(this, axis, exclusive, reverse5); -}; -getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) { - this.throwIfDisposed(); - return depthToSpace(this, blockSize, dataFormat); -}; -getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) { - this.throwIfDisposed(); - return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode); -}; -getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) { - this.throwIfDisposed(); - return dilation2d(this, filter, strides, pad3, dilations, dataFormat); -}; -getGlobalTensorClass().prototype.divNoNan = function(b) { - this.throwIfDisposed(); - return divNoNan(this, b); -}; -getGlobalTensorClass().prototype.div = function(b) { - this.throwIfDisposed(); - return div(this, b); -}; -getGlobalTensorClass().prototype.dot = function(b) { - this.throwIfDisposed(); - return dot(this, b); -}; -getGlobalTensorClass().prototype.elu = function() { - this.throwIfDisposed(); - return elu(this); -}; -getGlobalTensorClass().prototype.equal = function(b) { - this.throwIfDisposed(); - return equal(this, b); -}; -getGlobalTensorClass().prototype.erf = function() { - this.throwIfDisposed(); - return erf(this); -}; -getGlobalTensorClass().prototype.exp = function() { - this.throwIfDisposed(); - return exp(this); -}; -getGlobalTensorClass().prototype.expandDims = function(axis) { - this.throwIfDisposed(); - return expandDims(this, axis); -}; -getGlobalTensorClass().prototype.expm1 = function() { - this.throwIfDisposed(); - return expm1(this); -}; -getGlobalTensorClass().prototype.fft = function() { - this.throwIfDisposed(); - return fft(this); -}; -getGlobalTensorClass().prototype.flatten = function() { - this.throwIfDisposed(); - return reshape(this, [this.size]); -}; -getGlobalTensorClass().prototype.floor = function() { - this.throwIfDisposed(); - return floor(this); -}; -getGlobalTensorClass().prototype.floorDiv = function(b) { - this.throwIfDisposed(); - return floorDiv(this, b); -}; -getGlobalTensorClass().prototype.gather = function(indices, axis) { - this.throwIfDisposed(); - return gather(this, indices, axis); -}; -getGlobalTensorClass().prototype.greaterEqual = function(b) { - this.throwIfDisposed(); - return greaterEqual(this, b); -}; -getGlobalTensorClass().prototype.greater = function(b) { - this.throwIfDisposed(); - return greater(this, b); -}; -getGlobalTensorClass().prototype.ifft = function() { - this.throwIfDisposed(); - return ifft(this); -}; -getGlobalTensorClass().prototype.irfft = function() { - this.throwIfDisposed(); - return irfft(this); -}; -getGlobalTensorClass().prototype.isFinite = function() { - this.throwIfDisposed(); - return isFinite2(this); -}; -getGlobalTensorClass().prototype.isInf = function() { - this.throwIfDisposed(); - return isInf(this); -}; -getGlobalTensorClass().prototype.isNaN = function() { - this.throwIfDisposed(); - return isNaN2(this); -}; -getGlobalTensorClass().prototype.leakyRelu = function(alpha) { - this.throwIfDisposed(); - return leakyRelu(this, alpha); -}; -getGlobalTensorClass().prototype.lessEqual = function(b) { - this.throwIfDisposed(); - return lessEqual(this, b); -}; -getGlobalTensorClass().prototype.less = function(b) { - this.throwIfDisposed(); - return less(this, b); -}; -getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) { - this.throwIfDisposed(); - return localResponseNormalization(this, depthRadius, bias, alpha, beta); -}; -getGlobalTensorClass().prototype.logSigmoid = function() { - this.throwIfDisposed(); - return logSigmoid(this); -}; -getGlobalTensorClass().prototype.logSoftmax = function(axis) { - this.throwIfDisposed(); - return logSoftmax(this, axis); -}; -getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) { - this.throwIfDisposed(); - return logSumExp(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.log = function() { - this.throwIfDisposed(); - return log5(this); -}; -getGlobalTensorClass().prototype.log1p = function() { - this.throwIfDisposed(); - return log1p(this); -}; -getGlobalTensorClass().prototype.logicalAnd = function(b) { - this.throwIfDisposed(); - return logicalAnd(this, b); -}; -getGlobalTensorClass().prototype.logicalNot = function() { - this.throwIfDisposed(); - return logicalNot(this); -}; -getGlobalTensorClass().prototype.logicalOr = function(b) { - this.throwIfDisposed(); - return logicalOr(this, b); -}; -getGlobalTensorClass().prototype.logicalXor = function(b) { - this.throwIfDisposed(); - return logicalXor(this, b); -}; -getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) { - this.throwIfDisposed(); - return matMul(this, b, transposeA, transposeB); -}; -getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) { - this.throwIfDisposed(); - return maxPool(this, filterSize, strides, pad3, dimRoundingMode); -}; -getGlobalTensorClass().prototype.max = function(axis, keepDims) { - this.throwIfDisposed(); - return max(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.maximum = function(b) { - this.throwIfDisposed(); - return maximum(this, b); -}; -getGlobalTensorClass().prototype.mean = function(axis, keepDims) { - this.throwIfDisposed(); - return mean(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.min = function(axis, keepDims) { - this.throwIfDisposed(); - return min(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.minimum = function(b) { - this.throwIfDisposed(); - return minimum(this, b); -}; -getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) { - this.throwIfDisposed(); - return mirrorPad(this, paddings, mode); -}; -getGlobalTensorClass().prototype.mod = function(b) { - this.throwIfDisposed(); - return mod(this, b); -}; -getGlobalTensorClass().prototype.mul = function(b) { - this.throwIfDisposed(); - return mul(this, b); -}; -getGlobalTensorClass().prototype.neg = function() { - this.throwIfDisposed(); - return neg(this); -}; -getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) { - this.throwIfDisposed(); - return norm(this, ord, axis, keepDims); -}; -getGlobalTensorClass().prototype.notEqual = function(b) { - this.throwIfDisposed(); - return notEqual(this, b); -}; -getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) { - this.throwIfDisposed(); - return oneHot(this, depth, onValue, offValue); -}; -getGlobalTensorClass().prototype.onesLike = function() { - this.throwIfDisposed(); - return onesLike(this); -}; -getGlobalTensorClass().prototype.pad = function(paddings, constantValue) { - this.throwIfDisposed(); - return pad(this, paddings, constantValue); -}; -getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides) { - this.throwIfDisposed(); - return pool(this, windowShape, poolingType, padding, dilationRate, strides); -}; -getGlobalTensorClass().prototype.pow = function(exp4) { - this.throwIfDisposed(); - return pow(this, exp4); -}; -getGlobalTensorClass().prototype.prelu = function(alpha) { - this.throwIfDisposed(); - return prelu(this, alpha); -}; -getGlobalTensorClass().prototype.prod = function(axis, keepDims) { - this.throwIfDisposed(); - return prod(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.reciprocal = function() { - this.throwIfDisposed(); - return reciprocal(this); -}; -getGlobalTensorClass().prototype.relu = function() { - this.throwIfDisposed(); - return relu(this); -}; -getGlobalTensorClass().prototype.relu6 = function() { - this.throwIfDisposed(); - return relu6(this); -}; -getGlobalTensorClass().prototype.reshapeAs = function(x) { - this.throwIfDisposed(); - return reshape(this, x.shape); -}; -getGlobalTensorClass().prototype.reshape = function(shape) { - this.throwIfDisposed(); - return reshape(this, shape); -}; -getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) { - this.throwIfDisposed(); - return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters); -}; -getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) { - this.throwIfDisposed(); - return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters); -}; -getGlobalTensorClass().prototype.reverse = function(axis) { - this.throwIfDisposed(); - return reverse(this, axis); -}; -getGlobalTensorClass().prototype.rfft = function() { - this.throwIfDisposed(); - return rfft(this); -}; -getGlobalTensorClass().prototype.round = function() { - this.throwIfDisposed(); - return round2(this); -}; -getGlobalTensorClass().prototype.rsqrt = function() { - this.throwIfDisposed(); - return rsqrt(this); -}; -getGlobalTensorClass().prototype.selu = function() { - this.throwIfDisposed(); - return selu(this); -}; -getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) { - this.throwIfDisposed(); - return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat); -}; -getGlobalTensorClass().prototype.sigmoid = function() { - this.throwIfDisposed(); - return sigmoid(this); -}; -getGlobalTensorClass().prototype.sign = function() { - this.throwIfDisposed(); - return sign(this); -}; -getGlobalTensorClass().prototype.sin = function() { - this.throwIfDisposed(); - return sin(this); -}; -getGlobalTensorClass().prototype.sinh = function() { - this.throwIfDisposed(); - return sinh(this); -}; -getGlobalTensorClass().prototype.slice = function(begin, size) { - this.throwIfDisposed(); - return slice(this, begin, size); -}; -getGlobalTensorClass().prototype.softmax = function(dim) { - this.throwIfDisposed(); - return softmax(this, dim); -}; -getGlobalTensorClass().prototype.softplus = function() { - this.throwIfDisposed(); - return softplus(this); -}; -getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) { - this.throwIfDisposed(); - return spaceToBatchND(this, blockShape, paddings); -}; -getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) { - this.throwIfDisposed(); - return split(this, numOrSizeSplits, axis); -}; -getGlobalTensorClass().prototype.sqrt = function() { - this.throwIfDisposed(); - return sqrt(this); -}; -getGlobalTensorClass().prototype.square = function() { - this.throwIfDisposed(); - return square(this); -}; -getGlobalTensorClass().prototype.squaredDifference = function(b) { - this.throwIfDisposed(); - return squaredDifference(this, b); -}; -getGlobalTensorClass().prototype.squeeze = function(axis) { - this.throwIfDisposed(); - return squeeze(this, axis); -}; -getGlobalTensorClass().prototype.stack = function(x, axis) { - this.throwIfDisposed(); - const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x]; - return stack(tensorsToBeStacked, axis); -}; -getGlobalTensorClass().prototype.step = function(alpha) { - this.throwIfDisposed(); - return step(this, alpha); -}; -getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { - this.throwIfDisposed(); - return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); -}; -getGlobalTensorClass().prototype.sub = function(b) { - this.throwIfDisposed(); - return sub(this, b); -}; -getGlobalTensorClass().prototype.sum = function(axis, keepDims) { - this.throwIfDisposed(); - return sum2(this, axis, keepDims); -}; -getGlobalTensorClass().prototype.tan = function() { - this.throwIfDisposed(); - return tan(this); -}; -getGlobalTensorClass().prototype.tanh = function() { - this.throwIfDisposed(); - return tanh2(this); -}; -getGlobalTensorClass().prototype.tile = function(reps) { - this.throwIfDisposed(); - return tile(this, reps); -}; -getGlobalTensorClass().prototype.toBool = function() { - this.throwIfDisposed(); - return cast(this, "bool"); -}; -getGlobalTensorClass().prototype.toFloat = function() { - this.throwIfDisposed(); - return cast(this, "float32"); -}; -getGlobalTensorClass().prototype.toInt = function() { - this.throwIfDisposed(); - return cast(this, "int32"); -}; -getGlobalTensorClass().prototype.topk = function(k, sorted) { - this.throwIfDisposed(); - return topk(this, k, sorted); -}; -getGlobalTensorClass().prototype.transpose = function(perm) { - this.throwIfDisposed(); - return transpose(this, perm); -}; -getGlobalTensorClass().prototype.unique = function(axis) { - this.throwIfDisposed(); - return unique(this, axis); -}; -getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) { - this.throwIfDisposed(); - return unsortedSegmentSum(this, segmentIds, numSegments); -}; -getGlobalTensorClass().prototype.unstack = function(axis) { - this.throwIfDisposed(); - return unstack(this, axis); -}; -getGlobalTensorClass().prototype.where = function(condition, x) { - this.throwIfDisposed(); - return where(condition, this, x); -}; -getGlobalTensorClass().prototype.zerosLike = function() { - this.throwIfDisposed(); - return zerosLike(this); -}; var exports_constraints_exports = {}; __export2(exports_constraints_exports, { maxNorm: () => maxNorm, @@ -18953,21 +17780,21 @@ function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = } const nestedConfig = config["config"]; nestedConfig["customObjects"] = customObjectsCombined; - const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + const backupCustomObjects = { ..._GLOBAL_CUSTOM_OBJECTS }; for (const key of Object.keys(customObjects)) { _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; } convertNDArrayScalarsInConfig(config["config"]); const returnObj = fromConfig(cls, config["config"], customObjects, fastWeightInit); - _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + _GLOBAL_CUSTOM_OBJECTS = { ...backupCustomObjects }; return returnObj; } else { - const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS); + const backupCustomObjects = { ..._GLOBAL_CUSTOM_OBJECTS }; for (const key of Object.keys(customObjects)) { _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key]; } const returnObj = new cls(config["config"]); - _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects); + _GLOBAL_CUSTOM_OBJECTS = { ...backupCustomObjects }; return returnObj; } } @@ -19271,37 +18098,37 @@ function arrayProd(array2, begin, end) { if (end == null) { end = array2.length; } - let prod5 = 1; + let prod6 = 1; for (let i = begin; i < end; ++i) { - prod5 *= array2[i]; + prod6 *= array2[i]; } - return prod5; + return prod6; } function min2(array2) { if (array2.length === 0) { return Number.NaN; } - let min6 = Number.POSITIVE_INFINITY; + let min7 = Number.POSITIVE_INFINITY; for (let i = 0; i < array2.length; i++) { const value = array2[i]; - if (value < min6) { - min6 = value; + if (value < min7) { + min7 = value; } } - return min6; + return min7; } function max2(array2) { if (array2.length === 0) { return Number.NaN; } - let max6 = Number.NEGATIVE_INFINITY; + let max7 = Number.NEGATIVE_INFINITY; for (let i = 0; i < array2.length; i++) { const value = array2[i]; - if (value > max6) { - max6 = value; + if (value > max7) { + max7 = value; } } - return max6; + return max7; } function range2(begin, end) { if (end < begin) { @@ -19474,8 +18301,8 @@ function tile2(x, n) { } return tile(x, n); } -function randomNormal2(shape, mean4 = 0, stddev = 1, dtype, seed) { - return randomNormal(shape, mean4, stddev, dtype, seed); +function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) { + return randomNormal(shape, mean5, stddev, dtype, seed); } function dot2(a, b, activation2, bias) { if (a.rank < 2 || b.rank < 2) { @@ -20971,9 +19798,9 @@ function disposeTensorsInLogs(logs) { } } var ModelLoggingVerbosity; -(function(ModelLoggingVerbosity2) { - ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT"; - ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE"; +(function(ModelLoggingVerbosity4) { + ModelLoggingVerbosity4[ModelLoggingVerbosity4["SILENT"] = 0] = "SILENT"; + ModelLoggingVerbosity4[ModelLoggingVerbosity4["VERBOSE"] = 1] = "VERBOSE"; })(ModelLoggingVerbosity || (ModelLoggingVerbosity = {})); var DEFAULT_YIELD_EVERY_MS = 125; var BaseCallback = class { @@ -21260,20 +20087,20 @@ function standardizeCallbacks(callbacks2, yieldEvery) { const callbackConfigs = toList(callbacks2); return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery)); } -var CallbackConstructorRegistry = class { +var _CallbackConstructorRegistry = class { constructor() { } static registerCallbackConstructor(verbosityLevel, callbackConstructor) { util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`); - CallbackConstructorRegistry.checkForDuplicate(callbackConstructor); - if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) { - CallbackConstructorRegistry.constructors[verbosityLevel] = []; + _CallbackConstructorRegistry.checkForDuplicate(callbackConstructor); + if (_CallbackConstructorRegistry.constructors[verbosityLevel] == null) { + _CallbackConstructorRegistry.constructors[verbosityLevel] = []; } - CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor); + _CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor); } static checkForDuplicate(callbackConstructor) { - for (const levelName in CallbackConstructorRegistry.constructors) { - const constructors = CallbackConstructorRegistry.constructors[+levelName]; + for (const levelName in _CallbackConstructorRegistry.constructors) { + const constructors = _CallbackConstructorRegistry.constructors[+levelName]; constructors.forEach((ctor) => { if (ctor === callbackConstructor) { throw new ValueError("Duplicate callback constructor."); @@ -21282,19 +20109,20 @@ var CallbackConstructorRegistry = class { } } static clear() { - CallbackConstructorRegistry.constructors = {}; + _CallbackConstructorRegistry.constructors = {}; } static createCallbacks(verbosityLevel) { const constructors = []; - for (const levelName in CallbackConstructorRegistry.constructors) { + for (const levelName in _CallbackConstructorRegistry.constructors) { const level = +levelName; if (verbosityLevel >= level) { - constructors.push(...CallbackConstructorRegistry.constructors[level]); + constructors.push(..._CallbackConstructorRegistry.constructors[level]); } } return constructors.map((ctor) => new ctor()); } }; +var CallbackConstructorRegistry = _CallbackConstructorRegistry; CallbackConstructorRegistry.constructors = {}; function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) { const history = new History(); @@ -21350,9 +20178,9 @@ function meanAbsolutePercentageError(yTrue, yPred) { function meanSquaredLogarithmicError(yTrue, yPred) { return tidy(() => { const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE); - const firstLog = log5(add2(1, clippedPred)); + const firstLog = log4(add2(1, clippedPred)); const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE); - const secondLog = log5(add2(1, clippedTrue)); + const secondLog = log4(add2(1, clippedTrue)); return mean(square2(sub(firstLog, secondLog)), -1); }); } @@ -21371,8 +20199,8 @@ function hinge(yTrue, yPred) { function categoricalHinge(yTrue, yPred) { return tidy(() => { const pos = sum2(mul(yTrue, yPred), -1); - const neg4 = max(mul(sub(1, yTrue), yPred), -1); - return maximum(0, add2(1, sub(neg4, pos))); + const neg5 = max(mul(sub(1, yTrue), yPred), -1); + return maximum(0, add2(1, sub(neg5, pos))); }); } function logcosh(yTrue, yPred) { @@ -21392,7 +20220,7 @@ function categoricalCrossentropy(target, output, fromLogits = false) { output = div(output, outputSum); } output = clipByValue(output, epsilon(), 1 - epsilon()); - return neg(sum2(mul(cast(target, "float32"), log5(output)), output.shape.length - 1)); + return neg(sum2(mul(cast(target, "float32"), log4(output)), output.shape.length - 1)); }); } function sparseCategoricalCrossentropy(target, output, fromLogits = false) { @@ -21418,7 +20246,7 @@ function binaryCrossentropy(yTrue, yPred) { return tidy(() => { let y; y = clipByValue(yPred, epsilon(), 1 - epsilon()); - y = log5(div(y, sub(1, y))); + y = log4(div(y, sub(1, y))); return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1); }); } @@ -21426,12 +20254,12 @@ function kullbackLeiblerDivergence(yTrue, yPred) { return tidy(() => { const clippedTrue = clipByValue(yTrue, epsilon(), 1); const clippedPred = clipByValue(yPred, epsilon(), 1); - return sum2(mul(yTrue, log5(div(clippedTrue, clippedPred))), -1); + return sum2(mul(yTrue, log4(div(clippedTrue, clippedPred))), -1); }); } function poisson(yTrue, yPred) { return tidy(() => { - const logPred = log5(add2(epsilon(), yPred)); + const logPred = log4(add2(epsilon(), yPred)); return mean(sub(yPred, mul(yTrue, logPred)), -1); }); } @@ -21860,7 +20688,7 @@ function convertTsToPythonic(tsConfig, key) { return pyDict; } } -var version2 = "3.11.0"; +var version2 = "0.0.0"; function assertFeedCompatibility(key, val) { if (key.dtype == null || key.dtype === val.dtype) { return val; @@ -24376,7 +23204,7 @@ function decodeModelAndOptimizerWeights(buffer2, specs) { }); return { modelWeights, optimizerWeights }; } -var Sequential = class extends LayersModel { +var _Sequential = class extends LayersModel { constructor(args) { super({ inputs: [], outputs: [] }); args = args || {}; @@ -24396,7 +23224,7 @@ var Sequential = class extends LayersModel { } } add(layer) { - const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel; + const isLayerModelInstance = layer instanceof _Sequential || layer instanceof LayersModel; let modelLayer; if (isLayerModelInstance) { modelLayer = layer; @@ -24594,7 +23422,7 @@ var Sequential = class extends LayersModel { extraModelConfig = config; } const model2 = new cls(extraModelConfig); - if (!(model2 instanceof Sequential)) { + if (!(model2 instanceof _Sequential)) { throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`); } for (const conf of configArray) { @@ -24630,6 +23458,7 @@ var Sequential = class extends LayersModel { return { name: this.name, layers }; } }; +var Sequential = _Sequential; Sequential.className = "Sequential"; serialization_exports.registerClass(Sequential); function model(args) { @@ -24650,103 +23479,103 @@ function input(config) { function registerCallbackConstructor(verbosityLevel, callbackConstructor) { CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor); } -var Activation = class extends serialization_exports.Serializable { +var Activation2 = class extends serialization_exports.Serializable { getConfig() { return {}; } }; -var Elu2 = class extends Activation { +var Elu2 = class extends Activation2 { apply(x, alpha = 1) { return elu2(x, alpha); } }; Elu2.className = "elu"; serialization_exports.registerClass(Elu2); -var Selu2 = class extends Activation { +var Selu2 = class extends Activation2 { apply(x) { return selu(x); } }; Selu2.className = "selu"; serialization_exports.registerClass(Selu2); -var Relu2 = class extends Activation { +var Relu2 = class extends Activation2 { apply(x) { return relu(x); } }; Relu2.className = "relu"; serialization_exports.registerClass(Relu2); -var Relu62 = class extends Activation { +var Relu62 = class extends Activation2 { apply(x) { return tidy(() => minimum(6, relu(x))); } }; Relu62.className = "relu6"; serialization_exports.registerClass(Relu62); -var Linear = class extends Activation { +var Linear = class extends Activation2 { apply(x) { return x; } }; Linear.className = "linear"; serialization_exports.registerClass(Linear); -var Sigmoid2 = class extends Activation { +var Sigmoid2 = class extends Activation2 { apply(x) { return sigmoid(x); } }; Sigmoid2.className = "sigmoid"; serialization_exports.registerClass(Sigmoid2); -var HardSigmoid = class extends Activation { +var HardSigmoid = class extends Activation2 { apply(x) { return hardSigmoid(x); } }; HardSigmoid.className = "hardSigmoid"; serialization_exports.registerClass(HardSigmoid); -var Softplus2 = class extends Activation { +var Softplus2 = class extends Activation2 { apply(x) { return softplus(x); } }; Softplus2.className = "softplus"; serialization_exports.registerClass(Softplus2); -var Softsign = class extends Activation { +var Softsign = class extends Activation2 { apply(x) { return softsign(x); } }; Softsign.className = "softsign"; serialization_exports.registerClass(Softsign); -var Tanh2 = class extends Activation { +var Tanh2 = class extends Activation2 { apply(x) { return tanh2(x); } }; Tanh2.className = "tanh"; serialization_exports.registerClass(Tanh2); -var Softmax2 = class extends Activation { +var Softmax2 = class extends Activation2 { apply(x, axis = -1) { return softmax(x, axis); } }; Softmax2.className = "softmax"; serialization_exports.registerClass(Softmax2); -var LogSoftmax2 = class extends Activation { +var LogSoftmax2 = class extends Activation2 { apply(x, axis = -1) { return logSoftmax(x, axis); } }; LogSoftmax2.className = "logSoftmax"; serialization_exports.registerClass(LogSoftmax2); -var Swish = class extends Activation { +var Swish = class extends Activation2 { apply(x, alpha = 1) { return tidy(() => mul(sigmoid(mul(x, alpha)), x)); } }; Swish.className = "swish"; serialization_exports.registerClass(Swish); -var Mish = class extends Activation { +var Mish = class extends Activation2 { apply(x) { return tidy(() => mul(x, tanh2(softplus(x)))); } @@ -24771,7 +23600,7 @@ function getActivation(identifier) { config["className"] = identifier; config["config"] = {}; return deserializeActivation(config); - } else if (identifier instanceof Activation) { + } else if (identifier instanceof Activation2) { return identifier; } else { return deserializeActivation(identifier); @@ -25337,10 +24166,10 @@ var Conv = class extends BaseConv { } } }; -var Conv2D2 = class extends Conv { +var _Conv2D = class extends Conv { constructor(args) { super(2, args); - Conv2D2.verifyArgs(args); + _Conv2D.verifyArgs(args); } getConfig() { const config = super.getConfig(); @@ -25353,12 +24182,13 @@ var Conv2D2 = class extends Conv { } } }; +var Conv2D2 = _Conv2D; Conv2D2.className = "Conv2D"; serialization_exports.registerClass(Conv2D2); -var Conv3D2 = class extends Conv { +var _Conv3D = class extends Conv { constructor(args) { super(3, args); - Conv3D2.verifyArgs(args); + _Conv3D.verifyArgs(args); } getConfig() { const config = super.getConfig(); @@ -25373,6 +24203,7 @@ var Conv3D2 = class extends Conv { } } }; +var Conv3D2 = _Conv3D; Conv3D2.className = "Conv3D"; serialization_exports.registerClass(Conv3D2); var Conv2DTranspose = class extends Conv2D2 { @@ -25686,10 +24517,10 @@ var SeparableConv2D = class extends SeparableConv { }; SeparableConv2D.className = "SeparableConv2D"; serialization_exports.registerClass(SeparableConv2D); -var Conv1D = class extends Conv { +var _Conv1D = class extends Conv { constructor(args) { super(1, args); - Conv1D.verifyArgs(args); + _Conv1D.verifyArgs(args); this.inputSpec = [{ ndim: 3 }]; } getConfig() { @@ -25704,6 +24535,7 @@ var Conv1D = class extends Conv { } } }; +var Conv1D = _Conv1D; Conv1D.className = "Conv1D"; serialization_exports.registerClass(Conv1D); var Cropping2D = class extends Layer { @@ -25990,7 +24822,7 @@ function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, con return [lastOutput, outputs, states]; }); } -var RNN = class extends Layer { +var _RNN = class extends Layer { constructor(args) { super(args); let cell; @@ -26291,13 +25123,13 @@ var RNN = class extends Layer { config["numConstants"] = this.numConstants; } const cellConfig = this.cell.getConfig(); - if (this.getClassName() === RNN.className) { + if (this.getClassName() === _RNN.className) { config["cell"] = { "className": this.cell.getClassName(), "config": cellConfig }; } - return Object.assign({}, cellConfig, baseConfig, config); + return { ...cellConfig, ...baseConfig, ...config }; } static fromConfig(cls, config, customObjects = {}) { const cellConfig = config["cell"]; @@ -26305,6 +25137,7 @@ var RNN = class extends Layer { return new cls(Object.assign(config, { cell })); } }; +var RNN = _RNN; RNN.className = "RNN"; serialization_exports.registerClass(RNN); var RNNCell = class extends Layer { @@ -26415,7 +25248,7 @@ var SimpleRNNCell = class extends RNNCell { dropout: this.dropout, recurrentDropout: this.recurrentDropout }; - return Object.assign({}, baseConfig, config); + return { ...baseConfig, ...config }; } }; SimpleRNNCell.className = "SimpleRNNCell"; @@ -26572,7 +25405,7 @@ var GRUCell = class extends RNNCell { implementation: this.implementation, resetAfter: false }; - return Object.assign({}, baseConfig, config); + return { ...baseConfig, ...config }; } }; GRUCell.className = "GRUCell"; @@ -26655,7 +25488,7 @@ var LSTMCell = class extends RNNCell { if (this.unitForgetBias) { const capturedBiasInit = this.biasInitializer; const capturedUnits = this.units; - biasInitializer = new (_a = class CustomInit extends Initializer { + biasInitializer = new (_a = class extends Initializer { apply(shape, dtype) { const bI = capturedBiasInit.apply([capturedUnits]); const bF = new Ones().apply([capturedUnits]); @@ -26748,7 +25581,7 @@ var LSTMCell = class extends RNNCell { recurrentDropout: this.recurrentDropout, implementation: this.implementation }; - return Object.assign({}, baseConfig, config); + return { ...baseConfig, ...config }; } }; LSTMCell.className = "LSTMCell"; @@ -26864,7 +25697,7 @@ var StackedRNNCells = class extends RNNCell { }; const cellConfigs = this.cells.map(getCellConfig); const config = { "cells": cellConfigs }; - return Object.assign({}, baseConfig, config); + return { ...baseConfig, ...config }; } static fromConfig(cls, config, customObjects = {}) { const cells = []; @@ -26928,18 +25761,6 @@ function generateDropoutMask(args) { const masks = Array(count2).fill(void 0).map(createMask); return masks.map((m) => keep(m.clone())); } -var __rest = function(s, e) { - var t = {}; - for (var p2 in s) - if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0) - t[p2] = s[p2]; - if (s != null && typeof Object.getOwnPropertySymbols === "function") - for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) { - if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i])) - t[p2[i]] = s[p2[i]]; - } - return t; -}; var ConvRNN2D = class extends RNN { constructor(args) { if (args.unroll) { @@ -27063,8 +25884,15 @@ var ConvRNN2D = class extends RNN { ConvRNN2D.className = "ConvRNN2D"; var ConvLSTM2DCell = class extends LSTMCell { constructor(args) { - const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args; - super(Object.assign({}, args, { units: filters })); + const { + filters, + kernelSize, + strides, + padding, + dataFormat, + dilationRate + } = args; + super({ ...args, units: filters }); this.filters = filters; assertPositiveInteger(this.filters, "filters"); this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize"); @@ -27096,7 +25924,7 @@ var ConvLSTM2DCell = class extends LSTMCell { if (this.unitForgetBias) { const init2 = this.biasInitializer; const filters = this.filters; - biasInitializer = new (_a = class CustomInit extends Initializer { + biasInitializer = new (_a = class extends Initializer { apply(shape, dtype) { const biasI = init2.apply([filters]); const biasF = ones2([filters]); @@ -27175,7 +26003,7 @@ var ConvLSTM2DCell = class extends LSTMCell { }); } getConfig() { - const _a = super.getConfig(), { "units": _ } = _a, baseConfig = __rest(_a, ["units"]); + const { "units": _, ...baseConfig } = super.getConfig(); const config = { filters: this.filters, kernelSize: this.kernelSize, @@ -27184,7 +26012,7 @@ var ConvLSTM2DCell = class extends LSTMCell { dilationRate: this.dilationRate, strides: this.strides }; - return Object.assign({}, baseConfig, config); + return { ...baseConfig, ...config }; } inputConv(x, w, b, padding) { const out = conv2d(x, w, this.strides, padding || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate); @@ -27203,7 +26031,7 @@ serialization_exports.registerClass(ConvLSTM2DCell); var ConvLSTM2D = class extends ConvRNN2D { constructor(args) { const cell = new ConvLSTM2DCell(args); - super(Object.assign({}, args, { cell })); + super({ ...args, cell }); } static fromConfig(cls, config) { return new cls(config); @@ -27404,7 +26232,7 @@ var Flatten = class extends Layer { }; Flatten.className = "Flatten"; serialization_exports.registerClass(Flatten); -var Activation2 = class extends Layer { +var Activation5 = class extends Layer { constructor(args) { super(args); this.supportsMasking = true; @@ -27424,8 +26252,8 @@ var Activation2 = class extends Layer { return config; } }; -Activation2.className = "Activation"; -serialization_exports.registerClass(Activation2); +Activation5.className = "Activation"; +serialization_exports.registerClass(Activation5); var RepeatVector = class extends Layer { constructor(args) { super(args); @@ -28331,14 +27159,14 @@ var AlphaDropout = class extends Layer { }; AlphaDropout.className = "AlphaDropout"; serialization_exports.registerClass(AlphaDropout); -function batchNormalization(x, mean4, variance, beta, gamma, epsilon32 = 1e-3) { +function batchNormalization(x, mean5, variance, beta, gamma, epsilon32 = 1e-3) { let out; if (x.rank === 2) { - out = batchNorm2d(x, mean4, variance, beta, gamma, epsilon32); + out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon32); } else if (x.rank === 3) { - out = batchNorm3d(x, mean4, variance, beta, gamma, epsilon32); + out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon32); } else if (x.rank === 4) { - out = batchNorm4d(x, mean4, variance, beta, gamma, epsilon32); + out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon32); } else { throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`); } @@ -28347,16 +27175,16 @@ function batchNormalization(x, mean4, variance, beta, gamma, epsilon32 = 1e-3) { function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { return tidy(() => { const meanAndVariance = moments(x, reductionAxes); - const mean4 = meanAndVariance.mean; + const mean5 = meanAndVariance.mean; const variance = meanAndVariance.variance; - const normed = batchNormalization(x, mean4, variance, beta, gamma, epsilon32); - return [normed, mean4, variance]; + const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon32); + return [normed, mean5, variance]; }); } function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { return tidy(() => { const meanAndVariance = moments(x, reductionAxes); - const mean4 = meanAndVariance.mean; + const mean5 = meanAndVariance.mean; const variance = meanAndVariance.variance; const targetShape = []; for (const axis of range2(0, x.rank)) { @@ -28366,12 +27194,12 @@ function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilo targetShape.push(x.shape[axis]); } } - const broadcastMean = reshape(mean4, targetShape); + const broadcastMean = reshape(mean5, targetShape); const broadcastVariance = reshape(variance, targetShape); const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape); const broadcastBeta = beta == null ? null : reshape(beta, targetShape); const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon32); - return [normed, mean4, variance]; + return [normed, mean5, variance]; }); } function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon32 = 1e-3) { @@ -28449,7 +27277,7 @@ var BatchNormalization = class extends Layer { if (!training) { return normalizeInference(); } - const [normedTraining, mean4, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon); + const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon); const doMovingAverage = (variable2, value, momentum) => { tidy(() => { const decay = 1 - momentum; @@ -28459,7 +27287,7 @@ var BatchNormalization = class extends Layer { }); }; const updateMovingMeanAndVariance = () => { - doMovingAverage(this.movingMean, mean4, this.momentum); + doMovingAverage(this.movingMean, mean5, this.momentum); doMovingAverage(this.movingVariance, variance, this.momentum); }; updateMovingMeanAndVariance(); @@ -28557,7 +27385,7 @@ var LayerNormalization = class extends Layer { const nDims = inputShape.length; return tidy(() => { const keepDims = true; - let { mean: mean4, variance } = moments(input2, this.axis, keepDims); + let { mean: mean5, variance } = moments(input2, this.axis, keepDims); const broadcastShape = pyListRepeat(1, nDims); for (const dim of this.axis) { broadcastShape[dim] = inputShape[dim]; @@ -28582,11 +27410,11 @@ var LayerNormalization = class extends Layer { scaleOffsetTiling.push(inputShape[i]); } } - mean4 = tile(mean4, momentsTiling); + mean5 = tile(mean5, momentsTiling); variance = tile(variance, momentsTiling); scale22 = tile(scale22, scaleOffsetTiling); offset = tile(offset, scaleOffsetTiling); - return batchNormalization(input2, mean4, variance, offset, scale22, this.epsilon); + return batchNormalization(input2, mean5, variance, offset, scale22, this.epsilon); }); } getConfig() { @@ -29521,7 +28349,7 @@ function depthwiseConv2d4(args) { return new DepthwiseConv2D(args); } function activation(args) { - return new Activation2(args); + return new Activation5(args); } function dense(args) { return new Dense(args); @@ -29857,38 +28685,62 @@ function earlyStopping(args) { return new EarlyStopping(args); } var callbacks = { earlyStopping }; -var DataType; -(function(DataType2) { - DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID"; - DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT"; - DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE"; - DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32"; - DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8"; - DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16"; - DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8"; - DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING"; - DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; - DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64"; - DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL"; - DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8"; - DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8"; - DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32"; - DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; - DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; - DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; - DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF"; - DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; - DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF"; - DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF"; - DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF"; - DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; - DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF"; - DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; - DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; - DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; - DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; - DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; -})(DataType || (DataType = {})); +var ENV3 = env(); +ENV3.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => { + if (debugValue) { + console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance."); + } +}); +var DataType8; +(function(DataType48) { + DataType48[DataType48["DT_INVALID"] = 0] = "DT_INVALID"; + DataType48[DataType48["DT_FLOAT"] = 1] = "DT_FLOAT"; + DataType48[DataType48["DT_DOUBLE"] = 2] = "DT_DOUBLE"; + DataType48[DataType48["DT_INT32"] = 3] = "DT_INT32"; + DataType48[DataType48["DT_UINT8"] = 4] = "DT_UINT8"; + DataType48[DataType48["DT_INT16"] = 5] = "DT_INT16"; + DataType48[DataType48["DT_INT8"] = 6] = "DT_INT8"; + DataType48[DataType48["DT_STRING"] = 7] = "DT_STRING"; + DataType48[DataType48["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; + DataType48[DataType48["DT_INT64"] = 9] = "DT_INT64"; + DataType48[DataType48["DT_BOOL"] = 10] = "DT_BOOL"; + DataType48[DataType48["DT_QINT8"] = 11] = "DT_QINT8"; + DataType48[DataType48["DT_QUINT8"] = 12] = "DT_QUINT8"; + DataType48[DataType48["DT_QINT32"] = 13] = "DT_QINT32"; + DataType48[DataType48["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; + DataType48[DataType48["DT_QINT16"] = 15] = "DT_QINT16"; + DataType48[DataType48["DT_QUINT16"] = 16] = "DT_QUINT16"; + DataType48[DataType48["DT_UINT16"] = 17] = "DT_UINT16"; + DataType48[DataType48["DT_COMPLEX128"] = 18] = "DT_COMPLEX128"; + DataType48[DataType48["DT_HALF"] = 19] = "DT_HALF"; + DataType48[DataType48["DT_RESOURCE"] = 20] = "DT_RESOURCE"; + DataType48[DataType48["DT_VARIANT"] = 21] = "DT_VARIANT"; + DataType48[DataType48["DT_UINT32"] = 22] = "DT_UINT32"; + DataType48[DataType48["DT_UINT64"] = 23] = "DT_UINT64"; + DataType48[DataType48["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; + DataType48[DataType48["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; + DataType48[DataType48["DT_INT32_REF"] = 103] = "DT_INT32_REF"; + DataType48[DataType48["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; + DataType48[DataType48["DT_INT16_REF"] = 105] = "DT_INT16_REF"; + DataType48[DataType48["DT_INT8_REF"] = 106] = "DT_INT8_REF"; + DataType48[DataType48["DT_STRING_REF"] = 107] = "DT_STRING_REF"; + DataType48[DataType48["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; + DataType48[DataType48["DT_INT64_REF"] = 109] = "DT_INT64_REF"; + DataType48[DataType48["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; + DataType48[DataType48["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; + DataType48[DataType48["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; + DataType48[DataType48["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; + DataType48[DataType48["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; + DataType48[DataType48["DT_QINT16_REF"] = 115] = "DT_QINT16_REF"; + DataType48[DataType48["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF"; + DataType48[DataType48["DT_UINT16_REF"] = 117] = "DT_UINT16_REF"; + DataType48[DataType48["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF"; + DataType48[DataType48["DT_HALF_REF"] = 119] = "DT_HALF_REF"; + DataType48[DataType48["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF"; + DataType48[DataType48["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF"; + DataType48[DataType48["DT_UINT32_REF"] = 122] = "DT_UINT32_REF"; + DataType48[DataType48["DT_UINT64_REF"] = 123] = "DT_UINT64_REF"; +})(DataType8 || (DataType8 = {})); var SaverDef; (function(SaverDef2) { let CheckpointFormatVersion; @@ -36156,21 +35008,22 @@ function getNumberParam(attrs, name, def) { } function parseDtypeParam(value) { if (typeof value === "string") { - value = DataType[value]; + value = DataType8[value]; } switch (value) { - case DataType.DT_FLOAT: + case DataType8.DT_FLOAT: + case DataType8.DT_HALF: return "float32"; - case DataType.DT_INT32: - case DataType.DT_INT64: - case DataType.DT_INT8: - case DataType.DT_UINT8: + case DataType8.DT_INT32: + case DataType8.DT_INT64: + case DataType8.DT_INT8: + case DataType8.DT_UINT8: return "int32"; - case DataType.DT_BOOL: + case DataType8.DT_BOOL: return "bool"; - case DataType.DT_DOUBLE: + case DataType8.DT_DOUBLE: return "float32"; - case DataType.DT_STRING: + case DataType8.DT_STRING: return "string"; default: return null; @@ -36386,7 +35239,7 @@ var executeOp2 = (node2, tensorMap, context) => { case "Floor": return [floor(getParamValue("x", node2, tensorMap, context))]; case "Log": - return [log5(getParamValue("x", node2, tensorMap, context))]; + return [log4(getParamValue("x", node2, tensorMap, context))]; case "Log1p": { return [log1p(getParamValue("x", node2, tensorMap, context))]; } @@ -37175,7 +36028,16 @@ var executeOp4 = (node2, tensorMap, context) => { return [conv2d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])]; } case "_FusedConv2D": { - const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node2, tensorMap, context); + const { + stride, + pad: pad3, + dataFormat, + dilations, + biasArg, + preluArg, + activationFunc, + leakyreluAlpha + } = fusedConvAndDepthWiseParams(node2, tensorMap, context); return [fused_ops_exports.conv2d({ x: getParamValue("x", node2, tensorMap, context), filter: getParamValue("filter", node2, tensorMap, context), @@ -37190,7 +36052,16 @@ var executeOp4 = (node2, tensorMap, context) => { })]; } case "FusedDepthwiseConv2dNative": { - const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node2, tensorMap, context); + const { + stride, + pad: pad3, + dataFormat, + dilations, + biasArg, + preluArg, + activationFunc, + leakyreluAlpha + } = fusedConvAndDepthWiseParams(node2, tensorMap, context); return [fused_ops_exports.depthwiseConv2d({ x: getParamValue("x", node2, tensorMap, context), filter: getParamValue("filter", node2, tensorMap, context), @@ -37316,10 +36187,10 @@ var executeOp5 = (node2, tensorMap, context) => { } case "TruncatedNormal": { const shape = getParamValue("shape", node2, tensorMap, context); - const mean4 = getParamValue("mean", node2, tensorMap, context); + const mean5 = getParamValue("mean", node2, tensorMap, context); const stdDev = getParamValue("stdDev", node2, tensorMap, context); const seed = getParamValue("seed", node2, tensorMap, context); - return [truncatedNormal(shape, mean4, stdDev, getParamValue("dtype", node2, tensorMap, context), seed)]; + return [truncatedNormal(shape, mean5, stdDev, getParamValue("dtype", node2, tensorMap, context), seed)]; } case "Zeros": { return [zeros(getParamValue("shape", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))]; @@ -37350,7 +36221,14 @@ function nmsParams(node2, tensorMap, context) { var executeOp6 = async (node2, tensorMap, context) => { switch (node2.op) { case "NonMaxSuppressionV5": { - const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node2, tensorMap, context); + const { + boxes, + scores, + maxOutputSize, + iouThreshold, + scoreThreshold, + softNmsSigma + } = nmsParams(node2, tensorMap, context); const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); return [result.selectedIndices, result.selectedScores]; } @@ -37860,7 +36738,12 @@ var executeOp15 = (node2, tensorMap, context) => { var executeOp16 = (node2, tensorMap, context) => { switch (node2.op) { case "SparseFillEmptyRows": { - const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = sparse.sparseFillEmptyRows(getParamValue("indices", node2, tensorMap, context), getParamValue("values", node2, tensorMap, context), getParamValue("denseShape", node2, tensorMap, context), getParamValue("defaultValue", node2, tensorMap, context)); + const { + outputIndices, + outputValues, + emptyRowIndicator, + reverseIndexMap + } = sparse.sparseFillEmptyRows(getParamValue("indices", node2, tensorMap, context), getParamValue("values", node2, tensorMap, context), getParamValue("denseShape", node2, tensorMap, context), getParamValue("defaultValue", node2, tensorMap, context)); return [ outputIndices, outputValues, @@ -38247,6 +37130,8 @@ var GraphExecutor = class { this.SEPERATOR = ","; this._functions = {}; this._functionExecutorMap = {}; + this.intermediateTensors = {}; + this.keepTensorForDebug = false; this._outputs = graph2.outputs; this._inputs = graph2.inputs; this._initNodes = graph2.initNodes; @@ -38336,6 +37221,7 @@ var GraphExecutor = class { const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]); const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]); let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]); + this.resetIntermediateTensors(); if (outputNodes.length === 0) { outputNodes = this._outputs; } @@ -38349,7 +37235,7 @@ var GraphExecutor = class { const tensorListMap = {}; return tidy(() => { const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap); - const tensorsMap = Object.assign({}, this.weightMap); + const tensorsMap = { ...this.weightMap }; Object.keys(inputs).forEach((name) => { const [nodeName, index] = parseNodeName(name); const tensors = []; @@ -38396,7 +37282,17 @@ var GraphExecutor = class { if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) { const count2 = intermediateTensorConsumerCount[tensor2.id]; if (count2 === 1) { - tensor2.dispose(); + if (!this.keepTensorForDebug) { + tensor2.dispose(); + } else { + const [nodeName2, index] = getNodeNameAndIndex(node2.name, context); + if (this.intermediateTensors[nodeName2]) { + this.intermediateTensors[nodeName2][index] = tensor2; + } else { + this.intermediateTensors[nodeName2] = []; + this.intermediateTensors[nodeName2][index] = tensor2; + } + } delete intermediateTensorConsumerCount[tensor2.id]; } else if (count2 != null) { intermediateTensorConsumerCount[tensor2.id]--; @@ -38410,6 +37306,35 @@ var GraphExecutor = class { async executeAsync(inputs, outputs) { return this._executeAsync(inputs, outputs); } + disposeIntermediateTensors() { + if (!this.intermediateTensors) { + return; + } + Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose())); + this.disposeTensorsMap(); + } + disposeTensorsMap() { + if (!this.tensorsMap) { + return; + } + Object.keys(this.tensorsMap).forEach((key) => { + const tensorArray = this.tensorsMap[key]; + tensorArray.forEach((tensor2) => { + if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) { + tensor2.dispose(); + } + }); + }); + } + getIntermediateTensors() { + return this.tensorsMap; + } + resetIntermediateTensors() { + for (const key in this.intermediateTensors) { + this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()); + delete this.intermediateTensors[key]; + } + } async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) { if (!isFunctionExecution) { inputs = this.mapInputs(inputs); @@ -38418,22 +37343,23 @@ var GraphExecutor = class { outputs = this.mapOutputs(outputs); this.checkOutputs(outputs); } + try { + this.keepTensorForDebug = env().getBool("KEEP_INTERMEDIATE_TENSORS"); + } catch (e) { + console.warn(e.message); + } + this.resetIntermediateTensors(); const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap); - const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution); - const results = outputs.map((name) => getTensor(name, tensorMap, context)); + this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution); + const results = outputs.map((name) => getTensor(name, this.tensorsMap, context)); const outputIds = results.map((t) => t.id); const inputIds = Object.keys(inputs).map((name) => inputs[name].id); - const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]); - Object.keys(tensorMap).forEach((key) => { - const tensorArray = tensorMap[key]; - tensorArray.forEach((tensor2) => { - if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !keepIds.has(tensor2.id)) { - tensor2.dispose(); - } - }); - }); + this.keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]); + if (!this.keepTensorForDebug) { + this.disposeTensorsMap(); + } if (this.parent == null) { - context.dispose(keepIds); + context.dispose(this.keepIds); } return results; } @@ -38460,7 +37386,7 @@ var GraphExecutor = class { ].map((node2) => { return { node: node2, contexts: context.currentContext }; }); - const tensorsMap = Object.assign({}, this.weightMap); + const tensorsMap = { ...this.weightMap }; Object.keys(inputs).forEach((name) => { const [nodeName, index] = parseNodeName(name); const tensors = []; @@ -38756,6 +37682,12 @@ var GraphModel = class { const result = await this.executor.executeAsync(inputs, outputs); return result.length > 1 ? result : result[0]; } + getIntermediateTensors() { + return this.executor.getIntermediateTensors(); + } + disposeIntermediateTensors() { + this.executor.disposeIntermediateTensors(); + } convertTensorMapToTensorsMap(map) { return Object.keys(map).reduce((newMap, key) => { newMap[key] = [map[key]]; @@ -38789,9 +37721,9 @@ async function loadGraphModel(modelUrl, options = {}) { await model2.load(); return model2; } -var version3 = "3.11.0"; -var dist_exports = {}; -__export2(dist_exports, { +var version3 = "0.0.0"; +var src_exports = {}; +__export2(src_exports, { CSVDataset: () => CSVDataset, Dataset: () => Dataset, FileDataSource: () => FileDataSource, @@ -38806,8 +37738,8 @@ __export2(dist_exports, { webcam: () => webcam, zip: () => zip }); -var seedrandom3 = __toModule(require_seedrandom4()); -var seedrandom2 = __toModule(require_seedrandom4()); +var seedrandom3 = __toModule(require_seedrandom2()); +var seedrandom2 = __toModule(require_seedrandom2()); function deepMap(input2, mapFn) { return deepMapInternal(input2, mapFn); } @@ -39019,9 +37951,9 @@ var RingBuffer = class { return result; } }; -var GrowingRingBuffer = class extends RingBuffer { +var _GrowingRingBuffer = class extends RingBuffer { constructor() { - super(GrowingRingBuffer.INITIAL_CAPACITY); + super(_GrowingRingBuffer.INITIAL_CAPACITY); } isFull() { return false; @@ -39052,6 +37984,7 @@ var GrowingRingBuffer = class extends RingBuffer { this.end = len; } }; +var GrowingRingBuffer = _GrowingRingBuffer; GrowingRingBuffer.INITIAL_CAPACITY = 32; function iteratorFromItems(items) { return new ArrayIterator(items); @@ -39105,17 +38038,17 @@ var LazyIterator = class { filter(predicate) { return new FilterIterator(this, predicate); } - map(transform5) { - return new MapIterator(this, transform5); + map(transform6) { + return new MapIterator(this, transform6); } - mapAsync(transform5) { - return new AsyncMapIterator(this, transform5); + mapAsync(transform6) { + return new AsyncMapIterator(this, transform6); } - serialMapAsync(transform5) { - return new AsyncMapIterator(this, transform5).serial(); + serialMapAsync(transform6) { + return new AsyncMapIterator(this, transform6).serial(); } - flatmap(transform5) { - return new FlatmapIterator(this, transform5); + flatmap(transform6) { + return new FlatmapIterator(this, transform6); } async forEachAsync(f) { return this.map(f).resolveFully(); @@ -39305,10 +38238,10 @@ var FilterIterator = class extends LazyIterator { } }; var MapIterator = class extends LazyIterator { - constructor(upstream, transform5) { + constructor(upstream, transform6) { super(); this.upstream = upstream; - this.transform = transform5; + this.transform = transform6; } summary() { return `${this.upstream.summary()} -> Map`; @@ -39357,10 +38290,10 @@ var ErrorHandlingLazyIterator = class extends LazyIterator { } }; var AsyncMapIterator = class extends LazyIterator { - constructor(upstream, transform5) { + constructor(upstream, transform6) { super(); this.upstream = upstream; - this.transform = transform5; + this.transform = transform6; } summary() { return `${this.upstream.summary()} -> AsyncMap`; @@ -39401,10 +38334,10 @@ var OneToManyIterator = class extends LazyIterator { } }; var FlatmapIterator = class extends OneToManyIterator { - constructor(upstream, transform5) { + constructor(upstream, transform6) { super(); this.upstream = upstream; - this.transform = transform5; + this.transform = transform6; } summary() { return `${this.upstream.summary()} -> Flatmap`; @@ -39469,7 +38402,7 @@ var ZipMismatchMode; ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST"; })(ZipMismatchMode || (ZipMismatchMode = {})); var ZipIterator = class extends LazyIterator { - constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) { + constructor(iterators, mismatchMode = 0) { super(); this.iterators = iterators; this.mismatchMode = mismatchMode; @@ -39507,11 +38440,11 @@ var ZipIterator = class extends LazyIterator { } if (iteratorsDone > 0) { switch (this.mismatchMode) { - case ZipMismatchMode.FAIL: + case 0: throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`); - case ZipMismatchMode.SHORTEST: + case 1: return { value: null, done: true }; - case ZipMismatchMode.LONGEST: + case 2: default: } } @@ -39557,8 +38490,8 @@ var ShuffleIterator = class extends PrefetchIterator { this.lastRead = this.lastRead.then(() => this.serialNext()); return this.lastRead; } - randomInt(max6) { - return Math.floor(this.random() * max6); + randomInt(max7) { + return Math.floor(this.random() * max7); } chooseIndex() { return this.randomInt(this.buffer.length()); @@ -39627,16 +38560,16 @@ var Dataset = class { async forEachAsync(f) { return (await this.iterator()).forEachAsync(f); } - map(transform5) { + map(transform6) { const base2 = this; return datasetFromIteratorFn(async () => { - return (await base2.iterator()).map((x) => tidy(() => transform5(x))); + return (await base2.iterator()).map((x) => tidy(() => transform6(x))); }, this.size); } - mapAsync(transform5) { + mapAsync(transform6) { const base2 = this; return datasetFromIteratorFn(async () => { - return (await base2.iterator()).mapAsync(transform5); + return (await base2.iterator()).mapAsync(transform6); }, this.size); } prefetch(bufferSize) { @@ -40435,8 +39368,8 @@ var FileChunkIterator = class extends ByteChunkIterator { fileReader.onerror = (event) => { return reject(new Error(event.type)); }; - const slice5 = this.file.slice(this.offset, end); - fileReader.readAsArrayBuffer(slice5); + const slice6 = this.file.slice(this.offset, end); + fileReader.readAsArrayBuffer(slice6); } this.offset = end; }); @@ -40485,7 +39418,7 @@ var FileDataSource = class extends DataSource { } async iterator() { if (isLocalPath(this.input) && env().get("IS_NODE")) { - const fs = __require2("fs"); + const fs = require_fs(); this.input = fs.readFileSync(this.input.substr(7)); } return new FileChunkIterator(this.input, this.options); @@ -40524,7 +39457,7 @@ async function webcam(webcamVideoElement, webcamConfig) { async function microphone(microphoneConfig) { return MicrophoneIterator.create(microphoneConfig); } -var version4 = "3.11.0"; +var version4 = "0.0.0"; function assertNotComplex(tensor2, opName) { if (!Array.isArray(tensor2)) { tensor2 = [tensor2]; @@ -40536,7 +39469,7 @@ function assertNotComplex(tensor2, opName) { }); } var whereImpl2 = kernel_impls_exports.whereImpl; -var MathBackendCPU = class extends KernelBackend { +var _MathBackendCPU = class extends KernelBackend { constructor() { super(); this.blockSize = 48; @@ -40544,7 +39477,7 @@ var MathBackendCPU = class extends KernelBackend { this.data = new DataStorage(this, engine()); } nextDataId() { - return MathBackendCPU.nextDataId++; + return _MathBackendCPU.nextDataId++; } write(values, shape, dtype) { if (this.firstUse) { @@ -40608,7 +39541,7 @@ var MathBackendCPU = class extends KernelBackend { if (t.dtype === "string") { try { decodedData = data.map((d) => util_exports.decodeString(d)); - } catch (_a) { + } catch (e) { throw new Error("Failed to decode encoded string bytes into utf-8"); } } @@ -40662,6 +39595,7 @@ var MathBackendCPU = class extends KernelBackend { return super.epsilon(); } }; +var MathBackendCPU = _MathBackendCPU; MathBackendCPU.nextDataId = 0; var shared_exports = {}; __export2(shared_exports, { @@ -40764,14 +39698,14 @@ function createSimpleBinaryKernelImpl(op2) { } function complex2(args) { const { inputs, backend: backend2 } = args; - const { real: real4, imag: imag4 } = inputs; - const realVals = backend2.data.get(real4.dataId).values; - const imagVals = backend2.data.get(imag4.dataId).values; - const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); - const complex4 = backend2.data.get(complexInfo.dataId); - complex4.complexTensorInfos = { - real: backend2.makeTensorInfo(real4.shape, "float32", realVals), - imag: backend2.makeTensorInfo(imag4.shape, "float32", imagVals) + const { real: real5, imag: imag5 } = inputs; + const realVals = backend2.data.get(real5.dataId).values; + const imagVals = backend2.data.get(imag5.dataId).values; + const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64"); + const complex5 = backend2.data.get(complexInfo.dataId); + complex5.complexTensorInfos = { + real: backend2.makeTensorInfo(real5.shape, "float32", realVals), + imag: backend2.makeTensorInfo(imag5.shape, "float32", imagVals) }; return complexInfo; } @@ -40782,9 +39716,9 @@ var complexConfig = { }; function zeros3(backend2, shape, dtype = "float32") { if (dtype === "complex64") { - const real4 = zeros3(backend2, shape, "float32"); - const imag4 = zeros3(backend2, shape, "float32"); - return complex2({ inputs: { real: real4, imag: imag4 }, backend: backend2 }); + const real5 = zeros3(backend2, shape, "float32"); + const imag5 = zeros3(backend2, shape, "float32"); + return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 }); } const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype); return backend2.makeTensorInfo(shape, dtype, values); @@ -40803,9 +39737,9 @@ var identityConfig = { function real2(args) { const { inputs, backend: backend2 } = args; const { input: input2 } = inputs; - const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real; - const realVal = backend2.data.get(real4.dataId).values; - return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal); + const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real; + const realVal = backend2.data.get(real5.dataId).values; + return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal); } var realConfig = { kernelName: Real, @@ -41172,24 +40106,24 @@ function linSpaceImpl(start, stop, num) { return values; } var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi)); -var log6 = unaryKernelFuncFromImpl(Log, logImpl); +var log5 = unaryKernelFuncFromImpl(Log, logImpl); var logConfig = { kernelName: Log, backendName: "cpu", - kernelFunc: log6 + kernelFunc: log5 }; function maxImpl(aVals, reduceSize, outShape, dtype) { const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape)); for (let i = 0; i < vals.length; ++i) { const offset = i * reduceSize; - let max6 = aVals[offset]; + let max7 = aVals[offset]; for (let j = 0; j < reduceSize; ++j) { const value = aVals[offset + j]; - if (Number.isNaN(value) || value > max6) { - max6 = value; + if (Number.isNaN(value) || value > max7) { + max7 = value; } } - vals[i] = max6; + vals[i] = max7; } return vals; } @@ -41288,11 +40222,11 @@ function prodImpl(xShape, xDtype, xVals, reductionAxes) { const reduceSize = util_exports.sizeFromShape(reduceShape); for (let i = 0; i < outVals.length; ++i) { const offset = i * reduceSize; - let prod5 = 1; + let prod6 = 1; for (let j = 0; j < reduceSize; ++j) { - prod5 *= xVals[offset + j]; + prod6 *= xVals[offset + j]; } - outVals[i] = prod5; + outVals[i] = prod6; } return { outVals, outShape, outDtype }; } @@ -41406,8 +40340,7 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va const rank = indicesShape[1]; if (denseRows === 0) { if (indicesCount !== 0) { - throw new Error(`Received SparseTensor with denseShape[0] = 0 but - indices.shape[0] = ${indicesCount}`); + throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount)); } const outputIndices = util_exports.getArrayFromDType(indicesDType, 0); const outputValues = util_exports.getArrayFromDType(valuesDType, 0); @@ -41425,10 +40358,10 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va for (let i = 0; i < indicesCount; ++i) { const row = indices[i * rank]; if (row < 0) { - throw new Error(`indices(${i}, 0) is invalid: ${row} < 0`); + throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row)); } if (row >= denseRows) { - throw new Error(`indices(${i}, 0) is invalid: ${row} >= ${denseRows}`); + throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows)); } ++csrOffset[row]; rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow; @@ -41504,13 +40437,13 @@ function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputSha const size = targetShape[d]; if (size === -1) { if (unknownIndex !== -1) { - throw new Error(`only one output dimension may be -1, not both ${unknownIndex} and ${d}`); + throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d)); } unknownIndex = d; outputShape.push(1); } else { if (size < 0) { - throw new Error(`size ${d} must be non-negative, not ${size}`); + throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size)); } product *= size; outputShape.push(size); @@ -41518,18 +40451,17 @@ function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputSha } if (unknownIndex !== -1) { if (product <= 0) { - throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"); + throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); } const missing = Math.trunc(denseSize / product); if (product * missing !== denseSize) { - throw new Error(`Input to reshape is a SparseTensor with ${denseSize} - dense values, but the requested shape requires a multiple of ${product}. inputShape=${inputShape} outputShape= ${outputShape}`); + throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape)); } outputShape[unknownIndex] = missing; } const outputSize = util_exports.sizeFromShape(outputShape); if (outputSize !== denseSize) { - throw new Error(`Input to reshape is a tensor with ${denseSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`); + throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape)); } const inputRank = inputShape.length; const inputStrides = []; @@ -41561,15 +40493,12 @@ function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputSha } function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) { const numIndices = indices.length; - if (numIndices !== segmentIds.length) { - throw new Error(`segmentIds and indices should have same size.`); - } const inputFlat = [inputShape[0], input2.length / inputShape[0]]; const numCol = inputFlat[1]; const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0; const outputRows = lastSegmentIdPlusOne; if (outputRows < 0) { - throw new Error(`segment ids must be >= 0`); + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); } const outputShape = inputShape.slice(); outputShape[0] = outputRows; @@ -41582,7 +40511,7 @@ function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, seg return [output, outputShape]; } if (outputRows <= 0) { - throw new Error(`segment ids must be >= 0`); + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); } let start = 0, end = 1; let uninitializedIndex = 0; @@ -41596,11 +40525,11 @@ function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, seg continue; } if (outIndex >= nextIndex) { - throw new Error(`segment ids are not increasing`); + throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); } } if (outIndex < 0 || outIndex >= outputRows) { - throw new Error(`Segment id ${outIndex} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`); + throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows)); } if (outIndex > uninitializedIndex) { output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol); @@ -41608,7 +40537,7 @@ function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, seg for (let i = start; i < end; ++i) { const index = indices[i]; if (index < 0 || index >= inputFlat[0]) { - throw new Error(`Bad: indices[${i}] == ${indices[i]} out of range [0, ${inputFlat[0]})`); + throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0])); } for (let j = 0; j < numCol; j++) { output[outIndex * numCol + j] += input2[index * numCol + j]; @@ -42020,6 +40949,7 @@ function uniqueImpl(values, axis, shape, dtype) { indices }; } +var version5 = "0.0.0"; registerBackend("cpu", () => new MathBackendCPU(), 1); var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1); var eluConfig = { @@ -42101,10 +41031,10 @@ function reshape3(args) { backend2.incRef(x.dataId); const xData = backend2.data.get(x.dataId); if (xData.complexTensorInfos != null) { - const real4 = xData.complexTensorInfos.real; - const imag4 = xData.complexTensorInfos.imag; - real4.shape = $shape; - imag4.shape = $shape; + const real5 = xData.complexTensorInfos.real; + const imag5 = xData.complexTensorInfos.imag; + real5.shape = $shape; + imag5.shape = $shape; } return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; } @@ -42128,9 +41058,7 @@ function batchMatMul(args) { const outerDimsB = b.shape.slice(0, -2); const batchDimA = util_exports.sizeFromShape(outerDimsA); const batchDimB = util_exports.sizeFromShape(outerDimsB); - const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1; - util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`); - const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; @@ -42160,15 +41088,15 @@ function batchMatMul(args) { const kBlock = Math.min(k02 + blockSize, sharedDim); for (let i = i0; i < iBlock; i++) { for (let j = j0; j < jBlock; j++) { - let sum6 = 0; + let sum7 = 0; for (let k = k02; k < kBlock; k++) { const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch; const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch; const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep]; const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB]; - sum6 += aVal * bVal; + sum7 += aVal * bVal; } - resVals[bi * size + (i * rightDim + j)] += sum6; + resVals[bi * size + (i * rightDim + j)] += sum7; } } } @@ -42357,12 +41285,12 @@ function argMax2(args) { const aVals = backend2.data.get($x.dataId).values; for (let i = 0; i < vals.length; ++i) { const offset = i * reduceSize; - let max6 = aVals[offset]; + let max7 = aVals[offset]; let maxIndex = 0; for (let j = 0; j < reduceSize; ++j) { const value = aVals[offset + j]; - if (value > max6) { - max6 = value; + if (value > max7) { + max7 = value; maxIndex = j; } } @@ -42399,12 +41327,12 @@ function argMin2(args) { const aVals = backend2.data.get($x.dataId).values; for (let i = 0; i < vals.length; ++i) { const offset = i * reduceSize; - let min6 = aVals[offset]; + let min7 = aVals[offset]; let minIndex = 0; for (let j = 0; j < reduceSize; ++j) { const value = aVals[offset + j]; - if (value < min6) { - min6 = value; + if (value < min7) { + min7 = value; minIndex = j; } } @@ -42862,17 +41790,17 @@ var avgPoolGradConfig2 = { }; function batchNorm2(args) { const { inputs, backend: backend2, attrs } = args; - const { x, scale: scale22, offset, mean: mean4, variance } = inputs; - util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); - util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); - util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); - assertNotComplex([x, mean4, variance, scale22, offset], "batchNorm"); + const { x, scale: scale22, offset, mean: mean5, variance } = inputs; + util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale22 == null || mean5.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + assertNotComplex([x, mean5, variance, scale22, offset], "batchNorm"); let { varianceEpsilon } = attrs; if (varianceEpsilon == null) { varianceEpsilon = 1e-3; } const xVals = backend2.data.get(x.dataId).values; - const mVals = backend2.data.get(mean4.dataId).values; + const mVals = backend2.data.get(mean5.dataId).values; const varVals = backend2.data.get(variance.dataId).values; const sVals = scale22 ? backend2.data.get(scale22.dataId).values : new Float32Array([1]); const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]); @@ -42912,10 +41840,10 @@ function batchToSpaceND2(args) { const { x } = inputs; const { blockShape, crops } = attrs; assertNotComplex([x], "batchToSpaceND"); - const prod5 = blockShape.reduce((a, b) => a * b); - const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const prod6 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6); const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); - const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6); const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); @@ -42980,14 +41908,14 @@ var complexAbs = (args) => { const cpuBackend = args.backend; const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape)); const complexVals = cpuBackend.data.get(x.dataId); - const real4 = complexVals.complexTensorInfos.real; - const imag4 = complexVals.complexTensorInfos.imag; - const realVals = cpuBackend.data.get(real4.dataId).values; - const imagVals = cpuBackend.data.get(imag4.dataId).values; + const real5 = complexVals.complexTensorInfos.real; + const imag5 = complexVals.complexTensorInfos.imag; + const realVals = cpuBackend.data.get(real5.dataId).values; + const imagVals = cpuBackend.data.get(imag5.dataId).values; for (let i = 0; i < realVals.length; i++) { - const real5 = realVals[i]; - const imag5 = imagVals[i]; - resultValues[i] = Math.hypot(real5, imag5); + const real6 = realVals[i]; + const imag6 = imagVals[i]; + resultValues[i] = Math.hypot(real6, imag6); } return cpuBackend.makeOutput(resultValues, x.shape, "float32"); }; @@ -42999,9 +41927,9 @@ var complexAbsConfig = { function imag2(args) { const { inputs, backend: backend2 } = args; const { input: input2 } = inputs; - const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag; - const imagVal = backend2.data.get(imag4.dataId).values; - return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal); + const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag; + const imagVal = backend2.data.get(imag5.dataId).values; + return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal); } var imagConfig = { kernelName: Imag, @@ -43190,7 +42118,19 @@ function conv2DBackpropInput2(args) { const dyValues = backend2.data.get(dy.dataId).values; const fltValues = backend2.data.get(filter.dataId).values; const [fltS0, fltS1, fltS2] = filterStrides; - const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const { + batchSize, + filterHeight, + filterWidth, + inChannels, + inHeight, + inWidth, + outChannels, + outHeight, + outWidth, + strideHeight, + strideWidth + } = convInfo; $dataFormat = convInfo.dataFormat; const topPad = filterHeight - 1 - convInfo.padInfo.top; const leftPad = filterWidth - 1 - convInfo.padInfo.left; @@ -43246,7 +42186,15 @@ function conv3D(args) { const { strides, pad: pad3, dilations } = attrs; assertNotComplex([x, filter], "conv3d"); const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3); - const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo; + const { + filterDepth, + filterHeight, + filterWidth, + dilationDepth, + dilationHeight, + dilationWidth, + padInfo + } = convInfo; const padFront = padInfo.front; const padLeft = padInfo.left; const padTop = padInfo.top; @@ -43399,7 +42347,23 @@ function conv3DBackpropInputV2(args) { const [dyS0, dyS1, dyS2, dyS3] = dyStrides; const fltValues = backend2.data.get(filter.dataId).values; const [fltS0, fltS1, fltS2, fltS3] = filterStrides; - const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo; + const { + batchSize, + filterDepth, + filterHeight, + filterWidth, + inChannels, + inDepth, + inHeight, + inWidth, + outChannels, + outDepth, + outHeight, + outWidth, + strideDepth, + strideHeight, + strideWidth + } = convInfo; const frontPad = filterDepth - 1 - convInfo.padInfo.front; const topPad = filterHeight - 1 - convInfo.padInfo.top; const leftPad = filterWidth - 1 - convInfo.padInfo.left; @@ -43785,7 +42749,19 @@ function depthwiseConv2dNativeBackpropInput2(args) { const [dyS0, dyS1, dyS2] = dyStrides; const fltValues = backend2.data.get(filter.dataId).values; const [fltS0, fltS1, fltS2] = filterStrides; - const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const { + batchSize, + filterHeight, + filterWidth, + inChannels, + inHeight, + inWidth, + outChannels, + outHeight, + outWidth, + strideHeight, + strideWidth + } = convInfo; const topPad = filterHeight - 1 - convInfo.padInfo.top; const leftPad = filterWidth - 1 - convInfo.padInfo.left; const chMul = outChannels / inChannels; @@ -43855,7 +42831,22 @@ var dilation2dConfig = { const xRank = x.shape.length; const filterVals = cpuBackend.data.get(filter.dataId).values; const filterRank = filter.shape.length; - const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + const { + batchSize, + inHeight, + inWidth, + inChannels, + outHeight, + outWidth, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + dilationHeight, + dilationWidth, + outShape + } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); const outSize = util_exports.sizeFromShape(outShape); const outRank = outShape.length; const outputVals = util_exports.getArrayFromDType(x.dtype, outSize); @@ -43901,7 +42892,22 @@ var dilation2dBackpropFilterConfig = { const cpuBackend = backend2; const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); - const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + const { + batchSize, + inHeight, + inWidth, + inChannels, + outHeight, + outWidth, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + dilationHeight, + dilationWidth, + outShape + } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype); @@ -43948,7 +42954,22 @@ var dilation2dBackpropInputConfig = { const cpuBackend = backend2; const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); - const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); + const { + batchSize, + inHeight, + inWidth, + inChannels, + outHeight, + outWidth, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + dilationHeight, + dilationWidth, + outShape + } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations); util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`); const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype); @@ -44015,11 +43036,11 @@ function sum3(args) { const aVals = backend2.data.get(permutedX.dataId).values; for (let i = 0; i < vals.length; ++i) { const offset = i * reduceSize; - let sum6 = 0; + let sum7 = 0; for (let j = 0; j < reduceSize; ++j) { - sum6 += aVals[offset + j]; + sum7 += aVals[offset + j]; } - vals[i] = sum6; + vals[i] = sum7; } if (keepDims) { const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes); @@ -44190,8 +43211,8 @@ function fftBatch(input2, inverse, cpuBackend) { attrs: { begin: [b, 0], size: [1, innerDim] } }); const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend }); - const { real: real4, imag: imag4 } = fftImpl(input3, inverse, cpuBackend); - const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4); + const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend); + const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5); for (let d = 0; d < innerDim; d++) { const c = backend_util_exports.getComplexWithIndex(res, d); resultReal[b * innerDim + d] = c.real; @@ -44339,19 +43360,19 @@ function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) { function fourierTransformByMatmul(data, size, inverse) { const ret = new Float32Array(size * 2); for (let r = 0; r < size; r++) { - let real4 = 0; - let imag4 = 0; + let real5 = 0; + let imag5 = 0; for (let c = 0; c < size; c++) { const e = backend_util_exports.exponent(r * c, size, inverse); const term = backend_util_exports.getComplexWithIndex(data, c); - real4 += term.real * e.real - term.imag * e.imag; - imag4 += term.real * e.imag + term.imag * e.real; + real5 += term.real * e.real - term.imag * e.imag; + imag5 += term.real * e.imag + term.imag * e.real; } if (inverse) { - real4 /= size; - imag4 /= size; + real5 /= size; + imag5 /= size; } - backend_util_exports.assignToTypedArray(ret, real4, imag4, r); + backend_util_exports.assignToTypedArray(ret, real5, imag5, r); } return ret; } @@ -44440,7 +43461,15 @@ var floorDivConfig = { function fusedConv2D(args) { const { inputs, backend: backend2, attrs } = args; const { x, filter, bias, preluActivationWeights } = inputs; - const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; let result = conv2D({ inputs: { x, filter }, backend: backend2, @@ -44466,7 +43495,15 @@ var fusedConv2DConfig = { function fusedDepthwiseConv2D(args) { const { inputs, backend: backend2, attrs } = args; const { x, filter, bias, preluActivationWeights } = inputs; - const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; let result = depthwiseConv2dNative({ inputs: { x, filter }, backend: backend2, @@ -44653,16 +43690,16 @@ function lRN(args) { const currentChannel = offset % channels; let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius); const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD); - let sum6 = 0; + let sum7 = 0; for (; beginSumOffset <= endSumOffset; beginSumOffset++) { const z = xValues[beginSumOffset]; - sum6 += z * z; + sum7 += z * z; } - return sum6; + return sum7; } for (let offset = 0; offset < size; offset++) { - const sum6 = sumAcrossChannels(offset); - const val = xValues[offset] * Math.pow(bias + alpha * sum6, -beta); + const sum7 = sumAcrossChannels(offset); + const val = xValues[offset] * Math.pow(bias + alpha * sum7, -beta); result[offset] = val; } return backend2.makeTensorInfo(x.shape, x.dtype, result); @@ -44984,14 +44021,14 @@ function min3(args) { const aVals = backend2.data.get($x.dataId).values; for (let i = 0; i < vals.length; ++i) { const offset = i * reduceSize; - let min6 = aVals[offset]; + let min7 = aVals[offset]; for (let j = 0; j < reduceSize; ++j) { const value = aVals[offset + j]; - if (Number.isNaN(value) || value < min6) { - min6 = value; + if (Number.isNaN(value) || value < min7) { + min7 = value; } } - vals[i] = min6; + vals[i] = min7; } if (permutedAxes != null) { backend2.disposeIntermediateTensorInfo($x); @@ -45027,16 +44064,16 @@ function mirrorPad2(args) { const resultStrides = util_exports.computeStrides(outShape); const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize); for (let i = 0; i < resultSize; i++) { - let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides); + let coords3 = util_exports.indexToLoc(i, resultRank, resultStrides); for (let i2 = 0; i2 < resultRank; i2++) { - if (coords2[i2] < start[i2]) { - coords2[i2] = start[i2] * 2 - coords2[i2] - offset; - } else if (coords2[i2] >= end[i2]) { - coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset; + if (coords3[i2] < start[i2]) { + coords3[i2] = start[i2] * 2 - coords3[i2] - offset; + } else if (coords3[i2] >= end[i2]) { + coords3[i2] = (end[i2] - 1) * 2 - coords3[i2] + offset; } } - coords2 = coords2.map((c, i2) => c - start[i2]); - const inIndex = util_exports.locToIndex(coords2, xRank, xStrides); + coords3 = coords3.map((c, i2) => c - start[i2]); + const inIndex = util_exports.locToIndex(coords3, xRank, xStrides); resVals[i] = xVals[inIndex]; } const outId = backend2.write(resVals, outShape, x.dtype); @@ -45316,8 +44353,8 @@ function padV2(args) { resVals.fill(constantValue); } for (let i = 0; i < xSize; i++) { - const coords2 = util_exports.indexToLoc(i, xRank, xStrides); - const outCoords = coords2.map((c, i2) => c + start[i2]); + const coords3 = util_exports.indexToLoc(i, xRank, xStrides); + const outCoords = coords3.map((c, i2) => c + start[i2]); const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides); resVals[outIndex] = xVals[i]; } @@ -45653,9 +44690,9 @@ var rotateWithOffsetConfig = { for (let col = 0; col < imageWidth; col++) { const colOffset = col * numChannels; for (let channel = 0; channel < numChannels; channel++) { - const coords2 = [batch, row, col, channel]; - const x = coords2[2]; - const y = coords2[1]; + const coords3 = [batch, row, col, channel]; + const x = coords3[2]; + const y = coords3[1]; let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor; let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor; coordX = Math.round(coordX + centerX); @@ -45843,7 +44880,7 @@ function spaceToBatchND2(args) { const { x } = inputs; const { blockShape, paddings } = attrs; assertNotComplex([x], "spaceToBatchND"); - const prod5 = util_exports.sizeFromShape(blockShape); + const prod6 = util_exports.sizeFromShape(blockShape); const completePaddings = [[0, 0]]; completePaddings.push(...paddings); for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { @@ -45854,9 +44891,9 @@ function spaceToBatchND2(args) { backend: backend2, attrs: { paddings: completePaddings, constantValue: 0 } }); - const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false); const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); - const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false); const reshapeInputs = { x: paddedX }; const reshapeAttrs = { shape: reshapedPaddedShape }; const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); @@ -45899,7 +44936,13 @@ function sparseFillEmptyRows2(args) { const $values = backend2.data.get(values.dataId).values; const $denseShape = backend2.data.get(denseShape.dataId).values; const $defaultValue = backend2.data.get(defaultValue.dataId).values[0]; - const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + const [ + outputIndices, + outputIndicesShape, + outputValues, + emptyRowIndicator, + reverseIndexMap + ] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); return [ backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), @@ -45954,6 +44997,9 @@ function sparseSegmentMean2(args) { throw new Error(`Segment ids should be a vector but received shape ${segmentIds.shape}`); } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } const $data = backend2.data.get(data.dataId).values; const $indices = backend2.data.get(indices.dataId).values; const $segmentIds = backend2.data.get(segmentIds.dataId).values; @@ -45979,6 +45025,9 @@ function sparseSegmentSum2(args) { throw new Error(`Segment ids should be a vector but received shape ${segmentIds.shape}`); } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error(`segmentIds and indices should have same size.`); + } const $data = backend2.data.get(data.dataId).values; const $indices = backend2.data.get(indices.dataId).values; const $segmentIds = backend2.data.get(segmentIds.dataId).values; @@ -46061,9 +45110,27 @@ var stepConfig = { function stridedSlice2(args) { const { inputs, backend: backend2, attrs } = args; const { x } = inputs; - const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; + const { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + } = attrs; assertNotComplex(x, "stridedSlice"); - const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + const { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: $begin, + end: $end, + strides: $strides + } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); let result; if (isIdentity) { result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); @@ -46087,7 +45154,14 @@ var stridedSliceConfig = { }; function stringNGrams2(args) { const { inputs, backend: backend2, attrs } = args; - const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { + separator, + nGramWidths, + leftPad, + rightPad: rightPad2, + padWidth, + preserveShortSequences + } = attrs; const { data, dataSplits } = inputs; const $data = backend2.data.get(data.dataId).values; const $dataSplits = backend2.data.get(dataSplits.dataId).values; @@ -46207,17 +45281,17 @@ function transform2(args) { const imageVals = backend2.data.get(image3.dataId).values; const transformVals = backend2.data.get(transforms.dataId).values; for (let b = 0; b < batch; ++b) { - const transform5 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8); + const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8); for (let outY = 0; outY < outHeight; ++outY) { for (let outX = 0; outX < outWidth; ++outX) { for (let channel = 0; channel < numChannels; ++channel) { let val; - const projection = transform5[6] * outX + transform5[7] * outY + 1; + const projection = transform6[6] * outX + transform6[7] * outY + 1; if (projection === 0) { continue; } - const inX = (transform5[0] * outX + transform5[1] * outY + transform5[2]) / projection; - const inY = (transform5[3] * outX + transform5[4] * outY + transform5[5]) / projection; + const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection; + const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection; const x = mapCoord(inX, imageWidth, fillMode); const y = mapCoord(inY, imageHeight, fillMode); switch (interpolation) { @@ -46656,7 +45730,7 @@ function getWebGLContext(webGLVersion) { } } const gl = contexts[webGLVersion]; - if (gl.isContextLost()) { + if (gl == null || gl.isContextLost()) { delete contexts[webGLVersion]; return getWebGLContext(webGLVersion); } @@ -46757,6 +45831,7 @@ function getTextureConfig(gl, textureHalfFloatExtension) { defaultNumChannels = 1; textureTypeHalfFloat = glany.HALF_FLOAT; textureTypeFloat = glany.FLOAT; + downloadTextureFormat = glany.RGBA8; } else { internalFormatFloat = gl.RGBA; internalFormatHalfFloat = gl.RGBA; @@ -46767,8 +45842,8 @@ function getTextureConfig(gl, textureHalfFloatExtension) { defaultNumChannels = 4; textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null; textureTypeFloat = gl.FLOAT; + downloadTextureFormat = gl.RGBA; } - downloadTextureFormat = gl.RGBA; return { internalFormatFloat, internalFormatHalfFloat, @@ -46916,8 +45991,8 @@ function validateTextureSize(width, height) { } if (width > maxTextureSize || height > maxTextureSize) { const requested = `[${width}x${height}]`; - const max6 = `[${maxTextureSize}x${maxTextureSize}]`; - throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max6 + "."); + const max7 = `[${maxTextureSize}x${maxTextureSize}]`; + throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max7 + "."); } } function createFramebuffer(gl) { @@ -47235,9 +46310,9 @@ function assertNotComplex2(tensor2, opName) { } }); } -var ENV3 = env(); -ENV3.registerFlag("HAS_WEBGL", () => ENV3.getNumber("WEBGL_VERSION") > 0); -ENV3.registerFlag("WEBGL_VERSION", () => { +var ENV4 = env(); +ENV4.registerFlag("HAS_WEBGL", () => ENV4.getNumber("WEBGL_VERSION") > 0); +ENV4.registerFlag("WEBGL_VERSION", () => { if (isWebGLVersionEnabled(2)) { return 2; } else if (isWebGLVersionEnabled(1)) { @@ -47245,61 +46320,61 @@ ENV3.registerFlag("WEBGL_VERSION", () => { } return 0; }); -ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); -ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV3.get("WEBGL_VERSION") === 2); -ENV3.registerFlag("WEBGL_CPU_FORWARD", () => true); -ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); -ENV3.registerFlag("WEBGL_PACK", () => ENV3.getBool("HAS_WEBGL")); -ENV3.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_CLIP", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_PACK_REDUCE", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_CONV_IM2COL", () => ENV3.getBool("WEBGL_PACK")); -ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION"))); -ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION"))); -ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { - const webGLVersion = ENV3.getNumber("WEBGL_VERSION"); +ENV4.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false); +ENV4.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV4.get("WEBGL_VERSION") === 2); +ENV4.registerFlag("WEBGL_CPU_FORWARD", () => true); +ENV4.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false); +ENV4.registerFlag("WEBGL_PACK", () => ENV4.getBool("HAS_WEBGL")); +ENV4.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_CLIP", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_PACK_REDUCE", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_CONV_IM2COL", () => ENV4.getBool("WEBGL_PACK")); +ENV4.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV4.getNumber("WEBGL_VERSION"))); +ENV4.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV4.getNumber("WEBGL_VERSION"))); +ENV4.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => { + const webGLVersion = ENV4.getNumber("WEBGL_VERSION"); if (webGLVersion === 0) { return 0; } return getWebGLDisjointQueryTimerVersion(webGLVersion); }); -ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile()); -ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION"))); -ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => { - return ENV3.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"); +ENV4.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV4.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile()); +ENV4.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV4.getNumber("WEBGL_VERSION"))); +ENV4.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => { + return ENV4.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV4.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"); }); -ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION"))); -ENV3.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION"))); -ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => { - const useUniforms = ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED"); +ENV4.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV4.getNumber("WEBGL_VERSION"))); +ENV4.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV4.getNumber("WEBGL_VERSION"))); +ENV4.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => { + const useUniforms = ENV4.getBool("WEBGL_RENDER_FLOAT32_ENABLED"); return useUniforms ? 4 : 0; }); -ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => { +ENV4.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => { return -1; }, (threshold3) => { if (threshold3 < 0 && threshold3 !== -1) { throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`); } }); -ENV3.registerFlag("WEBGL_FLUSH_THRESHOLD", () => { +ENV4.registerFlag("WEBGL_FLUSH_THRESHOLD", () => { return device_util_exports.isMobile() ? 1 : -1; }, (threshold3) => { if (threshold3 < 0 && threshold3 !== -1) { throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`); } }); -ENV3.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); -ENV3.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); -ENV3.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); -ENV3.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); +ENV4.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128); +ENV4.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false); +ENV4.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5); +ENV4.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128); function getGlslDifferences() { - let version10; + let version92; let attribute; let varyingVs; let varyingFs; @@ -47310,7 +46385,7 @@ function getGlslDifferences() { let defineSpecialInf; let defineRound; if (env().getNumber("WEBGL_VERSION") === 2) { - version10 = "#version 300 es"; + version92 = "#version 300 es"; attribute = "in"; varyingVs = "out"; varyingFs = "in"; @@ -47341,7 +46416,7 @@ function getGlslDifferences() { } `; } else { - version10 = ""; + version92 = ""; attribute = "attribute"; varyingVs = "varying"; varyingFs = "varying"; @@ -47378,7 +46453,7 @@ function getGlslDifferences() { `; } return { - version: version10, + version: version92, attribute, varyingVs, varyingFs, @@ -47390,19 +46465,19 @@ function getGlslDifferences() { defineRound }; } -function getLogicalCoordinatesFromFlatIndex(coords2, shape, index = "index") { +function getLogicalCoordinatesFromFlatIndex(coords3, shape, index = "index") { const strides = util_exports.computeStrides(shape); return strides.map((stride, i) => { - const line1 = `int ${coords2[i]} = ${index} / ${stride}`; - const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`; + const line1 = `int ${coords3[i]} = ${index} / ${stride}`; + const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${stride}` : `index -= ${coords3[i]} * ${stride}`; return `${line1}; ${line2};`; }).join(""); } -function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords2, shape, index = "index") { +function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = "index") { const strides = util_exports.computeStrides(shape); return strides.map((_, i) => { - const line1 = `int ${coords2[i]} = ${index} / outShapeStrides[${i}]`; - const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * outShapeStrides[${i}]` : `index -= ${coords2[i]} * outShapeStrides[${i}]`; + const line1 = `int ${coords3[i]} = ${index} / outShapeStrides[${i}]`; + const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * outShapeStrides[${i}]` : `index -= ${coords3[i]} * outShapeStrides[${i}]`; return `${line1}; ${line2};`; }).join(""); } @@ -47416,12 +46491,12 @@ function symbolicallyComputeStrides(indicesArr, variableName) { } return strides; } -function getLogicalCoordinatesFromFlatIndexByUniform(coords2, variableName, index = "index") { - const indicesArray = coords2.map((_, i) => i); +function getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = "index") { + const indicesArray = coords3.map((_, i) => i); const strides = symbolicallyComputeStrides(indicesArray, variableName); return strides.map((_, i) => { - const line1 = `int ${coords2[i]} = ${index} / ${strides[i]}`; - const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${strides[i]}` : `index -= ${coords2[i]} * ${strides[i]}`; + const line1 = `int ${coords3[i]} = ${index} / ${strides[i]}`; + const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${strides[i]}` : `index -= ${coords3[i]} * ${strides[i]}`; return `${line1}; ${line2};`; }).join(""); } @@ -47678,7 +46753,7 @@ function getFloatTextureSetRGBASnippet(glsl) { `; } function getShaderPrefix(glsl) { - const SHADER_PREFIX = `${glsl.version} + const SHADER_PREFIX2 = `${glsl.version} precision highp float; precision highp int; precision highp sampler2D; @@ -47737,7 +46812,7 @@ function getShaderPrefix(glsl) { ${SAMPLE_2D_SNIPPET} ${SAMPLE_3D_SNIPPET} `; - return SHADER_PREFIX; + return SHADER_PREFIX2; } var SAMPLE_1D_SNIPPET = ` vec2 uvFromFlat(int texNumR, int texNumC, int index) { @@ -47979,14 +47054,14 @@ function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) { const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2); let texelsInBatchN = texelsInBatch; let batches = ``; - let coords2 = "b, r, c"; + let coords3 = "b, r, c"; for (let b = 2; b < shape.length - 1; b++) { texelsInBatchN *= shape[shape.length - b - 1]; batches = ` int b${b} = index / ${texelsInBatchN}; index -= b${b} * ${texelsInBatchN}; ` + batches; - coords2 = `b${b}, ` + coords2; + coords3 = `b${b}, ` + coords3; } return ` ivec${shape.length} getOutputCoords() { @@ -48002,7 +47077,7 @@ function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) { int r = 2 * (index / ${texelsInLogicalRow}); int c = imod(index, ${texelsInLogicalRow}) * 2; - return ivec${shape.length}(${coords2}); + return ivec${shape.length}(${coords3}); } `; } @@ -49089,7 +48164,8 @@ function compileProgram(gpgpu, program, inputs, output) { flatOffset: null }; const source = makeShader(inputInfos, outShapeInfo, program); - const webGLProgram = gpgpu.createProgram(source); + const fragmentShader = createFragmentShader(gpgpu.gl, source); + const webGLProgram = gpgpu.createProgram(fragmentShader); let infLoc = null; const nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false); if (env().getNumber("WEBGL_VERSION") === 1) { @@ -49124,6 +48200,7 @@ function compileProgram(gpgpu, program, inputs, output) { } return { program, + fragmentShader, source, webGLProgram, uniformLocations, @@ -49598,7 +48675,11 @@ function createAndConfigureTexture(gl, width, height, internalFormat, textureFor callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE)); callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST)); callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST)); - callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null)); + if (env().getNumber("WEBGL_VERSION") === 1) { + callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null)); + } else { + callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height)); + } callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); return texture; } @@ -49658,15 +48739,29 @@ function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureCon internalFormat = textureConfig.internalFormatPackedFloat; } dataForUpload.set(data); - callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload)); + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload)); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload)); + } callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); } function uploadPixelDataToTexture(gl, texture, pixels) { callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture)); if (pixels.data instanceof Uint8Array) { - callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + gl.flush(); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data)); + } } else { - callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + if (env().getNumber("WEBGL_VERSION") === 2) { + callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + gl.flush(); + } else { + callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels)); + } } callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null)); } @@ -49856,10 +48951,9 @@ var GPGPUContext = class { downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) { return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols)); } - createProgram(fragmentShaderSource) { + createProgram(fragmentShader) { this.throwIfDisposed(); const gl = this.gl; - const fragmentShader = createFragmentShader(gl, fragmentShaderSource); if (this.vertexShader == null) { this.vertexShader = createVertexShader2(gl); } @@ -50103,7 +49197,50 @@ function linearSearchLastTrue(arr) { } return i - 1; } -var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports; +var { + addImpl: addImplCPU, + bincountImpl: bincountImplCPU, + bincountReduceImpl: bincountReduceImplCPU, + ceilImpl: ceilImplCPU, + concatImpl: concatImplCPU, + equalImpl: equalImplCPU, + expImpl: expImplCPU, + expm1Impl: expm1ImplCPU, + floorImpl: floorImplCPU, + gatherNdImpl: gatherNdImplCPU, + gatherV2Impl: gatherV2ImplCPU, + greaterImpl: greaterImplCPU, + greaterEqualImpl: greaterEqualImplCPU, + lessImpl: lessImplCPU, + lessEqualImpl: lessEqualImplCPU, + linSpaceImpl: linSpaceImplCPU, + logImpl: logImplCPU, + maxImpl: maxImplCPU, + maximumImpl: maximumImplCPU, + minimumImpl: minimumImplCPU, + multiplyImpl: multiplyImplCPU, + negImpl: negImplCPU, + notEqualImpl: notEqualImplCPU, + prodImpl: prodImplCPU, + rangeImpl: rangeImplCPU, + rsqrtImpl: rsqrtImplCPU, + sigmoidImpl: sigmoidImplCPU, + simpleAbsImpl: simpleAbsImplCPU, + sliceImpl: sliceImplCPU, + sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, + sparseReshapeImpl: sparseReshapeImplCPU, + sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, + sqrtImpl: sqrtImplCPU, + stridedSliceImpl: stridedSliceImplCPU, + stringNGramsImpl: stringNGramsImplCPU, + stringSplitImpl: stringSplitImplCPU, + stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, + subImpl: subImplCPU, + tileImpl: tileImplCPU, + topKImpl: topKImplCPU, + transposeImpl: transposeImplCPU, + uniqueImpl: uniqueImplCPU +} = shared_exports; function getVecChannels(name, rank) { return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`); } @@ -50117,14 +49254,14 @@ function getSourceCoords(rank, dims) { if (rank === 1) { return "rc"; } - let coords2 = ""; + let coords3 = ""; for (let i = 0; i < rank; i++) { - coords2 += dims[i]; + coords3 += dims[i]; if (i < rank - 1) { - coords2 += ","; + coords3 += ","; } } - return coords2; + return coords3; } var PackProgram = class { constructor(outputShape) { @@ -50132,19 +49269,20 @@ var PackProgram = class { this.packedInputs = false; this.packedOutput = true; this.outputShape = outputShape; - const rank = outputShape.length; - if (rank === 0) { + this.rank = outputShape.length; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); + if (this.rank === 0) { this.userCode = ` void main() { setOutput(vec4(getA(), 0., 0., 0.)); } `; } else { - const channels = getChannels("rc", rank); - const dtype = getCoordsDataType(rank); - const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels); - const setup46 = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels); - const output = getOutput(outputShape, channels); + const channels = getChannels("rc", this.rank); + const dtype = getCoordsDataType(this.rank); + const outOfBoundsCondition = this.getOutOfBoundsCondition(channels); + const setup49 = this.getSetup(channels); + const output = this.getOutput(channels); this.userCode = ` void main() { ${dtype} rc = getOutputCoords(); @@ -50152,7 +49290,7 @@ var PackProgram = class { if(${outOfBoundsCondition}) { setOutput(vec4(0)); } else { - ${setup46} + ${setup49} setOutput(vec4(${output})); } @@ -50160,61 +49298,62 @@ var PackProgram = class { `; } } -}; -function getSourceCoordsArr(rank, dims) { - const coords2 = []; - for (let row = 0; row <= 1; row++) { - for (let col = 0; col <= 1; col++) { - let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`; - for (let d = 2; d < rank; d++) { - coord = `${dims[dims.length - 1 - d]},` + coord; + getSourceCoordsArr(dims) { + const coords3 = []; + for (let row = 0; row <= 1; row++) { + for (let col = 0; col <= 1; col++) { + let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`; + for (let d = 2; d < this.rank; d++) { + coord = `${dims[dims.length - 1 - d]},` + coord; + } + coords3.push(coord); } - coords2.push(coord); } + return coords3; } - return coords2; -} -function getOutOfBoundsCondition(rank, shape, dims) { - if (rank === 1) { - return `rc > ${shape[0]}`; - } - let cond = ""; - for (let i = rank - 2; i < rank; i++) { - cond += `${dims[i]} >= ${shape[i]}`; - if (i < rank - 1) { - cond += "||"; + getOutOfBoundsCondition(dims) { + if (this.rank === 1) { + return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`; } + let cond = ""; + for (let i = this.rank - 2; i < this.rank; i++) { + cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`; + if (i < this.rank - 1) { + cond += "||"; + } + } + return cond; } - return cond; -} -function getSetup(rank, cols, rows, dims) { - if (rank === 1) { - return ""; - } - const innerDims = dims.slice(-2); - return ` - int r = ${innerDims[0]}; - int c = ${innerDims[1]}; - int rp1 = r + 1; - int cp1 = c + 1; + getSetup(dims) { + if (this.rank === 1) { + return ""; + } + const innerDims = dims.slice(-2); + const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1]; + const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2]; + return ` + int r = ${innerDims[0]}; + int c = ${innerDims[1]}; + int rp1 = r + 1; + int cp1 = c + 1; - bool cEdge = cp1 >= ${cols}; - bool rEdge = rp1 >= ${rows}; - `; -} -function getOutput(shape, dims) { - const rank = shape.length; - const sourceCoords = getSourceCoordsArr(rank, dims); - if (rank === 1) { - return `getA(rc), - rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1), - 0, 0`; + bool cEdge = cp1 >= ${col}; + bool rEdge = rp1 >= ${row}; + `; } - return `getA(${sourceCoords[0]}), - cEdge ? 0. : getA(${sourceCoords[1]}), - rEdge ? 0. : getA(${sourceCoords[2]}), - rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`; -} + getOutput(dims) { + const sourceCoords = this.getSourceCoordsArr(dims); + if (this.rank === 1) { + return `getA(rc), + rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1), + 0, 0`; + } + return `getA(${sourceCoords[0]}), + cEdge ? 0. : getA(${sourceCoords[1]}), + rEdge ? 0. : getA(${sourceCoords[2]}), + rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`; + } +}; var ReshapePackedProgram = class { constructor(outputShape, inputShape) { this.variableNames = ["A"]; @@ -50406,6 +49545,8 @@ function numBytesForInternalFormat(gl, internalFormat) { return 16; } else if (internalFormat === glany.RGBA16F) { return 8; + } else if (internalFormat === glany.RGBA8) { + return 4; } throw new Error(`Unknown internal format ${internalFormat}`); } @@ -50555,18 +49696,19 @@ var UnpackProgram = class { this.packedInputs = true; this.packedOutput = false; this.outputShape = outputShape; + this.enableShapeUniforms = useShapeUniforms(this.outputShape.length); const rank = outputShape.length; const channels = getChannels("rc", rank); const dtype = getCoordsDataType(rank); const sourceCoords = getSourceCoords(rank, channels); const innerDims = channels.slice(-2); - const coords2 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`; + const coords3 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`; this.userCode = ` void main() { ${dtype} rc = getOutputCoords(); vec4 packedInput = getA(${sourceCoords}); - setOutput(getChannel(packedInput, ${coords2})); + setOutput(getChannel(packedInput, ${coords3})); } `; } @@ -50590,7 +49732,7 @@ function numMBBeforeWarning() { } return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024; } -var MathBackendWebGL = class extends KernelBackend { +var _MathBackendWebGL = class extends KernelBackend { constructor(gpgpu) { super(); this.pendingRead = new WeakMap(); @@ -50623,7 +49765,7 @@ var MathBackendWebGL = class extends KernelBackend { this.texData = new DataStorage(this, engine()); } nextDataId() { - return MathBackendWebGL.nextDataId++; + return _MathBackendWebGL.nextDataId++; } numDataIds() { return this.texData.numDataIds() - this.pendingDeletes; @@ -50670,8 +49812,8 @@ var MathBackendWebGL = class extends KernelBackend { } readSync(dataId) { const texData = this.texData.get(dataId); - const { values, dtype, complexTensorInfos, slice: slice5, shape, isPacked } = texData; - if (slice5 != null) { + const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData; + if (slice6 != null) { let program; if (isPacked) { program = new UnaryOpPackedProgram(shape, CLONE); @@ -50713,8 +49855,8 @@ var MathBackendWebGL = class extends KernelBackend { return new Promise((resolve) => subscribers2.push(resolve)); } const texData = this.texData.get(dataId); - const { values, shape, slice: slice5, dtype, complexTensorInfos, isPacked } = texData; - if (slice5 != null) { + const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData; + if (slice6 != null) { let program; if (isPacked) { program = new UnaryOpPackedProgram(shape, CLONE); @@ -50729,8 +49871,10 @@ var MathBackendWebGL = class extends KernelBackend { if (values != null) { return this.convertAndCacheOnCPU(dataId); } - if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) { - throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`); + if (env().getBool("DEBUG")) { + if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) { + throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`); + } } let buffer2 = null; let tmpDownloadTarget; @@ -50784,7 +49928,7 @@ var MathBackendWebGL = class extends KernelBackend { if (t.dtype === "string") { try { decodedData = data.map((d) => util_exports.decodeString(d)); - } catch (_a) { + } catch (e) { throw new Error("Failed to decode encoded string bytes into utf-8"); } } @@ -50922,8 +50066,8 @@ var MathBackendWebGL = class extends KernelBackend { return true; } releaseGPUData(dataId) { - const { texture, dtype, texShape, usage, isPacked, slice: slice5 } = this.texData.get(dataId); - const key = slice5 && slice5.origDataId || dataId; + const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId); + const key = slice6 && slice6.origDataId || dataId; const refCount = this.dataRefCount.get(key); if (refCount > 1) { this.dataRefCount.set(key, refCount - 1); @@ -51071,7 +50215,9 @@ var MathBackendWebGL = class extends KernelBackend { texData.isPacked = true; texData.shape = input2.shape; } - } else if (!!texData.isPacked !== !!program.packedInputs) { + } + this.uploadToGPU(input2.dataId); + if (!!texData.isPacked !== !!program.packedInputs) { input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2); dataToDispose.push(input2); texData = this.texData.get(input2.dataId); @@ -51084,7 +50230,6 @@ var MathBackendWebGL = class extends KernelBackend { texData = this.texData.get(input2.dataId); savedInput.shape = targetShape; } - this.uploadToGPU(input2.dataId); return { shape: input2.shape, texData, isUniform: false }; }); this.uploadToGPU(output.dataId); @@ -51197,18 +50342,23 @@ var MathBackendWebGL = class extends KernelBackend { let program; let width = texShape[1], height = texShape[0]; const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray; - if (isPacked) { + if (isPacked || !isByteArray) { [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]); + } + if (isPacked) { program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray); } else { program = new EncodeMatrixProgram(shapeAs3D, isByteArray); } - const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype); + const tempDenseInputTexShape = isByteArray ? [height, width] : texShape; + const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype); + const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId); if (isByteArray) { - this.texData.get(tempDenseInputHandle.dataId).usage = TextureUsage.PIXELS; + tempDenseInputTexData.usage = TextureUsage.PIXELS; } else { - this.texData.get(tempDenseInputHandle.dataId).usage = TextureUsage.UPLOAD; + tempDenseInputTexData.usage = TextureUsage.UPLOAD; } + tempDenseInputTexData.texShape = tempDenseInputTexShape; this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values); const customValues = [[height, width]]; const preventEagerUnpacking = true; @@ -51251,6 +50401,7 @@ var MathBackendWebGL = class extends KernelBackend { return shape[0] * shape[1] * util_exports.bytesPerElement(dtype); } }; +var MathBackendWebGL = _MathBackendWebGL; MathBackendWebGL.nextDataId = 0; function float32ToTypedArray(a, dtype) { if (dtype === "float32" || dtype === "complex64") { @@ -51265,7 +50416,7 @@ function float32ToTypedArray(a, dtype) { throw new Error(`Unknown dtype ${dtype}`); } } -var version5 = "3.11.0"; +var version6 = "0.0.0"; function forceHalfFloat() { env().set("WEBGL_FORCE_F16_TEXTURES", true); } @@ -51393,12 +50544,12 @@ var identityConfig2 = { }; function complex3(args) { const { inputs, backend: backend2 } = args; - const { real: real4, imag: imag4 } = inputs; - const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64"); - const complex4 = backend2.texData.get(complexInfo.dataId); - const realTensorInfo = identity3({ inputs: { x: real4 }, backend: backend2 }); - const imagTensorInfo = identity3({ inputs: { x: imag4 }, backend: backend2 }); - complex4.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo }; + const { real: real5, imag: imag5 } = inputs; + const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64"); + const complex5 = backend2.texData.get(complexInfo.dataId); + const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 }); + const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 }); + complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo }; return complexInfo; } var complexConfig2 = { @@ -51473,14 +50624,21 @@ function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) return webglBackend.runWebGLProgram(program, [x], $dtype); }; } -function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) { +function binaryKernelFunc2({ + opSnippet, + packedOpSnippet, + checkOutOfBounds = false, + supportsComplex = false, + cpuKernelImpl, + dtype +}) { return ({ inputs, backend: backend2 }) => { const { a, b } = inputs; const webglBackend = backend2; if (supportsComplex && a.dtype === "complex64") { const aData = webglBackend.texData.get(a.dataId); const bData = webglBackend.texData.get(b.dataId); - const [real4, imag4] = [ + const [real5, imag5] = [ [aData.complexTensorInfos.real, bData.complexTensorInfos.real], [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag] ].map((complexParts) => { @@ -51498,9 +50656,9 @@ function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = fals const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape); return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype)); }); - const complexOutput = complex3({ inputs: { real: real4, imag: imag4 }, backend: webglBackend }); - webglBackend.disposeIntermediateTensorInfo(real4); - webglBackend.disposeIntermediateTensorInfo(imag4); + const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend }); + webglBackend.disposeIntermediateTensorInfo(real5); + webglBackend.disposeIntermediateTensorInfo(imag5); return complexOutput; } const $dtype = dtype || upcastType(a.dtype, b.dtype); @@ -52166,7 +51324,17 @@ var transposeConfig2 = { kernelFunc: transpose3 }; var MATMUL_SHARED_DIM_THRESHOLD = 1e3; -function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { +function batchMatMulImpl({ + a, + b, + transposeA, + transposeB, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { const aRank = a.shape.length; const bRank = b.shape.length; const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; @@ -52177,9 +51345,7 @@ function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias const outerDimsB = b.shape.slice(0, -2); const batchDimA = util_exports.sizeFromShape(outerDimsA); const batchDimB = util_exports.sizeFromShape(outerDimsB); - const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1; - util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`); - const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; @@ -52524,32 +51690,32 @@ var ArgMinMaxPackedProgram = class { const outShape = this.outputShape; const rank = outShape.length; const dtype = getCoordsDataType(rank); - const coords2 = getChannels("coords", rank); + const coords3 = getChannels("coords", rank); let sourceLocSetup; let sourceRank; if (outSize === 1) { sourceRank = rank + 1; const sourceLocDType = getCoordsDataType(sourceRank); sourceLocSetup = ` - ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0); - ++${coords2[rank - 1]}; - ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0); - ++${coords2[rank - 2]}; - ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0); - --${coords2[rank - 1]}; - ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0); - --${coords2[rank - 2]};`; + ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0); + ++${coords3[rank - 1]}; + ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0); + ++${coords3[rank - 2]}; + ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0); + --${coords3[rank - 1]}; + ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0); + --${coords3[rank - 2]};`; } else { sourceRank = rank; sourceLocSetup = ` ${dtype} sourceLocR = coords; - ++${coords2[rank - 1]}; + ++${coords3[rank - 1]}; ${dtype} sourceLocG = coords; - ++${coords2[rank - 2]}; + ++${coords3[rank - 2]}; ${dtype} sourceLocA = coords; - --${coords2[rank - 1]}; + --${coords3[rank - 1]}; ${dtype} sourceLocB = coords; - --${coords2[rank - 2]};`; + --${coords3[rank - 2]};`; } const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank); const inChannel = "." + channels[sourceRank - 1]; @@ -52582,8 +51748,8 @@ var ArgMinMaxPackedProgram = class { ${getBestIndicesAChannelSnippet} void main() { ${dtype} coords = getOutputCoords(); - bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1}; - bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1}; + bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1}; + bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1}; ${sourceLocSetup} ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel}, sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize}; @@ -53435,15 +52601,15 @@ var BatchNormPackedProgram = class { } }; var batchNorm3 = ({ inputs, backend: backend2, attrs }) => { - const { x, mean: mean4, variance, offset, scale: scale22 } = inputs; - util_exports.assert(mean4.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); - util_exports.assert(offset == null || mean4.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); - util_exports.assert(scale22 == null || mean4.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); + const { x, mean: mean5, variance, offset, scale: scale22 } = inputs; + util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."); + util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."); + util_exports.assert(scale22 == null || mean5.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."); let { varianceEpsilon } = attrs; if (varianceEpsilon == null) { varianceEpsilon = 1e-3; } - const finalInputs = [x, mean4, variance]; + const finalInputs = [x, mean5, variance]; let offsetShape = null; if (offset != null) { offsetShape = offset.shape; @@ -53454,7 +52620,7 @@ var batchNorm3 = ({ inputs, backend: backend2, attrs }) => { scaleShape = scale22.shape; finalInputs.push(scale22); } - const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean4.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon); + const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon); const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype); return output; }; @@ -53507,31 +52673,31 @@ var SlicePackedProgram = class { this.rank = destSize.length; this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }]; const dtype = getCoordsDataType(this.rank); - const coords2 = getChannels("coords", this.rank); + const coords3 = getChannels("coords", this.rank); const sourceLoc = getChannels("sourceLoc", this.rank); const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`; const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`; const upperRow = ` result.x = ${getChannel}; - if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) { ++${sourceLoc[this.rank - 1]}; result.y = ${getChannel}; --${sourceLoc[this.rank - 1]}; } `; const lowerRow = this.rank === 1 ? "" : ` - --${coords2[this.rank - 1]}; - if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) { + --${coords3[this.rank - 1]}; + if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) { ++${sourceLoc[this.rank - 2]}; result.z = ${getChannel}; - if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) { + if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) { ++${sourceLoc[this.rank - 1]}; result.w = ${getChannel}; } } `; const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords + - ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join("\n"); + ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords3[i]} + start[${i}];`).join("\n"); this.userCode = ` void main() { ${dtype} coords = getOutputCoords(); @@ -53599,10 +52765,10 @@ var batchToSpaceND3 = (args) => { const { x } = inputs; const { blockShape, crops } = attrs; util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"); - const prod5 = blockShape.reduce((a, b) => a * b); - const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const prod6 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6); const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); - const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6); const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); const toDispose = []; @@ -53874,7 +53040,7 @@ var ConcatPackedProgram = class { const shape = this.outputShape; const rank = shape.length; const dtype = getCoordsDataType(rank); - const coords2 = getChannels("coords", rank); + const coords3 = getChannels("coords", rank); const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank); this.variableNames = shapes.map((_, i) => `T${i}`); const offsets = new Array(shapes.length - 1); @@ -53911,22 +53077,22 @@ var ConcatPackedProgram = class { void main() { ${dtype} coords = getOutputCoords(); - vec4 result = vec4(getValue(${coords2}), 0., 0., 0.); + vec4 result = vec4(getValue(${coords3}), 0., 0., 0.); - ${coords2[rank - 1]} = ${coords2[rank - 1]} + 1; - if (${coords2[rank - 1]} < ${shape[rank - 1]}) { - result.g = getValue(${coords2}); + ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1; + if (${coords3[rank - 1]} < ${shape[rank - 1]}) { + result.g = getValue(${coords3}); } - ${coords2[rank - 2]} = ${coords2[rank - 2]} + 1; - if (${coords2[rank - 2]} < ${shape[rank - 2]}) { - result.a = getValue(${coords2}); + ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1; + if (${coords3[rank - 2]} < ${shape[rank - 2]}) { + result.a = getValue(${coords3}); } - ${coords2[rank - 1]} = ${coords2[rank - 1]} - 1; - if (${coords2[rank - 2]} < ${shape[rank - 2]} && - ${coords2[rank - 1]} < ${shape[rank - 1]}) { - result.b = getValue(${coords2}); + ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1; + if (${coords3[rank - 2]} < ${shape[rank - 2]} && + ${coords3[rank - 1]} < ${shape[rank - 1]}) { + result.b = getValue(${coords3}); } setOutput(result); } @@ -54404,7 +53570,16 @@ var Im2ColPackedProgram = class { `; } }; -function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { +function conv2dByMatMul({ + x, + filter, + convInfo, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { const xShape = x.shape; const xTexData = backend2.texData.get(x.dataId); const sharedMatMulDim = convInfo.inChannels; @@ -54485,8 +53660,24 @@ function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, p } return out; } -function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) { - const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo; +function conv2dWithIm2Row({ + x, + filter, + convInfo, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { + const { + filterWidth, + filterHeight, + inChannels, + outWidth, + outHeight, + dataFormat + } = convInfo; const isChannelsLast = dataFormat === "channelsLast"; const sharedDim = filterWidth * filterHeight * inChannels; const numCols = outHeight * outWidth; @@ -55803,7 +54994,17 @@ var Dilation2DProgram = class { constructor(convInfo) { this.variableNames = ["x", "W"]; this.outputShape = convInfo.outShape; - const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo; + const { + inHeight, + inWidth, + padInfo, + strideHeight, + strideWidth, + filterHeight, + filterWidth, + dilationHeight, + dilationWidth + } = convInfo; const { top: padTop, left: padLeft } = padInfo; this.userCode = ` const ivec2 strides = ivec2(${strideHeight}, ${strideWidth}); @@ -56355,7 +55556,15 @@ function fromPixels2(args) { function fusedConv2d(args) { const { inputs, backend: backend2, attrs } = args; const { x, filter, bias, preluActivationWeights } = inputs; - const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); let out; @@ -56715,11 +55924,11 @@ var LOG_PACKED = ` return result; `; -var log7 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU }); +var log6 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU }); var logConfig2 = { kernelName: Log, backendName: "webgl", - kernelFunc: log7 + kernelFunc: log6 }; var LOG1P = `return log(1.0 + x);`; var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P }); @@ -57470,9 +56679,9 @@ var MirrorPadPackedProgram = class { const dtype = getCoordsDataType(rank); const start = paddings.map((p2) => p2[0]).join(","); const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); - const coords2 = getChannels("rc", rank); + const coords3 = getChannels("rc", rank); const source = getChannels("source", rank); - const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`; const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; const offset = mode === "reflect" ? 0 : 1; let mainLoop = ""; @@ -57490,7 +56699,7 @@ var MirrorPadPackedProgram = class { ${dtype} rc = outputLoc; ${padSetup} result[0] = getChannel(getX(${source.join()}), ${innerDims}); - ${coords2[rank - 1]} += 1; + ${coords3[rank - 1]} += 1; if(${cLimit}) { ${padSetup} result[1] = getChannel(getX(${source.join()}), ${innerDims}); @@ -57511,17 +56720,17 @@ var MirrorPadPackedProgram = class { ${dtype} rc = outputLoc; ${padSetup} result[0] = getChannel(getX(${source.join()}), ${innerDims}); - ${coords2[rank - 1]} += 1; + ${coords3[rank - 1]} += 1; if(${cLimit}) { ${padSetup} result[1] = getChannel(getX(${source.join()}), ${innerDims}); } rc = outputLoc; - ${coords2[rank - 2]} += 1; - if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) { + ${coords3[rank - 2]} += 1; + if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) { ${padSetup} result[2] = getChannel(getX(${source.join()}), ${innerDims}); - ${coords2[rank - 1]} += 1; + ${coords3[rank - 1]} += 1; if(${cLimit}) { ${padSetup} result[3] = getChannel(getX(${source.join()}), ${innerDims}); @@ -57939,20 +57148,20 @@ var PadPackedProgram = class { const dtype = getCoordsDataType(rank); const start = paddings.map((p2) => p2[0]).join(","); const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(","); - const coords2 = getChannels("rc", rank); + const coords3 = getChannels("rc", rank); const source = getChannels("source", rank); - const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`; + const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`; const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`; const componentSetup = [ `${dtype} rc = outputLoc;`, - `${coords2[rank - 1]} += 1; + `${coords3[rank - 1]} += 1; if(${cLimit}) { `, rank === 1 ? "" : `} rc = outputLoc; - ${coords2[rank - 2]} += 1; - if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {`, - rank === 1 ? "" : ` ${coords2[rank - 1]} += 1; + ${coords3[rank - 2]} += 1; + if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`, + rank === 1 ? "" : ` ${coords3[rank - 1]} += 1; if(${cLimit}) {` ]; const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))"; @@ -59069,7 +58278,7 @@ var spaceToBatchND3 = (args) => { const { x } = inputs; const { blockShape, paddings } = attrs; util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"); - const prod5 = blockShape.reduce((a, b) => a * b); + const prod6 = blockShape.reduce((a, b) => a * b); const completePaddings = [[0, 0]]; completePaddings.push(...paddings); for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { @@ -59081,9 +58290,9 @@ var spaceToBatchND3 = (args) => { backend: backend2, attrs: { paddings: completePaddings, constantValue: 0 } }); - const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false); const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); - const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false); const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } }); const paddedXT = transpose3({ inputs: { x: reshapedPaddedX }, @@ -59125,7 +58334,13 @@ function sparseFillEmptyRows3(args) { const $values = backend2.readSync(values.dataId); const $denseShape = backend2.readSync(denseShape.dataId); const $defaultValue = backend2.readSync(defaultValue.dataId)[0]; - const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); + const [ + outputIndices, + outputIndicesShape, + outputValues, + emptyRowIndicator, + reverseIndexMap + ] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue); return [ backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), @@ -59318,8 +58533,26 @@ var StridedSliceProgram = class { function stridedSlice3(args) { const { inputs, backend: backend2, attrs } = args; const { x } = inputs; - const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; - const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + const { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + } = attrs; + const { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: $begin, + end: $end, + strides: $strides + } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); let result; if (isIdentity) { result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); @@ -59352,7 +58585,14 @@ var stridedSliceConfig2 = { }; function stringNGrams3(args) { const { inputs, backend: backend2, attrs } = args; - const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs; + const { + separator, + nGramWidths, + leftPad, + rightPad: rightPad2, + padWidth, + preserveShortSequences + } = attrs; const { data, dataSplits } = inputs; const $data = backend2.readSync(data.dataId); const $dataSplits = backend2.readSync(dataSplits.dataId); @@ -60259,6 +59499,7931 @@ var kernelConfigs2 = [ for (const kernelConfig of kernelConfigs2) { registerKernel(kernelConfig); } +var ENV5 = env(); +ENV5.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15); +ENV5.registerFlag("WEBGPU_CPU_FORWARD", () => true); +ENV5.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4); +ENV5.registerFlag("WEBGPU_USE_NAIVE_CONV2D", () => false); +ENV5.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false); +ENV5.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false); +ENV5.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false); +ENV5.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3); +ENV5.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false); +ENV5.registerFlag("WEBGPU_USE_IMPORT", () => false); +function symbolicallyComputeStrides2(indicesArr, variableName) { + if (Math.max(...indicesArr) > 3) { + throw new Error("Cannot symbolically compute strides for rank > 4 tensor."); + } + const numCoords = indicesArr.length; + const shape = indicesArr.map((d) => `${variableName}[${d}]`); + const strides = new Array(numCoords - 1); + strides[numCoords - 2] = shape[numCoords - 1]; + for (let i = numCoords - 3; i >= 0; --i) { + strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`; + } + return strides; +} +function getCoordsDataType2(rank) { + if (rank <= 1) { + return "i32"; + } else if (rank === 2) { + return `vec2`; + } else if (rank === 3) { + return `vec3`; + } else if (rank === 4) { + return `vec4`; + } else { + throw Error(`GPU for rank ${rank} is not yet supported`); + } +} +function mapToWgslTypes(type, isVec4) { + if (type === "float32") { + return isVec4 ? "vec4" : "f32"; + } else if (type === "int32") { + return isVec4 ? "vec4" : "i32"; + } else if (type === "bool") { + return isVec4 ? "vec4" : "i32"; + } + return type; +} +function getWorkGroupSizeString() { + return ` + [[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]] +`; +} +function getFlatDispatchLayoutMainHeaderString() { + return ` + ${getWorkGroupSizeString()} + fn main([[builtin(local_invocation_id)]] localId : vec3, + [[builtin(global_invocation_id)]] globalId : vec3, + [[builtin(num_workgroups)]] numWorkgroups: vec3) +`; +} +function getNonFlatDispatchLayoutMainHeaderString() { + return ` + ${getWorkGroupSizeString()} + fn main([[builtin(local_invocation_id)]] localId : vec3, + [[builtin(global_invocation_id)]] globalId : vec3) +`; +} +function getMainHeaderAndGlobalIndexString() { + return ` + ${getFlatDispatchLayoutMainHeaderString()} { + let index = getGlobalIndex(globalId, localId, numWorkgroups); +`; +} +function makeShader2(inputInfo, outputData, program, isFromPixel = false) { + const workGroupSizeSnippet = ` + let workGroupSizeX = ${program.workGroupSize[0]}u; + let workGroupSizeY = ${program.workGroupSize[1]}u; + let workGroupSizeZ = ${program.workGroupSize[2]}u;`; + if (isFromPixel === true) { + const getCoords5 = generateGetCoordsFromFlatIndex(outputData.shape); + const outputBufferStr = ` + [[block]] struct Matrix0 { + numbers: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>; + }; + [[block]] struct Uniform { + size : i32; + numChannels : i32; + outShapeStrides : vec2; + dispatchSize : vec3; + }; + + [[group(0), binding(0)]] var result : Matrix0; + [[group(0), binding(2)]] var uniforms: Uniform; + `; + return [ + SHADER_PREFIX, + outputBufferStr, + workGroupSizeSnippet, + SAMPLING_SNIPPETS, + getCoords5, + program.getUserCode() + ].join("\n"); + } + const prefixSnippets = []; + let uniformDeclaration = "[[block]] struct Uniforms { NAN : f32; "; + program.variableNames.forEach((x, i) => { + uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${getCoordsDataType2(inputInfo[i].shape.length)}; `; + }); + uniformDeclaration += `outShape : ${getCoordsDataType2(outputData.shape.length)} ; `; + const stridesLength = outputData.shape.length - 1; + uniformDeclaration += ` + outShapeStrides: ${getCoordsDataType2(stridesLength)}; `; + if (program.size) { + uniformDeclaration += "size : i32; "; + } + if (program.uniforms) { + uniformDeclaration += program.uniforms; + } + uniformDeclaration += "};"; + prefixSnippets.push(uniformDeclaration); + if (program.atomic) { + prefixSnippets.push(` + [[block]] struct Matrix0 { + numbers: array>; + }; + + [[group(0), binding(0)]] var result : Matrix0; + `); + } else { + prefixSnippets.push(` + [[block]] struct Matrix0 { + numbers: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>; + }; + + [[group(0), binding(0)]] var result : Matrix0; + `); + } + program.variableNames.forEach((x, i) => { + prefixSnippets.push(` + [[block]] struct Matrix${1 + i} { + numbers: array<${mapToWgslTypes(inputInfo[i].dtype, program.isVec4)}>; + }; + [[group(0), binding(${1 + i})]] var ${x} : Matrix${1 + i}; + `); + }); + if (uniformDeclaration !== "") { + prefixSnippets.push(` + [[group(0), binding(${1 + program.variableNames.length})]] var uniforms : Uniforms; + `); + } + prefixSnippets.push(workGroupSizeSnippet); + const [getOutputCoords, dispatchLayoutRank] = generateGetOutputCoords(outputData.shape, program.dispatchLayout); + const getCoords4 = generateGetCoordsFromFlatIndex(outputData.shape); + const sources = [ + SHADER_PREFIX, + prefixSnippets.join("\n"), + SAMPLING_SNIPPETS, + getCoords4, + getOutputCoords, + getOutputFlatIndexSnippet(outputData.shape.length) + ]; + if (!program.atomic) { + sources.push(getSetOutputSnippet(outputData.shape, outputData.dtype, program.isVec4)); + } + if (dispatchLayoutRank === outputData.shape.length) { + const inputSamplingSnippet = inputInfo.map((x) => getInputSamplingSnippet2(x, outputData.shape, program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n"); + sources.push(inputSamplingSnippet); + } + sources.push(program.getUserCode()); + const source = sources.join("\n"); + return source; +} +var SHADER_PREFIX = ` + fn idiv(a: i32, b: i32, sign: f32) -> i32 { + var res: i32 = a / b; + let mod: i32 = a % b; + if (sign < 0. && mod != 0) { + res = res - 1; + } + return res; + } + + fn isNanCustom(val : f32) -> bool { + if (val > 0.0) { + return false; + } + if (val < 0.0) { + return false; + } + if (val == 0.0) { + return false; + } + return true; + } + + fn isNanCustomVec4F32(val : vec4) -> vec4 { + var res = vec4 (0.0); + for (var i = 0u; i < 4u; i = i + 1u) { + if (isNanCustom(val[i])) { + res[i] = 1.0; + } else { + res[i] = 0.0; + } + } + return res; + } + + // Checks whether coordinates lie within the bounds of the shape. + fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool { + return all(coord >= vec4(0)) && + all(coord < shape); + } + + fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool { + return all(coord >= vec3(0)) && + all(coord < shape); + } + + fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool { + return all(coord >= vec2(0)) && + all(coord < shape); + } + `; +var SAMPLING_SNIPPETS = ` + fn getFlatIndex1D(coord : i32, shape : i32) -> i32 { + return coord; + } + + fn getFlatIndex2D(coords : vec2, shape : vec2) -> i32 { + return dot(coords, vec2(shape.y, 1)); + } + + fn getFlatIndex3D(coords : vec3, shape : vec3) -> i32 { + return dot(coords, vec3(shape.y * shape.z, shape.z, 1)); + } + + fn getFlatIndex4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); + } + + // Only used when the y/z dimension of workgroup size is 1. + fn getGlobalIndex(globalId : vec3, localId : vec3, numWorkgroups: vec3) -> i32 { + if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) { + return i32(globalId.x); + } + + let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY + + localId.y * workGroupSizeX + localId.x; + let workGroupID = (globalId - localId)/vec3( + workGroupSizeX, workGroupSizeY, workGroupSizeZ); + + return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y + + workGroupID.y * numWorkgroups.x + workGroupID.x) * + (workGroupSizeX * workGroupSizeY * workGroupSizeZ) + + localInvocationIndex); + } +`; +function getOutputFlatIndexSnippet(outRank) { + let snippet = ""; + switch (outRank) { + case 0: + case 1: + snippet += ` + fn getOutputFlatIndex(coords : i32) -> i32 { + return coords; + } + `; + break; + case 2: + snippet += ` + fn getOutputFlatIndex(coords : vec2) -> i32 { + return dot(coords, vec2(uniforms.outShapeStrides, 1)); + } + `; + break; + case 3: + snippet += ` + fn getOutputFlatIndex(coords : vec3) -> i32 { + return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1)); + } + `; + break; + case 4: + snippet += ` + fn getOutputFlatIndex(coords : vec4) -> i32 { + return dot(coords, vec4( + uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1)); + } + `; + break; + default: + util_exports.assert(false, () => `Unsupported ${outRank}D shape`); + break; + } + return snippet; +} +function getSetOutputSnippet(outShape, outBufferType, isVec4) { + const outRank = outShape.length; + const wgslType = mapToWgslTypes(outBufferType, isVec4); + let snippet; + if (isVec4) { + snippet = `fn setOutputFlat(flatIndex : i32, value : vec4) { + result.numbers[flatIndex] = ${wgslType}(value); + } + fn setOutputFlatI32(flatIndex : i32, value : vec4) { + result.numbers[flatIndex] = ${wgslType}(value); + }`; + } else { + snippet = `fn setOutputFlat(flatIndex : i32, value : f32) { + result.numbers[flatIndex] = ${wgslType}(value); + } + fn setOutputFlatI32(flatIndex : i32, value : i32) { + result.numbers[flatIndex] = ${wgslType}(value); + }`; + } + if (outRank >= 2) { + const dims = ["d0", "d1", "d2", "d3"].slice(0, outRank); + const type = getCoordsDataType2(outRank); + if (isVec4) { + snippet += ` + fn setOutput(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4) { + let flatIndex = getOutputFlatIndex(${type}(${dims.join(", ")})); + setOutputFlat(flatIndex / 4, value); + } + fn setOutputI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4) { + let flatIndex = getOutputFlatIndex(${type}(${dims.join(", ")})); + setOutputFlatI32(flatIndex / 4, value); + } + `; + } else { + snippet += ` + fn setOutput(${dims.map((d) => `${d} : i32`).join(", ")}, value : f32) { + let flatIndex = getOutputFlatIndex(${type}(${dims.join(", ")})); + setOutputFlat(flatIndex, value); + } + fn setOutputI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : i32) { + let flatIndex = getOutputFlatIndex(${type}(${dims.join(", ")})); + setOutputFlatI32(flatIndex, value); + } + `; + } + } + return snippet; +} +function getInputSamplingSnippet2(inInfo, outShape, isVec4, isFlatDispatchLayout) { + let res = getSamplerFromInInfo2(inInfo, isVec4); + const inShape = inInfo.shape; + if (inShape.length <= outShape.length) { + res += getSamplerAtOutputCoords2(inInfo, outShape, isVec4, isFlatDispatchLayout); + } + return res; +} +function getSamplerFromInInfo2(inInfo, isVec4) { + const texName = inInfo.name; + const rank = inInfo.shape.length; + const type = getCoordsDataType2(rank); + const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1); + const dims = ["d0", "d1", "d2", "d3"].slice(0, rank); + const inputs = dims.map((d) => `${d} : i32`).join(", "); + if (rank < 1) { + if (isVec4) { + return ` + fn ${funcName}() -> vec4 { + return vec4(${texName}.numbers[0]); + } + `; + } + return ` + fn ${funcName}() ->f32 { + return f32(${texName}.numbers[0]); + } + `; + } + const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`; + let rankStr = `${rank}D`; + if (rank === 0) { + rankStr = "1D"; + } + if (isVec4) { + return ` + fn ${funcName}(${inputs}) -> vec4 { + return vec4(${texName}.numbers[getFlatIndex${rankStr}(${type}(${dims.join(",")}), + ${shapeStr}) / 4]); + } + `; + } + return ` + fn ${funcName}(${inputs}) -> f32 { + return f32(${texName}.numbers[getFlatIndex${rankStr}(${type}(${dims.join(",")}), + ${shapeStr})]); + } + `; +} +function getSamplerAtOutputCoords2(inInfo, outShape, isVec4, isFlatDispatchLayout) { + const texName = inInfo.name; + const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1); + const funcName = "get" + texFuncSnippet + "AtOutCoords"; + const inRank = inInfo.shape.length; + const outRank = outShape.length; + const type = getCoordsDataType2(outRank); + if (util_exports.arraysEqual(inInfo.shape, outShape) && isFlatDispatchLayout) { + if (isVec4) { + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> vec4 { + return vec4(${texName}.numbers[globalIndex]); + } + + fn ${funcName}ByCoords(coords : ${type}) -> vec4 { + return vec4(${texName}.numbers[${outRank > 1 ? "getOutputFlatIndex(coords)" : "coords"} / 4]); + } + `; + } else { + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> f32 { + return f32(${texName}.numbers[globalIndex]); + } + + fn ${funcName}ByCoords(coords : ${type}) -> f32 { + return f32(${texName}.numbers[${outRank > 1 ? "getOutputFlatIndex(coords)" : "coords"}]); + } + `; + } + } + const broadcastDims = backend_util_exports.getBroadcastDims(inInfo.shape, outShape); + const rankDiff = outRank - inRank; + let coordsSnippet = ""; + if (inRank === 0) { + if (isVec4) { + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> vec4 { + return get${texFuncSnippet}(); + } + + fn ${funcName}ByCoords(coords : ${type}) -> vec4 { + return get${texFuncSnippet}(); + } + `; + } + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> f32{ + return get${texFuncSnippet}(); + } + + fn ${funcName}ByCoords(coords : ${type}) -> f32{ + return get${texFuncSnippet}(); + } + `; + } else { + if (outRank < 2 && broadcastDims.length >= 1) { + coordsSnippet = "coords = 0;"; + } else { + coordsSnippet = broadcastDims.map((d) => `coords[${d + rankDiff}] = 0;`).join("\n"); + } + } + let unpackedCoordsSnippet = ""; + if (outRank < 2 && inRank > 0) { + unpackedCoordsSnippet = "coords"; + } else { + if (outRank > 1) { + const coordsType = getCoordsDataType2(inRank); + const coordsValues = inInfo.shape.map((s, i) => `coords[${i + rankDiff}]`).join(", "); + unpackedCoordsSnippet = `${coordsType}(${coordsValues})`; + } else { + unpackedCoordsSnippet = "coords"; + } + } + const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`; + const rankStr = `${inRank}D`; + if (isVec4) { + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> vec4 { + var coords = getCoordsFromFlatIndex(globalIndex); + ${coordsSnippet} + return ${texName}.numbers[getFlatIndex${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4]; + } + + fn ${funcName}ByCoords(coordsIn : ${type}) -> vec4 { + var coords = coordsIn; + ${coordsSnippet} + return ${texName}.numbers[getFlatIndex${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4]; + } + `; + } + return ` + fn ${funcName}ByGlobalIndex(globalIndex : i32) -> f32 { + var coords = getCoordsFromFlatIndex(globalIndex); + ${coordsSnippet} + return f32(${texName}.numbers[getFlatIndex${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]); + } + + fn ${funcName}ByCoords(coordsIn : ${type}) -> f32 { + var coords = coordsIn; + ${coordsSnippet} + return f32(${texName}.numbers[getFlatIndex${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]); + } + `; +} +function generateGetOutputCoords(outShape, dispatchLayout) { + const { x, y = [], z = [] } = dispatchLayout; + const outRank = outShape.length; + if (x.length === outRank) { + const dtype2 = getCoordsDataType2(outRank); + const snippet2 = `fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3, localId : vec3, numWorkgroups: vec3) -> ${dtype2}{ + let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups); + return getCoordsFromFlatIndex(globalIndex); + } + `; + return [snippet2, outRank]; + } + let gatherDimensionsStr = ""; + const dims = [x, y, z]; + let rank = 0; + for (let i = 0; i < dims.length; i++) { + const arr = dims[i]; + if (arr.length === 0) { + continue; + } + rank += arr.length; + if (arr.length === 1) { + gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i}]);`; + } else { + const strides = symbolicallyComputeStrides2(arr, "uniforms.outShape"); + gatherDimensionsStr += `var index${i} = i32(globalId[${i}]);`; + for (let j = 0; j < strides.length; j++) { + gatherDimensionsStr += `let d${arr[j]} = index${i} / ${strides[j]};`; + if (j === strides.length - 1) { + gatherDimensionsStr += `let d${arr[j + 1]} = index${i} - d${arr[j]} * ${strides[j]};`; + } else { + gatherDimensionsStr += `index${i} = index${i} - d${arr[j]} * ${strides[j]};`; + } + } + } + } + const dimensions = []; + for (let i = 0; i < rank; i++) { + dimensions.push(`d${i}`); + } + const dtype = getCoordsDataType2(rank); + let snippet = `fn getOutputCoordsWithNonFlatDispatchLayout(globalId : vec3) -> ${dtype} { + ${gatherDimensionsStr} + `; + if (dimensions.length === 0) { + snippet += `return ${dtype}(0); }`; + } else { + snippet += `return ${dtype}(${dimensions.join(",")}); }`; + } + return [snippet, rank]; +} +function generateGetCoordsFromFlatIndex(shape) { + const rank = shape.length; + if (rank <= 1) { + return `fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }`; + } + const strides = util_exports.computeStrides(shape); + const dtype = getCoordsDataType2(rank); + const coords3 = []; + for (let i = 0; i < rank; i++) { + coords3.push(`d${i}`); + } + if (strides.length === 1) { + return ` fn getCoordsFromFlatIndex(index : i32) -> vec2 { + let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides; + return vec2(d0, d1); + }`; + } + const snippet = "var index2 = index;" + strides.map((_, i) => { + const line1 = `let ${coords3[i]} = index2 / uniforms.outShapeStrides[${i}]`; + const line2 = i === strides.length - 1 ? `let ${coords3[i + 1]} = index2 - ${coords3[i]} * uniforms.outShapeStrides[${i}]` : `index2 = index2 - ${coords3[i]} * uniforms.outShapeStrides[${i}]`; + return `${line1}; ${line2};`; + }).join(""); + return ` + fn getCoordsFromFlatIndex(index : i32) -> ${dtype} { + ${snippet} + return ${dtype}(${coords3.join(",")}); + } + `; +} +var webgpu_util_exports = {}; +__export2(webgpu_util_exports, { + ArrayBufferToTypedArray: () => ArrayBufferToTypedArray, + GPUBytesPerElement: () => GPUBytesPerElement, + computeDispatch: () => computeDispatch, + computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d, + computeWorkGroupSizeForMatMul: () => computeWorkGroupSizeForMatMul, + computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d, + flatDispatchLayout: () => flatDispatchLayout, + isWebGPUSupported: () => isWebGPUSupported, + tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape +}); +var MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = 65535; +var arrayProduct = (arr) => { + let product = 1; + for (let i = 0; i < arr.length; i++) { + product *= arr[i]; + } + return product; +}; +function tilesFitEvenlyIntoShape(tileSize, shape) { + if (tileSize.length !== shape.length) { + throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`); + } + return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0); +} +function computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) { + const [dispatchX, dispatchY, dispatchZ] = [ + Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])), + layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1, + layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1 + ]; + if (dispatchX <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && dispatchY <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && dispatchZ <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) { + return [dispatchX, dispatchY, dispatchZ]; + } + util_exports.assert(dispatchX > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension."); + let dispatchAverage = Math.ceil(Math.sqrt(dispatchX)); + if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) { + dispatchAverage = Math.ceil(Math.cbrt(dispatchX)); + util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => "Total dispatch size exceeds WebGPU maximum."); + return [dispatchAverage, dispatchAverage, dispatchAverage]; + } else { + return [dispatchAverage, dispatchAverage, 1]; + } +} +function computeWorkGroupSizeForConv2d(layout, outputShape) { + const dim0 = arrayProduct(layout.x.map((d) => outputShape[d])); + const dim1 = arrayProduct(layout.y.map((d) => outputShape[d])); + if (dim0 <= 4) { + return [4, 16, 1]; + } + if (dim1 <= 4) { + return [16, 4, 1]; + } + return [16, 16, 1]; +} +function computeWorkGroupSizeForMatMul(dimAOuter, dimInner, dimBOuter) { + if (dimAOuter === 1) { + return [32, 1, 1]; + } else if (dimBOuter === 1) { + return [1, 32, 1]; + } + return [8, 8, 1]; +} +function computeWorkPerThreadForConv2d(layout, outputShape) { + const dim0 = arrayProduct(layout.x.map((d) => outputShape[d])); + const dim1 = arrayProduct(layout.y.map((d) => outputShape[d])); + if (dim0 <= 4) { + return [1, 2, 1]; + } + if (dim1 <= 4) { + return [2, 1, 1]; + } + return [2, 2, 1]; +} +function flatDispatchLayout(shape) { + return { x: shape.map((d, i) => i) }; +} +function GPUBytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32" || dtype === "bool" || dtype === "string") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function ArrayBufferToTypedArray(data, dtype) { + if (dtype === "float32") { + return new Float32Array(data); + } else if (dtype === "int32") { + return new Int32Array(data); + } else if (dtype === "bool" || dtype === "string") { + const dataAsInt32Array = new Int32Array(data); + const boolData = new ArrayBuffer(dataAsInt32Array.length); + const dataAsTypedArray = new Uint8Array(boolData); + for (let i = 0; i < dataAsInt32Array.length; i++) { + dataAsTypedArray[i] = dataAsInt32Array[i]; + } + return dataAsTypedArray; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function isWebGPUSupported() { + if (!navigator.gpu) { + return false; + } + return true; +} +var BinaryOpType; +(function(BinaryOpType6) { + BinaryOpType6[BinaryOpType6["MUL"] = 0] = "MUL"; + BinaryOpType6[BinaryOpType6["ADD"] = 1] = "ADD"; + BinaryOpType6[BinaryOpType6["SUB"] = 2] = "SUB"; + BinaryOpType6[BinaryOpType6["DIV"] = 3] = "DIV"; + BinaryOpType6[BinaryOpType6["EQUAL"] = 4] = "EQUAL"; + BinaryOpType6[BinaryOpType6["GREATER"] = 5] = "GREATER"; + BinaryOpType6[BinaryOpType6["GREATER_EQUAL"] = 6] = "GREATER_EQUAL"; + BinaryOpType6[BinaryOpType6["LESS"] = 7] = "LESS"; + BinaryOpType6[BinaryOpType6["LESS_EQUAL"] = 8] = "LESS_EQUAL"; + BinaryOpType6[BinaryOpType6["LOGICAL_AND"] = 9] = "LOGICAL_AND"; + BinaryOpType6[BinaryOpType6["NOT_EQUAL"] = 10] = "NOT_EQUAL"; + BinaryOpType6[BinaryOpType6["SQUARED_DIFFERENCE"] = 11] = "SQUARED_DIFFERENCE"; + BinaryOpType6[BinaryOpType6["INT_DIV"] = 12] = "INT_DIV"; + BinaryOpType6[BinaryOpType6["POW"] = 13] = "POW"; + BinaryOpType6[BinaryOpType6["PRELU"] = 14] = "PRELU"; + BinaryOpType6[BinaryOpType6["MAX"] = 15] = "MAX"; + BinaryOpType6[BinaryOpType6["MIN"] = 16] = "MIN"; + BinaryOpType6[BinaryOpType6["COMPLEX_MULTIPLY_REAL"] = 17] = "COMPLEX_MULTIPLY_REAL"; + BinaryOpType6[BinaryOpType6["COMPLEX_MULTIPLY_IMAG"] = 18] = "COMPLEX_MULTIPLY_IMAG"; +})(BinaryOpType || (BinaryOpType = {})); +var ADD2 = "return a + b;"; +var COMPLEX_MULTIPLY_REAL = "return areal * breal - aimag * bimag;"; +var COMPLEX_MULTIPLY_IMAG = "return areal * bimag + aimag * breal;"; +var DIV2 = "return a / b;"; +var MUL2 = "return a * b;"; +var SQUARED_DIFFERENCE2 = "return (a - b) * (a - b);"; +var SUB2 = "return a - b;"; +var EQUAL2 = "return f32(a == b);"; +var EQUAL_VEC4 = "return vec4(a == b);"; +var GREATER2 = "return f32(a > b);"; +var GREATER_VEC4 = "return vec4(a > b);"; +var GREATER_EQUAL2 = "return f32(a >= b);"; +var GREATER_EQUAL_VEC4 = "return vec4(a >= b);"; +var LESS2 = "return f32(a < b);"; +var LESS_VEC4 = "return vec4(a < b);"; +var LESS_EQUAL2 = "return f32(a <= b);"; +var LESS_EQUAL_VEC4 = "return vec4(a <= b);"; +var LOGICAL_AND2 = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);"; +var LOGICAL_AND_VEC4 = `return (vec4(a >= vec4(1.0)) * + vec4(b >= vec4(1.0)));`; +var CHECK_NAN_SNIPPET4 = ` + if (isNanCustom(a)) { return a; } + if (isNanCustom(b)) { return b; } + `; +var CHECK_NAN_SNIPPET_VEC4 = ` + if (isNaN.r > 0.) { + resultTemp.r = uniforms.NAN; + } + if (isNaN.g > 0.) { + resultTemp.g = uniforms.NAN; + } + if (isNaN.b > 0.) { + resultTemp.b = uniforms.NAN; + } + if (isNaN.a > 0.) { + resultTemp.a = uniforms.NAN; + } + `; +var INT_DIV2 = ` + let s = sign(a) * sign(b); + let ia = i32(round(a)); + let ib = i32(round(b)); + return f32(idiv(ia, ib, s)); + `; +var INT_DIV_VEC4 = ` + let ia = vec4(round(a)); + let ib = vec4(round(b)); + let cond = ib != vec4(0); + var resultTemp = vec4(0); + let s = sign(a) * sign(b); + + // Windows (D3D) wants guaranteed non-zero int division at compile-time. + if (cond[0]) { + resultTemp[0] = idiv(ia[0], ib[0], s[0]); + } + if (cond[1]) { + resultTemp[1] = idiv(ia[1], ib[1], s[1]); + } + if (cond[2]) { + resultTemp[2] = idiv(ia[2], ib[2], s[2]); + } + if (cond[3]) { + resultTemp[3] = idiv(ia[3], ib[3], s[3]); + } + return vec4(resultTemp); + `; +var NOT_EQUAL2 = "return f32(a != b);"; +var NOT_EQUAL_VEC4 = "return vec4(a != b);"; +var POW2 = ` + if(a < 0.0 && floor(b) < b) { + return uniforms.NAN; + } + if (b == 0.0) { + return 1.0; + } + if (round(abs(b) % 2.0) != 1.0) { + return pow(abs(a), b); + } + return sign(a) * pow(abs(a), b); + `; +var POW_VEC4 = ` + let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); + let isModRound1 = vec4(isModRound1Bool); + let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); + var resultTemp = multiplier * pow(abs(a), b); + + // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS + let isExpZero = b == vec4(0.0); + if (isExpZero.r) { + resultTemp.r = 1.0; + } + if (isExpZero.g) { + resultTemp.g = 1.0; + } + if (isExpZero.b) { + resultTemp.b = 1.0; + } + if (isExpZero.a) { + resultTemp.a = 1.0; + } + let isNaN = vec4(a < vec4(0.0)) * vec4(floor(b) < b); + ${CHECK_NAN_SNIPPET_VEC4} + return resultTemp; + `; +var PRELU2 = `if (a < 0.0) { return b * a; } return a;`; +var PRELU_VEC4 = ` + let aLessThanZero = vec4(a < vec4(0.0)); + return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); + `; +function getMinMaxString(op2, useVec4) { + const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4; + return useVec4 ? ` + var resultTemp = vec4(${op2}(a, b)); + let isNaN = min(vec4(isNanCustomVec4F32(a)) + vec4(isNanCustomVec4F32(b)), vec4(1.0)); + ` + checkNanSnippet + ` + return resultTemp; + ` : checkNanSnippet + ` + return ${op2}(a, b); + `; +} +function getBinaryOpString(type, useVec4) { + switch (type) { + case 0: + return MUL2; + case 1: + return ADD2; + case 2: + return SUB2; + case 3: + return DIV2; + case 4: + return useVec4 ? EQUAL_VEC4 : EQUAL2; + case 5: + return useVec4 ? GREATER_VEC4 : GREATER2; + case 6: + return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2; + case 7: + return useVec4 ? LESS_VEC4 : LESS2; + case 8: + return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2; + case 9: + return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2; + case 10: + return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2; + case 11: + return SQUARED_DIFFERENCE2; + case 12: + return useVec4 ? INT_DIV_VEC4 : INT_DIV2; + case 14: + return useVec4 ? PRELU_VEC4 : PRELU2; + case 15: + return getMinMaxString("max", useVec4); + case 16: + return getMinMaxString("min", useVec4); + case 13: + return useVec4 ? POW_VEC4 : POW2; + case 17: + return COMPLEX_MULTIPLY_REAL; + case 18: + return COMPLEX_MULTIPLY_IMAG; + default: + throw new Error(`BinaryType ${type} is not implemented!`); + } +} +var UnaryOpType; +(function(UnaryOpType3) { + UnaryOpType3[UnaryOpType3["ABS"] = 0] = "ABS"; + UnaryOpType3[UnaryOpType3["CEIL"] = 1] = "CEIL"; + UnaryOpType3[UnaryOpType3["COS"] = 2] = "COS"; + UnaryOpType3[UnaryOpType3["COSH"] = 3] = "COSH"; + UnaryOpType3[UnaryOpType3["ELU"] = 4] = "ELU"; + UnaryOpType3[UnaryOpType3["EXP"] = 5] = "EXP"; + UnaryOpType3[UnaryOpType3["EXPM1"] = 6] = "EXPM1"; + UnaryOpType3[UnaryOpType3["FLOOR"] = 7] = "FLOOR"; + UnaryOpType3[UnaryOpType3["LINEAR"] = 8] = "LINEAR"; + UnaryOpType3[UnaryOpType3["LOG"] = 9] = "LOG"; + UnaryOpType3[UnaryOpType3["LOGICAL_NOT"] = 10] = "LOGICAL_NOT"; + UnaryOpType3[UnaryOpType3["NEG"] = 11] = "NEG"; + UnaryOpType3[UnaryOpType3["PRELU"] = 12] = "PRELU"; + UnaryOpType3[UnaryOpType3["RELU"] = 13] = "RELU"; + UnaryOpType3[UnaryOpType3["RELU6"] = 14] = "RELU6"; + UnaryOpType3[UnaryOpType3["RSQRT"] = 15] = "RSQRT"; + UnaryOpType3[UnaryOpType3["SIN"] = 16] = "SIN"; + UnaryOpType3[UnaryOpType3["SINH"] = 17] = "SINH"; + UnaryOpType3[UnaryOpType3["SIGMOID"] = 18] = "SIGMOID"; + UnaryOpType3[UnaryOpType3["SQRT"] = 19] = "SQRT"; + UnaryOpType3[UnaryOpType3["SQUARE"] = 20] = "SQUARE"; + UnaryOpType3[UnaryOpType3["TANH"] = 21] = "TANH"; + UnaryOpType3[UnaryOpType3["TO_INT"] = 22] = "TO_INT"; +})(UnaryOpType || (UnaryOpType = {})); +var ABS3 = `return abs(a);`; +var CEIL2 = `return ceil(a);`; +var COS2 = `return cos(a);`; +var COSH2 = ` + let e2x = exp(-a); + return (e2x + 1.0 / e2x) / 2.0; +`; +var EXPM12 = `return exp(a) - 1.0;`; +var ELU5 = `if (a >= 0.0) { return a; } return (exp(a) - 1.0);`; +var ELU_VEC4 = ` + var resFloat = exp(a) - vec4(1.0); + if (a.r >= 0.0) { + resFloat.r = a.r; + } + if (a.g >= 0.0) { + resFloat.g = a.g; + } + if (a.b >= 0.0) { + resFloat.b = a.b; + } + if (a.a >= 0.0) { + resFloat.a = a.a; + } + return resFloat; +`; +var EXP2 = `return exp(a);`; +var FLOOR2 = `return floor(a);`; +var LINEAR3 = `return a;`; +var LOG2 = `if (a < 0.0) { return 1.0/0.0; } + return log(a);`; +var LOGICAL_NOT2 = `return f32(!(a >= 1.0));`; +var NEG2 = `return -a;`; +var PRELU3 = `return (a < 0.0) ? b * a : a;`; +var RELU4 = "return max(a, 0.0);"; +var RELU64 = "return clamp(a, 0.0, 6.0);"; +var RELU6_VEC4 = "return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));"; +var RELU_VEC4 = ` + var resFloat = a * vec4(a >= vec4(0.0)); + let isNaN = isNan(a); + + if (isNaN.r) { + resFloat.r = a.r; + } + if (isNaN.g) { + resFloat.g = a.g; + } + if (isNaN.b) { + resFloat.b = a.b; + } + if (isNaN.a) { + resFloat.a = a.a; + } + return resFloat; +`; +var RSQRT2 = `return 1.0/sqrt(a);`; +var SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`; +var SIN2 = `return sin(a);`; +var SINH2 = ` + let e2x = exp(a); + return (e2x - 1.0 / e2x) / 2.0; +`; +var SQRT2 = `return sqrt(a);`; +var SQUARE2 = `return a * a;`; +var TANH2 = ` + let e2x = exp(-2.0 * abs(a)); + return sign(a) * (1.0 - e2x) / (1.0 + e2x); +`; +var TO_INT2 = `return f32(i32((a)));`; +function getUnaryOpString(type, useVec4) { + switch (type) { + case 0: + return ABS3; + case 2: + return COS2; + case 3: + return COSH2; + case 1: + return CEIL2; + case 4: + return useVec4 ? ELU_VEC4 : ELU5; + case 5: + return EXP2; + case 6: + return EXPM12; + case 7: + return FLOOR2; + case 8: + return LINEAR3; + case 9: + return LOG2; + case 10: + return LOGICAL_NOT2; + case 11: + return NEG2; + case 12: + return PRELU3; + case 13: + return useVec4 ? RELU_VEC4 : RELU4; + case 14: + return useVec4 ? RELU6_VEC4 : RELU64; + case 15: + return RSQRT2; + case 18: + return SIGMOID4; + case 16: + return SIN2; + case 17: + return SINH2; + case 19: + return SQRT2; + case 20: + return SQUARE2; + case 21: + return TANH2; + case 22: + return TO_INT2; + default: + throw new Error(`BinaryType ${type} is not implemented!`); + } +} +function mapActivationToShaderProgram2(activation2, packed = false) { + if (activation2 === null) { + return null; + } else if (activation2 === "linear") { + return getUnaryOpString(UnaryOpType.LINEAR); + } else if (activation2 === "relu") { + return getUnaryOpString(UnaryOpType.RELU, packed); + } else if (activation2 === "elu") { + return getUnaryOpString(UnaryOpType.ELU, packed); + } else if (activation2 === "relu6") { + return getUnaryOpString(UnaryOpType.RELU6, packed); + } else if (activation2 === "prelu") { + return getBinaryOpString(BinaryOpType.PRELU, packed); + } else if (activation2 === "sigmoid") { + return getUnaryOpString(UnaryOpType.SIGMOID); + } + throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`); +} +function makeMatMulPackedVec4Source(workPerThread, workGroupSize) { + const tileInfo = { + RowPerThread: workPerThread[1], + ColPerThread: workPerThread[0], + TileAOuter: workGroupSize[1] * workPerThread[1], + TileBOuter: workGroupSize[0] * workPerThread[0], + TileInner: workGroupSize[0] * workPerThread[0] + }; + return ` + var mm_Asub : array, ${tileInfo.TileInner / tileInfo.ColPerThread}>, ${tileInfo.TileAOuter}>; + var mm_Bsub : array, ${tileInfo.TileBOuter / tileInfo.ColPerThread}>, ${tileInfo.TileInner}>; + + let RowPerThread = ${tileInfo.RowPerThread}; + let ColPerThread = ${tileInfo.ColPerThread}; // only support ColPerThread = 4 + let TileAOuter = ${tileInfo.TileAOuter}; + let TileBOuter = ${tileInfo.TileBOuter}; + let TileInner = ${tileInfo.TileInner}; + + ${getNonFlatDispatchLayoutMainHeaderString()} { + + let tileRow = i32(localId.y) * RowPerThread; + let tileCol = i32(localId.x); + + let globalRow = i32(globalId.y) * RowPerThread; + let globalCol = i32(globalId.x); + let numTiles = (uniforms.dimInner - 1) / TileInner + 1; + + var acc: array, ${tileInfo.RowPerThread}>; + var ACached : vec4; + var BCached : array, 4>; + + // Loop over shared dimension. + var globalColA = tileCol; + let RowPerThreadB = TileInner / ${workGroupSize[1]}; + let tileRowB = i32(localId.y) * RowPerThreadB; + for (var t = 0; t < numTiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileCol; + mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId); + } + globalColA = globalColA + TileInner / ColPerThread; + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId); + } + + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < TileInner / ColPerThread; k = k + 1) { + BCached[0] = mm_Bsub[k * ColPerThread][tileCol]; + BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol]; + BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol]; + BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol]; + + for (var i = 0; i < RowPerThread; i = i + 1) { + ACached = mm_Asub[tileRow + i][k]; + acc[i] = BCached[0] * ACached.x + acc[i]; + acc[i] = BCached[1] * ACached.y + acc[i]; + acc[i] = BCached[2] * ACached.z + acc[i]; + acc[i] = BCached[3] * ACached.w + acc[i]; + } + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { + mm_write(globalRow + innerRow, + globalCol, + acc[innerRow], globalId); + } +}`; +} +function makeMatMulVectorVec4Source(workGroupSize) { + return ` + var mm_Asub : array, ${workGroupSize[0]}>; + let tileSize = ${workGroupSize[0] * 4}; + ${getNonFlatDispatchLayoutMainHeaderString()} { + let tileCol = i32(localId.x); + let globalCol = i32(globalId.x); + let globalRow = i32(globalId.y); + + let numTiles = (uniforms.dimInner - 1) / tileSize + 1; + + // Without this initialization strange values show up in acc. + var acc = vec4(0.0); + + // Loop over shared dimension. + for (var t = 0; t < numTiles; t = t + 1) { + // Load one tile of A into local memory. + let colA = t * tileSize / 4 + tileCol; + mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId); + + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileSize / 4; k = k + 1) { + let rowB = t * tileSize + k * 4; + let BCached0 = mm_readB(rowB, globalCol, globalId); + let BCached1 = mm_readB(rowB + 1, globalCol, globalId); + let BCached2 = mm_readB(rowB + 2, globalCol, globalId); + let BCached3 = mm_readB(rowB + 3, globalCol, globalId); + + let ACached = mm_Asub[k]; + acc = acc + BCached0 * ACached.x; + acc = acc + BCached1 * ACached.y; + acc = acc + BCached2 * ACached.z; + acc = acc + BCached3 * ACached.w; + } + + workgroupBarrier(); + } + + if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) { + mm_write(globalRow, globalCol, acc, globalId); + } + } +`; +} +var MatMulPackedVec4Program = class { + constructor(aShape, outputShape, rowPerThread, bias = null, activation2 = null, preluActivationWeights = null) { + this.variableNames = ["A", "B"]; + this.uniforms = `dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.workGroupSize = [16, 16, 1]; + this.isVec4 = true; + this.vecSize = 4; + this.outputShape = outputShape; + this.workGroupSize = computeWorkGroupSizeForMatMul(outputShape[1], aShape[2], outputShape[2]); + this.dispatchLayout = { x: [2], y: [1], z: [0] }; + if (outputShape[1] === 1) { + rowPerThread = 1; + } + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.vecSize, rowPerThread, 1]); + const addBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.workPerThread = rowPerThread; + this.aShape = aShape; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + [this.fitA, this.fitB] = this.getShapeFit(); + this.shaderKey = `matMulPackedVec4_${rowPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}`; + } + getShapeFit() { + const dimInner = this.aShape[2]; + const dimBOuter = this.outputShape[2]; + const bShape = [this.outputShape[0], dimInner, dimBOuter]; + const tileAOuter = this.workGroupSize[1] * this.workPerThread; + const tileBOuter = this.workGroupSize[0] * this.vecSize; + const tileInner = tileBOuter; + const tileSizeA = [tileAOuter, tileInner]; + const tileSizeB = [tileInner, tileBOuter]; + return [ + tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)), + tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1)) + ]; + } + getUserCode() { + const sampleA = this.fitA ? `return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]` : `if (coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimAOuter, uniforms.dimInner))) { + return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]; + } + return vec4(0.0)`; + const sampleB = this.fitB ? `return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]` : `if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]; + } + return vec4(0.0)`; + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : vec4, outCoord : vec3) -> vec4 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : vec4, outCoord : vec3) -> vec4 { + ${activationOp} + }`; + } + applyActivationSnippet = "value = activation(value, outCoord);"; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { + let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize}; + let batch = i32(globalId.z); + ${sampleA}; + } + + fn mm_readB(row : i32, col : i32, globalId : vec3) -> vec4 { + let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize}; + let batch = i32(globalId.z); + ${sampleB}; + } + + fn mm_write(row : i32, col : i32, valueIn : vec4, globalId : vec3) { + if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2]) + { + var value = valueIn; + let batch = i32(globalId.z); + let outCoord = vec3(batch, row, col * 4); + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(outCoord[0], outCoord[1], outCoord[2], value); + } + } + ${this.outputShape[1] > 1 ? makeMatMulPackedVec4Source([this.vecSize, this.workPerThread, 1], this.workGroupSize) : makeMatMulVectorVec4Source(this.workGroupSize)} + + `; + return userCode; + } +}; +function makeMatMulPackedSource(workPerThread, workGroupSize) { + const tileAOuter = workGroupSize[1] * workPerThread[1]; + const tileBOuter = workGroupSize[0] * workPerThread[0]; + const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter; + return ` + var mm_Asub : array, ${tileAOuter}>; + var mm_Bsub : array, ${tileInner}>; + ${getNonFlatDispatchLayoutMainHeaderString()} { + let tileRow = i32(localId.y) * ${workPerThread[1]}; + let tileCol = i32(localId.x) * ${workPerThread[0]}; + + let globalRow = i32(globalId.y) * ${workPerThread[1]}; + let globalCol = i32(globalId.x) * ${workPerThread[0]}; + + let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1; + + var acc : array, ${workPerThread[1]}>; + var ACached : f32; + var BCached : array; + + // Without this initialization strange values show up in acc. + for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = 0.0; + } + } + + let ColPerThreadA = ${tileInner} / ${workGroupSize[0]}; + let tileColA = i32(localId.x) * ColPerThreadA; + let RowPerThreadB = ${tileInner} / ${workGroupSize[1]}; + let tileRowB = i32(localId.y) * RowPerThreadB; + + // Loop over shared dimension. + for (var t = 0; t < numTiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileColA + innerCol; + + mm_Asub[inputRow][inputCol] = mm_readA( + globalRow + innerRow, + t * ${tileInner} + inputCol, globalId); + } + } + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + + mm_Bsub[inputRow][inputCol] = mm_readB( + t * ${tileInner} + inputRow, + globalCol + innerCol, globalId); + } + } + + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < ${tileInner}; k = k + 1) { + for (var inner = 0; inner < ${workPerThread[0]}; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) { + ACached = mm_Asub[tileRow + innerRow][k]; + for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) { + + if ((globalCol + innerCol) < uniforms.dimBOuter && + (globalRow + innerRow) < uniforms.dimAOuter) { + mm_write(globalRow + innerRow, + globalCol + innerCol, + acc[innerRow][innerCol], globalId); + } + } + } + } + `; +} +function makeMatMulVectorSource(workGroupSize) { + return ` + let TileSize = ${workGroupSize[0] * 4}; + var mm_Asub : array, ${workGroupSize[0]}>; + + ${getNonFlatDispatchLayoutMainHeaderString()} { + let tileCol = i32(localId.x); + let globalCol = i32(globalId.x); + let globalRow = i32(globalId.y); + + let numTiles = (uniforms.dimInner - 1) / TileSize + 1; + + // Without this initialization strange values show up in acc. + var acc = 0.0; + + // Loop over shared dimension. + for (var t = 0; t < numTiles; t = t + 1) { + // Load one tile of A into local memory. + let colA = t * TileSize + tileCol * 4; + mm_Asub[tileCol] = vec4(mm_readA(globalRow, colA, globalId), + mm_readA(globalRow, colA + 1, globalId), + mm_readA(globalRow, colA + 2, globalId), + mm_readA(globalRow, colA + 3, globalId)); + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < TileSize / 4; k = k + 1) { + let rowB = t * TileSize + k * 4; + let BCached = vec4(mm_readB(rowB, globalCol, globalId), + mm_readB(rowB + 1, globalCol, globalId), + mm_readB(rowB + 2, globalCol, globalId), + mm_readB(rowB + 3, globalCol, globalId)); + + let ACached = mm_Asub[k]; + acc = acc + dot(ACached, BCached); + } + + workgroupBarrier(); + } + + if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) { + mm_write(globalRow, globalCol, acc, globalId); + } + } + `; +} +var MatMulPackedProgram2 = class { + constructor(aShape, outputShape, workPerThread, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) { + this.variableNames = ["A", "B"]; + this.uniforms = `dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.workGroupSize = [16, 16, 1]; + this.outputShape = outputShape; + this.dispatchLayout = { x: [2], y: [1], z: [0] }; + const dimInner = transposeA ? aShape[1] : aShape[2]; + this.workGroupSize = computeWorkGroupSizeForMatMul(outputShape[1], dimInner, outputShape[2]); + if (outputShape[1] === 1 || outputShape[2] === 1) { + workPerThread = 1; + } + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]); + if (util_exports.arraysEqual(this.dispatch, [1, 1, 1])) { + workPerThread = 1; + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]); + } + const addBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.workPerThread = workPerThread; + this.aShape = aShape; + this.transposeA = transposeA; + this.transposeB = transposeB; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + const dimBOuter = this.outputShape[2]; + const bShape = this.transposeB ? [this.outputShape[0], dimBOuter, dimInner] : [this.outputShape[0], dimInner, dimBOuter]; + [this.fitA, this.fitB] = this.getShapeFit(bShape); + this.shaderKey = `matMulPacked_${this.workPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}`; + } + getShapeFit(bShape) { + const tileAOuter = this.workGroupSize[1] * this.workPerThread; + const tileBOuter = this.workGroupSize[0] * this.workPerThread; + let tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter; + if (this.outputShape[1] === 1) { + tileInner *= 4; + } + util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => `tileInner must be multiple of workgroupsize.x and workgroupsize.y`); + const tileSizeA = [tileAOuter, tileInner]; + const tileSizeB = [tileInner, tileBOuter]; + return [ + tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)), + tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1)) + ]; + } + getUserCode() { + let sampleA; + if (this.transposeA === false) { + sampleA = this.fitA ? `return A.numbers[batch * batchASize + row * uniforms.dimInner + col];` : `if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { + return A.numbers[batch * batchASize + row * uniforms.dimInner + col]; + } + return 0.0;`; + } else { + sampleA = this.fitA ? `return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];` : `if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { + return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row]; + } + return 0.0;`; + } + let sampleB; + if (this.transposeB === false) { + sampleB = this.fitB ? `return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col]; + } + return 0.0;`; + } else { + sampleB = this.fitB ? `return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];` : `if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return B.numbers[batch * batchBSize + col * uniforms.dimInner + row]; + } + return 0.0;`; + } + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, false); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : f32, outCoord : vec3) -> f32 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : f32, outCoord : vec3) -> f32 { + ${activationOp} + } + `; + } + applyActivationSnippet = "value = activation(value, outCoord);"; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + + fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { + let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; + let batch = i32(globalId.z); + ${sampleA} + } + + fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { + let batch = i32(globalId.z); + let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; + ${sampleB} + } + + fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3) { + var value = valueIn; + let batch = i32(globalId.z); + let outCoord = vec3(batch, row, col); + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(batch, row, col, value); + } + ${this.outputShape[1] > 1 ? makeMatMulPackedSource([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : makeMatMulVectorSource(this.workGroupSize)} + `; + return userCode; + } +}; +function makeMatMulReduceSource() { + return ` + var sumValues : array; + ${getNonFlatDispatchLayoutMainHeaderString()} { + let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId); + let batch = coords[0]; + let row = coords[1]; + let col = coords[2]; + var sum = 0.0; + let Length = uniforms.dimInner; + for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) { + let dataA = mm_readA(batch, row, k); + let dataB = mm_readB(batch, k, col); + sum = sum + dataA * dataB; + } + sumValues[localId.x] = sum; + workgroupBarrier(); + + for(var currentSize = workGroupSizeX / 2u; currentSize > 1u; + currentSize = currentSize / 2u) { + if (localId.x < currentSize) + { + sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; + } + workgroupBarrier(); + } + + if (localId.x == 0u) { + sum = sumValues[0] + sumValues[1]; + mm_write(batch, row, col, sum); + } + } + `; +} +var MatMulReduceProgram = class { + constructor(outputShape, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) { + this.variableNames = ["A", "B"]; + this.uniforms = `dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.workGroupSize = [256, 1, 1]; + this.outputShape = outputShape; + this.dispatchLayout = { x: [], y: [1, 2], z: [0] }; + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + const addBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.transposeA = transposeA; + this.transposeB = transposeB; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}`; + } + getUserCode() { + let sampleA; + if (this.transposeA === false) { + sampleA = `return A.numbers[batch * batchASize + row * uniforms.dimInner + col];`; + } else { + sampleA = `return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];`; + } + let sampleB; + if (this.transposeB === false) { + sampleB = `return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];`; + } else { + sampleB = `return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];`; + } + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, false); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : f32, outCoord : vec3) -> f32 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : f32, outCoord : vec3) -> f32 { + ${activationOp} + } + `; + } + applyActivationSnippet = "value = activation(value, outCoord);"; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + + fn mm_readA(batch: i32, row : i32, col : i32) -> f32 { + let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; + ${sampleA} + } + + fn mm_readB(batch: i32, row : i32, col : i32) -> f32 { + let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; + ${sampleB} + } + + fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) { + var value = valueIn; + let outCoord = vec3(batch, row, col); + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(batch, row, col, value); + } + ${makeMatMulReduceSource()} + `; + return userCode; + } +}; +function makeMatMulSmallOutputSizeSource(workGroupSize) { + const tileAOuter = workGroupSize[1] / 2; + const tileBOuter = workGroupSize[0]; + const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter; + return ` + var mm_Asub1 : array, ${tileAOuter}>; + var mm_Bsub1 : array, ${tileInner}>; + var mm_Asub2 : array, ${tileAOuter}>; + var mm_Bsub2 : array, ${tileInner}>; + + // If the output size is small for matrix multiplication, avoid to use vec4 + // and handle some elements per thread to optimally utilize the ALU. + // Introduces two shared memory buffers, some logical threads could handle + // arithmetic operations and others handle IO operations between barrier api, + // makes ALUs and load/store units work simultaneously, could improves + // the performance. + ${getNonFlatDispatchLayoutMainHeaderString()} { + let tileRow = i32(localId.y); + let tileCol = i32(localId.x); + let globalRow = i32(globalId.y); + let globalCol = i32(globalId.x); + + // uniforms.dimInner should be greater than 0. + let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1; + var acc = 0.0; + + var globalColA = tileCol; + var globalRowB = tileRow; + for (var t = 0; t < numTiles; t = t + 1) { + if (t == 0) { + if (tileRow < ${tileAOuter}) { + // Load one tile of A and B into local memory. + // globalRow is always greater than or equal tileRow. + mm_Asub1[tileRow][tileCol] = + mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); + globalColA = globalColA + ${tileInner}; + mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); + globalRowB = globalRowB + ${tileInner}; + } + } else { + if (tileRow < ${tileAOuter}) { + // Load one tile of A and B into local memory. + // globalRow is always greater than or equal tileRow. + mm_Asub1[tileRow][tileCol] = + mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); + globalColA = globalColA + ${tileInner}; + mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); + globalRowB = globalRowB + ${tileInner}; + } else { + // Compute acc values for a single thread. + for (var k = 0; k < ${tileInner}; k = k + 1) { + let subRow = tileRow - ${tileAOuter}; + if (subRow < 0) { + continue; + } + acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol]; + } + } + } + workgroupBarrier(); + if (t != 0) { + t = t + 1; + } + + if (t < numTiles) { + if (tileRow < ${tileAOuter}) { + // Load one tile of A and B into local memory. + // globalRow is always greater than or equal tileRow. + mm_Asub2[tileRow][tileCol] = + mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId); + globalColA = globalColA + ${tileInner}; + mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId); + globalRowB = globalRowB + ${tileInner}; + } else { + // Compute acc values for a single thread. + for (var k = 0; k < ${tileInner}; k = k + 1) { + let subRow = tileRow - ${tileAOuter}; + if (subRow < 0) { + continue; + } + acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol]; + } + } + } + workgroupBarrier(); + } + let writeCol = (globalRow - tileRow) / 2 + tileRow - ${tileAOuter}; + if (tileRow >= ${tileAOuter} && writeCol >= 0) { + mm_write(writeCol, globalCol, acc, globalId); + } + } + `; +} +var MatMulSmallOutputSizeProgram = class { + constructor(aShape, bShape, outputShape, bias = null, activation2 = null, preluActivationWeights = null) { + this.variableNames = ["A", "B"]; + this.uniforms = `dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.workGroupSize = [8, 16, 1]; + util_exports.assert(aShape[1] <= 16 || bShape[2] <= 16, () => "This program can be only used when A width or B Height are small"); + this.outputShape = outputShape; + this.dispatchLayout = { x: [2], y: [1], z: [0] }; + this.dispatch = [ + Math.ceil(outputShape[2] / this.workGroupSize[0]), + Math.ceil(outputShape[1] * 2 / this.workGroupSize[1]), + outputShape[0] + ]; + const addBias = bias != null; + if (addBias) { + this.variableNames.push("bias"); + } + const hasPreluActivationWeights = preluActivationWeights != null; + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + this.shaderKey = `matMulSmallOutputSize_${this.activation}`; + } + getUserCode() { + const sampleA = `if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimInner))) { + return A.numbers[batch * batchASize + row * uniforms.dimInner + col]; + } + return 0.0;`; + const sampleB = `if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col]; + } + return 0.0;`; + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, false); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : f32, outCoord : vec3) -> f32 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = `fn activation(a : f32, outCoord : vec3) -> f32 { + ${activationOp} + }`; + } + applyActivationSnippet = "value = activation(value, outCoord);"; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + + fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { + let batchASize = uniforms.aShape[1] * uniforms.aShape[2]; + let batch = i32(globalId.z); + ${sampleA} + } + fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { + let batch = i32(globalId.z); + let batchBSize = uniforms.bShape[1] * uniforms.bShape[2]; + ${sampleB} + } + fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3) { + if (coordsInBounds2D(vec2(row, col), vec2(uniforms.dimAOuter, uniforms.dimBOuter))) { + let batch = i32(globalId.z); + let outCoord = vec3(batch, row, col); + var value = valueIn; + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(batch, row, col, value); + } + } + ${makeMatMulSmallOutputSizeSource(this.workGroupSize)} + `; + return userCode; + } +}; +function reshape5(args) { + const { inputs, attrs } = args; + const { x } = inputs; + const { shape } = attrs; + const xSize = util_exports.sizeFromShape(x.shape); + const $shape = util_exports.inferFromImplicitShape(shape, xSize); + const $xSize = util_exports.sizeFromShape($shape); + util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`); + args.backend.incRef(x.dataId); + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; +} +var reshapeConfig3 = { + kernelName: Reshape, + backendName: "webgpu", + kernelFunc: reshape5 +}; +function batchMatMulImpl2({ + a, + b, + transposeA, + transposeB, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { + const aRank = a.shape.length; + const bRank = b.shape.length; + const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + const outerDimsA = a.shape.slice(0, -2); + const outerDimsB = b.shape.slice(0, -2); + const batchDimA = util_exports.sizeFromShape(outerDimsA); + const batchDimB = util_exports.sizeFromShape(outerDimsB); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); + const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; + const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; + const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const intermediates = [a3d, b3d]; + const batchDim = Math.max(batchDimA, batchDimB); + const useVec4 = innerShapeA % 4 === 0 && outerShapeB % 4 === 0 && !transposeA && !transposeB && outerShapeB >= 32; + let program; + if (outerShapeA * outerShapeB <= 32) { + program = new MatMulReduceProgram([batchDim, outerShapeA, outerShapeB], transposeA, transposeB, bias, activation2, preluActivationWeights); + } else if (!transposeA && !transposeB && (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA))) { + program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], bias, activation2, preluActivationWeights); + } else if (useVec4) { + program = new MatMulPackedVec4Program(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), bias, activation2, preluActivationWeights); + } else { + program = new MatMulPackedProgram2(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), transposeA, transposeB, bias, activation2, preluActivationWeights); + } + const inputs = [a3d, b3d]; + if (bias) { + inputs.push(bias); + } + if (preluActivationWeights) { + inputs.push(preluActivationWeights); + } + const dimensions = [ + { type: "int32", data: [outerShapeA] }, + { type: "int32", data: [outerShapeB] }, + { type: "int32", data: [innerShapeA] } + ]; + const out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions); + const outReshaped = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } }); + intermediates.push(out); + for (const i of intermediates) { + backend2.disposeData(i.dataId); + } + return outReshaped; +} +function _fusedMatMul3(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b, bias, preluActivationWeights } = inputs; + const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs; + return batchMatMulImpl2({ + a, + b, + transposeA, + transposeB, + backend: backend2, + bias, + preluActivationWeights, + leakyreluAlpha, + activation: activation2 + }); +} +var _fusedMatMulConfig3 = { + kernelName: _FusedMatMul, + backendName: "webgpu", + kernelFunc: _fusedMatMul3 +}; +var BinaryOpComplexProgram2 = class { + constructor(op2, aShape, bShape) { + this.variableNames = ["AReal", "AImag", "BReal", "BImag"]; + this.workGroupSize = [128, 1, 1]; + this.size = true; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `binaryOpComplex_${op2}`; + this.op = op2; + } + getUserCode() { + const opStr = getBinaryOpString(this.op, false); + const userCode = ` + fn binaryOpComplex( + areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { + ${opStr} + } + + ${getMainHeaderAndGlobalIndexString()} + if(index < uniforms.size) { + let areal = getARealAtOutCoordsByGlobalIndex(index); + let aimag = getAImagAtOutCoordsByGlobalIndex(index); + let breal = getBRealAtOutCoordsByGlobalIndex(index); + let bimag = getBImagAtOutCoordsByGlobalIndex(index); + setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag)); + } + } + `; + return userCode; + } +}; +var BinaryOpSharedProgram = class { + constructor(op2, aShape, bShape, useSharedMemoryWithB) { + this.variableNames = ["A", "B"]; + this.size = true; + const workGroupSizeX = 256; + this.workGroupSize = [workGroupSizeX, 1, 1]; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.lastDimensionSize = useSharedMemoryWithB ? bShape[0] : aShape[0]; + if (this.lastDimensionSize < 256) { + this.workPerThread = 1; + } else if (this.lastDimensionSize < 512) { + this.workPerThread = 2; + } else { + this.workPerThread = 4; + } + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.useSharedMemoryWithB = useSharedMemoryWithB; + this.op = op2; + this.shaderKey = `binaryShared_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`; + } + getUserCode() { + const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0"; + const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAAtOutCoordsByCoords(coords); + let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}]; + let b = getBAtOutCoordsByCoords(coords);`; + const opStr = getBinaryOpString(this.op, false); + const userCode = ` + fn binaryOperation(a : f32, b : f32) -> f32 { + ${opStr} + } + var sharedBuf : array; + ${getMainHeaderAndGlobalIndexString()} + + // Fill in the shared memory buffer. Here we need a loop to make sure + // that all data in A|B are uploaded when |sharedMemorySize| is larger + // than work group size. + for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) { + sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}.numbers[localIndex]); + } + workgroupBarrier(); + + for(var i = 0; i < ${this.workPerThread}; i = i + 1) { + let flatIndex = index * ${this.workPerThread} + i; + if(flatIndex < uniforms.size) { + let coords = getCoordsFromFlatIndex(flatIndex); + + ${accessDataSnippet} + setOutputFlat(flatIndex, binaryOperation(a, b)); + } + } + } + `; + return userCode; + } +}; +var BinaryOpVec4Program = class { + constructor(op2, aShape, bShape) { + this.variableNames = ["A", "B"]; + this.workPerThread = 4; + this.isVec4 = true; + this.size = true; + const workGroupSizeX = 128; + this.workGroupSize = [workGroupSizeX, 1, 1]; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.op = op2; + this.shaderKey = `binaryVec4_${op2}`; + } + getUserCode() { + const opStr = getBinaryOpString(this.op, this.isVec4); + const userCode = ` + fn binaryOperation(a : vec4, b : vec4) -> vec4 { + ${opStr} + } + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let a = getAAtOutCoordsByGlobalIndex(index); + let b = getBAtOutCoordsByGlobalIndex(index); + setOutputFlat(index, binaryOperation(a, b)); + } + } + `; + return userCode; + } +}; +var BinaryOpProgram2 = class { + constructor(op2, aShape, bShape) { + this.variableNames = ["A", "B"]; + this.size = true; + const workGroupSizeX = 128; + this.workGroupSize = [workGroupSizeX, 1, 1]; + this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `binary_${op2}`; + this.op = op2; + } + getUserCode() { + const opStr = getBinaryOpString(this.op, false); + const userCode = ` + fn binaryOperation(a : f32, b : f32) -> f32 { + ${opStr} + } + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let a = getAAtOutCoordsByGlobalIndex(index); + let b = getBAtOutCoordsByGlobalIndex(index); + setOutputFlat(index, binaryOperation(a, b)); + } + } + `; + return userCode; + } +}; +function getBinaryProgram(op2, aShape, bShape) { + const useVec4 = util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0; + if (useVec4) { + return new BinaryOpVec4Program(op2, aShape, bShape); + } + const useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024; + const useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024; + if (useSharedMemoryWithA || useSharedMemoryWithB) { + return new BinaryOpSharedProgram(op2, aShape, bShape, useSharedMemoryWithB); + } else { + return new BinaryOpProgram2(op2, aShape, bShape); + } +} +function identity4(args) { + const { inputs } = args; + const { x } = inputs; + args.backend.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; +} +var identityConfig3 = { + kernelName: Identity, + backendName: "webgpu", + kernelFunc: identity4 +}; +function complex4(args) { + const { inputs, backend: backend2 } = args; + const { real: real5, imag: imag5 } = inputs; + const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64"); + const complex5 = backend2.tensorMap.get(complexInfo.dataId); + const realTensorInfo = identity4({ inputs: { x: real5 }, backend: backend2 }); + const imagTensorInfo = identity4({ inputs: { x: imag5 }, backend: backend2 }); + complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo }; + return complexInfo; +} +var complexConfig3 = { + kernelName: Complex, + backendName: "webgpu", + kernelFunc: complex4 +}; +var UnaryOpProgram2 = class { + constructor(outputShape, op2) { + this.variableNames = ["A"]; + this.size = true; + const workGroupSizeX = 128; + this.workGroupSize = [workGroupSizeX, 1, 1]; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.op = op2; + this.shaderKey = `unary_${op2}`; + } + getUserCode() { + return ` + fn unaryOperation(a : f32) -> f32 { + ${getUnaryOpString(this.op, false)} + } + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let a = getAAtOutCoordsByGlobalIndex(index); + setOutputFlat(index, unaryOperation(a)); + } + } + `; + } +}; +function unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const webgpuBackend = backend2; + const $dtype = dtype || x.dtype; + if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) { + const xData = webgpuBackend.tensorMap.get(x.dataId); + const outValues = cpuKernelImpl(xData.values, $dtype); + return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues); + } + const program = new UnaryOpProgram2(x.shape, opType); + return webgpuBackend.runWebGPUProgram(program, [x], $dtype); + }; +} +function binaryKernelFunc3({ opSnippet, cpuKernelImpl, supportsComplex = false, dtype }) { + return ({ inputs, backend: backend2 }) => { + const { a, b } = inputs; + const webgpuBackend = backend2; + if (supportsComplex && a.dtype === "complex64") { + const aData = webgpuBackend.tensorMap.get(a.dataId); + const bData = webgpuBackend.tensorMap.get(b.dataId); + let real5, imag5; + if (opSnippet !== BinaryOpType.MUL) { + [real5, imag5] = [ + [aData.complexTensorInfos.real, bData.complexTensorInfos.real], + [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag] + ].map((complexParts) => { + const [aPart, bPart] = complexParts; + const aHandle = { + dataId: aPart.dataId, + dtype: aPart.dtype, + shape: a.shape + }; + const bHandle = { + dataId: bPart.dataId, + dtype: bPart.dtype, + shape: b.shape + }; + const program2 = getBinaryProgram(opSnippet, a.shape, b.shape); + return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype)); + }); + } else { + const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a.shape, b.shape); + const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a.shape, b.shape); + const inputs2 = [ + { + dataId: aData.complexTensorInfos.real.dataId, + dtype: aData.complexTensorInfos.real.dtype, + shape: a.shape + }, + { + dataId: aData.complexTensorInfos.imag.dataId, + dtype: aData.complexTensorInfos.imag.dtype, + shape: a.shape + }, + { + dataId: bData.complexTensorInfos.real.dataId, + dtype: bData.complexTensorInfos.real.dtype, + shape: b.shape + }, + { + dataId: bData.complexTensorInfos.imag.dataId, + dtype: bData.complexTensorInfos.imag.dtype, + shape: b.shape + } + ]; + real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, "float32"); + imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, "float32"); + } + const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend }); + webgpuBackend.disposeData(real5.dataId); + webgpuBackend.disposeData(imag5.dataId); + return complexOutput; + } + const $dtype = dtype || upcastType(a.dtype, b.dtype); + if ((a.dtype === "string" || b.dtype === "string" || webgpuBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) { + const aData = webgpuBackend.tensorMap.get(a.dataId).values; + const bData = webgpuBackend.tensorMap.get(b.dataId).values; + const decodedAVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aData) : aData; + const decodedBVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bData) : bData; + const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype); + return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues); + } + const program = getBinaryProgram(opSnippet, a.shape, b.shape); + return webgpuBackend.runWebGPUProgram(program, [a, b], $dtype); + }; +} +var { + addImpl: addImplCPU2, + ceilImpl: ceilImplCPU2, + concatImpl: concatImplCPU2, + equalImpl: equalImplCPU2, + expImpl: expImplCPU2, + expm1Impl: expm1ImplCPU2, + floorImpl: floorImplCPU2, + gatherNdImpl: gatherNdImplCPU2, + gatherV2Impl: gatherV2ImplCPU2, + greaterEqualImpl: greaterEqualImplCPU2, + greaterImpl: greaterImplCPU2, + lessEqualImpl: lessEqualImplCPU2, + lessImpl: lessImplCPU2, + logImpl: logImplCPU2, + maxImpl: maxImplCPU2, + maximumImpl: maximumImplCPU2, + minimumImpl: minimumImplCPU2, + multiplyImpl: multiplyImplCPU2, + negImpl: negImplCPU2, + notEqualImpl: notEqualImplCPU2, + prodImpl: prodImplCPU2, + rangeImpl: rangeImplCPU2, + rsqrtImpl: rsqrtImplCPU2, + simpleAbsImpl: simpleAbsImplCPU2, + sliceImpl: sliceImplCPU2, + stridedSliceImpl: stridedSliceImplCPU2, + stringNGramsImpl: stringNGramsImplCPU2, + subImpl: subImplCPU2, + tileImpl: tileImplCPU2, + topKImpl: topKImplCPU2, + transposeImpl: transposeImplCPU2, + uniqueImpl: uniqueImplCPU2 +} = shared_exports; +var abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 }); +var absConfig3 = { + kernelName: Abs, + backendName: "webgpu", + kernelFunc: abs4 +}; +var addKernelFunc2 = binaryKernelFunc3({ + opSnippet: BinaryOpType.ADD, + cpuKernelImpl: addImplCPU2, + supportsComplex: true +}); +var addConfig3 = { + kernelName: Add, + backendName: "webgpu", + kernelFunc: addKernelFunc2 +}; +var AddNPackedProgram2 = class { + constructor(shapes) { + this.workPerThread = 4; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = shapes[0]; + this.variableNames = shapes.map((_, i) => `T${i}`); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.shaderKey = "addN"; + } + getUserCode() { + const snippets = []; + this.variableNames.forEach((variable2) => { + snippets.push(`let v${variable2} = get${variable2}AtOutCoordsByCoords(coords);`); + }); + const operation = this.variableNames.map((variable2) => { + return `v${variable2}`; + }).join(" + "); + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + for (var i = 0; i < ${this.workPerThread}; i = i + 1) { + let flatIndex = index * ${this.workPerThread} + i; + if (flatIndex < uniforms.size) { + let coords = getCoordsFromFlatIndex(flatIndex); + ${snippets.join("\n ")} + setOutputFlat(flatIndex, ${operation}); + } + } + } + `; + return userCode; + } +}; +function addN4(args) { + const { inputs, backend: backend2 } = args; + const tensors = inputs; + if (tensors.length === 1) { + return identity4({ inputs: { x: tensors[0] }, backend: backend2 }); + } + const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2)); + const shapes = tensors.map((t) => t.shape); + const program = new AddNPackedProgram2(shapes); + return backend2.runWebGPUProgram(program, tensors, dtype); +} +var addNConfig3 = { + kernelName: AddN, + backendName: "webgpu", + kernelFunc: addN4 +}; +var ArgMinMaxProgram2 = class { + constructor(inputShape, axis, reduceType) { + this.workGroupSize = [64, 1, 1]; + this.variableNames = ["x"]; + this.uniforms = "axis : i32; infinityValue : f32;"; + this.size = true; + const axes = [axis]; + backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, inputShape.length); + this.op = reduceType === "min" ? "<" : ">"; + const [outputShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes); + this.outputShape = outputShape.length === 0 ? [1] : outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]); + this.inputShape = inputShape; + this.shaderKey = `argMinMax${this.op}`; + } + getUserCode() { + const sharedMemorySnippet = ` + var xBestIndices : array; + var xBestValues : array; + `; + const indexOutputCoords = (outputCoords, index) => { + if (this.outputShape.length === 1) { + return outputCoords; + } else { + return `${outputCoords}[${index}]`; + } + }; + const indexInputShape = (index) => { + if (this.inputShape.length === 1) { + return "uniforms.xShape"; + } else { + return `uniforms.xShape[${index}]`; + } + }; + const userCode = ` + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + + ${sharedMemorySnippet} + + // In order to get a flattened index into the input tensor, we need to + // add back the index along the reduced dimension to |outputCoords|. + // This function outputs the offset to the first value along + // |axis| and the stride to get the next value of the input along |axis|. + fn getInputCoordInfo(outputIndex : i32) -> vec2{ + let outputCoords = getCoordsFromFlatIndex(outputIndex); + var i = ${this.outputShape.length - 1}; + + var stride = 1; + var inputStride = 1; + var offset = 0; + + for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) { + let length = ${indexInputShape(`${this.inputShape.length} - r`)}; + if (${this.inputShape.length} - r == uniforms.axis) { + inputStride = stride; + } else { + offset = offset + ${indexOutputCoords("outputCoords", "i")} * stride; + i = i - 1; + } + stride = stride * length; + } + + return vec2(offset, inputStride); + } + + fn getInputIndex(coordInfo : vec2, index : i32) -> i32{ + return coordInfo[0] + coordInfo[1] * index; + } + + ${getMainHeaderAndGlobalIndexString()} + let outputIndex = index / i32(workGroupSizeX); + let coordInfo = getInputCoordInfo(outputIndex); + let Length = ${indexInputShape("uniforms.axis")}; + + var bestIndex = i32(localId.x); + var bestValue = uniforms.infinityValue; + + for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; + k = k + i32(workGroupSizeX)) { + let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]); + if (!isNanCustom(candidate) && candidate ${this.op} bestValue) { + bestValue = candidate; + bestIndex = k; + } + } + xBestValues[localId.x] = bestValue; + xBestIndices[localId.x] = bestIndex; + workgroupBarrier(); + + var reduceSize = min(u32(Length), workGroupSizeX); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (localId.x < currentSize) { + let candidate = xBestValues[localId.x + interval]; + if (candidate ${this.op} bestValue) { + bestValue = candidate; + xBestValues[localId.x] = bestValue; + xBestIndices[localId.x] = xBestIndices[localId.x + interval]; + } + } + reduceSize = interval; + workgroupBarrier(); + } + + if (localId.x == 0u && outputIndex < uniforms.size) { + setOutputFlatI32(outputIndex, xBestIndices[localId.x]); + } + } + `; + return userCode; + } +}; +var TransposeSharedProgram = class { + constructor(aShape, newDim) { + this.variableNames = ["A"]; + this.workGroupSize = [16, 16, 1]; + const outputShape = new Array(aShape.length); + for (let i = 0; i < outputShape.length; i++) { + outputShape[i] = aShape[newDim[i]]; + } + this.outputShape = outputShape; + this.dispatchLayout = { x: [0], y: [1] }; + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]); + this.shaderKey = "transposeShared"; + } + getUserCode() { + const userCode = ` + let TILE_DIM = ${this.workGroupSize[0]}; + var tile : array, ${this.workGroupSize[0]}>; + ${getWorkGroupSizeString()} + fn main([[builtin(local_invocation_id)]] localId : vec3, + [[builtin(workgroup_id)]] workgroupId : vec3) { + var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x); + var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y); + let width = uniforms.outShape[0]; + let height = uniforms.outShape[1]; + if (x < width && y < height) { + tile[localId.y][localId.x] = + A.numbers[y * width + x]; + } + workgroupBarrier(); + + x = i32(workgroupId.y) * TILE_DIM + i32(localId.x); + y = i32(workgroupId.x) * TILE_DIM + i32(localId.y); + if (x < height && y < width) { + setOutputFlat((y * height + x), tile[localId.x] + [localId.y]); + } + } + `; + return userCode; + } +}; +var TransposeProgram2 = class { + constructor(aShape, newDim) { + this.variableNames = ["A"]; + this.workPerThread = 4; + this.workGroupSize = [64, 1, 1]; + this.size = true; + const outputShape = new Array(aShape.length); + for (let i = 0; i < outputShape.length; i++) { + outputShape[i] = aShape[newDim[i]]; + } + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.newDim = newDim; + this.shaderKey = `transpose_${newDim}`; + } + getUserCode() { + const dtype = getCoordsDataType2(this.outputShape.length); + const switched = getSwitchedCoords2(this.newDim); + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + + for(var i = 0; i < ${this.workPerThread}; i = i + 1) { + let flatIndex = index * ${this.workPerThread} + i; + if(flatIndex < uniforms.size) { + let resRC = getCoordsFromFlatIndex(flatIndex); + setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D( + ${dtype}(${switched}), uniforms.aShape)]); + } + } + } + `; + return userCode; + } +}; +function getSwitchedCoords2(newDim) { + const rank = newDim.length; + if (rank > 4) { + throw Error(`Transpose for rank ${rank} is not yet supported`); + } + const switchedCoords = new Array(rank); + for (let i = 0; i < newDim.length; i++) { + switchedCoords[newDim[i]] = `resRC[${i}]`; + } + return switchedCoords.join(); +} +function transpose4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { perm } = attrs; + const webgpuBackend = backend2; + const xRank = x.shape.length; + const newShape = new Array(xRank); + for (let i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + if (backend2.shouldExecuteOnCPU([x])) { + const xData = webgpuBackend.tensorMap.get(x.dataId); + const values = xData.values; + const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape); + return backend2.makeTensorInfo(newShape, x.dtype, outValues); + } + if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) { + const program2 = new TransposeSharedProgram(x.shape, perm); + return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype); + } + const program = new TransposeProgram2(x.shape, perm); + return webgpuBackend.runWebGPUProgram(program, [x], x.dtype); +} +var transposeConfig3 = { + kernelName: Transpose, + backendName: "webgpu", + kernelFunc: transpose4 +}; +function argMax4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose4({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length); + const program = new ArgMinMaxProgram2($x.shape, axes[0], "max"); + const uniformData = [ + { type: "int32", data: [axes[0]] }, + { type: "float32", data: [Number.NEGATIVE_INFINITY] } + ]; + const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData); + intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); + return out; +} +var argMaxConfig3 = { + kernelName: ArgMax, + backendName: "webgpu", + kernelFunc: argMax4 +}; +function argMin4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis } = attrs; + let axes = util_exports.parseAxisParam(axis, x.shape); + const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length); + let $x = x; + const intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose4({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length); + } + backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length); + const program = new ArgMinMaxProgram2($x.shape, axes[0], "min"); + const uniformData = [ + { type: "int32", data: [axes[0]] }, + { type: "float32", data: [Number.POSITIVE_INFINITY] } + ]; + const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData); + intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); + return out; +} +var argMinConfig3 = { + kernelName: ArgMin, + backendName: "webgpu", + kernelFunc: argMin4 +}; +var Pool2DProgram2 = class { + constructor(convInfo, poolType) { + this.variableNames = ["x"]; + this.uniforms = `stride : vec2; pad : vec2; dilation : vec2; convDims : vec2; filterDims : vec2;`; + this.workGroupSize = [128, 1, 1]; + this.size = true; + this.outputShape = convInfo.outShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `pool2D_${poolType}`; + this.poolType = poolType; + } + getUserCode() { + let updateSnippet = `resultValue = max(value, resultValue);`; + if (this.poolType === "avg") { + updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`; + } + let returnValue = `resultValue`; + if (this.poolType === "avg") { + returnValue = `resultValue / count`; + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let batch = coords[0]; + let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; + let xRCorner = xRCCorner.x; + let xCCorner = xRCCorner.y; + + var resultValue = ${this.poolType === "avg" ? "0.0" : "-1.0 / pow(10.0, -20.0)"}; + var count = 0.0; + + for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) { + let xR = xRCorner + wR; + + if (xR < 0 || xR >= uniforms.convDims.x) { + continue; + } + + for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) { + let xC = xCCorner + wC; + if (xC < 0 || xC >= uniforms.convDims.y) { + continue; + } + + let value = getX(batch, xR, xC, coords[3]); + ${updateSnippet} + } + } + + setOutputFlat(index, ${returnValue}); + } + } + `; + return userCode; + } +}; +var PoolWithFilterSizeEqualsOneProgram = class { + constructor(convInfo) { + this.variableNames = ["x"]; + this.uniforms = `stride : vec2;`; + this.workGroupSize = [256, 1, 1]; + this.size = true; + this.outputShape = convInfo.outShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = "poolWithFilterSizeEqualsOne"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let batch = coords[0]; + let d = coords[3]; + + let xRCCorner = coords.yz * uniforms.stride; + let xRCorner = xRCCorner.x; + let xCCorner = xRCCorner.y; + + let value = getX(batch, xRCorner, xCCorner, d); + setOutputFlat(index, value); + } + } + `; + return userCode; + } +}; +function avgPool4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity4({ inputs: { x }, backend: backend2 }); + } + let program; + const dimensions = [{ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }]; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) { + program = new PoolWithFilterSizeEqualsOneProgram(convInfo); + } else { + program = new Pool2DProgram2(convInfo, "avg"); + dimensions.push({ type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, { + type: "int32", + data: [convInfo.dilationHeight, convInfo.dilationWidth] + }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, { + type: "int32", + data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth] + }); + } + return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions); +} +var avgPoolConfig3 = { + kernelName: AvgPool, + backendName: "webgpu", + kernelFunc: avgPool4 +}; +function batchMatMul3(args) { + const { inputs, backend: backend2, attrs } = args; + const { a, b } = inputs; + const { transposeA, transposeB } = attrs; + return batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2 }); +} +var batchMatMulConfig3 = { + kernelName: BatchMatMul, + backendName: "webgpu", + kernelFunc: batchMatMul3 +}; +var SliceProgram2 = class { + constructor(start, destSize) { + this.variableNames = ["source"]; + this.workPerThread = 1; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = destSize; + this.rank = destSize.length; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.start = start; + this.uniforms = `start : ${getCoordsDataType2(start.length)}; `; + this.shaderKey = "slice"; + } + getUserCode() { + const dtype = getCoordsDataType2(this.rank); + const sourceCoords = getCoords3(this.rank); + let coordSum; + if (this.start.length === 1) { + coordSum = this.outputShape.map((_, i) => { + return `sourceLoc = uniforms.start + coords;`; + }); + } else { + coordSum = this.outputShape.map((_, i) => { + return `sourceLoc.${coords2[i]} = uniforms.start[${i}] + coords.${coords2[i]};`; + }); + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + var sourceLoc : ${dtype}; + let coords = getCoordsFromFlatIndex(index); + ${coordSum.join("\n")} + setOutputFlat(index, getSource(${sourceCoords})); + } + } + `; + return userCode; + } +}; +var coords2 = ["x", "y", "z", "w", "u", "v"]; +function getCoords3(rank) { + if (rank === 1) { + return "sourceLoc"; + } else if (rank <= 6) { + return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(","); + } else { + throw Error(`Slicing for rank ${rank} is not yet supported`); + } +} +function slice4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { begin, size } = attrs; + const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size); + slice_util_exports.assertParamsValid(x, $begin, $size); + if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") { + const xBufferInfo = backend2.tensorMap.get(x.dataId); + const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype); + return backend2.makeTensorInfo($size, x.dtype, outValues); + } + if (util_exports.sizeFromShape($size) === 0) { + return backend2.makeTensorInfo($size, x.dtype, []); + } + const program = new SliceProgram2($begin, $size); + const uniformData = [{ type: "int32", data: $begin }]; + return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData); +} +var sliceConfig3 = { + kernelName: Slice, + backendName: "webgpu", + kernelFunc: slice4 +}; +var batchToSpaceND4 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, crops } = attrs; + util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet"); + const prod6 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6); + const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6); + const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); + const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); + const toDispose = []; + const reshapedIntermediate = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const transposedIntermediate = transpose4({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } }); + const reshapedIntermediate2 = reshape5({ + inputs: { x: transposedIntermediate }, + backend: backend2, + attrs: { shape: reshapedPermuted } + }); + const sliced = slice4({ + inputs: { x: reshapedIntermediate2 }, + backend: backend2, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + toDispose.push(reshapedIntermediate); + toDispose.push(transposedIntermediate); + toDispose.push(reshapedIntermediate2); + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return sliced; +}; +var batchToSpaceNDConfig3 = { + kernelName: BatchToSpaceND, + backendName: "webgpu", + kernelFunc: batchToSpaceND4 +}; +var notEqual4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.NOT_EQUAL, + dtype: "bool", + cpuKernelImpl: notEqualImplCPU2 +}); +var notEqualConfig3 = { + kernelName: NotEqual, + backendName: "webgpu", + kernelFunc: notEqual4 +}; +function real4(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.tensorMap.get(input2.dataId); + return identity4({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 }); +} +var realConfig3 = { + kernelName: Real, + backendName: "webgpu", + kernelFunc: real4 +}; +function int2(input2, backend2) { + const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT); + const output = backend2.runWebGPUProgram(program, [input2], "int32"); + return { dataId: output.dataId, shape: output.shape, dtype: output.dtype }; +} +function cast5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { dtype } = attrs; + if (dtype === "complex64") { + if (x.dtype === "complex64") { + return identity4({ inputs: { x }, backend: backend2 }); + } + const zerosTensor = zeros(x.shape); + const floatX = cast5({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } }); + const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 }); + zerosTensor.dispose(); + backend2.disposeData(floatX.dataId); + return result; + } + if (x.dtype === "complex64") { + const realPart = real4({ inputs: { input: x }, backend: backend2 }); + const result = cast5({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } }); + backend2.disposeData(realPart.dataId); + return result; + } + if (!util_exports.hasEncodingLoss(x.dtype, dtype)) { + const result = identity4({ inputs: { x }, backend: backend2 }); + return { dataId: result.dataId, shape: result.shape, dtype }; + } + if (dtype === "int32") { + return int2(x, backend2); + } + if (dtype === "bool") { + const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1)); + const binaryInputs = { a: x, b: zerosTensorInfo }; + const result = notEqual4({ inputs: binaryInputs, backend: backend2 }); + backend2.disposeData(zerosTensorInfo.dataId); + return result; + } + throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`); +} +var castConfig3 = { + kernelName: Cast, + backendName: "webgpu", + kernelFunc: cast5 +}; +var ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 }); +var ceilConfig3 = { + kernelName: Ceil, + backendName: "webgpu", + kernelFunc: ceil4 +}; +var ClipVec4Program = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.uniforms = "minVal : f32; maxVal : f32;"; + this.workPerThread = 4; + this.workGroupSize = [64, 1, 1]; + this.isVec4 = true; + this.size = true; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.shaderKey = "clipVec4"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if(index < uniforms.size) { + let value = getAAtOutCoordsByGlobalIndex(index); + var clampedValue : vec4; + for (var i = 0; i < 4; i = i + 1) { + if (isNanCustom(value[i])) { + clampedValue[i] = value[i]; + } else { + clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); + } + } + + setOutputFlat(index, clampedValue); + } + } + `; + return userCode; + } +}; +var ClipProgram2 = class { + constructor(outputShape) { + this.variableNames = ["A"]; + this.uniforms = "minVal : f32; maxVal : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = "clip"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if(index < uniforms.size) { + let value = getAAtOutCoordsByGlobalIndex(index); + if (isNanCustom(value)) { + setOutputFlat(index, value); + return; + } + setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal)); + } + } + `; + return userCode; + } +}; +function clipByValue3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { clipValueMin, clipValueMax } = attrs; + let program; + const uniformData = [ + { type: "float32", data: [clipValueMin] }, + { type: "float32", data: [clipValueMax] } + ]; + if (util_exports.sizeFromShape(x.shape) % 4 === 0) { + program = new ClipVec4Program(x.shape); + } else { + program = new ClipProgram2(x.shape); + } + return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData); +} +var clipByValueConfig2 = { + kernelName: ClipByValue, + backendName: "webgpu", + kernelFunc: clipByValue3 +}; +var ConcatProgram2 = class { + constructor(shapes) { + this.uniforms = ""; + this.workPerThread = 4; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = backend_util_exports.computeOutShape(shapes, 1); + this.variableNames = shapes.map((_, i) => `T${i}`); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.offsetLength = shapes.length - 1; + for (let i = 0; i < this.offsetLength; i++) { + this.uniforms += `offset${i} : i32;`; + } + this.shaderKey = "concat"; + } + getUserCode() { + const snippets = []; + if (this.offsetLength > 0) { + snippets.push(`if (yC < uniforms.offset0){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`); + for (let i = 1; i < this.offsetLength; i++) { + snippets.push(`elseif (yC < uniforms.offset${[i]}){ setOutput(coords.x, coords.y, getT${i}(yR, yC - uniforms.offset${i - 1})); }`); + } + const lastIndex = this.offsetLength; + const lastShiftIndex = this.offsetLength - 1; + snippets.push(`else { setOutput(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`); + } else { + snippets.push(`setOutput(coords.x, coords.y, getT0(yR, yC));`); + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + for(var i = 0; i < ${this.workPerThread}; i = i + 1) { + let flatIndex = index * ${this.workPerThread} + i; + if(flatIndex < uniforms.size) { + let coords = getCoordsFromFlatIndex(flatIndex); + let yR = coords.x; + let yC = coords.y; + + ${snippets.join("\n ")} + } + } + } + `; + return userCode; + } +}; +function imag4(args) { + const { inputs, backend: backend2 } = args; + const { input: input2 } = inputs; + const inputData = backend2.tensorMap.get(input2.dataId); + return identity4({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 }); +} +var imagConfig3 = { + kernelName: Imag, + backendName: "webgpu", + kernelFunc: imag4 +}; +function concatImpl3(inputs, axis, backend2) { + const dtype = inputs[0].dtype; + if (dtype === "complex64") { + const reals = inputs.map((t) => real4({ inputs: { input: t }, backend: backend2 })); + const imags = inputs.map((t) => imag4({ inputs: { input: t }, backend: backend2 })); + const realConcated = concatImpl3(reals, axis, backend2); + const imagConcated = concatImpl3(imags, axis, backend2); + const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 }); + reals.forEach((r) => backend2.disposeData(r.dataId)); + imags.forEach((i) => backend2.disposeData(i.dataId)); + backend2.disposeData(realConcated.dataId); + backend2.disposeData(imagConcated.dataId); + return result; + } + let runOnCpu = backend2.shouldExecuteOnCPU(inputs); + if (dtype === "string") { + runOnCpu = true; + } + if (runOnCpu) { + const tensors2D2 = inputs.map((t) => { + const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); + const shape = [-1, innerSize]; + return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + }); + const inputsValShapes = tensors2D2.map((t) => { + return { vals: backend2.readSync(t.dataId), shape: t.shape }; + }); + const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1); + const simplyConcat = tensors2D2[0].shape[0] === 1; + const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat); + const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals); + tensors2D2.forEach((t) => backend2.disposeData(t.dataId)); + return outInfo; + } + const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2); + const shapes = tensors2D.map((t) => t.shape); + const program = new ConcatProgram2(shapes); + const uniformData = []; + const offsets = new Array(shapes.length - 1); + if (offsets.length > 0) { + offsets[0] = shapes[0][1]; + uniformData.push({ type: "int32", data: [offsets[0]] }); + for (let i = 1; i < offsets.length; i++) { + offsets[i] = offsets[i - 1] + shapes[i][1]; + uniformData.push({ type: "int32", data: [offsets[i]] }); + } + } + const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData); + tensors2D.forEach((r) => backend2.disposeData(r.dataId)); + const reshapedResult = reshape5({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } }); + backend2.disposeData(res.dataId); + return reshapedResult; +} +function computeTensors2D2(inputs, axis, backend2) { + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); + const tensors2D = inputs.map((t) => reshape5({ + inputs: { x: t }, + backend: backend2, + attrs: { + shape: [ + util_exports.sizeFromShape(t.shape.slice(0, axis)), + util_exports.sizeFromShape(t.shape.slice(axis)) + ] + } + })); + return { tensors2D, outShape }; +} +function concat4(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0]; + const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis); + if (util_exports.sizeFromShape(outShape) === 0) { + return backend2.makeTensorInfo(outShape, inputs[0].dtype, []); + } + const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); + if ($inputs.length === 1) { + return identity4({ inputs: { x: $inputs[0] }, backend: backend2 }); + } + const shapes = $inputs.map((t) => t.shape); + backend_util_exports.assertParamsConsistent(shapes, $axis); + return concatImpl3($inputs, $axis, backend2); +} +var concatConfig3 = { + kernelName: Concat, + backendName: "webgpu", + kernelFunc: concat4 +}; +var Im2ColProgram = class { + constructor(outputShape, isChannelsLast) { + this.variableNames = ["A"]; + this.uniforms = `pad : vec2; stride : vec2; dilation : vec2; outWidth : i32; itemsPerBlockRow : i32; + inChannels : i32;`; + this.workPerThread = 4; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + this.isChannelsLast = isChannelsLast; + this.shaderKey = `im2col_${this.isChannelsLast}`; + } + getUserCode() { + const rowDim = this.isChannelsLast ? 0 : 1; + const colDim = this.isChannelsLast ? 1 : 2; + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + + for(var i = 0; i<${this.workPerThread}; i = i + 1) { + let flatIndex = index * ${this.workPerThread} + i; + + let rc = getCoordsFromFlatIndex(flatIndex); + + if(flatIndex < uniforms.size) { + let blockIndex = rc[0]; + let pos = rc[1]; + + let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1]; + let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow; + var value = 0.0; + if(d0 < uniforms.aShape[${rowDim}] && d0 >= 0) { + let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] - + uniforms.pad[0]; + let d1 = offsetX + uniforms.dilation[0] * ((pos % + uniforms.itemsPerBlockRow) / uniforms.inChannels); + let ch = pos % uniforms.inChannels; + if(d1 < uniforms.aShape[${colDim}] && d1 >= 0) { + value = getA(d0, d1, ch); + } + } + setOutputFlat(flatIndex, value); + } + } + } + `; + return userCode; + } +}; +function conv2dByMatMul2({ + x, + filter, + convInfo, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { + const xShape = x.shape; + const isChannelsLast = convInfo.dataFormat === "channelsLast"; + const transposeA = false; + const transposeB = false; + const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] : xShape[0] * xShape[2] * xShape[3]; + const xReshaped = reshape5({ + inputs: { x }, + backend: backend2, + attrs: { shape: [1, targetShape, convInfo.inChannels] } + }); + const filterReshaped = reshape5({ + inputs: { x: filter }, + backend: backend2, + attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] } + }); + const result = batchMatMulImpl2({ + a: xReshaped, + b: filterReshaped, + transposeA, + transposeB, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + const out = reshape5({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } }); + backend2.disposeData(xReshaped.dataId); + backend2.disposeData(filterReshaped.dataId); + backend2.disposeData(result.dataId); + return out; +} +function conv2dWithIm2Col({ + x, + filter, + convInfo, + backend: backend2, + bias = null, + preluActivationWeights = null, + leakyreluAlpha = 0, + activation: activation2 = null +}) { + const { + filterWidth, + filterHeight, + inChannels, + strideWidth, + strideHeight, + padInfo, + outWidth, + outHeight, + dilationWidth, + dilationHeight, + dataFormat + } = convInfo; + const isChannelsLast = dataFormat === "channelsLast"; + const sharedDim = filterWidth * filterHeight * inChannels; + const numCols = outHeight * outWidth; + const x2ColShape = [numCols, sharedDim]; + const transposeA = false; + const transposeB = false; + const intermediates = []; + const xSqueezed = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: x.shape.slice(1) } }); + const w2Row = reshape5({ inputs: { x: filter }, backend: backend2, attrs: { shape: [1, sharedDim, -1] } }); + intermediates.push(xSqueezed); + intermediates.push(w2Row); + const im2ColProgram = new Im2ColProgram(x2ColShape, isChannelsLast); + const dimensions = [ + { type: "int32", data: [padInfo.left, padInfo.top] }, + { type: "int32", data: [strideWidth, strideHeight] }, + { type: "int32", data: [dilationWidth, dilationHeight] }, + { type: "int32", data: [outWidth] }, + { type: "int32", data: [inChannels * filterWidth] }, + { type: "int32", data: [inChannels] } + ]; + const im2Col = backend2.runWebGPUProgram(im2ColProgram, [xSqueezed], xSqueezed.dtype, dimensions); + const im2Col3D = reshape5({ + inputs: { x: im2Col }, + backend: backend2, + attrs: { shape: [1, x2ColShape[0], x2ColShape[1]] } + }); + intermediates.push(im2Col); + intermediates.push(im2Col3D); + const a3dShape = [1, x2ColShape[0], x2ColShape[1]]; + const matMulProgram = new MatMulPackedProgram2(a3dShape, [1, numCols, convInfo.outChannels], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), transposeA, transposeB); + const dimAOuter = a3dShape[1]; + const dimInner = a3dShape[2]; + const dimBOuter = convInfo.outChannels; + const matmulDimensions = [ + { type: "int32", data: [dimAOuter] }, + { type: "int32", data: [dimBOuter] }, + { type: "int32", data: [dimInner] } + ]; + const result = backend2.runWebGPUProgram(matMulProgram, [im2Col3D, w2Row], im2Col3D.dtype, matmulDimensions); + const outShape = isChannelsLast ? [1, outHeight, outWidth, convInfo.outChannels] : [1, convInfo.outChannels, outHeight, outWidth]; + const out = reshape5({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } }); + intermediates.push(result); + for (const i of intermediates) { + backend2.disposeData(i.dataId); + } + return out; +} +var Conv2DMMVec4Program = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) { + this.variableNames = ["x", "W"]; + this.uniforms = `filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; + dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.isVec4 = true; + this.outputShape = convInfo.outShape; + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }; + this.workGroupSize = [8, 8, 1]; + const elementsPerThread = [4, 4, 1]; + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, elementsPerThread); + this.convInfo = convInfo; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + this.hasLeakyreluAlpha = hasLeakyreluAlpha; + if (this.addBias) { + this.variableNames.push("bias"); + } + if (this.hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + if (this.hasLeakyreluAlpha) { + this.variableNames.push("leakyreluAlpha"); + } + [this.fitA, this.fitB] = this.getShapeFit(elementsPerThread); + this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`; + } + getShapeFit(elementsPerThread) { + const tileAOuter = this.workGroupSize[1] * elementsPerThread[1]; + const tileBOuter = this.workGroupSize[0] * elementsPerThread[0]; + const tileInner = tileBOuter; + const tileSizeA = [tileAOuter, tileInner]; + const tileSizeB = [tileInner, tileBOuter]; + const dimAOuter = this.outputShape[1] * this.outputShape[2]; + const dimBOuter = this.outputShape[3]; + const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels; + return [ + tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]), + tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter]) + ]; + } + getSampleAWithRemainder(index) { + return `let flatIndex${index} = getFlatIndex4D(coord, uniforms.xShape); + let divBy4Remainder${index} = flatIndex${index} % 4; + let divBy4Index${index} = flatIndex${index} / 4; + let curData${index} = x.numbers[divBy4Index${index}]; + if (divBy4Remainder${index} == 0) { + temp = curData${index}; + } else { + // TODO: This could end up being a redundant load with another one in + // the same shader invocation. Perhaps there's an opportunity for + // optimization + let nextData${index} = x.numbers[divBy4Index${index} + 1]; + if (divBy4Remainder${index} == 1) { + temp = vec4(curData${index}.yzw, nextData${index}.x); + } elseif (divBy4Remainder${index} == 2) { + temp = vec4(curData${index}.zw, nextData${index}.xy); + } elseif (divBy4Remainder${index} == 3) { + temp = vec4(curData${index}.w, nextData${index}.xyz); + } + } + `; + } + getUserCode() { + const elementsPerThread = [4, 4, 1]; + const matMulSource = makeMatMulPackedVec4Source(elementsPerThread, this.workGroupSize); + const remainder = this.convInfo.inChannels % 4; + const remainderSnippet = remainder === 0 ? `// The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (coordsInBounds4D(coord, uniforms.xShape)) { + resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4]; + } else { + resData = vec4(0.0); }` : `var temp = vec4(0.0); + ${this.getSampleAWithRemainder(1)} + resData = temp; + if (WCol == (uniforms.filterDims[1] - 1)) { + coord = vec4( + coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0); + ${this.getSampleAWithRemainder(2)} + if (inChCoord == 0) { + resData = vec4(resData.xyz, temp.x); + } elseif (inChCoord == 1) { + resData = vec4(resData.xy, temp.xy); + } else { + resData = vec4(resData.x, temp.xyz); + } + } + `; + const readASnippet = `let outRow = r / uniforms.outShape[2]; + let outCol = r % uniforms.outShape[2]; + let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]); + let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1]; + let inChCoord = c % uniforms.xShape[3]; + var coord = vec4( + batch, + outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0], + outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1], + inChCoord); + var resData = vec4(0.0); + ${remainderSnippet} + return resData;`; + const sampleA = this.fitA ? `${readASnippet}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) { + ${readASnippet} + } + return vec4(0.0); + `; + const sampleB = this.fitB ? `return W.numbers[row * uniforms.dimBOuter / 4 + col];` : `if(coordsInBounds2D(vec2(row, col * 4), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return W.numbers[row * uniforms.dimBOuter / 4 + col]; + } + return vec4(0.0); + `; + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : vec4, outCoord : vec4) -> vec4 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else if (this.hasLeakyreluAlpha) { + activationSnippet = `fn activation(a: vec4) -> vec4 { + let b = getLeakyreluAlphaAtOutCoords(); + ${activationOp} + }`; + throw new Error("Leakyrelu is not supported."); + } else { + activationSnippet = ` + fn activation(a : vec4, outCoord : vec4) -> vec4 { + ${activationOp} + }`; + } + applyActivationSnippet = `value = activation(value, outCoord);`; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + fn mm_readA(row : i32, col : i32, globalId : vec3) -> vec4 { + let r = row; + let c = col * 4; + var batch = i32(globalId.z); + ${sampleA} + } + + fn mm_readB(row : i32, col : i32, globalId : vec3) -> vec4 { + ${sampleB} + } + + fn mm_write(row : i32, col : i32, valueInput : vec4, globalId : vec3) { + var batch = i32(globalId.z); + var value = valueInput; + if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter) + { + let outCoord = vec4( + batch, + row / uniforms.outShape[2], + row % uniforms.outShape[2], + col * 4); + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3], + value); + } + } + ${matMulSource} + `; + return userCode; + } +}; +var Conv2DMMProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) { + this.variableNames = ["x", "W"]; + this.uniforms = `filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2; dimAOuter : i32; dimBOuter : i32; dimInner : i32;`; + this.outputShape = convInfo.outShape; + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }; + this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape); + this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread); + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.convInfo = convInfo; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + [this.fitA, this.fitB] = this.getShapeFit(); + this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`; + } + getShapeFit() { + const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1]; + const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0]; + const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter; + util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => "tileInner must be multiple of workgroupsize.x and workgroupsize.y"); + const tileSizeA = [tileAOuter, tileInner]; + const tileSizeB = [tileInner, tileBOuter]; + const dimAOuter = this.outputShape[1] * this.outputShape[2]; + const dimBOuter = this.outputShape[3]; + const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels; + return [ + tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]), + tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter]) + ]; + } + getUserCode() { + const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize); + const readASnippet = ` + let outRow = row / uniforms.outShape[2]; + let outCol = row % uniforms.outShape[2]; + + let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]); + let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1]; + let coord = vec4( + batch, + outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0], + outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1], + col % uniforms.xShape[3]); + // The bounds checking is always needed since we use it to pad zero for the + // 'same' padding type. + if(coordsInBounds4D(coord, uniforms.xShape)) { + return x.numbers[getFlatIndex4D(coord, uniforms.xShape)]; + } + return 0.0;`; + const sampleA = this.fitA ? `${readASnippet}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) { + ${readASnippet} + } + return 0.0; + `; + const sampleB = this.fitB ? `return W.numbers[row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2(row, col), vec2(uniforms.dimInner, uniforms.dimBOuter))) { + return W.numbers[row * uniforms.dimBOuter + col]; + } + return 0.0; + `; + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, false); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a: f32, outCoord : vec4) -> f32 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : f32, outCoord : vec4) -> f32 { + ${activationOp} + } + `; + } + applyActivationSnippet = `value = activation(value, outCoord);`; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { + var batch = i32(globalId.z); + ${sampleA} + } + + fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { + ${sampleB} + } + + fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3) { + var batch = i32(globalId.z); + var value = valueInput; + let outCoord = vec4( + batch, + row / uniforms.outShape[2], + row % uniforms.outShape[2], + col); + ${addBiasSnippet} + ${applyActivationSnippet} + result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; + } + ${matMulSource} + `; + return userCode; + } +}; +var Conv2DNaiveProgram = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) { + this.variableNames = ["x", "W"]; + this.uniforms = `filterDims : vec2; pad : vec2; stride : vec2; dilation : vec2;`; + this.workGroupSize = [128, 1, 1]; + this.outputShape = convInfo.outShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivationWeights) { + this.variableNames.push("preluActivationWeights"); + } + this.convInfo = convInfo; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivationWeights = hasPreluActivationWeights; + this.shaderKey = `conv2DNaive_${this.activation}`; + } + getUserCode() { + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation); + if (this.hasPreluActivationWeights) { + activationSnippet = `fn activation(a : f32, outCoord : vec4) -> f32{ + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : f32, outCoord : vec4) -> f32{ + ${activationOp} + } + `; + } + applyActivationSnippet = `value = activation(value, outCoord);`; + } + const addBiasSnippet = this.addBias ? "value = value + getBiasAtOutCoordsByCoords(outCoord);" : ""; + const userCode = ` + ${activationSnippet} + fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 { + let coord = vec4(batch, row, col, chan); + if(coordsInBounds4D(coord, uniforms.xShape)) { + return getX(batch, row, col, chan); + } + return 0.0; + } + + fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{ + let coord = vec4(row, col, xChannel, outChannel); + if(coordsInBounds4D(coord, uniforms.wShape)) { + return getW(row, col, xChannel, outChannel); + } + return 0.0; + } + + fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) { + let coord = vec4(batch, row, col, chan); + if (coordsInBounds4D(coord, uniforms.outShape)) { + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(batch, row, col, chan, value); + } + } + + ${getFlatDispatchLayoutMainHeaderString()} { + let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups); + let batch = coords[0]; + let outChannel = coords[3]; + + var acc = 0.0; + + for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) { + for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) { + for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) { + let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0]; + let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1]; + let v = readInp(batch, coordRow, coordCol, xChannel); + let f = readFilt(row, col, xChannel, outChannel); + acc = acc + v * f; + } + } + } + + writeResult(batch, coords[1], coords[2], outChannel, acc); + } + `; + return userCode; + } +}; +function conv2d5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + return conv2dByMatMul2({ x, filter, convInfo, backend: backend2 }); + } + if (env().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") && x.shape[0] === 1) { + return conv2dWithIm2Col({ x, filter, convInfo, backend: backend2 }); + } + let program; + const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left]; + const dimensions = [ + { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] }, + { type: "int32", data: [...padInfo] }, + { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, + { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] } + ]; + const useNaive = env().getBool("WEBGPU_USE_NAIVE_CONV2D"); + if (useNaive) { + program = new Conv2DNaiveProgram(convInfo); + } else if ((convInfo.inChannels % 4 === 0 || convInfo.inChannels === 3 && convInfo.padInfo.type === "VALID") && convInfo.outChannels % 4 === 0 && convInfo.outChannels >= 64) { + program = new Conv2DMMVec4Program(convInfo); + } else { + program = new Conv2DMMProgram(convInfo); + } + if (!useNaive) { + const dimAOuter = convInfo.outShape[1] * convInfo.outShape[2]; + const dimBOuter = convInfo.outShape[3]; + const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inShape[3]; + dimensions.push({ type: "int32", data: [dimAOuter] }, { type: "int32", data: [dimBOuter] }, { type: "int32", data: [dimInner] }); + } + return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions); +} +var conv2DConfig3 = { + kernelName: Conv2D, + backendName: "webgpu", + kernelFunc: conv2d5 +}; +var Conv2DDerInputMMProgram = class { + constructor(convInfo) { + this.variableNames = ["x", "W"]; + this.uniforms = "filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4; dimAOuter : i32; dimBOuter : i32; dimInner : i32;"; + this.outputShape = convInfo.inShape; + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }; + this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape); + this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread); + this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`; + } + getUserCode() { + const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize); + const readASnippet = ` + let outRow = row / uniforms.outShape[2]; + let outCol = row % uniforms.outShape[2]; + + let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); + let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; + let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); + let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); + if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { + return 0.0; + } + if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { + return 0.0; + } + let coord = vec4( + batch, + i32(xR), + i32(xC), + col % uniforms.outBackprop[3]); + return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];`; + const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) { + ${readASnippet} + } + return 0.0;`; + const userCode = ` + fn mm_readA(row : i32, col : i32, globalId : vec3) -> f32 { + var batch = i32(globalId.z); + ${sampleA} + } + + fn mm_readB(row : i32, col : i32, globalId : vec3) -> f32 { + let coordX = uniforms.filterDims.x - 1 - + row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); + let coordY = uniforms.filterDims.y - 1 - + (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; + if (row < uniforms.dimInner && col < uniforms.dimBOuter && + coordX >= 0 && coordY >= 0) { + let coord = vec4(coordX, coordY, col, + row % uniforms.outBackprop[3]); + return W.numbers[getFlatIndex4D(coord, uniforms.wShape)]; + } + return 0.0; + } + + fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3) { + var batch = i32(globalId.z); + var value = valueInput; + let outCoord = vec4( + batch, + row / uniforms.outShape[2], + row % uniforms.outShape[2], + col); + result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value; + } + + ${matMulSource} + `; + return userCode; + } +}; +var Conv2DDerInputProgram2 = class { + constructor(convInfo) { + this.variableNames = ["dy", "W"]; + this.uniforms = "filterDims : vec2; pads : vec2; stride : vec2; outBackprop : vec4;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = convInfo.inShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.isChannelsLast = convInfo.dataFormat === "channelsLast"; + this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`; + } + getUserCode() { + const rowDim = this.isChannelsLast ? 1 : 2; + const colDim = this.isChannelsLast ? 2 : 3; + const channelDim = this.isChannelsLast ? 3 : 1; + return ` + ${getMainHeaderAndGlobalIndexString()} { + if(index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let batch = coords[0]; + let d1 = coords[${channelDim}]; + + let dyCorner = vec2(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { + let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); + let wRPerm = uniforms.filterDims.x - 1 - wR; + if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR = dyR; + + for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { + let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); + let wCPerm = uniforms.filterDims.y - 1 - wC; + if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC = dyC; + + for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { + if (${this.isChannelsLast}) { + let xValue = getDy(batch, idyR, idyC, d2); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } else { + let xValue = getDy(batch, d2, idyR, idyC); + let wValue = getW(wRPerm, wCPerm, d1, d2); + dotProd = dotProd + xValue * wValue; + } + + } + } + } + setOutputFlat(index, dotProd); + } + } + `; + } +}; +function conv2DBackpropInput4(args) { + const { inputs, backend: backend2, attrs } = args; + const { dy, filter } = inputs; + const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat); + const dimensions = [ + { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] }, + { + type: "int32", + data: [ + convInfo.filterHeight - 1 - convInfo.padInfo.top, + convInfo.filterWidth - 1 - convInfo.padInfo.left + ] + }, + { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, + { + type: "int32", + data: [ + convInfo.batchSize, + convInfo.outHeight, + convInfo.outWidth, + convInfo.outChannels + ] + } + ]; + let program; + if (env().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")) { + program = new Conv2DDerInputProgram2(convInfo); + } else { + program = new Conv2DDerInputMMProgram(convInfo); + const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2]; + const dimBOuter = convInfo.inShape[3]; + const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels; + dimensions.push({ type: "uint32", data: [dimAOuter] }, { type: "uint32", data: [dimBOuter] }, { type: "uint32", data: [dimInner] }); + } + return backend2.runWebGPUProgram(program, [dy, filter], "float32", dimensions); +} +var conv2DBackpropInputConfig3 = { + kernelName: Conv2DBackpropInput, + backendName: "webgpu", + kernelFunc: conv2DBackpropInput4 +}; +var cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS }); +var cosConfig3 = { + kernelName: Cos, + backendName: "webgpu", + kernelFunc: cos4 +}; +var cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH }); +var coshConfig3 = { + kernelName: Cosh, + backendName: "webgpu", + kernelFunc: cosh4 +}; +var CropAndResizeProgram2 = class { + constructor(channnel, boxShape, cropSize, method) { + this.variableNames = ["Image", "Boxes", "BoxInd"]; + this.uniforms = "extrapolationValue : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + const [numBoxes] = boxShape; + this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel]; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.methodId = method === "bilinear" ? 1 : 0; + this.cropHeightBiggerThan1 = this.outputShape[1] > 1; + this.cropWidthBiggerThan1 = this.outputShape[2] > 1; + this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`; + } + getUserCode() { + const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`]; + const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [ + `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`, + "(y2-y1) * height_ratio", + `y1*${inputHeightFloat} + f32(y)*(height_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (y1+y2) * ${inputHeightFloat}` + ]; + const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [ + `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`, + "(x2-x1) * width_ratio", + `x1*${inputWidthFloat} + f32(x)*(width_scale)` + ] : [ + "0.0", + "0.0", + `0.5 * (x1+x2) * ${inputWidthFloat}` + ]; + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let height_ratio = f32(${heightRatio}); + let width_ratio = f32(${widthRatio}); + let b = coords[0]; + let y = coords[1]; + let x = coords[2]; + let d = coords[3]; + // get box vals + let y1 = getBoxes(b, 0); + let x1 = getBoxes(b, 1); + let y2 = getBoxes(b, 2); + let x2 = getBoxes(b, 3); + // get image in batch index + let bInd = i32(round(getBoxInd(b))); + if(bInd < 0 || bInd >= uniforms.outShape[0]) { + return; + } + let height_scale = ${heightScale}; + let width_scale = ${widthScale}; + let in_y = ${inY}; + if( in_y < 0.0 || in_y > ${inputHeightFloat} ) { + setOutputFlat(index, uniforms.extrapolationValue); + return; + } + let in_x = ${inX}; + if( in_x < 0.0 || in_x > ${inputWidthFloat} ) { + setOutputFlat(index, uniforms.extrapolationValue); + return; + } + let sourceFracIndexCR = vec2(in_x,in_y); + if(${this.methodId} == 1) { + // Compute the four integer indices. + let sourceFloorCR = vec2(sourceFracIndexCR); + let sourceCeilCR = vec2(ceil(sourceFracIndexCR)); + let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d); + let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d); + let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d); + let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d); + let fracCR = sourceFracIndexCR - vec2(sourceFloorCR); + let top = topLeft + (topRight - topLeft) * fracCR.x; + let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; + let newValue = top + (bottom - top) * fracCR.y; + setOutputFlat(index, newValue); + } else { + // Compute the coordinators of nearest neighbor point. + let sourceNearestCR = vec2(floor( + sourceFracIndexCR + vec2(0.5,0.5))); + let newValue = getImage( + bInd, sourceNearestCR.y, sourceNearestCR.x, d); + setOutputFlat(index, newValue); + } + } + } + `; + return userCode; + } +}; +var cropAndResize4 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { image: image3, boxes, boxInd } = inputs; + const { cropSize, method, extrapolationValue } = attrs; + const program = new CropAndResizeProgram2(image3.shape[3], boxes.shape, cropSize, method); + const uniformData = [{ type: "float32", data: [extrapolationValue] }]; + return backend2.runWebGPUProgram(program, [image3, boxes, boxInd], "float32", uniformData); +}; +var cropAndResizeConfig3 = { + kernelName: CropAndResize, + backendName: "webgpu", + kernelFunc: cropAndResize4 +}; +var DepthToSpaceProgram2 = class { + constructor(outputShape, dataFormat) { + this.variableNames = ["x"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.uniforms = "blockSize : i32;"; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `depthToSpace_${dataFormat}`; + this.dataFormat = dataFormat; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let b = coords[0]; + let h = ${this.getHeightCoordString()}; + let w = ${this.getWidthCoordString()}; + let d = ${this.getDepthCoordString()}; + + let in_h = h / uniforms.blockSize; + let offset_h = h % uniforms.blockSize; + let in_w = w / uniforms.blockSize; + let offset_w = w % uniforms.blockSize; + let offset_d = (offset_h * uniforms.blockSize + offset_w) * + ${this.getOutputDepthSize()}; + let in_d = d + offset_d; + + let rlt = ${this.getInputSamplingString()}; + setOutputFlat(index, rlt); + } + }`; + return userCode; + } + getHeightCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[1]`; + } else { + return `coords[2]`; + } + } + getWidthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[2]`; + } else { + return `coords[3]`; + } + } + getDepthCoordString() { + if (this.dataFormat === "NHWC") { + return `coords[3]`; + } else { + return `coords[1]`; + } + } + getOutputDepthSize() { + if (this.dataFormat === "NHWC") { + return `uniforms.outShape[3]`; + } else { + return `uniforms.outShape[1]`; + } + } + getInputSamplingString() { + if (this.dataFormat === "NHWC") { + return `getX(b, in_h, in_w, in_d)`; + } else { + return `getX(b, in_d, in_h, in_w)`; + } + } +}; +function depthToSpace4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockSize, dataFormat } = attrs; + const batchSize = x.shape[0]; + const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2]; + const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3]; + const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1]; + const outputHeight = inputHeight * blockSize; + const outputWidth = inputWidth * blockSize; + const outputDepth = inputDepth / (blockSize * blockSize); + const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth]; + const uniformData = [ + { type: "int32", data: [blockSize] } + ]; + const program = new DepthToSpaceProgram2(outputShape, dataFormat); + return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData); +} +var depthToSpaceConfig3 = { + kernelName: DepthToSpace, + backendName: "webgpu", + kernelFunc: depthToSpace4 +}; +var DepthwiseConv2D3x3Program = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) { + this.variableNames = ["x", "W"]; + this.uniforms = "pad : vec2; stride : vec2; dilation : vec2; inDims : vec2;"; + this.workGroupSize = [4, 4, 4]; + this.isVec4 = true; + this.outputShape = convInfo.outShape; + this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }; + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]); + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivation) { + this.variableNames.push("preluActivationWeights"); + } + this.convInfo = convInfo; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivation = hasPreluActivation; + this.shaderKey = `depthwise3x3_${activation2}`; + } + getUserCode() { + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4); + if (this.hasPreluActivation) { + activationSnippet = `fn activation(a : vec4, outCoord : vec4) -> vec4 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : vec4, outCoord : vec4) -> vec4 { + ${activationOp} + } + `; + } + applyActivationSnippet = `dotProd[i] = activation(dotProd[i], coords);`; + } + const addBiasSnippet = this.addBias ? "dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);" : ""; + const userCode = ` + ${activationSnippet} + + ${getWorkGroupSizeString()} + fn main([[builtin(global_invocation_id)]] globalId: vec3) { + let batch = 0; + let r = i32(globalId.x); + let c = i32(globalId.y) * 4; + let d2 = i32(globalId.z) * 4; + let xRCCorner = vec2(r, c) * uniforms.stride - uniforms.pad; + let d1 = d2; + let q = 0; + + let xRCorner = xRCCorner.x; + let xCCorner = xRCCorner.y; + + var wVals : array, 9>; + wVals[0] = getW(0, 0, d1, q); + wVals[1] = getW(0, 1, d1, q); + wVals[2] = getW(0, 2, d1, q); + wVals[3] = getW(1, 0, d1, q); + wVals[4] = getW(1, 1, d1, q); + wVals[5] = getW(1, 2, d1, q); + wVals[6] = getW(2, 0, d1, q); + wVals[7] = getW(2, 1, d1, q); + wVals[8] = getW(2, 2, d1, q); + + var xVals : array, 6>, 3>; + for (var wR = 0; wR < 3; wR = wR + 1) { + let xR = xRCorner + wR * uniforms.dilation[0]; + for (var wC = 0; wC < 6; wC = wC + 1) { + let xC = xCCorner + wC * uniforms.dilation[1]; + if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) { + xVals[wR][wC] = vec4(0.0); + } else { + xVals[wR][wC] = getX(batch, xR, xC, d1); + } + } + } + + var dotProd : array, 4>; + dotProd[0] = vec4(0.0); + dotProd[1] = vec4(0.0); + dotProd[2] = vec4(0.0); + dotProd[3] = vec4(0.0); + + for (var wR = 0; wR < 3; wR = wR + 1) { + for (var wC = 0; wC < 3; wC = wC + 1) { + let indexW = wR * 3 + wC; + dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW]; + dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW]; + dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW]; + dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW]; + } + } + + for (var i = 0; i < 4; i = i + 1) { + let coords = vec4(batch, r, c + i, d2); + if (coordsInBounds4D(coords, uniforms.outShape)) { + ${addBiasSnippet} + ${applyActivationSnippet} + setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]); + } + } + } + `; + return userCode; + } +}; +var DepthwiseConv2DProgram2 = class { + constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) { + this.variableNames = ["x", "W"]; + this.uniforms = `pad : vec2; stride : vec2; dilation : vec2; + inDims : vec2; filterHeight : i32; filterWidth : i32; + channelMul : i32;`; + this.workGroupSize = [256, 1, 1]; + this.outputShape = convInfo.outShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"); + if (addBias) { + this.variableNames.push("bias"); + } + if (hasPreluActivation) { + this.variableNames.push("preluActivationWeights"); + } + this.convInfo = convInfo; + this.addBias = addBias; + this.activation = activation2; + this.hasPreluActivation = hasPreluActivation; + this.shaderKey = `depthwise_${this.activation}`; + } + getUserCode() { + let activationSnippet = "", applyActivationSnippet = ""; + if (this.activation) { + const activationOp = mapActivationToShaderProgram2(this.activation, false); + if (this.hasPreluActivation) { + activationSnippet = `fn activation(a : f32, outCoord : vec4) -> f32 { + let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord); + ${activationOp} + }`; + } else { + activationSnippet = ` + fn activation(a : f32, outCoord : vec4) -> f32 { + ${activationOp} + } + `; + } + applyActivationSnippet = `dotProd = activation(dotProd, coords);`; + } + const addBiasSnippet = this.addBias ? "dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);" : ""; + const userCode = ` + ${activationSnippet} + + fn writeResult(batch : i32, row : i32, col : i32, chan : i32, + value : f32) { + let coord = vec4(batch, row, col, chan); + if (coordsInBounds4D(coord, uniforms.outShape)) { + setOutput(batch, row, col, chan, value); + } + } + + ${getFlatDispatchLayoutMainHeaderString()} { + let coords = getOutputCoordsWithFlatDispatchLayout(globalId, + localId, numWorkgroups); + let batch = coords[0]; + let xRCCorner = vec2(coords.yz) * uniforms.stride - uniforms.pad; + let d2 = coords[3]; + let d1 = d2 / uniforms.channelMul; + let q = d2 - d1 * uniforms.channelMul; + + let inputRowStart = xRCCorner.x; + let inputColStart = xRCCorner.y; + let inputRowEnd = inputRowStart + uniforms.filterHeight * + uniforms.dilation[0]; + let inputColEnd = inputColStart + uniforms.filterWidth * + uniforms.dilation[1]; + + // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). + // ? = to be determined. : = across all values in that axis. + var dotProd = 0.0; + + // Extract if checking out of for loop for performance. + if (inputRowStart >= 0 && inputColStart >= 0 && + inputRowEnd < uniforms.inDims[0] && + inputColEnd < uniforms.inDims[1]) { + // Here using a constant value |this.convInfo.filterHeight| instead + // of uniform value is in order to loop unrolling. + for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { + let xR = inputRowStart + wR * uniforms.dilation[0]; + + for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { + let xC = inputColStart + wC * uniforms.dilation[1]; + + let xVal = getX(batch, xR, xC, d1); + let wVal = getW(wR, wC, d1, q); + dotProd = dotProd + xVal * wVal; + } + } + } else { + for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { + let xR = inputRowStart + wR * uniforms.dilation[0]; + + if (xR < 0 || xR >= uniforms.inDims[0]) { + continue; + } + + for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { + let xC = inputColStart + wC * uniforms.dilation[1]; + + if (xC < 0 || xC >= uniforms.inDims[1]) { + continue; + } + + let xVal = getX(batch, xR, xC, d1); + let wVal = getW(wR, wC, d1, q); + dotProd = dotProd + xVal * wVal; + } + } + } + + ${addBiasSnippet} + ${applyActivationSnippet} + writeResult(batch, coords[1], coords[2], d2, dotProd); + } + `; + return userCode; + } +}; +function depthwiseConv2dNative3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode } = attrs; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true); + const dimensions = [ + { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, + { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, + { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] }, + { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] } + ]; + let program; + if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) { + program = new DepthwiseConv2D3x3Program(convInfo); + } else { + program = new DepthwiseConv2DProgram2(convInfo); + dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] }); + } + return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions); +} +var depthwiseConv2dNativeConfig3 = { + kernelName: DepthwiseConv2dNative, + backendName: "webgpu", + kernelFunc: depthwiseConv2dNative3 +}; +var multiplyKernelFunc = binaryKernelFunc3({ + opSnippet: BinaryOpType.MUL, + cpuKernelImpl: multiplyImplCPU2, + supportsComplex: true +}); +var multiplyConfig3 = { + kernelName: Multiply, + backendName: "webgpu", + kernelFunc: multiplyKernelFunc +}; +var ReduceProgram2 = class { + constructor(reduceInfo, reduceType) { + this.workGroupSize = [64, 1, 1]; + this.variableNames = ["x"]; + this.uniforms = "reduceSize : i32;"; + this.size = true; + this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize]; + const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]); + this.outputShape = outputShape.length === 0 ? [1] : outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]); + this.reduceType = reduceType; + this.shaderKey = `reduce_${reduceType}`; + } + getUserCode() { + let reduceOp = ``; + let initValue = "0.0"; + if (this.reduceType === "min" || this.reduceType === "max") { + reduceOp = ` + if (isNanCustom(candidate)) { + bestValue = uniforms.NAN; + } elseif (!isNanCustom(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue) + { bestValue = candidate; }`; + initValue = "f32(x.numbers[offset])"; + } else if (this.reduceType === "sum" || this.reduceType === "mean") { + reduceOp = " bestValue = bestValue + candidate; "; + } else if (this.reduceType === "prod") { + reduceOp = " bestValue = bestValue * candidate; "; + initValue = "1.0"; + } + const outputSnippet = this.reduceType === "mean" ? `setOutputFlat(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputFlat(outputIndex, bestValue);`; + const sharedMemorySnippet = ` + var xBestValues : array; + `; + const userCode = ` + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + + ${sharedMemorySnippet} + fn getOffset(outputIndex : i32) -> i32 { + let outputCoords = getCoordsFromFlatIndex(outputIndex); + let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize; + return offset; + } + ${getMainHeaderAndGlobalIndexString()} + let outputIndex = index / i32(workGroupSizeX); + let offset = getOffset(outputIndex); + var bestValue = ${initValue}; + let Length = uniforms.reduceSize; + let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX); + for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; + k = k + i32(workGroupSizeX)) { + let candidate = f32(x.numbers[offset + k]); + ${reduceOp} + } + xBestValues[localId.x] = bestValue; + workgroupBarrier(); + + var reduceSize = min(u32(Length), workGroupSizeX); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (localId.x < currentSize) { + let candidate = xBestValues[localId.x + interval]; + ${reduceOp} + xBestValues[localId.x] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (localId.x == 0u && outputIndex < uniforms.size) { + ${outputSnippet} + } + } + `; + return userCode; + } +}; +function reduce2(x, axis, keepDims, reduceType, backend2) { + const xRank = x.shape.length; + const toDispose = []; + const origAxes = util_exports.parseAxisParam(axis, x.shape); + let axes = origAxes; + const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank); + let input2 = x; + if (permutedAxes != null) { + input2 = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 }); + axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); + toDispose.push(input2); + } + backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank); + const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes); + let resOutShape = reduceOutShape; + if (keepDims) { + resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes); + } + let res; + if ((reduceType === "max" || reduceType === "prod") && backend2.shouldExecuteOnCPU([input2])) { + const xVals = backend2.tensorMap.get(input2.dataId).values; + switch (reduceType) { + case "max": + const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype); + res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues); + break; + case "prod": + const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes); + res = backend2.makeTensorInfo(outShape, outDtype, outVals); + break; + default: + throw new Error(`${reduceType} CPU implementation is not yet supported.`); + } + } else { + const inSize = util_exports.sizeFromShape(reduceShape); + const xSize = util_exports.sizeFromShape(input2.shape); + const batchSize = xSize / inSize; + const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 }; + const dtype = reduceType === "mean" ? "float32" : sumOutType(x.dtype); + const uniformData = [ + { type: "int32", data: [inSize] } + ]; + const program = new ReduceProgram2(reduceInfo, reduceType); + const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData); + toDispose.push(reduced); + res = reshape5({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 }); + } + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return res; +} +function sum5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + return reduce2(x, axis, keepDims, "sum", backend2); +} +var sumConfig3 = { + kernelName: Sum, + backendName: "webgpu", + kernelFunc: sum5 +}; +function einsum4(args) { + const { inputs, backend: backend2, attrs } = args; + const { equation } = attrs; + const tensors = inputs; + const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length); + backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors); + const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims); + const nSteps = steps.length; + let out = null; + let numDimsRemaining = allDims.length; + const tensorsToDispose = []; + for (let i = 0; i < nSteps; ++i) { + for (const idTerm of steps[i]) { + const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]); + let x; + if (backend_util_exports.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } else { + x = transpose4({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } }); + tensorsToDispose.push(x); + } + const targetShape = x.shape.slice(); + for (let k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!util_exports.arraysEqual(x.shape, targetShape)) { + x = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } else { + out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 }); + tensorsToDispose.push(out); + } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum5({ + inputs: { x: out }, + backend: backend2, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + for (const tensorInfo of tensorsToDispose) { + if (tensorInfo === out) { + continue; + } + backend2.disposeData(tensorInfo.dataId); + } + return out; +} +var einsumConfig3 = { + kernelName: Einsum, + backendName: "webgpu", + kernelFunc: einsum4 +}; +var elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU }); +var eluConfig3 = { + kernelName: Elu, + backendName: "webgpu", + kernelFunc: elu6 +}; +var equal4 = binaryKernelFunc3({ opSnippet: BinaryOpType.EQUAL, dtype: "bool", cpuKernelImpl: equalImplCPU2 }); +var equalConfig3 = { + kernelName: Equal, + backendName: "webgpu", + kernelFunc: equal4 +}; +var exp4 = unaryKernelFunc3({ + opType: UnaryOpType.EXP, + cpuKernelImpl: expImplCPU2, + dtype: "float32" +}); +var expConfig3 = { + kernelName: Exp, + backendName: "webgpu", + kernelFunc: exp4 +}; +function expandDims5(args) { + const { inputs, attrs, backend: backend2 } = args; + const { dim } = attrs; + const { input: input2 } = inputs; + const inputRank = input2.shape.length; + const newShape = input2.shape.slice(); + let $dim = dim; + if (dim < 0) { + util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); +} +var expandDimsConfig3 = { + kernelName: ExpandDims, + backendName: "webgpu", + kernelFunc: expandDims5 +}; +var expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 }); +var expm1Config3 = { + kernelName: Expm1, + backendName: "webgpu", + kernelFunc: expm14 +}; +var FillProgram2 = class { + constructor(shape) { + this.variableNames = []; + this.outputShape = []; + this.uniforms = "value : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = "fill"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + setOutputFlat(index, uniforms.value); + } + } + `; + return userCode; + } +}; +function fill4(args) { + const { backend: backend2, attrs } = args; + const { shape, value } = attrs; + let { dtype } = attrs; + dtype = dtype || util_exports.inferDtype(value); + if (dtype === "string") { + const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape)); + values.fill(value); + return backend2.makeTensorInfo(shape, dtype, values); + } else { + const program = new FillProgram2(shape); + const uniformData = [{ type: "float32", data: [value] }]; + return backend2.runWebGPUProgram(program, [], dtype, uniformData); + } +} +var fillConfig3 = { + kernelName: Fill, + backendName: "webgpu", + kernelFunc: fill4 +}; +var FlipLeftRightProgram2 = class { + constructor(imageShape) { + this.outputShape = []; + this.variableNames = ["x"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = imageShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = "flipLeftRight"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let coordX = uniforms.xShape[2] - coords[2] - 1; + let outputValue = getX(coords[0], coords[1], coordX, coords[3]); + setOutputFlat(index, outputValue); + } + } + `; + return userCode; + } +}; +var flipLeftRightConfig3 = { + kernelName: FlipLeftRight, + backendName: "webgpu", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { image: image3 } = inputs; + const webgpuBackend = backend2; + const program = new FlipLeftRightProgram2(image3.shape); + const output = webgpuBackend.runWebGPUProgram(program, [image3], image3.dtype); + return output; + } +}; +var floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 }); +var floorConfig3 = { + kernelName: Floor, + backendName: "webgpu", + kernelFunc: floor4 +}; +var floorDiv4 = binaryKernelFunc3({ opSnippet: BinaryOpType.INT_DIV, dtype: "int32" }); +var floorDivConfig3 = { + kernelName: FloorDiv, + backendName: "webgpu", + kernelFunc: floorDiv4 +}; +var makeBindGroup = (device, bindGroupLayout, inputs, output, uniforms) => { + const bindings = [output, ...inputs]; + if (uniforms) { + bindings.push(uniforms); + } + return device.createBindGroup({ + layout: bindGroupLayout, + entries: bindings.map((b, i) => ({ binding: i, resource: b })) + }); +}; +var compileProgram2 = (device, program, pipelineLayout, inputsData, output, isFromPixel = false) => { + const outputData = { dtype: output.dtype, shape: output.shape }; + const source = makeShader2(inputsData, outputData, program, isFromPixel); + const module2 = device.createShaderModule({ code: source }); + const pipeline = device.createComputePipeline({ layout: pipelineLayout, compute: { module: module2, entryPoint: "main" } }); + return pipeline; +}; +function makeShaderKey2(program, shapes, types, broadcastDimsKey = "", inputShapesEqualsOutShape = "") { + const key = program.shaderKey + "_" + (program.workGroupSize ? program.workGroupSize.join(",") : "") + shapes.map((shape) => shape.length).join(",") + types.join(",") + program.variableNames.join(",") + broadcastDimsKey + inputShapesEqualsOutShape; + return key; +} +function fromPixelsExternalImage(args) { + const { externalImage, backend: backend2, attrs, outShape, useImport } = args; + const { numChannels } = attrs; + const size = util_exports.sizeFromShape(outShape); + const strides = util_exports.computeStrides(outShape); + const output = backend2.makeTensorInfo(outShape, "int32"); + const program = backend2.getFromPixelsProgram(useImport ? "import" : "copyExternal"); + program.updateOutputShape(outShape); + const outputShapes = [output.shape]; + const outputTypes = [output.dtype, useImport ? "import" : "copyExternal"]; + const key = makeShaderKey2(program, outputShapes, outputTypes); + const layout = program.getLayout(backend2.device); + const pipeline = backend2.getAndSavePipeline(key, () => { + return compileProgram2(backend2.device, program, layout.pipelineLayout, [], output, true); + }); + program.setPipeline(pipeline); + if (!useImport) { + backend2.queue.copyExternalImageToTexture({ source: externalImage, origin: { x: 0, y: 0 } }, { + texture: program.makeInputTexture(backend2.device, outShape[1], outShape[0]) + }, [outShape[1], outShape[0]]); + } + const info = backend2.tensorMap.get(output.dataId); + info.bufferInfo.buffer = backend2.acquireBuffer(info.bufferInfo.byteSize); + const uniformData = [size, numChannels, ...strides, ...program.dispatch]; + program.setUniform(backend2.device, uniformData); + let externalResource; + if (useImport) { + const externalTextureDescriptor = { + source: externalImage + }; + externalResource = backend2.device.importExternalTexture(externalTextureDescriptor); + } else { + externalResource = program.inputTexture.createView(); + } + backend2.runFromPixelsProgram(program, info.bufferInfo.buffer, layout, externalResource, output.dataId); + return output; +} +var fromPixelsConfig2 = { + kernelName: FromPixels, + backendName: "webgpu", + kernelFunc: fromPixels3 +}; +var fromPixels2DContext3; +function fromPixels3(args) { + const { inputs, backend: backend2, attrs } = args; + let { pixels } = inputs; + const { numChannels } = attrs; + if (pixels == null) { + throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); + } + const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement; + const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement; + const isCanvas = typeof HTMLCanvasElement !== "undefined" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== "undefined" && pixels instanceof OffscreenCanvas; + const isImageBitmap = typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap; + const [width, height] = isVideo ? [ + pixels.videoWidth, + pixels.videoHeight + ] : [pixels.width, pixels.height]; + const outShape = [height, width, numChannels]; + if (env().getBool("WEBGPU_USE_IMPORT")) { + if (isVideo) { + return fromPixelsExternalImage({ + externalImage: pixels, + backend: backend2, + attrs, + outShape, + useImport: true + }); + } + } + if (isVideo || isImage) { + if (fromPixels2DContext3 == null) { + fromPixels2DContext3 = document.createElement("canvas").getContext("2d"); + } + fromPixels2DContext3.canvas.width = width; + fromPixels2DContext3.canvas.height = height; + fromPixels2DContext3.drawImage(pixels, 0, 0, width, height); + pixels = fromPixels2DContext3.canvas; + } + if (isImageBitmap || isCanvas || isVideo || isImage) { + return fromPixelsExternalImage({ + externalImage: pixels, + backend: backend2, + attrs, + outShape, + useImport: false + }); + } + const imageData = pixels.data; + let pixelArray = imageData; + if (numChannels != null && numChannels !== 4) { + pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels); + const dataLength = imageData.length; + let j = 0; + for (let i = 0; i < dataLength; i++) { + if (i % 4 < numChannels) { + pixelArray[j++] = imageData[i]; + } + } + } + const output = backend2.makeTensorInfo(outShape, "int32"); + const info = backend2.tensorMap.get(output.dataId); + info.values = new Int32Array(pixelArray); + backend2.maybeReleaseBuffer(output.dataId); + backend2.uploadToGPU(output.dataId); + return output; +} +var BatchNormProgram2 = class { + constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) { + this.uniforms = "varianceEpsilon : f32;"; + this.workGroupSize = [128, 1, 1]; + this.size = true; + this.variableNames = ["x", "mean", "variance"]; + backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape); + backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape); + this.outputShape = xShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + if (offsetShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape); + this.variableNames.push("offset"); + } + if (scaleShape != null) { + backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape); + this.variableNames.push("scale"); + } + this.offsetShape = offsetShape; + this.scaleShape = scaleShape; + this.shaderKey = "batchNorm"; + } + getUserCode() { + let offsetSnippet = "0.0"; + if (this.offsetShape != null) { + offsetSnippet = "getOffsetAtOutCoordsByGlobalIndex(index)"; + } + let scaleSnippet = "1.0"; + if (this.scaleShape != null) { + scaleSnippet = "getScaleAtOutCoordsByGlobalIndex(index)"; + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) + { + let xValue = getXAtOutCoordsByGlobalIndex(index); + let meanValue = getMeanAtOutCoordsByGlobalIndex(index); + let varianValue = getVarianceAtOutCoordsByGlobalIndex(index); + let offsetValue = ${offsetSnippet}; + let scaleValue = ${scaleSnippet}; + let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon)); + setOutputFlat(index,dot(vec3(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); + } + } + `; + return userCode; + } +}; +var fusedBatchNormConfig = { + kernelName: FusedBatchNorm, + backendName: "webgpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x, scale: scale22, offset, mean: mean5, variance } = inputs; + const { varianceEpsilon } = attrs; + const webGPUBackend = backend2; + const batchNormInputs = [x, mean5, variance]; + let offsetShape = null; + if (offset != null) { + offsetShape = offset.shape; + batchNormInputs.push(offset); + } + let scaleShape = null; + if (scale22 != null) { + scaleShape = scale22.shape; + batchNormInputs.push(scale22); + } + const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape); + const uniformData = [{ type: "float32", data: [varianceEpsilon] }]; + return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData); + } +}; +function fusedConv2d2(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { + strides, + pad: pad3, + dataFormat, + dilations, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; + const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + let program; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) { + return conv2dByMatMul2({ + x, + filter, + convInfo, + backend: backend2, + bias, + activation: activation2, + preluActivationWeights, + leakyreluAlpha + }); + } + const useNaive = env().getBool("WEBGPU_USE_NAIVE_CONV2D"); + const useVec4 = convInfo.inChannels % 4 === 0 && convInfo.outChannels % 4 === 0; + const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left]; + const dimensions = [ + { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] }, + { type: "int32", data: [...padInfo] }, + { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, + { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] } + ]; + if (useNaive) { + program = new Conv2DNaiveProgram(convInfo, hasBias, activation2, hasPreluActivationWeights); + } else { + if (useVec4) { + program = new Conv2DMMVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights); + } else { + program = new Conv2DMMProgram(convInfo, hasBias, activation2, hasPreluActivationWeights); + } + const dimAOuter = convInfo.outShape[1] * convInfo.outShape[2]; + const dimBOuter = convInfo.outShape[3]; + const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inShape[3]; + dimensions.push({ type: "int32", data: [dimAOuter] }, { type: "int32", data: [dimBOuter] }, { type: "int32", data: [dimInner] }); + } + const inputVar = [x, filter]; + if (hasBias) { + inputVar.push(bias); + } + if (hasPreluActivationWeights) { + inputVar.push(preluActivationWeights); + } + return backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions); +} +var fusedConv2DConfig3 = { + kernelName: FusedConv2D, + backendName: "webgpu", + kernelFunc: fusedConv2d2 +}; +function fusedDepthwiseConv2D3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, filter, bias, preluActivationWeights } = inputs; + const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2 } = attrs; + let $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`); + const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true); + const programInputs = [x, filter]; + const hasBias = bias != null; + const hasPreluActivationWeights = preluActivationWeights != null; + if (hasBias) { + programInputs.push(bias); + } + if (hasPreluActivationWeights) { + programInputs.push(preluActivationWeights); + } + const dimensions = [ + { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, + { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, + { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] }, + { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] } + ]; + let program; + if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) { + program = new DepthwiseConv2D3x3Program(convInfo, hasBias, activation2, hasPreluActivationWeights); + } else { + program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights); + dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] }); + } + const result = backend2.runWebGPUProgram(program, programInputs, "float32", dimensions); + return result; +} +var fusedDepthwiseConv2DConfig3 = { + kernelName: FusedDepthwiseConv2D, + backendName: "webgpu", + kernelFunc: fusedDepthwiseConv2D3 +}; +var GatherNDProgram2 = class { + constructor(sliceDim, shape) { + this.variableNames = ["A", "indices"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `gathernd_${sliceDim}`; + this.sliceDim = sliceDim; + this.uniforms = `sliceDim : i32; strides : ${getCoordsDataType2(sliceDim)};`; + } + getUserCode() { + let strideString; + if (this.sliceDim > 1) { + strideString = "uniforms.strides[j]"; + } else { + strideString = "uniforms.strides"; + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + var flattenIndex = 0; + for (var j = 0; j < uniforms.sliceDim; j = j + 1) { + let indexTemp = i32(round(getIndices(coords[0], j))); + let strideNum = ${strideString}; + flattenIndex = flattenIndex + indexTemp * strideNum; + } + + setOutputFlat(index, getA(flattenIndex, coords[1])); + } + } + `; + return userCode; + } +}; +function gatherNd3(args) { + const { inputs, backend: backend2 } = args; + const { params, indices } = inputs; + const indicesShape = indices.shape; + const sliceRank = indicesShape[indicesShape.length - 1]; + const paramsSize = util_exports.sizeFromShape(params.shape); + const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices); + const flattenIndices = reshape5({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } }); + const flattenX = reshape5({ + inputs: { x: params }, + backend: backend2, + attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] } + }); + if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") { + const indicesData = backend2.readSync(indices.dataId); + const paramsBuf = backend2.bufferSync(params); + const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values); + } + const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]); + const uniformData = [{ type: "int32", data: [sliceRank] }, { type: "int32", data: strides }]; + const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData); + const reshaped = reshape5({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } }); + backend2.disposeData(flattenIndices.dataId); + backend2.disposeData(flattenX.dataId); + backend2.disposeData(res.dataId); + return reshaped; +} +var gatherNdConfig3 = { + kernelName: GatherNd, + backendName: "webgpu", + kernelFunc: gatherNd3 +}; +var GatherProgram2 = class { + constructor(aShape, outputShape) { + this.variableNames = ["A", "indices"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = aShape.slice(); + this.aShape = aShape; + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `gather`; + } + getUserCode() { + const sourceCoords = getSourceCoords4(this.aShape, "i32"); + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let resRC = getCoordsFromFlatIndex(index); + setOutputFlat(index, getA(${sourceCoords})); + } + } + `; + return userCode; + } +}; +function getSourceCoords4(aShape, typePrefix = "int") { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const sourceCoords = []; + for (let i = 0; i < aShape.length; i++) { + if (i === 2) { + sourceCoords.push(`${typePrefix}(getIndices(resRC.x, resRC.z))`); + } else { + sourceCoords.push(`${currentCoords[i]}`); + } + } + return sourceCoords.join(); +} +function gatherV23(args) { + const { inputs, backend: backend2, attrs } = args; + const { x, indices } = inputs; + const { axis, batchDims } = attrs; + const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0]; + const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); + const indicesSize = util_exports.sizeFromShape(indices.shape); + const toDispose = []; + const flattenX = reshape5({ + inputs: { x }, + backend: backend2, + attrs: { + shape: [ + shapeInfo.batchSize, + shapeInfo.outerSize, + shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + const flattenIndex = reshape5({ + inputs: { x: indices }, + backend: backend2, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + toDispose.push(flattenX); + toDispose.push(flattenIndex); + const flattenOutputShape = [ + shapeInfo.batchSize, + shapeInfo.outerSize, + indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + if (backend2.shouldExecuteOnCPU([x, indices])) { + const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId); + const indicesValues = indicesBufferInfo.values; + const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues); + const xBufferInfo = backend2.tensorMap.get(flattenX.dataId); + const xValues = xBufferInfo.values; + const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues); + const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape); + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); + } + const program = new GatherProgram2(flattenX.shape, flattenOutputShape); + const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype); + toDispose.push(res); + const reshaped = reshape5({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } }); + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return reshaped; +} +var gatherV2Config3 = { + kernelName: GatherV2, + backendName: "webgpu", + kernelFunc: gatherV23 +}; +var greater5 = binaryKernelFunc3({ + opSnippet: BinaryOpType.GREATER, + cpuKernelImpl: greaterImplCPU2, + dtype: "bool" +}); +var greaterConfig3 = { + kernelName: Greater, + backendName: "webgpu", + kernelFunc: greater5 +}; +var greaterEqual4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.GREATER_EQUAL, + dtype: "bool", + cpuKernelImpl: greaterEqualImplCPU2 +}); +var greaterEqualConfig3 = { + kernelName: GreaterEqual, + backendName: "webgpu", + kernelFunc: greaterEqual4 +}; +var less5 = binaryKernelFunc3({ opSnippet: BinaryOpType.LESS, dtype: "bool", cpuKernelImpl: lessImplCPU2 }); +var lessConfig3 = { + kernelName: Less, + backendName: "webgpu", + kernelFunc: less5 +}; +var lessEqual4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.LESS_EQUAL, + dtype: "bool", + cpuKernelImpl: lessEqualImplCPU2 +}); +var lessEqualConfig3 = { + kernelName: LessEqual, + backendName: "webgpu", + kernelFunc: lessEqual4 +}; +var log7 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 }); +var logConfig3 = { + kernelName: Log, + backendName: "webgpu", + kernelFunc: log7 +}; +var logicalAnd4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.LOGICAL_AND, + dtype: "bool" +}); +var logicalAndConfig3 = { + kernelName: LogicalAnd, + backendName: "webgpu", + kernelFunc: logicalAnd4 +}; +var logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT }); +var logicalNotConfig3 = { + kernelName: LogicalNot, + backendName: "webgpu", + kernelFunc: logicalNot4 +}; +function max5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { reductionIndices, keepDims } = attrs; + return reduce2(x, reductionIndices, keepDims, "max", backend2); +} +var maxConfig3 = { + kernelName: Max, + backendName: "webgpu", + kernelFunc: max5 +}; +var maximum5 = binaryKernelFunc3({ + opSnippet: BinaryOpType.MAX, + cpuKernelImpl: maximumImplCPU2 +}); +var maximumConfig3 = { + kernelName: Maximum, + backendName: "webgpu", + kernelFunc: maximum5 +}; +function maxPool4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs; + const dilations = 1; + const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode); + let program; + const dimensions = []; + if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) { + if (util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) { + return identity4({ inputs: { x }, backend: backend2 }); + } + program = new PoolWithFilterSizeEqualsOneProgram(convInfo); + dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }); + } else { + program = new Pool2DProgram2(convInfo, "max"); + dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, { + type: "int32", + data: [convInfo.dilationHeight, convInfo.dilationWidth] + }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, { + type: "int32", + data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth] + }); + } + return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions); +} +var maxPoolConfig3 = { + kernelName: MaxPool, + backendName: "webgpu", + kernelFunc: maxPool4 +}; +function mean3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { keepDims, axis } = attrs; + return reduce2(x, axis, keepDims, "mean", backend2); +} +var meanConfig3 = { + kernelName: Mean, + backendName: "webgpu", + kernelFunc: mean3 +}; +function min5(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + return reduce2(x, axis, keepDims, "min", backend2); +} +var minConfig3 = { + kernelName: Min, + backendName: "webgpu", + kernelFunc: min5 +}; +var minimum5 = binaryKernelFunc3({ + opSnippet: BinaryOpType.MIN, + cpuKernelImpl: minimumImplCPU2 +}); +var minimumConfig3 = { + kernelName: Minimum, + backendName: "webgpu", + kernelFunc: minimum5 +}; +var MirrorPadProgram2 = class { + constructor(xShape, paddings, mode) { + this.uniforms = ""; + this.variableNames = ["x"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.xShape = xShape; + paddings.map((_, i) => { + this.uniforms += ` pad${i} : vec2;`; + }); + this.offset = mode === "reflect" ? 0 : 1; + this.shaderKey = `mirrorPad_${mode}`; + } + getUserCode() { + const rank = this.xShape.length; + const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(","); + const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(","); + const shaderStart = rank === 1 ? "start" : "start[i]"; + const shaderEnd = rank === 1 ? "end" : "end[i]"; + const shaderOutC = rank === 1 ? "outC" : "outC[i]"; + const dtype = getCoordsDataType2(rank); + const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords"; + return ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let start = ${dtype}(${start}); + let end = ${dtype}(${end}); + var outC = getCoordsFromFlatIndex(index); + for (var i = 0; i < ${rank}; i = i + 1) { + if (${shaderOutC} < ${shaderStart}) { + ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset}; + } elseif(${shaderOutC} >= ${shaderEnd}) { + ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset}; + } + } + let coords = outC - start; + setOutputFlat(index, getX(${unpackedCoords})); + } + } + `; + } +}; +var mirrorPadConfig3 = { + kernelName: MirrorPad, + backendName: "webgpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { x } = inputs; + const { paddings, mode } = attrs; + const webGPUBackend = backend2; + const uniformData = paddings.map((p2) => { + return { type: "int32", data: [p2[0], p2[1]] }; + }); + const program = new MirrorPadProgram2(x.shape, paddings, mode); + const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData); + return output; + } +}; +function neg4(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (backend2.shouldExecuteOnCPU([x])) { + const xData = backend2.tensorMap.get(x.dataId); + const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype); + return backend2.makeTensorInfo(newShape, x.dtype, outValues); + } + const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG); + return backend2.runWebGPUProgram(program, [x], x.dtype); +} +var negConfig3 = { + kernelName: Neg, + backendName: "webgpu", + kernelFunc: neg4 +}; +function nonMaxSuppressionV33(args) { + console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold); + return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)); +} +var nonMaxSuppressionV3Config3 = { + kernelName: NonMaxSuppressionV3, + backendName: "webgpu", + kernelFunc: nonMaxSuppressionV33 +}; +function nonMaxSuppressionV53(args) { + console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"); + const { inputs, backend: backend2, attrs } = args; + const { boxes, scores } = inputs; + const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs; + const boxesVals = backend2.readSync(boxes.dataId); + const scoresVals = backend2.readSync(scores.dataId); + const maxOutputSizeVal = maxOutputSize; + const iouThresholdVal = iouThreshold; + const scoreThresholdVal = scoreThreshold; + const softNmsSigmaVal = softNmsSigma; + const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal); + return [ + backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)), + backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores)) + ]; +} +var nonMaxSuppressionV5Config3 = { + kernelName: NonMaxSuppressionV5, + backendName: "webgpu", + kernelFunc: nonMaxSuppressionV53 +}; +function zerosLike4(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "complex64") { + const realPart = real4({ inputs: { input: x }, backend: backend2 }); + const r = zerosLike4({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag4({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike4({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeData(realPart.dataId); + backend2.disposeData(r.dataId); + backend2.disposeData(imagPart.dataId); + backend2.disposeData(i.dataId); + return result; + } else { + return fill4({ + attrs: { + shape: x.shape, + dtype: x.dtype, + value: x.dtype === "string" ? "" : 0 + }, + backend: backend2 + }); + } +} +var zerosLikeConfig3 = { + kernelName: ZerosLike, + backendName: "webgpu", + kernelFunc: zerosLike4 +}; +function onesLike4(args) { + const { inputs, backend: backend2 } = args; + const { x } = inputs; + if (x.dtype === "string") { + throw new Error("onesLike is not supported under string dtype"); + } else if (x.dtype === "complex64") { + const realPart = real4({ inputs: { input: x }, backend: backend2 }); + const r = onesLike4({ inputs: { x: realPart }, backend: backend2 }); + const imagPart = imag4({ inputs: { input: x }, backend: backend2 }); + const i = zerosLike4({ inputs: { x: imagPart }, backend: backend2 }); + const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 }); + backend2.disposeData(realPart.dataId); + backend2.disposeData(r.dataId); + backend2.disposeData(imagPart.dataId); + backend2.disposeData(i.dataId); + return result; + } else { + return fill4({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 }); + } +} +var onesLikeConfig3 = { + kernelName: OnesLike, + backendName: "webgpu", + kernelFunc: onesLike4 +}; +function pack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { axis } = attrs; + if (inputs.length === 1) { + return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + } + const shape = inputs[0].shape; + const dtype = inputs[0].dtype; + inputs.forEach((t) => { + util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes"); + util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes"); + }); + const intermediateTensorInfos = []; + const expandedTensors = inputs.map((t) => { + const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); + return result; +} +var packConfig3 = { + kernelName: Pack, + backendName: "webgpu", + kernelFunc: pack3 +}; +var PadProgram2 = class { + constructor(xShape, paddings) { + this.variableNames = ["x"]; + this.uniforms = "constantValue : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]); + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + paddings.map((_, i) => { + this.uniforms += ` pad${i} : vec2;`; + }); + this.xShape = xShape; + this.shaderKey = "pad"; + } + getUserCode() { + const rank = this.xShape.length; + const type = getCoordsDataType2(rank); + const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(","); + const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(","); + const startValue = rank > 1 ? `${type}(${start})` : `${start}`; + const endValue = rank > 1 ? `${type}(${end})` : `${end}`; + const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`; + const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`; + const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords"; + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let start = ${startValue}; + let end = ${endValue}; + let outC = getCoordsFromFlatIndex(index); + + if (${leftPadCondition} || ${rightPadCondition}) { + setOutputFlat(index, uniforms.constantValue); + } else { + let coords = outC - start; + setOutputFlat(index, getX(${unpackedCoords})); + } + } + } + `; + return userCode; + } +}; +var padV23 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { paddings, constantValue } = attrs; + if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) { + return identity4({ inputs: { x }, backend: backend2 }); + } + if (util_exports.sizeFromShape(x.shape) === 0) { + const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]); + return fill4({ + backend: backend2, + attrs: { shape: outputShape, value: constantValue, dtype: x.dtype } + }); + } + const uniformData = [{ type: "float32", data: [constantValue] }]; + paddings.map((p2) => uniformData.push({ type: "int32", data: [p2[0], p2[1]] })); + const program = new PadProgram2(x.shape, paddings); + return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData); +}; +var padV2Config3 = { + kernelName: PadV2, + backendName: "webgpu", + kernelFunc: padV23 +}; +var pow4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.POW +}); +var powConfig3 = { + kernelName: Pow, + backendName: "webgpu", + kernelFunc: pow4 +}; +function prelu5(args) { + const { inputs, backend: backend2 } = args; + const { x, alpha } = inputs; + const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha.shape); + return backend2.runWebGPUProgram(program, [x, alpha], "float32"); +} +var preluConfig3 = { + kernelName: Prelu, + backendName: "webgpu", + kernelFunc: prelu5 +}; +function prod4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { axis, keepDims } = attrs; + return reduce2(x, axis, keepDims, "prod", backend2); +} +var prodConfig3 = { + kernelName: Prod, + backendName: "webgpu", + kernelFunc: prod4 +}; +var range5 = (args) => { + const { backend: backend2, attrs } = args; + const { start, stop, step: step5, dtype } = attrs; + const values = rangeImplCPU2(start, stop, step5, dtype); + return backend2.makeTensorInfo([values.length], dtype, values); +}; +var rangeConfig3 = { + kernelName: Range, + backendName: "webgpu", + kernelFunc: range5 +}; +var realDiv2 = binaryKernelFunc3({ opSnippet: BinaryOpType.DIV }); +var realDivConfig3 = { + kernelName: RealDiv, + backendName: "webgpu", + kernelFunc: realDiv2 +}; +var relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU }); +var reluConfig3 = { + kernelName: Relu, + backendName: "webgpu", + kernelFunc: relu4 +}; +var relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 }); +var relu6Config3 = { + kernelName: Relu6, + backendName: "webgpu", + kernelFunc: relu64 +}; +var ResizeBilinearProgram2 = class { + constructor(inputShape, newHeight, newWidth) { + this.variableNames = ["x"]; + this.uniforms = "adjustHeightWidth : vec2; halfPixelCenters : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]]; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = `resizeBilinear`; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let b = coords[0]; + let d = coords[3]; + let rc = coords.yz; + + let effectiveInSize = vec2( + f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], + f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); + + let effectiveOutSize = vec2( + f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], + f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); + + let effectiveInputOverOutputRatioRC = + effectiveInSize / effectiveOutSize; + + // Fractional source index + let sourceFracIndexRC = + (vec2(rc) + vec2(uniforms.halfPixelCenters)) * + effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters); + + // Compute the four integer indices. + let sourceFloorRC = vec2(sourceFracIndexRC); + let sourceCeilRC = vec2( + min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); + + let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); + let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); + let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); + let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); + + let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); + + let top = topLeft + (topRight - topLeft) * fracRC.y; + let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; + let newValue = top + (bottom - top) * fracRC.x; + + setOutputFlat(index, newValue); + } + } + `; + return userCode; + } +}; +function resizeBilinear4(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, size, halfPixelCenters } = attrs; + const [newHeight, newWidth] = size; + const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0; + const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0; + const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0; + const uniformData = [ + { type: "float32", data: [adjustHeight, adjustWidth] }, + { type: "float32", data: [halfPixelCentersValue] } + ]; + const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth); + return backend2.runWebGPUProgram(program, [images], "float32", uniformData); +} +var resizeBilinearConfig3 = { + kernelName: ResizeBilinear, + backendName: "webgpu", + kernelFunc: resizeBilinear4 +}; +var ResizeNearestNeighborProgram2 = class { + constructor(inputShape, newHeight, newWidth, halfPixelCenters) { + this.variableNames = ["x"]; + this.uniforms = "adjustHeightWidth : vec2; roundBase : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]]; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.halfPixelCenters = halfPixelCenters; + this.shaderKey = `resizeNearest_${halfPixelCenters}`; + } + getUserCode() { + let sourceFracIndexRC; + if (this.halfPixelCenters) { + sourceFracIndexRC = `max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`; + } else { + sourceFracIndexRC = `vec2(rc) * effectiveInputOverOutputRatioRC`; + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let b = coords[0]; + let d = coords[3]; + let rc = coords.yz; + + let effectiveInSize = vec2( + f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], + f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); + + let effectiveOutSize = vec2( + f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], + f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); + + let effectiveInputOverOutputRatioRC = + effectiveInSize / effectiveOutSize; + + // Fractional source index + let sourceFracIndexRC = ${sourceFracIndexRC}; + + // Compute the coordinators of nearest neighbor point. + let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); + let sourceNearestRC = vec2( + min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); + let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); + + setOutputFlat(index, newValue); + } + } + `; + return userCode; + } +}; +function resizeNearestNeighbor4(args) { + const { inputs, backend: backend2, attrs } = args; + const { images } = inputs; + const { alignCorners, halfPixelCenters, size } = attrs; + const [newHeight, newWidth] = size; + const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0; + const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0; + const roundBase = alignCorners ? 0.5 : 0; + const uniformData = [ + { type: "float32", data: [adjustHeight, adjustWidth] }, + { type: "float32", data: [roundBase] } + ]; + const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters); + return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData); +} +var resizeNearestNeighborConfig3 = { + kernelName: ResizeNearestNeighbor, + backendName: "webgpu", + kernelFunc: resizeNearestNeighbor4 +}; +var RotateProgram2 = class { + constructor(imageShape, fillValue) { + this.outputShape = []; + this.variableNames = ["x"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = imageShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.uniforms = `centerX : f32; centerY : f32; sinRadians : f32; + cosRadians : f32;`; + this.shaderKey = "rotate"; + this.outputShape = imageShape; + if (typeof fillValue === "number") { + this.uniforms += ` fillValue : f32;`; + this.fillSnippet = `var outputValue = uniforms.fillValue;`; + this.shaderKey += "_float"; + } else { + this.uniforms += ` fillValue : vec3;`; + this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`; + this.shaderKey += "_vec3"; + } + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + let coordXFloat = (f32(coords[2]) - uniforms.centerX) * + uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) * + uniforms.sinRadians; + let coordYFloat = (f32(coords[2]) - uniforms.centerX) * + uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) * + uniforms.cosRadians; + let coordX = i32(round(coordXFloat + uniforms.centerX)); + let coordY = i32(round(coordYFloat + uniforms.centerY)); + ${this.fillSnippet} + if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && + coordY < uniforms.xShape[1]) { + outputValue = getX(coords[0], coordY, coordX, coords[3]); + } + setOutputFlat(index, outputValue); + } + } + `; + return userCode; + } +}; +var rotateWithOffsetConfig3 = { + kernelName: RotateWithOffset, + backendName: "webgpu", + kernelFunc: ({ inputs, attrs, backend: backend2 }) => { + const { image: image3 } = inputs; + const { radians, fillValue, center } = attrs; + const webgpuBackend = backend2; + const program = new RotateProgram2(image3.shape, fillValue); + const [centerX, centerY] = backend_util_exports.getImageCenter(center, image3.shape[1], image3.shape[2]); + const uniformData = [ + { type: "float32", data: [centerX] }, + { type: "float32", data: [centerY] }, + { type: "float32", data: [Math.sin(radians)] }, + { type: "float32", data: [Math.cos(radians)] } + ]; + if (typeof fillValue === "number") { + uniformData.push({ type: "float32", data: [Number.parseFloat(fillValue.toFixed(2))] }); + } else { + uniformData.push({ type: "float32", data: fillValue }); + } + const output = webgpuBackend.runWebGPUProgram(program, [image3], image3.dtype, uniformData); + return output; + } +}; +var rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 }); +var rsqrtConfig3 = { + kernelName: Rsqrt, + backendName: "webgpu", + kernelFunc: rsqrt4 +}; +var ScatterOptimizedProgram = class { + constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides, shape, outputDtype) { + this.variableNames = ["updates", "indices"]; + this.workGroupSize = [64, 1, 1]; + this.atomic = true; + this.outputShape = shape; + this.type = outputDtype; + this.dispatchLayout = flatDispatchLayout(flattenXShape); + this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize); + this.sliceDimGreaterThanOne = sliceDim > 1; + this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}`; + const stridesType = getCoordsDataType2(strides.length); + this.uniforms = `sliceDim : i32; strides: ${stridesType}; size: i32;`; + this.updatesRank = updatesRank; + this.indicesRank = indicesRank; + } + getUserCode() { + let indicesString = ""; + if (this.indicesRank === 1) { + indicesString = "coords[0]"; + } else if (this.indicesRank === 2) { + indicesString = "coords[0], j"; + } + const indicesSnippet = `getIndices(${indicesString})`; + const strideString = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides"; + let updatesString = ""; + let outCoordsString = ""; + let getUpdatesCoordsFromFlatIndex = ""; + if (this.updatesRank === 1) { + updatesString = "coords[0]"; + outCoordsString = "flattenedIndex"; + getUpdatesCoordsFromFlatIndex = ` + fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { + return index; + } + `; + } else if (this.updatesRank === 2) { + updatesString = "coords[0], coords[1]"; + outCoordsString = "vec2(flattenedIndex, coords[1])"; + getUpdatesCoordsFromFlatIndex = ` + fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 { + let d0 = index / uniforms.updatesShape[1]; + let d1 = index - d0 * uniforms.updatesShape[1]; + return vec2(d0, d1); + } + `; + } + const updatesSnippet = `getUpdates(${updatesString})`; + const atomicAddSnippet = this.type === "int32" ? `atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));` : ` + var assumed = atomicLoad(&(result.numbers[flatIndex])); + var success = 0; + for (; success == 0;) { + let new = bitcast(assumed) + updateValue; + let newI32 = bitcast(new); + let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32); + assumed = resValue[0]; + success = resValue[1]; + } + `; + const userCode = ` + ${getUpdatesCoordsFromFlatIndex} + + ${getMainHeaderAndGlobalIndexString()} + + if (index < uniforms.size) { + let coords = getUpdatesCoordsFromFlatIndex(index); + var flattenedIndex = 0; + for (var j = 0; j < uniforms.sliceDim; j = j + 1) { + let indexInside = i32(round(${indicesSnippet})); + flattenedIndex = flattenedIndex + indexInside * ${strideString}; + } + let updateValue = ${updatesSnippet}; + let flatIndex = getOutputFlatIndex(${outCoordsString}); + + ${atomicAddSnippet} + } + }`; + return userCode; + } +}; +function scatterNd3(args) { + const { inputs, backend: backend2, attrs } = args; + const { indices, updates } = inputs; + const { shape } = attrs; + const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape); + const flattenShape = [outputSize / sliceSize, sliceSize]; + if (outputSize === 0) { + return backend2.makeTensorInfo(shape, indices.dtype); + } + const flattenIndices = reshape5({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } }); + const flattenX = reshape5({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } }); + const type = flattenX.dtype; + const output = fill4({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } }); + const size = util_exports.sizeFromShape(flattenX.shape); + const uniformData = [ + { type: "int32", data: [sliceRank] }, + { type: "int32", data: strides }, + { type: "int32", data: [size] } + ]; + const program = new ScatterOptimizedProgram(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, type); + const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output); + const reshaped = reshape5({ inputs: { x: res }, backend: backend2, attrs: { shape } }); + backend2.disposeData(flattenIndices.dataId); + backend2.disposeData(flattenX.dataId); + backend2.disposeData(res.dataId); + return reshaped; +} +var scatterNdConfig3 = { + kernelName: ScatterNd, + backendName: "webgpu", + kernelFunc: scatterNd3 +}; +var SelectProgram2 = class { + constructor(cRank, shape, rank) { + this.variableNames = ["c", "a", "b"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.cRank = cRank; + this.rank = rank; + this.shaderKey = "select"; + } + getUserCode() { + let cCoords; + let abCoords; + if (this.rank > 4) { + throw Error(`Where for rank ${this.rank} is not yet supported`); + } + if (this.rank === 1) { + abCoords = `resRC`; + cCoords = `resRC`; + } else { + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const cCoordVars = []; + const abCoordVars = []; + for (let i = 0; i < this.outputShape.length; i++) { + abCoordVars.push(`${currentCoords[i]}`); + if (i < this.cRank) { + cCoordVars.push(`${currentCoords[i]}`); + } + } + cCoords = cCoordVars.join(); + abCoords = abCoordVars.join(); + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let resRC = getCoordsFromFlatIndex(index); + let cVal = getC(${cCoords}); + if (cVal >= 1.0) { + setOutputFlat(index, getA(${abCoords})); + } else { + setOutputFlat(index, getB(${abCoords})); + } + } + } + `; + return userCode; + } +}; +function select4(args) { + const { inputs, backend: backend2 } = args; + const { condition, t, e } = inputs; + const program = new SelectProgram2(condition.shape.length, t.shape, t.shape.length); + return backend2.runWebGPUProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype)); +} +var selectConfig3 = { + kernelName: Select, + backendName: "webgpu", + kernelFunc: select4 +}; +var sigmoid4 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID }); +var sigmoidConfig3 = { + kernelName: Sigmoid, + backendName: "webgpu", + kernelFunc: sigmoid4 +}; +var sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN }); +var sinConfig3 = { + kernelName: Sin, + backendName: "webgpu", + kernelFunc: sin4 +}; +var sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH }); +var sinhConfig3 = { + kernelName: Sinh, + backendName: "webgpu", + kernelFunc: sinh4 +}; +var sub4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.SUB, + cpuKernelImpl: subImplCPU2, + supportsComplex: true +}); +var subConfig3 = { + kernelName: Sub, + backendName: "webgpu", + kernelFunc: sub4 +}; +function softmax5(args) { + const { inputs, backend: backend2, attrs } = args; + const { logits } = inputs; + const { dim } = attrs; + const axes = util_exports.parseAxisParam([dim], logits.shape); + const maxLogit = max5({ + inputs: { x: logits }, + backend: backend2, + attrs: { reductionIndices: axes, keepDims: false } + }); + const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes); + const maxLogitsReshaped = reshape5({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } }); + const a = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 }); + const b = exp4({ inputs: { x: a }, backend: backend2 }); + const sumExp = sum5({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } }); + const sumExpReshaped = reshape5({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } }); + const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 }); + backend2.disposeData(maxLogit.dataId); + backend2.disposeData(maxLogitsReshaped.dataId); + backend2.disposeData(a.dataId); + backend2.disposeData(b.dataId); + backend2.disposeData(sumExp.dataId); + backend2.disposeData(sumExpReshaped.dataId); + return res; +} +var softmaxConfig3 = { + kernelName: Softmax, + backendName: "webgpu", + kernelFunc: softmax5 +}; +var spaceToBatchND4 = (args) => { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { blockShape, paddings } = attrs; + util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet"); + const prod6 = blockShape.reduce((a, b) => a * b); + const completePaddings = [[0, 0]]; + completePaddings.push(...paddings); + for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + const toDispose = []; + const paddedX = padV23({ + inputs: { x }, + backend: backend2, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false); + const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false); + const reshapedPaddedX = reshape5({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } }); + const paddedXT = transpose4({ + inputs: { x: reshapedPaddedX }, + backend: backend2, + attrs: { perm: permutedReshapedPaddedPermutation } + }); + const result = reshape5({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } }); + toDispose.push(paddedX); + toDispose.push(reshapedPaddedX); + toDispose.push(paddedXT); + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return result; +}; +var spaceToBatchNDConfig3 = { + kernelName: SpaceToBatchND, + backendName: "webgpu", + kernelFunc: spaceToBatchND4 +}; +var ScatterProgram2 = class { + constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) { + this.variableNames = ["updates", "indices", "defaultValue"]; + this.workGroupSize = [64, 1, 1]; + this.workPerThread = 4; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + const sliceDimGreaterThanOne = sliceDim > 1; + this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${sliceDimGreaterThanOne}`; + const stridesType = getCoordsDataType2(strides.length); + this.uniforms = `updateSize : i32; sliceDim : i32; strides: ${stridesType};`; + let indicesString = ""; + if (indicesRank === 1) { + indicesString = "i"; + } else if (indicesRank === 2) { + indicesString = "i, j"; + } + this.indicesSnippet = `getIndices(${indicesString})`; + let updatesString = ""; + if (updatesRank === 1) { + updatesString = "i"; + } else if (updatesRank === 2) { + updatesString = "i, coords[1]"; + } + this.updatesSnippet = `getUpdates(${updatesString})`; + this.strideString = sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + + let globalIndex = index * ${this.workPerThread}; + if (globalIndex < uniforms.size) { + var sum = vec4(0.0); + var found = vec4(false); + for (var i = 0; i < uniforms.updateSize; i = i + 1) { + var flattenedIndex = 0; + for (var j = 0; j < uniforms.sliceDim; j = j + 1) { + let indexInside = i32(round(${this.indicesSnippet})); + flattenedIndex = flattenedIndex + indexInside * ${this.strideString}; + } + for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) { + let curIndex = globalIndex + innerIndex; + let coords = getCoordsFromFlatIndex(curIndex); + if (flattenedIndex == coords[0]) { + sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet}; + found[innerIndex] = true; + } + } + } + for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) { + let curIndex = globalIndex + innerIndex; + if (curIndex < uniforms.size) + { + setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex]))); + } + } + } + }`; + return userCode; + } +}; +function sparseToDense4(args) { + const { inputs, backend: backend2, attrs } = args; + const { sparseIndices, sparseValues, defaultValue } = inputs; + const { outputShape } = attrs; + const { sliceRank, numUpdates, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape); + const sumDupeIndices = false; + const uniformData = [ + { type: "int32", data: [numUpdates] }, + { type: "int32", data: [sliceRank] }, + { type: "int32", data: strides } + ]; + const program = new ScatterProgram2(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices); + const res = backend2.runWebGPUProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype, uniformData); + const reshaped = reshape5({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } }); + backend2.disposeData(res.dataId); + return reshaped; +} +var sparseToDenseConfig3 = { + kernelName: SparseToDense, + backendName: "webgpu", + kernelFunc: sparseToDense4 +}; +function splitV3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { numOrSizeSplits, axis } = attrs; + const $axis = util_exports.parseAxisParam(axis, x.shape)[0]; + const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis); + const xRank = x.shape.length; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + return splitSizes.map((s) => { + const sliceSize = [...size]; + sliceSize[$axis] = s; + const sliceT = slice4({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); +} +var splitVConfig3 = { + kernelName: SplitV, + backendName: "webgpu", + kernelFunc: splitV3 +}; +var sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT }); +var sqrtConfig3 = { + kernelName: Sqrt, + backendName: "webgpu", + kernelFunc: sqrt4 +}; +var squareConfig3 = { + kernelName: Square, + backendName: "webgpu", + kernelFunc: ({ inputs, backend: backend2 }) => { + const { x } = inputs; + const webGPUBackend = backend2; + const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE); + return webGPUBackend.runWebGPUProgram(program, [x], x.dtype); + } +}; +var squaredDifference4 = binaryKernelFunc3({ + opSnippet: BinaryOpType.SQUARED_DIFFERENCE +}); +var squaredDifferenceConfig3 = { + kernelName: SquaredDifference, + backendName: "webgpu", + kernelFunc: squaredDifference4 +}; +var StridedSliceProgram2 = class { + constructor(destSize) { + this.variableNames = ["x"]; + this.workPerThread = 1; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = destSize; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + const dtype = getCoordsDataType2(this.outputShape.length); + this.uniforms = `begin : ${dtype}; strides : ${dtype}; `; + this.shaderKey = "stridedSlice"; + } + getUserCode() { + const rank = this.outputShape.length; + let newCoords = ""; + if (rank === 1) { + newCoords = "coords * uniforms.strides + uniforms.begin"; + } else { + let outputAxis = 0; + newCoords = this.outputShape.map((_, i) => { + outputAxis++; + return this.outputShape.length === 1 ? `coords * uniforms.strides[${i}] + uniforms.begin[${i}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i}] + uniforms.begin[${i}]`; + }).join(","); + } + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + setOutputFlat(index, getX(${newCoords})); + } + } + `; + return userCode; + } +}; +function stridedSlice4(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + } = attrs; + const { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: $begin, + end: $end, + strides: $strides + } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + let result; + if (isIdentity) { + result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + } else if (sliceDim0 || isSimpleSlice) { + util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); + const size = slice_util_exports.computeOutShape($begin, $end, $strides); + const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(sliced.dataId); + } else { + const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]); + if (shouldExecuteOnCPU) { + const values = backend2.readSync(x.dataId); + const xBuf = buffer(x.shape, x.dtype, values); + const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin); + result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values); + } else { + const program = new StridedSliceProgram2(finalShapeSparse); + const uniformData = [{ type: "int32", data: $begin }, { type: "int32", data: $strides }]; + const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData); + result = reshape5({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } }); + backend2.disposeData(resultValues.dataId); + } + } + return result; +} +var stridedSliceConfig3 = { + kernelName: StridedSlice, + backendName: "webgpu", + kernelFunc: stridedSlice4 +}; +function stringNGrams4(args) { + const { inputs, backend: backend2, attrs } = args; + const { + separator, + nGramWidths, + leftPad, + rightPad: rightPad2, + padWidth, + preserveShortSequences + } = attrs; + const { data, dataSplits } = inputs; + const $data = backend2.readSync(data.dataId); + const $dataSplits = backend2.readSync(dataSplits.dataId); + const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences); + return [ + backend2.makeTensorInfo([nGrams.length], "string", nGrams), + backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits) + ]; +} +var stringNGramsConfig3 = { + kernelName: StringNGrams, + backendName: "webgpu", + kernelFunc: stringNGrams4 +}; +var tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH }); +var tanhConfig3 = { + kernelName: Tanh, + backendName: "webgpu", + kernelFunc: tanh5 +}; +var TileProgram2 = class { + constructor(aShape, reps) { + this.variableNames = ["A"]; + this.workGroupSize = [64, 1, 1]; + this.size = true; + const outputShape = new Array(aShape.length); + for (let i = 0; i < outputShape.length; i++) { + outputShape[i] = aShape[i] * reps[i]; + } + this.outputShape = outputShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.rank = this.outputShape.length; + this.shaderKey = "tile"; + } + getUserCode() { + const sourceCoords = getSourceCoords5(this.rank, "uniforms."); + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let resRC = getCoordsFromFlatIndex(index); + setOutputFlat(index, getA(${sourceCoords})); + } + } + `; + return userCode; + } +}; +function getSourceCoords5(rank, uniformPrefix = "") { + if (rank >= 5) { + throw Error(`Tile for rank ${rank} is not yet supported`); + } + if (rank === 1) { + return `(resRC % ${uniformPrefix}aShape)`; + } + const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"]; + const sourceCoords = []; + for (let i = 0; i < rank; i++) { + sourceCoords.push(`(${currentCoords[i]} % ${uniformPrefix}aShape[${i}])`); + } + return sourceCoords.join(); +} +function tile5(params) { + const { inputs, backend: backend2, attrs } = params; + const { x } = inputs; + const { reps } = attrs; + if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string" || x.shape.length >= 5) { + const data = backend2.readSync(x.dataId); + const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data; + const buf = buffer(x.shape, x.dtype, value); + const outBuf = tileImplCPU2(buf, reps); + return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + const program = new TileProgram2(x.shape, reps); + const output = backend2.runWebGPUProgram(program, [x], x.dtype); + return output; +} +var tileConfig3 = { + kernelName: Tile, + backendName: "webgpu", + kernelFunc: tile5 +}; +var SwapProgram2 = class { + constructor(shape) { + this.variableNames = ["x", "indices"]; + this.workGroupSize = [256, 1, 1]; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.uniforms = `inputSize : i32; firstPass : i32; negativeInf : f32; + dir : i32; inc : i32;`; + this.shaderKey = "swap"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let outC = getCoordsFromFlatIndex(index); + let batch = outC[0]; + let elemIdx = outC[1]; + // We compare elements pair-wise within a group of size 2 * inc. + // The comparing rule for each group alternates between ascending + // and descending. Within each group, we compare each pair at + // positions i and i+inc. To decide whether an element at position i + // is x0 or x1, we mod it by 2 * inc, if the result is smaller than + // inc, it is in the first half of the group, we denote it as x0, + // otherwise we denote it as x1. + // For example, as shown in the Bitonic top K paper referenced + // above, Figure5(a) shows that element[1] is in the second half of + // the group when group size is 2, but it is in the first half of + // the group when group size is 4. + let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; + var i = 0; + if (isFirstInPair) { + i = elemIdx; + } else { + i = elemIdx - uniforms.inc; + } + + var i0 = 0; + if (uniforms.firstPass == 1) { + i0 = i; + } else { + i0 = i32(getIndices(batch, i)); + } + + var i1 = 0; + if (uniforms.firstPass == 1) { + i1 = i + uniforms.inc; + } else { + i1 = i32(getIndices(batch, i + uniforms.inc)); + } + + var x0 = f32(0.0); + var x1 = f32(0.0); + if (i0 < uniforms.inputSize) { + x0 = getX(batch, i0); + } else { + x0 = uniforms.negativeInf; + } + if (i1 < uniforms.inputSize) { + x1 = getX(batch, i1); + } else { + x1 = uniforms.negativeInf; + } + + let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; + let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); + if (reverse == isGreater) { + // Elements in opposite order of direction + let iTemp = i0; + i0 = i1; + i1 = iTemp; + } + if (isFirstInPair) { + setOutputFlat(index, f32(i0)); + } else { + setOutputFlat(index, f32(i1)); + } + } + } + `; + return userCode; + } +}; +var MergeProgram2 = class { + constructor(shape) { + this.variableNames = ["x", "indices"]; + this.workGroupSize = [256, 1, 1]; + this.size = true; + this.outputShape = shape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.uniforms = `inputSize : i32; firstPass : i32; k : i32;`; + this.shaderKey = "merge"; + } + getUserCode() { + const userCode = ` + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let outC = getCoordsFromFlatIndex(index); + let batch = outC[0]; + let elemIdx = outC[1]; + // The output size is half of the previous size. + // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ + // (k=4), we only need to output the indices at positions |, the + // indices at positions _ can be thrown away, see Figure5(b) After + // Phase 2 (Merge phase) in the Bitonic Top K paper referenced + // above. + // For example, the paper shows we only need to output the orange + // bars. The output sequence should look like this | | | | | | | |. + // Because the sequence is halved, to map the output index back to + // the previous sequence to find the corresponding value, we need + // to double the index. When we double the index, we basically + // interpolate a position, so 2i looks like + // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k + // position of each 2k positions by - elemIdx % k. E.g. for output + // at index 4,5,6,7, we want to get the corresponding element at + // original index 8,9,10,11, for output at index 8,9,10,11, + // we want to get the corresponding element at original index + // 16,17,18,19, so on and so forth. + + var i = 0; + if (elemIdx < uniforms.k) { + i = elemIdx; + } else { + i = elemIdx * 2 - elemIdx % uniforms.k; + } + var i0 = 0; + if (uniforms.firstPass == 1) { + i0 = i; + } else { + i0 = i32(getIndices(batch, i)); + } + var i1 = 0; + if (uniforms.firstPass == 1) { + i1 = i + uniforms.k; + } else { + i1 = i32(getIndices(batch, i + uniforms.k)); + } + + let x0 = getX(batch, i0); + var x1 = f32(0.0); + if (i1 < uniforms.inputSize) { + x1 = getX(batch, i1); + } else { + x1 = x0; + } + + if (x0 >= x1) { + setOutputFlat(index, f32(i0)); + } else { + setOutputFlat(index, f32(i1)); + } + } + } + `; + return userCode; + } +}; +function disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) { + if (tensorInfo !== null) { + backend2.disposeData(tensorInfo.dataId); + } +} +function roundUpToPow22(num) { + let pow22 = 1; + while (pow22 < num) { + pow22 *= 2; + } + return pow22; +} +function topK3(args) { + const { inputs, backend: backend2, attrs } = args; + const { x } = inputs; + const { k, sorted } = attrs; + const xShape = x.shape; + const lastDim = xShape[xShape.length - 1]; + if (backend2.shouldExecuteOnCPU([x])) { + const xVals = backend2.readSync(x.dataId); + const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted); + return [ + backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; + } + if (k === 0) { + xShape[xShape.length - 1] = 0; + return [ + backend2.makeTensorInfo(xShape, x.dtype, []), + backend2.makeTensorInfo(xShape, "int32", []) + ]; + } + if (lastDim === 1) { + return [ + x, + fill4({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 }) + ]; + } + const xSize = util_exports.sizeFromShape(xShape); + const batch = xSize / lastDim; + const x2D = reshape5({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 }); + const kPow2 = roundUpToPow22(k); + const lastDimPow2 = roundUpToPow22(lastDim); + let indices = null; + const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices]; + const runSwap = (dir, inc, shape) => { + const inputs2 = getInputs(); + const program = new SwapProgram2(shape); + const firstPass = indices === null ? 1 : 0; + const uniformDataSwap = [ + { type: "int32", data: [lastDim] }, + { type: "int32", data: [firstPass] }, + { type: "float32", data: [Number.NEGATIVE_INFINITY] }, + { type: "int32", data: [dir] }, + { type: "int32", data: [inc] } + ]; + const prevIndices2 = indices; + indices = backend2.runWebGPUProgram(program, inputs2, "int32", uniformDataSwap); + disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2); + }; + for (let len = 1; len < kPow2; len *= 2) { + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, [batch, lastDimPow2]); + } + } + for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) { + const inputs2 = getInputs(); + const mergeProgram = new MergeProgram2([batch, indicesSize / 2]); + const firstPass = indices === null ? 1 : 0; + const uniformDataMerge = [ + { type: "int32", data: [lastDim] }, + { type: "int32", data: [firstPass] }, + { type: "int32", data: [kPow2] } + ]; + const prevIndices2 = indices; + indices = backend2.runWebGPUProgram(mergeProgram, inputs2, "int32", uniformDataMerge); + disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2); + const len = kPow2 / 2; + const dir = len * 2; + for (let inc = len; inc >= 1; inc /= 2) { + runSwap(dir, inc, indices.shape); + } + } + let prevIndices = indices; + indices = slice4({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } }); + disposeIntermediateTensorInfoOrNull2(backend2, prevIndices); + let values = gatherV23({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } }); + disposeIntermediateTensorInfoOrNull2(backend2, x2D); + const newShape = xShape.slice(0, -1); + newShape.push(k); + prevIndices = indices; + indices = reshape5({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull2(backend2, prevIndices); + const prevValues = values; + values = reshape5({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 }); + disposeIntermediateTensorInfoOrNull2(backend2, prevValues); + return [values, indices]; +} +var topKConfig3 = { + kernelName: TopK, + backendName: "webgpu", + kernelFunc: topK3 +}; +var TransformProgram2 = class { + constructor(outShape) { + this.variableNames = ["Image", "Transforms"]; + this.uniforms = "interpolationModeId : i32; fillModeId : i32; fillValue : f32;"; + this.workGroupSize = [64, 1, 1]; + this.size = true; + this.outputShape = outShape; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize); + this.shaderKey = "transform"; + } + getUserCode() { + const userCode = ` + fn mapCoord(outCoord : f32, len : f32) -> f32{ + var inCoord = outCoord; + if(uniforms.fillModeId == 2) { + if (inCoord < 0.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + let sz2 = 2.0 * len; + if (inCoord < sz2) { + inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) + + inCoord; + } + if (inCoord < -len) { + inCoord = inCoord + sz2; + } else { + inCoord = -inCoord - 1.0; + } + } + } elseif (inCoord > len - 1.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + let sz2 = 2.0 * len; + inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2))); + if (inCoord >= len) { + inCoord = sz2 - inCoord - 1.0; + } + } + } + return clamp(inCoord, 0.0, len - 1.0); + } elseif (uniforms.fillModeId == 3) { + if (inCoord < 0.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + let sz = len - 1.0; + inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0); + } + } elseif (inCoord > len - 1.0) { + if (len <= 1.0) { + inCoord = 0.0; + } else { + let sz = len - 1.0; + inCoord = inCoord - len * f32(i32(f32(inCoord / sz))); + } + } + return clamp(inCoord, 0.0, len - 1.0); + } elseif (uniforms.fillModeId == 4) { + return clamp(outCoord, 0.0, len - 1.0); + } + return outCoord; + } + fn readWithFillValue(batch : i32, coordY : i32, coordX : i32, + channel : i32) -> f32 { + var outputValue : f32; + if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) { + outputValue = getImage(batch, coordY, coordX, channel); + } else { + outputValue = uniforms.fillValue; + } + return outputValue; + } + + ${getMainHeaderAndGlobalIndexString()} + if (index < uniforms.size) { + let coords = getCoordsFromFlatIndex(index); + var outputValue : f32; + let batch = coords[0]; + let x = coords[2]; + let y = coords[1]; + let channel = coords[3]; + let xf = f32(x); + let yf = f32(y); + let a1 = getTransforms(batch, 0); + let a2 = getTransforms(batch, 1); + let a3 = getTransforms(batch, 2); + let b1 = getTransforms(batch, 3); + let b2 = getTransforms(batch, 4); + let b3 = getTransforms(batch, 5); + let c1 = getTransforms(batch, 6); + let c2 = getTransforms(batch, 7); + let projection = c1 * xf + c2 * yf + 1.0; + if (projection == 0.0) { + outputValue = uniforms.fillValue; + } else { + let inX = (a1 * xf + a2 * yf + a3) / projection; + let inY = (b1 * xf + b2 * yf + b3) / projection; + let mapX = mapCoord(inX, f32(uniforms.imageShape[2])); + let mapY = mapCoord(inY, f32(uniforms.imageShape[1])); + + if (uniforms.interpolationModeId == 1) { + let coordY = i32(round(mapY)); + let coordX = i32(round(mapX)); + outputValue = readWithFillValue(batch, coordY, coordX, + channel); + } else { + let yFloor = floor(mapY); + let xFloor = floor(mapX); + let yCeil = yFloor + 1.0; + let xCeil = xFloor + 1.0; + let valueYFloor = (xCeil - mapX) * + readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) + + (mapX - xFloor) * + readWithFillValue(batch, i32(yFloor), i32(xCeil), channel); + let valueYCeil = (xCeil - mapX) * + readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) + + (mapX - xFloor) * + readWithFillValue(batch, i32(yCeil), i32(xCeil), channel); + outputValue = (yCeil - mapY) * valueYFloor + + (mapY - yFloor) * valueYCeil; + } + } + setOutputFlat(index, outputValue); + } + } + `; + return userCode; + } +}; +function transform4(args) { + const { inputs, backend: backend2, attrs } = args; + const { image: image3, transforms } = inputs; + const { interpolation, fillMode, fillValue, outputShape } = attrs; + const [batch, imageHeight, imageWidth, numChannels] = image3.shape; + const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth]; + const outShape = [ + batch, + outHeight, + outWidth, + numChannels + ]; + const program = new TransformProgram2(outShape); + const interpolationModeId = interpolation === "nearest" ? 1 : 2; + let fillModeId; + switch (fillMode) { + case "constant": + fillModeId = 1; + break; + case "reflect": + fillModeId = 2; + break; + case "wrap": + fillModeId = 3; + break; + case "nearest": + fillModeId = 4; + break; + default: + fillModeId = 1; + break; + } + const uniformData = [ + { type: "int32", data: [interpolationModeId] }, + { type: "int32", data: [fillModeId] }, + { type: "float32", data: [fillValue] } + ]; + return backend2.runWebGPUProgram(program, [image3, transforms], "float32", uniformData); +} +var transformConfig3 = { + kernelName: Transform, + backendName: "webgpu", + kernelFunc: transform4 +}; +function unpack3(args) { + const { inputs, backend: backend2, attrs } = args; + const { value } = inputs; + let { axis } = attrs; + if (axis < 0) { + axis += value.shape.length; + } + const x = value; + const xRank = x.shape.length; + const num = value.shape[axis]; + const outShape = new Array(xRank - 1); + let outIndex = 0; + for (let i = 0; i < xRank; i++) { + if (i !== axis) { + outShape[outIndex++] = x.shape[i]; + } + } + const toDispose = []; + const begin = new Array(xRank).fill(0); + const size = x.shape.slice(); + size[axis] = 1; + const res = new Array(num); + for (let i = 0; i < res.length; i++) { + begin[axis] = i; + const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin, size } }); + const reshaped = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } }); + res[i] = reshaped; + toDispose.push(sliced); + } + toDispose.forEach((t) => backend2.disposeData(t.dataId)); + return res; +} +var unpackConfig3 = { + kernelName: Unpack, + backendName: "webgpu", + kernelFunc: unpack3 +}; +var kernelConfigs3 = [ + _fusedMatMulConfig3, + absConfig3, + addConfig3, + addNConfig3, + argMaxConfig3, + argMinConfig3, + avgPoolConfig3, + batchMatMulConfig3, + batchToSpaceNDConfig3, + castConfig3, + ceilConfig3, + clipByValueConfig2, + complexConfig3, + concatConfig3, + conv2DConfig3, + conv2DBackpropInputConfig3, + cosConfig3, + coshConfig3, + cropAndResizeConfig3, + depthToSpaceConfig3, + depthwiseConv2dNativeConfig3, + einsumConfig3, + eluConfig3, + equalConfig3, + expandDimsConfig3, + expConfig3, + expm1Config3, + fillConfig3, + flipLeftRightConfig3, + fromPixelsConfig2, + floorConfig3, + floorDivConfig3, + fusedBatchNormConfig, + fusedConv2DConfig3, + fusedDepthwiseConv2DConfig3, + gatherNdConfig3, + gatherV2Config3, + greaterConfig3, + greaterEqualConfig3, + identityConfig3, + imagConfig3, + lessConfig3, + lessEqualConfig3, + logConfig3, + logicalAndConfig3, + logicalNotConfig3, + maxConfig3, + maximumConfig3, + maxPoolConfig3, + meanConfig3, + minConfig3, + minimumConfig3, + mirrorPadConfig3, + multiplyConfig3, + negConfig3, + nonMaxSuppressionV3Config3, + nonMaxSuppressionV5Config3, + notEqualConfig3, + onesLikeConfig3, + packConfig3, + padV2Config3, + preluConfig3, + prodConfig3, + powConfig3, + rangeConfig3, + realConfig3, + realDivConfig3, + reluConfig3, + relu6Config3, + reshapeConfig3, + resizeBilinearConfig3, + resizeNearestNeighborConfig3, + rotateWithOffsetConfig3, + rsqrtConfig3, + scatterNdConfig3, + selectConfig3, + sigmoidConfig3, + sinConfig3, + sinhConfig3, + sliceConfig3, + stridedSliceConfig3, + stringNGramsConfig3, + softmaxConfig3, + spaceToBatchNDConfig3, + splitVConfig3, + sparseToDenseConfig3, + sqrtConfig3, + squareConfig3, + squaredDifferenceConfig3, + subConfig3, + sumConfig3, + tanhConfig3, + tileConfig3, + topKConfig3, + transformConfig3, + transposeConfig3, + unpackConfig3, + zerosLikeConfig3 +]; +for (const kernelConfig of kernelConfigs3) { + registerKernel(kernelConfig); +} +var BufferManager = class { + constructor(device) { + this.device = device; + this.numUsedBuffers = 0; + this.numFreeBuffers = 0; + this.freeBuffers = new Map(); + this.usedBuffers = new Map(); + this.numBytesUsed = 0; + this.numBytesAllocated = 0; + } + acquireBuffer(byteSize, usage) { + const key = getBufferKey(byteSize, usage); + if (!this.freeBuffers.has(key)) { + this.freeBuffers.set(key, []); + } + if (!this.usedBuffers.has(key)) { + this.usedBuffers.set(key, []); + } + this.numBytesUsed += byteSize; + this.numUsedBuffers++; + if (this.freeBuffers.get(key).length > 0) { + this.numFreeBuffers--; + const newBuffer2 = this.freeBuffers.get(key).shift(); + this.usedBuffers.get(key).push(newBuffer2); + return newBuffer2; + } + this.numBytesAllocated += byteSize; + const newBuffer = this.device.createBuffer({ size: byteSize, usage }); + this.usedBuffers.get(key).push(newBuffer); + return newBuffer; + } + releaseBuffer(buffer2, byteSize, usage) { + if (this.freeBuffers == null) { + return; + } + const key = getBufferKey(byteSize, usage); + if (!this.freeBuffers.has(key)) { + this.freeBuffers.set(key, []); + } + this.freeBuffers.get(key).push(buffer2); + this.numFreeBuffers++; + this.numUsedBuffers--; + const bufferList = this.usedBuffers.get(key); + const bufferIndex = bufferList.indexOf(buffer2); + if (bufferIndex < 0) { + throw new Error("Cannot release a buffer that was never provided by this buffer manager"); + } + bufferList.splice(bufferIndex, 1); + this.numBytesUsed -= byteSize; + } + getNumUsedBuffers() { + return this.numUsedBuffers; + } + getNumFreeBuffers() { + return this.numFreeBuffers; + } + reset() { + this.freeBuffers = new Map(); + this.usedBuffers = new Map(); + this.numUsedBuffers = 0; + this.numFreeBuffers = 0; + this.numBytesUsed = 0; + this.numBytesAllocated = 0; + } + dispose() { + if (this.freeBuffers == null && this.usedBuffers == null) { + return; + } + this.freeBuffers.forEach((buffers, key) => { + buffers.forEach((buff) => { + buff.destroy(); + }); + }); + this.usedBuffers.forEach((buffers, key) => { + buffers.forEach((buff) => { + buff.destroy(); + }); + }); + this.freeBuffers = null; + this.usedBuffers = null; + this.numUsedBuffers = 0; + this.numFreeBuffers = 0; + this.numBytesUsed = 0; + this.numBytesAllocated = 0; + } +}; +function getBufferKey(byteSize, usage) { + return `${byteSize}_${usage}`; +} +var FromPixelsProgram2 = class { + constructor() { + this.outputShape = [0]; + this.variableNames = []; + this.workGroupSize = [256, 1, 1]; + this.lastUniformData = []; + this.inputTexture = null; + this.layout = null; + this.lastPixelSize = { width: 0, height: 0 }; + this.disposed = false; + this.shaderKey = "fromPixels"; + this.useImport = false; + } + updateOutputShape(outputShape) { + if (util_exports.arraysEqual(this.outputShape, outputShape)) { + return; + } + this.outputShape = outputShape; + this.workPerThread = outputShape[2]; + this.dispatchLayout = flatDispatchLayout(this.outputShape); + this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]); + } + makeFromPixelsSource() { + const textureLoad = this.useImport ? "textureLoad(src, vec2(coords.yx));" : "textureLoad(src, vec2(coords.yx), 0)"; + const textureType = this.useImport ? "texture_external" : "texture_2d"; + return ` + [[binding(1), group(0)]] var src: ${textureType}; + + ${getMainHeaderAndGlobalIndexString()} + let flatIndexBase = index * uniforms.numChannels; + for (var i = 0; i < uniforms.numChannels; i = i + 1) { + let flatIndex = flatIndexBase + i; + if (flatIndex < uniforms.size) { + let coords = getCoordsFromFlatIndex(flatIndexBase); + let values = ${textureLoad}; + result.numbers[flatIndex] = i32(floor(255.0 * values[i])); + } + } + } + `; + } + getUserCode() { + return this.makeFromPixelsSource(); + } + setPipeline(pipeline) { + this.pipeline = pipeline; + } + setUniform(device, uniformData) { + if (!this.uniform) { + const uniformBuffer = device.createBuffer({ + size: uniformData.length * 4, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST + }); + this.uniform = uniformBuffer; + } + if (!uniformData || uniformData.length === this.lastUniformData.length && uniformData.every((v, i) => v === this.lastUniformData[i])) { + return; + } + device.queue.writeBuffer(this.uniform, 0, new Uint32Array(uniformData)); + this.lastUniformData = uniformData; + } + makeInputTexture(device, pixelWidth, pixelHeight) { + if (!this.inputTexture || this.lastPixelSize.width !== pixelWidth || this.lastPixelSize.height !== pixelHeight) { + if (this.inputTexture) { + this.inputTexture.destroy(); + } + this.inputTexture = device.createTexture({ + size: [pixelWidth, pixelHeight], + format: "rgba8unorm", + usage: GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING + }); + this.lastPixelSize.width = pixelWidth; + this.lastPixelSize.height = pixelHeight; + } + return this.inputTexture; + } + dispose() { + if (this.disposed) { + return; + } + if (this.uniform) { + this.uniform.destroy(); + } + if (this.inputTexture) { + this.inputTexture.destroy(); + } + this.disposed = true; + } + getLayout(device) { + if (this.layout === null) { + this.layout = this.createTextureLayout(device); + } + return this.layout; + } + createTextureLayout(device) { + const bindGroupLayoutEntries = []; + bindGroupLayoutEntries.push({ + binding: 0, + visibility: GPUShaderStage.COMPUTE, + buffer: { type: "storage" } + }); + bindGroupLayoutEntries.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} }); + bindGroupLayoutEntries.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} }); + const fromPixelBindGroupLayout = device.createBindGroupLayout({ entries: bindGroupLayoutEntries }); + const fromPixelPipelineLayout = device.createPipelineLayout({ bindGroupLayouts: [fromPixelBindGroupLayout] }); + return { + bindGroupLayout: fromPixelBindGroupLayout, + pipelineLayout: fromPixelPipelineLayout + }; + } +}; +var FromPixelsImportProgram = class extends FromPixelsProgram2 { + constructor() { + super(...arguments); + this.layout = null; + this.useImport = true; + } + getUserCode() { + return this.makeFromPixelsSource(); + } + getLayout(device) { + if (this.layout === null) { + this.layout = this.createTextureImportLayout(device); + } + return this.layout; + } + createTextureImportLayout(device) { + const bindGroupLayoutEntries = []; + bindGroupLayoutEntries.push({ + binding: 0, + visibility: GPUShaderStage.COMPUTE, + buffer: { type: "storage" } + }); + bindGroupLayoutEntries.push({ + binding: 1, + visibility: GPUShaderStage.COMPUTE, + externalTexture: {} + }); + bindGroupLayoutEntries.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} }); + const fromPixelImportBindGroupLayout = device.createBindGroupLayout({ entries: bindGroupLayoutEntries }); + const fromPixelImportPipelineLayout = device.createPipelineLayout({ bindGroupLayouts: [fromPixelImportBindGroupLayout] }); + return { + bindGroupLayout: fromPixelImportBindGroupLayout, + pipelineLayout: fromPixelImportPipelineLayout + }; + } +}; +var CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"); +var _WebGPUBackend = class extends KernelBackend { + constructor(device, supportTimeQuery = false) { + super(); + this.commandQueueOwnedIds = new WeakSet(); + this.tensorDisposalQueue = []; + this.uniformDisposalQueue = []; + this.disposed = false; + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + this.dispatchNumberInEncoder = 0; + if (!isWebGPUSupported()) { + throw new Error("WebGPU is not supported on this device"); + } + this.layoutCache = {}; + this.pipelineCache = {}; + this.device = device; + this.queue = device.queue; + this.currentCommandEncoder = null; + this.currentComputePass = null; + this.supportTimeQuery = supportTimeQuery; + this.bufferManager = new BufferManager(this.device); + this.tensorMap = new DataStorage(this, engine()); + if (this.supportTimeQuery) { + this.querySet = this.device.createQuerySet({ + type: "timestamp", + count: 2 + }); + } + if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) { + this.dummyCanvas = document.createElement("canvas"); + this.dummyCanvas.width = 1; + this.dummyCanvas.height = 1; + this.dummyContext = this.dummyCanvas.getContext("webgpu"); + this.dummyContext.configure({ + device, + format: "bgra8unorm" + }); + document.body.appendChild(this.dummyCanvas); + } + } + nextDataId() { + return _WebGPUBackend.nextDataId++; + } + floatPrecision() { + return 32; + } + defaultGpuBufferUsage() { + return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST; + } + flushDisposalQueue() { + this.tensorDisposalQueue.forEach((d) => { + this.maybeReleaseBuffer(d); + this.tensorMap.delete(d); + }); + this.uniformDisposalQueue.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.byteSize, d.usage)); + this.tensorDisposalQueue = []; + this.uniformDisposalQueue = []; + } + disposeData(dataId, force = false) { + if (this.tensorMap.has(dataId)) { + const data = this.tensorMap.get(dataId); + data.refCount--; + if (!force && data.refCount > 0) { + return false; + } + if (this.commandQueueOwnedIds.has(dataId)) { + this.tensorDisposalQueue.push(dataId); + return false; + } else { + this.maybeReleaseBuffer(dataId); + } + const { complexTensorInfos } = this.tensorMap.get(dataId); + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, true); + this.disposeData(complexTensorInfos.imag.dataId, true); + } + this.tensorMap.delete(dataId); + } + return true; + } + memory() { + return { + numBytesInGPU: this.bufferManager.numBytesUsed, + numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, + unreliable: false + }; + } + getBufferManager() { + return this.bufferManager; + } + acquireBuffer(byteSize, usage = this.defaultGpuBufferUsage()) { + return this.bufferManager.acquireBuffer(byteSize, usage); + } + maybeReleaseBuffer(dataId) { + const info = this.tensorMap.get(dataId); + if (info != null && info.bufferInfo.buffer != null) { + this.bufferManager.releaseBuffer(info.bufferInfo.buffer, info.bufferInfo.byteSize, info.bufferInfo.usage); + info.bufferInfo.buffer = null; + } + } + refCount(dataId) { + if (this.tensorMap.has(dataId)) { + const tensorData = this.tensorMap.get(dataId); + return tensorData.refCount; + } + return 0; + } + incRef(dataId) { + const tensorData = this.tensorMap.get(dataId); + tensorData.refCount++; + } + decRef(dataId) { + if (this.tensorMap.has(dataId)) { + const tensorData = this.tensorMap.get(dataId); + tensorData.refCount--; + } + } + write(values, shape, dtype) { + if (dtype === "complex64" && values != null) { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + const dataId = { id: this.nextDataId() }; + const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype); + if (dtype === "bool" && values instanceof Uint8Array) { + values = Int32Array.from(values); + } + this.tensorMap.set(dataId, { + dtype, + values, + bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() }, + refCount: 1 + }); + return dataId; + } + move(dataId, values, shape, dtype, refCount) { + if (dtype === "complex64") { + throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`); + } + const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype); + this.tensorMap.set(dataId, { + dtype, + values, + bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() }, + refCount + }); + } + submitQueue() { + this.ensureComputePassEnded(); + this.queue.submit([this.currentCommandEncoder.finish()]); + this.currentCommandEncoder = null; + this.dispatchNumberInEncoder = 0; + this.commandQueueOwnedIds = new WeakSet(); + this.flushDisposalQueue(); + } + getBuffer(dataId) { + this.uploadToGPU(dataId); + return this.tensorMap.get(dataId).bufferInfo.buffer; + } + getFromPixelsProgram(type) { + switch (type) { + case "copyExternal": { + if (!this.fromPixelProgram) { + this.fromPixelProgram = new FromPixelsProgram2(); + } + return this.fromPixelProgram; + } + case "import": { + if (!this.fromPixelImportProgram) { + this.fromPixelImportProgram = new FromPixelsImportProgram(); + } + return this.fromPixelImportProgram; + } + default: + util_exports.assert(false, () => `Unsupported fromPixels shape`); + return void 0; + } + } + ensureCommandEncoderReady() { + if (!this.currentCommandEncoder) { + this.currentCommandEncoder = this.device.createCommandEncoder(); + } + } + ensureComputePassEnded() { + if (this.currentComputePass) { + this.currentComputePass.endPass(); + this.currentComputePass = null; + } + } + getComputePass() { + if (!this.currentComputePass) { + this.currentComputePass = this.currentCommandEncoder.beginComputePass(); + } + return this.currentComputePass; + } + async getBufferData(info) { + if (info.values != null) { + return info.values; + } + const staging = this.acquireBuffer(info.bufferInfo.byteSize, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); + this.ensureCommandEncoderReady(); + this.ensureComputePassEnded(); + this.currentCommandEncoder.copyBufferToBuffer(info.bufferInfo.buffer, 0, staging, 0, info.bufferInfo.byteSize); + this.submitQueue(); + await staging.mapAsync(GPUMapMode.READ); + const values = staging.getMappedRange().slice(0); + staging.unmap(); + if (staging != null) { + this.bufferManager.releaseBuffer(staging, info.bufferInfo.byteSize, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ); + } + if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) { + util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`); + this.dummyContext.getCurrentTexture(); + } + return values; + } + convertAndCacheOnCPU(dataId, data) { + const info = this.tensorMap.get(dataId); + this.maybeReleaseBuffer(dataId); + info.values = data; + return info.values; + } + readSync(dataId) { + const texData = this.tensorMap.get(dataId); + const { values } = texData; + if (values == null) { + throw new Error("WebGPU readSync is only available for CPU-resident tensors."); + } + return values; + } + async read(dataId) { + if (!this.tensorMap.has(dataId)) { + throw new Error(`Tensor ${dataId} was not registered!`); + } + const info = this.tensorMap.get(dataId); + const { values } = info; + if (values != null) { + return this.convertAndCacheOnCPU(dataId, values); + } + let vals; + if (info.dtype === "complex64") { + const ps = await Promise.all([ + this.read(info.complexTensorInfos.real.dataId), + this.read(info.complexTensorInfos.imag.dataId) + ]); + const realValues = ps[0]; + const imagValues = ps[1]; + vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues); + } else { + const data = await this.getBufferData(info); + vals = ArrayBufferToTypedArray(data, info.dtype); + } + this.convertAndCacheOnCPU(dataId, vals); + return vals; + } + bufferSync(t) { + const data = this.readSync(t.dataId); + let decodedData = data; + if (t.dtype === "string") { + try { + decodedData = data.map((d) => util_exports.decodeString(d)); + } catch (e) { + throw new Error("Failed to decode encoded string bytes into utf-8"); + } + } + return buffer(t.shape, t.dtype, decodedData); + } + async time(f) { + const oldActiveTimers = this.activeTimers; + const newActiveTimers = []; + let outerMostTime = false; + if (this.programTimersStack == null) { + this.programTimersStack = newActiveTimers; + outerMostTime = true; + } else { + this.activeTimers.push(newActiveTimers); + } + this.activeTimers = newActiveTimers; + f(); + const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null); + const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null); + this.activeTimers = oldActiveTimers; + if (outerMostTime) { + this.programTimersStack = null; + } + const res = { + uploadWaitMs: this.uploadWaitMs, + downloadWaitMs: this.downloadWaitMs, + kernelMs: null, + wallMs: null + }; + const kernelMs = await Promise.all(flattenedActiveTimerQueries); + res["kernelMs"] = util_exports.sum(kernelMs); + res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", "); + this.uploadWaitMs = 0; + this.downloadWaitMs = 0; + return res; + } + getAndSavePipeline(key, getPipeline) { + if (!(key in this.pipelineCache)) { + this.pipelineCache[key] = getPipeline(); + } + return this.pipelineCache[key]; + } + makeTensorInfo(shape, dtype, values) { + let dataId; + if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) { + const encodedValues = values.map((d) => util_exports.encodeString(d)); + dataId = this.write(encodedValues, shape, dtype); + } else { + dataId = this.write(values, shape, dtype); + } + return { dataId, shape, dtype }; + } + tensorToBinding(tensor2) { + if (!tensor2) { + return null; + } + const tensorData = this.tensorMap.get(tensor2.dataId); + return { + offset: 0, + size: tensorData.bufferInfo.byteSize, + buffer: tensorData.bufferInfo.buffer + }; + } + async getQueryTime(query) { + if (this.supportTimeQuery) { + return this.getTimeFromQuerySet(query); + } else { + return 0; + } + } + uploadToGPU(dataId) { + const info = this.tensorMap.get(dataId); + if (info.bufferInfo.buffer != null) { + return; + } + info.bufferInfo.buffer = this.acquireBuffer(info.bufferInfo.byteSize); + if (info.values) { + this.queue.writeBuffer(info.bufferInfo.buffer, 0, info.values); + } + } + makeUniformsDataView(data) { + const dimensionsBuffer = this.acquireBuffer(data.byteLength, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM); + this.queue.writeBuffer(dimensionsBuffer, 0, data); + return { offset: 0, size: data.byteLength, buffer: dimensionsBuffer }; + } + arrayToDataView(arrays, length) { + const BYTES_PER_ELEMENT = 4; + const uniformDataView = new DataView(new ArrayBuffer(length * BYTES_PER_ELEMENT)); + let dataViewIndex = 0; + arrays.forEach((array2) => { + const arrayData = array2.data; + if (array2.type !== "int32" && array2.type !== "float32" && array2.type !== "uint32") { + throw new Error(`${array2.type} not supported!`); + } + if (array2.type === "int32") { + arrayData.forEach((d) => { + uniformDataView.setInt32(dataViewIndex * BYTES_PER_ELEMENT, d, true); + dataViewIndex++; + }); + } else if (array2.type === "uint32") { + arrayData.forEach((d) => { + uniformDataView.setUint32(dataViewIndex * BYTES_PER_ELEMENT, d, true); + dataViewIndex++; + }); + } else { + arrayData.forEach((d) => { + uniformDataView.setFloat32(dataViewIndex * BYTES_PER_ELEMENT, d, true); + dataViewIndex++; + }); + } + }); + return uniformDataView; + } + computePadding(uniformsWithType) { + let currentOffset = 0; + let padding = 0; + let dataViewIndex = 0; + const dimUniformsData = []; + uniformsWithType.forEach((d, i) => { + if (d.data.length === 0) { + d.data = [1]; + } + let baseAlignment; + switch (d.data.length) { + case 0: + baseAlignment = 1; + break; + case 1: + baseAlignment = 1; + break; + case 2: + baseAlignment = 2; + break; + case 3: + baseAlignment = 4; + break; + case 4: + baseAlignment = 4; + break; + default: + util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`); + } + padding = Math.ceil(currentOffset / baseAlignment) * baseAlignment - currentOffset; + for (let p2 = 0; p2 < padding; ++p2) { + dimUniformsData.push({ type: d.type, data: [0] }); + dataViewIndex++; + } + dimUniformsData.push({ type: d.type, data: d.data }); + dataViewIndex = dataViewIndex + d.data.length; + currentOffset += d.data.length + padding; + }); + return this.arrayToDataView(dimUniformsData, dataViewIndex); + } + createLayout(inputEntrySize) { + const bindGroupLayoutEntries = []; + bindGroupLayoutEntries.push({ + binding: 0, + visibility: GPUShaderStage.COMPUTE, + buffer: { type: "storage" } + }); + for (let i = 0; i < inputEntrySize; i++) { + bindGroupLayoutEntries.push({ + binding: i + 1, + visibility: GPUShaderStage.COMPUTE, + buffer: { type: "read-only-storage" } + }); + } + bindGroupLayoutEntries.push({ + binding: inputEntrySize + 1, + visibility: GPUShaderStage.COMPUTE, + buffer: { type: "uniform" } + }); + const bindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries }); + const pipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [bindGroupLayout] }); + return { bindGroupLayout, pipelineLayout }; + } + getCachedOrCreateLayout(inputEntrySize) { + if (!(inputEntrySize in this.layoutCache)) { + this.layoutCache[inputEntrySize] = this.createLayout(inputEntrySize); + } + return this.layoutCache[inputEntrySize]; + } + runWebGPUProgram(program, inputs, outputDtype, programUniforms, output) { + if (!output) { + output = this.makeTensorInfo(program.outputShape, outputDtype); + if (util_exports.sizeFromShape(output.shape) === 0) { + const outData = this.tensorMap.get(output.dataId); + outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0); + return output; + } + this.uploadToGPU(output.dataId); + } + let uniformsWithType = [{ type: "float32", data: [NaN] }]; + const bufferShapes = inputs.concat(output).map((d) => d.shape); + const uniformsType = "int32"; + bufferShapes.map((d) => { + uniformsWithType.push({ type: uniformsType, data: d }); + }); + const strides = util_exports.computeStrides(output.shape); + uniformsWithType.push({ type: uniformsType, data: strides }); + if (program.size) { + const size = util_exports.sizeFromShape(program.outputShape); + uniformsWithType.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] }); + } + if (programUniforms) { + uniformsWithType = [...uniformsWithType, ...programUniforms]; + } + let uniforms = null; + const uniformsDataView = this.computePadding(uniformsWithType); + const uniformsByteLength = uniformsDataView.byteLength; + uniforms = this.makeUniformsDataView(uniformsDataView); + const inputsData = inputs.map((input2, i) => { + if (input2.dtype === "complex64") { + throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`); + } + this.uploadToGPU(input2.dataId); + return { + dtype: this.tensorMap.get(input2.dataId).dtype, + shape: input2.shape, + name: program.variableNames[i] + }; + }); + const bufferTypes = inputsData.map((d) => d.dtype).concat(output.dtype); + const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape)); + const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join("_"); + const broadcastDimsKey = broadcastDims.map((d) => d.join("_")).join(";"); + const key = makeShaderKey2(program, bufferShapes, bufferTypes, broadcastDimsKey, inputShapesEqualsOutShape); + const { bindGroupLayout, pipelineLayout } = this.getCachedOrCreateLayout(program.variableNames.length); + const pipeline = this.getAndSavePipeline(key, () => { + return compileProgram2(this.device, program, pipelineLayout, inputsData, output); + }); + const shouldTimeProgram = this.activeTimers != null; + const bg = makeBindGroup(this.device, bindGroupLayout, inputs.map((t) => this.tensorToBinding(t)), this.tensorToBinding(output), uniforms); + this.ensureCommandEncoderReady(); + const pass = this.getComputePass(); + if (shouldTimeProgram) { + if (this.supportTimeQuery) { + pass.writeTimestamp(this.querySet, 0); + } + } + pass.setPipeline(pipeline); + pass.setBindGroup(0, bg); + pass.dispatch(program.dispatch[0], program.dispatch[1], program.dispatch[2]); + if (shouldTimeProgram) { + if (this.supportTimeQuery) { + pass.writeTimestamp(this.querySet, 1); + } + } + this.dispatchNumberInEncoder++; + inputs.forEach((input2) => { + this.commandQueueOwnedIds.add(input2.dataId); + }); + this.commandQueueOwnedIds.add(output.dataId); + if (uniforms) { + const uniformInfo = { + byteSize: uniformsByteLength, + usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, + buffer: uniforms.buffer + }; + this.uniformDisposalQueue.push(uniformInfo); + } + if (env().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder) { + this.submitQueue(); + } + if (shouldTimeProgram) { + this.activeTimers.push({ + name: program.constructor.name, + query: this.getQueryTime(this.querySet) + }); + } + return output; + } + runFromPixelsProgram(program, output, layout, externalResource, outputId) { + const bindGroup = this.device.createBindGroup({ + layout: layout.bindGroupLayout, + entries: [ + { + binding: 0, + resource: { + buffer: output + } + }, + { + binding: 1, + resource: externalResource + }, + { + binding: 2, + resource: { + buffer: program.uniform + } + } + ] + }); + this.ensureCommandEncoderReady(); + const passEncoder = this.getComputePass(); + const shouldTimeProgram = this.activeTimers != null; + if (shouldTimeProgram) { + if (this.supportTimeQuery) { + passEncoder.writeTimestamp(this.querySet, 0); + } + } + passEncoder.setPipeline(program.pipeline); + passEncoder.setBindGroup(0, bindGroup); + passEncoder.dispatch(program.dispatch[0], program.dispatch[1], program.dispatch[2]); + if (shouldTimeProgram) { + if (this.supportTimeQuery) { + passEncoder.writeTimestamp(this.querySet, 1); + } + } + this.commandQueueOwnedIds.add(outputId); + this.submitQueue(); + if (shouldTimeProgram) { + this.activeTimers.push({ + name: program.constructor.name, + query: this.getQueryTime(this.querySet) + }); + } + } + async getTimeFromQuerySet(querySet) { + const queryBuffer = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE); + const dst = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); + this.ensureCommandEncoderReady(); + this.ensureComputePassEnded(); + this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0); + this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16); + this.submitQueue(); + await dst.mapAsync(GPUMapMode.READ); + const arrayBuf = new BigUint64Array(dst.getMappedRange()); + const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]); + dst.unmap(); + this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST); + this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE); + return timeElapsedNanos / 1e6; + } + shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) { + return env().getBool("WEBGPU_CPU_FORWARD") && inputs.every((input2) => this.tensorMap.get(input2.dataId).bufferInfo.buffer == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold); + } + numDataIds() { + return this.tensorMap.numDataIds() - this.tensorDisposalQueue.length; + } + dispose() { + if (this.disposed) { + return; + } + this.bufferManager.dispose(); + if (this.fromPixelProgram) { + this.fromPixelProgram.dispose(); + } + if (this.fromPixelImportProgram) { + this.fromPixelImportProgram.dispose(); + } + this.disposed = true; + } +}; +var WebGPUBackend = _WebGPUBackend; +WebGPUBackend.nextDataId = 0; +var webgpu_exports = {}; +__export2(webgpu_exports, { + WebGPUBackend: () => WebGPUBackend, + webgpu_util: () => webgpu_util_exports +}); +if (device_util_exports.isBrowser() && isWebGPUSupported()) { + registerBackend("webgpu", async () => { + env().set("CHECK_COMPUTATION_FOR_ERRORS", false); + const gpuDescriptor = { + powerPreference: env().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" + }; + const adapter = await navigator.gpu.requestAdapter(gpuDescriptor); + let deviceDescriptor = {}; + const supportTimeQuery = adapter.features.has("timestamp-query"); + if (supportTimeQuery) { + deviceDescriptor = { requiredFeatures: ["timestamp-query"] }; + } else { + console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.`); + } + const device = await adapter.requestDevice(deviceDescriptor); + return new WebGPUBackend(device, supportTimeQuery); + }, 3); +} var CppDType; (function(CppDType2) { CppDType2[CppDType2["float32"] = 0] = "float32"; @@ -60319,8 +67484,8 @@ function fusedBatchMatMul(args) { } const leftDim = transposeA ? a.shape[2] : a.shape[1]; const rightDim = transposeB ? b.shape[1] : b.shape[2]; - const batchDim = a.shape[0]; - const out = backend2.makeOutput([batchDim, leftDim, rightDim], a.dtype); + const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype); const outId = backend2.dataIdMap.get(out.dataId).id; const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer); const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); @@ -60355,7 +67520,7 @@ function createUnaryKernelConfig(kernelName, outType) { } return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; } -var absConfig3 = createUnaryKernelConfig(Abs); +var absConfig4 = createUnaryKernelConfig(Abs); function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) { let wasmFunc9; function setupFunc3(backend2) { @@ -60385,25 +67550,13 @@ function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) { const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer); const outId = backend2.dataIdMap.get(out.dataId).id; const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId); - if (supportsFullBroadcast17 && a.dtype === "float32") { - kernelFunc4(); - return out; - } - const aBroadcastDims = backend_util_exports.getBroadcastDims(a.shape, newShape); - const bBroadcastDims = backend_util_exports.getBroadcastDims(b.shape, newShape); - const loopsOverAllOfA = aBroadcastDims.every((v, i) => v === i); - const loopsOverAllOfB = bBroadcastDims.every((v, i) => v === i); - if (loopsOverAllOfA && loopsOverAllOfB) { - kernelFunc4(); - return out; - } else { - throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`); - } + kernelFunc4(); + return out; } return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 }; } var supportsFullBroadcast = true; -var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast); +var addConfig4 = createBinaryKernelConfig(Add, supportsFullBroadcast); var wasmFunc; function setupFunc(backend2) { wasmFunc = backend2.wasm.cwrap(AddN, null, [ @@ -60425,13 +67578,13 @@ function addn(args) { wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId); return out; } -var addNConfig3 = { +var addNConfig4 = { kernelName: AddN, backendName: "wasm", setupFunc, kernelFunc: addn }; -function identity4(args) { +function identity5(args) { const { inputs: { x }, backend: backend2 } = args; const out = backend2.makeOutput(x.shape, x.dtype); const inVals = backend2.typedArrayFromHeap(x); @@ -60439,10 +67592,10 @@ function identity4(args) { outVals.set(inVals); return out; } -var identityConfig3 = { +var identityConfig4 = { kernelName: Identity, backendName: "wasm", - kernelFunc: identity4 + kernelFunc: identity5 }; var wasmTranspose; function setup2(backend2) { @@ -60456,7 +67609,7 @@ function setup2(backend2) { "number" ]); } -function transpose4(args) { +function transpose5(args) { const { inputs, backend: backend2, attrs } = args; const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm); let permIsNoOp = true; @@ -60472,7 +67625,7 @@ function transpose4(args) { dtype: inputs.x.dtype }; if (permIsNoOp) { - const cloned = identity4({ inputs, backend: backend2 }); + const cloned = identity5({ inputs, backend: backend2 }); cloned.shape = outShape; return cloned; } @@ -60513,10 +67666,10 @@ function removeOneSizeDims(shape, perm) { } return [newShape, newPerm]; } -var transposeConfig3 = { +var transposeConfig4 = { kernelName: Transpose, backendName: "wasm", - kernelFunc: transpose4, + kernelFunc: transpose5, setupFunc: setup2 }; function permuteAxesAndTranspose(x, axis, backend2) { @@ -60533,7 +67686,7 @@ function permuteAxesAndTranspose(x, axis, backend2) { newShape[i] = xShape[permutedAxes[i]]; } axes = backend_util_exports.getInnerMostAxes(axes.length, xRank); - xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 }); + xTransposed = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 }); const xId = backend2.dataIdMap.get(x.dataId).id; const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id; if (transposedId !== xId) { @@ -60660,7 +67813,7 @@ function argmax(args) { } return out; } -var argMaxConfig3 = { +var argMaxConfig4 = { kernelName: ArgMax, backendName: "wasm", kernelFunc: argmax, @@ -60685,7 +67838,7 @@ function setup6(backend2) { "number" ]); } -function avgPool4(args) { +function avgPool5(args) { const { inputs, attrs, backend: backend2 } = args; const x = inputs.x; const xId = backend2.dataIdMap.get(x.dataId).id; @@ -60711,13 +67864,13 @@ function avgPool4(args) { wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId); return out; } -var avgPoolConfig3 = { +var avgPoolConfig4 = { kernelName: AvgPool, backendName: "wasm", setupFunc: setup6, - kernelFunc: avgPool4 + kernelFunc: avgPool5 }; -function reshape5(args) { +function reshape6(args) { const { inputs, attrs } = args; const { x } = inputs; const { shape } = attrs; @@ -60727,10 +67880,10 @@ function reshape5(args) { args.backend.incRef(x.dataId); return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; } -var reshapeConfig3 = { +var reshapeConfig4 = { kernelName: Reshape, backendName: "wasm", - kernelFunc: reshape5 + kernelFunc: reshape6 }; var wasmBatchMatMul; function setup7(backend2) { @@ -60746,7 +67899,7 @@ function setup7(backend2) { "number" ]); } -function batchMatMul3(args) { +function batchMatMul4(args) { const { inputs, backend: backend2, attrs } = args; const { a, b } = inputs; const { transposeA, transposeB } = attrs; @@ -60763,15 +67916,13 @@ function batchMatMul3(args) { const outerDimsB = b.shape.slice(0, -2); const batchDimA = util_exports.sizeFromShape(outerDimsA); const batchDimB = util_exports.sizeFromShape(outerDimsB); - const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1; - util_exports.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`); - const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2); + const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`); const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA]; const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB]; - const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); - const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); + const a3d = reshape6({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } }); + const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } }); const a3dId = backend2.dataIdMap.get(a3d.dataId).id; const b3dId = backend2.dataIdMap.get(b3d.dataId).id; const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; @@ -60787,13 +67938,13 @@ function batchMatMul3(args) { out.shape = outShape; return out; } -var batchMatMulConfig3 = { +var batchMatMulConfig4 = { kernelName: BatchMatMul, backendName: "wasm", setupFunc: setup7, - kernelFunc: batchMatMul3 + kernelFunc: batchMatMul4 }; -function slice4(args) { +function slice5(args) { const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args; const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size); const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_); @@ -60875,25 +68026,25 @@ function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) { } } } -var sliceConfig3 = { +var sliceConfig4 = { kernelName: Slice, backendName: "wasm", - kernelFunc: slice4 + kernelFunc: slice5 }; -function batchToSpaceND4(args) { +function batchToSpaceND5(args) { const { inputs, backend: backend2, attrs } = args; const { x } = inputs; const { blockShape, crops } = attrs; - const prod5 = blockShape.reduce((a, b) => a * b); - const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod5); + const prod6 = blockShape.reduce((a, b) => a * b); + const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6); const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length); - const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod5); + const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6); const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length); const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length); - const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); - const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); - const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); - const result = slice4({ + const xReshaped = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } }); + const xTransposed = transpose5({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } }); + const xTransposedReshaped = reshape6({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } }); + const result = slice5({ inputs: { x: xTransposedReshaped }, backend: backend2, attrs: { begin: sliceBeginCoords, size: sliceSize } @@ -60903,12 +68054,12 @@ function batchToSpaceND4(args) { backend2.disposeData(xReshaped.dataId); return result; } -var batchToSpaceNDConfig3 = { +var batchToSpaceNDConfig4 = { kernelName: BatchToSpaceND, backendName: "wasm", - kernelFunc: batchToSpaceND4 + kernelFunc: batchToSpaceND5 }; -function cast5(args) { +function cast6(args) { const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args; const out = backend2.makeOutput(x.shape, dtype); const inVals = backend2.typedArrayFromHeap(x); @@ -60916,12 +68067,12 @@ function cast5(args) { outVals.set(inVals); return out; } -var castConfig3 = { +var castConfig4 = { kernelName: Cast, backendName: "wasm", - kernelFunc: cast5 + kernelFunc: cast6 }; -var ceilConfig3 = createUnaryKernelConfig(Ceil); +var ceilConfig4 = createUnaryKernelConfig(Ceil); var wasmClip; function setup8(backend2) { wasmClip = backend2.wasm.cwrap(ClipByValue, null, [ @@ -60941,19 +68092,19 @@ function clip2(args) { wasmClip(xId, clipValueMin, clipValueMax, outId); return out; } -var clipByValueConfig2 = { +var clipByValueConfig3 = { kernelName: ClipByValue, backendName: "wasm", setupFunc: setup8, kernelFunc: clip2 }; -function concat4(args) { +function concat5(args) { const { inputs, backend: backend2 } = args; const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0]; let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis); const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0); if ($inputs.length === 1) { - return identity4({ inputs: { x: $inputs[0] }, backend: backend2 }); + return identity5({ inputs: { x: $inputs[0] }, backend: backend2 }); } const out = backend2.makeOutput(outShape, inputs[0].dtype); if (util_exports.sizeFromShape(outShape) === 0) { @@ -60965,7 +68116,7 @@ function concat4(args) { const inputs2D = $inputs.map((t) => { const innerSize = util_exports.sizeFromShape(t.shape.slice(axis)); const shape = [-1, innerSize]; - return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } }); + return reshape6({ inputs: { x: t }, backend: backend2, attrs: { shape } }); }); const inputsValShapes = inputs2D.map((t) => { return { vals: backend2.readSync(t.dataId), shape: t.shape }; @@ -61001,10 +68152,10 @@ function concat4(args) { } return out; } -var concatConfig3 = { +var concatConfig4 = { kernelName: Concat, backendName: "wasm", - kernelFunc: concat4 + kernelFunc: concat5 }; var wasmConv2d; function setup9(backend2) { @@ -61030,7 +68181,7 @@ function setup9(backend2) { "number" ]); } -function conv2d5(args) { +function conv2d6(args) { const { inputs, attrs, backend: backend2 } = args; const { x, filter } = inputs; const xId = backend2.dataIdMap.get(x.dataId).id; @@ -61059,11 +68210,11 @@ function conv2d5(args) { wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); return out; } -var conv2DConfig3 = { +var conv2DConfig4 = { kernelName: Conv2D, backendName: "wasm", setupFunc: setup9, - kernelFunc: conv2d5 + kernelFunc: conv2d6 }; var wasmConv2DBackpropInput; function setup10(backend2) { @@ -61097,14 +68248,26 @@ function setup10(backend2) { "number" ]); } -function conv2DBackpropInput4(args) { +function conv2DBackpropInput5(args) { const { backend: backend2, inputs, attrs } = args; const { dy, filter } = inputs; const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs; const dilations = 1; const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat); const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat); - const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo; + const { + batchSize, + filterHeight, + filterWidth, + inChannels, + inHeight, + inWidth, + outChannels, + outHeight, + outWidth, + strideHeight, + strideWidth + } = convInfo; const topPad = filterHeight - 1 - convInfo.padInfo.top; const leftPad = filterWidth - 1 - convInfo.padInfo.left; const isChannelsLast = convInfo.dataFormat === "channelsLast"; @@ -61126,14 +68289,14 @@ function conv2DBackpropInput4(args) { wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId); return out; } -var conv2DBackpropInputConfig3 = { +var conv2DBackpropInputConfig4 = { kernelName: Conv2DBackpropInput, backendName: "wasm", setupFunc: setup10, - kernelFunc: conv2DBackpropInput4 + kernelFunc: conv2DBackpropInput5 }; -var cosConfig3 = createUnaryKernelConfig(Cos); -var coshConfig3 = createUnaryKernelConfig(Cosh); +var cosConfig4 = createUnaryKernelConfig(Cos); +var coshConfig4 = createUnaryKernelConfig(Cosh); var InterpolationMethod; (function(InterpolationMethod2) { InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear"; @@ -61154,7 +68317,7 @@ function setup11(backend2) { "number" ]); } -function cropAndResize4(args) { +function cropAndResize5(args) { const { backend: backend2, inputs, attrs } = args; const { method, extrapolationValue, cropSize } = attrs; const { image: image3, boxes, boxInd } = inputs; @@ -61164,7 +68327,7 @@ function cropAndResize4(args) { let imagesData = backend2.dataIdMap.get(image3.dataId); let castedData; if (image3.dtype !== "float32") { - castedData = cast5({ backend: backend2, inputs: { x: image3 }, attrs: { dtype: "float32" } }); + castedData = cast6({ backend: backend2, inputs: { x: image3 }, attrs: { dtype: "float32" } }); imagesData = backend2.dataIdMap.get(castedData.dataId); } const imagesId = imagesData.id; @@ -61179,11 +68342,11 @@ function cropAndResize4(args) { } return out; } -var cropAndResizeConfig3 = { +var cropAndResizeConfig4 = { kernelName: CropAndResize, backendName: "wasm", setupFunc: setup11, - kernelFunc: cropAndResize4 + kernelFunc: cropAndResize5 }; var wasmCumsum; function setup12(backend2) { @@ -61205,7 +68368,7 @@ function cumsum4(args) { const permutation = backend_util_exports.getAxesPermutation([axis], xRank); let permutedX = x; if (permutation !== null) { - permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); + permutedX = transpose5({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 }); } const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0]; backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank); @@ -61217,7 +68380,7 @@ function cumsum4(args) { let out = permutedOut; if (permutation !== null) { const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation); - out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); + out = transpose5({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 }); backend2.disposeData(permutedX.dataId); backend2.disposeData(permutedOut.dataId); } @@ -61243,7 +68406,7 @@ function setup13(backend2) { "number" ]); } -function depthToSpace4(args) { +function depthToSpace5(args) { const { backend: backend2, inputs, attrs } = args; const { x } = inputs; const { blockSize, dataFormat } = attrs; @@ -61266,11 +68429,11 @@ function depthToSpace4(args) { wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId); return out; } -var depthToSpaceConfig3 = { +var depthToSpaceConfig4 = { kernelName: DepthToSpace, backendName: "wasm", setupFunc: setup13, - kernelFunc: depthToSpace4 + kernelFunc: depthToSpace5 }; var wasmDepthwiseConv2d; function setup14(backend2) { @@ -61325,17 +68488,17 @@ function depthwiseConv2d5(args) { wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); return out; } -var depthwiseConv2dNativeConfig3 = { +var depthwiseConv2dNativeConfig4 = { kernelName: DepthwiseConv2dNative, backendName: "wasm", setupFunc: setup14, kernelFunc: depthwiseConv2d5 }; -var eluConfig3 = createUnaryKernelConfig(Elu); +var eluConfig4 = createUnaryKernelConfig(Elu); var supportsFullBroadcast2 = false; -var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool"); -var expConfig3 = createUnaryKernelConfig(Exp, "float32"); -function expandDims5(args) { +var equalConfig4 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool"); +var expConfig4 = createUnaryKernelConfig(Exp, "float32"); +function expandDims6(args) { const { inputs, attrs, backend: backend2 } = args; const { input: input2 } = inputs; const { dim } = attrs; @@ -61347,24 +68510,24 @@ function expandDims5(args) { $dim = inputRank + dim + 1; } newShape.splice($dim, 0, 1); - return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); + return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } }); } -var expandDimsConfig3 = { +var expandDimsConfig4 = { kernelName: ExpandDims, backendName: "wasm", - kernelFunc: expandDims5 + kernelFunc: expandDims6 }; -function fill4(args) { +function fill5(args) { const { attrs: { shape, value, dtype }, backend: backend2 } = args; const out = backend2.makeOutput(shape, dtype); const outVals = backend2.typedArrayFromHeap(out); outVals.fill(value); return out; } -var fillConfig3 = { +var fillConfig4 = { kernelName: Fill, backendName: "wasm", - kernelFunc: fill4 + kernelFunc: fill5 }; var wasmFlipLeftRight; function setup15(backend2) { @@ -61387,15 +68550,15 @@ function flipLeftRight2(args) { wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId); return out; } -var flipLeftRightConfig3 = { +var flipLeftRightConfig4 = { kernelName: FlipLeftRight, backendName: "wasm", kernelFunc: flipLeftRight2, setupFunc: setup15 }; -var floorConfig3 = createUnaryKernelConfig(Floor); +var floorConfig4 = createUnaryKernelConfig(Floor); var supportsFullBroadcast3 = false; -var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3); +var floorDivConfig4 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3); var wasmBatchNorm; function setup16(backend2) { wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]); @@ -61403,9 +68566,9 @@ function setup16(backend2) { function fusedBatchNorm(args) { const { backend: backend2, inputs, attrs } = args; const { varianceEpsilon } = attrs; - const { x, mean: mean4, variance, offset, scale: scale22 } = inputs; + const { x, mean: mean5, variance, offset, scale: scale22 } = inputs; const xId = backend2.dataIdMap.get(x.dataId).id; - const meanId = backend2.dataIdMap.get(mean4.dataId).id; + const meanId = backend2.dataIdMap.get(mean5.dataId).id; const varianceId = backend2.dataIdMap.get(variance.dataId).id; const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0; const scaleId = scale22 != null ? backend2.dataIdMap.get(scale22.dataId).id : 0; @@ -61417,7 +68580,7 @@ function fusedBatchNorm(args) { wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId); return out; } -var fusedBatchNormConfig = { +var fusedBatchNormConfig2 = { kernelName: FusedBatchNorm, backendName: "wasm", setupFunc: setup16, @@ -61451,10 +68614,18 @@ function setup17(backend2) { "number" ]); } -function fusedConv2d2(args) { +function fusedConv2d3(args) { const { inputs, attrs, backend: backend2 } = args; const { x, filter, bias, preluActivationWeights } = inputs; - const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const { + strides, + pad: pad3, + dilations, + dataFormat, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode); const fusedActivation = FusableActivation[activation2]; if (fusedActivation == null) { @@ -61498,11 +68669,11 @@ function fusedConv2d2(args) { wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); return out; } -var fusedConv2DConfig3 = { +var fusedConv2DConfig4 = { kernelName: FusedConv2D, backendName: "wasm", setupFunc: setup17, - kernelFunc: fusedConv2d2 + kernelFunc: fusedConv2d3 }; var wasmFusedDepthwiseConv2d; function setup18(backend2) { @@ -61535,7 +68706,15 @@ function setup18(backend2) { function fusedDepthwiseConv2d(args) { const { inputs, attrs, backend: backend2 } = args; const { x, filter, bias, preluActivationWeights } = inputs; - const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs; + const { + strides, + pad: pad3, + dilations, + dataFormat, + dimRoundingMode, + activation: activation2, + leakyreluAlpha + } = attrs; const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true); const fusedActivation = FusableActivation[activation2]; if (fusedActivation == null) { @@ -61579,7 +68758,7 @@ function fusedDepthwiseConv2d(args) { wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId); return out; } -var fusedDepthwiseConv2DConfig3 = { +var fusedDepthwiseConv2DConfig4 = { kernelName: FusedDepthwiseConv2D, backendName: "wasm", setupFunc: setup18, @@ -61598,7 +68777,7 @@ function setup19(backend2) { "number" ]); } -function gatherNd3(args) { +function gatherNd4(args) { const { backend: backend2, inputs } = args; const { params, indices } = inputs; const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices); @@ -61617,11 +68796,11 @@ function gatherNd3(args) { wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId); return out; } -var gatherNdConfig3 = { +var gatherNdConfig4 = { kernelName: GatherNd, backendName: "wasm", setupFunc: setup19, - kernelFunc: gatherNd3 + kernelFunc: gatherNd4 }; var wasmGather; function setup20(backend2) { @@ -61636,7 +68815,7 @@ function setup20(backend2) { "number" ]); } -function gatherV23(args) { +function gatherV24(args) { const { backend: backend2, inputs, attrs } = args; const { x, indices } = inputs; const { axis, batchDims } = attrs; @@ -61648,7 +68827,7 @@ function gatherV23(args) { util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`); } const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims); - const flattenX = reshape5({ + const flattenX = reshape6({ inputs: { x }, attrs: { shape: [ @@ -61661,7 +68840,7 @@ function gatherV23(args) { backend: backend2 }); const indicesSize = util_exports.sizeFromShape(indices.shape); - const flattenIndex = reshape5({ + const flattenIndex = reshape6({ inputs: { x: indices }, attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }, backend: backend2 @@ -61690,16 +68869,16 @@ function gatherV23(args) { out.shape = shapeInfo.outputShape; return out; } -var gatherV2Config3 = { +var gatherV2Config4 = { kernelName: GatherV2, backendName: "wasm", setupFunc: setup20, - kernelFunc: gatherV23 + kernelFunc: gatherV24 }; var supportsFullBroadcast4 = false; -var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool"); +var greaterConfig4 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool"); var supportsFullBroadcast5 = false; -var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool"); +var greaterEqualConfig4 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool"); var wasmFunc3; function setupFunc2(backend2) { wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [ @@ -61726,12 +68905,12 @@ var leakyReluConfig3 = { kernelFunc: leakyRelu4 }; var supportsFullBroadcast6 = false; -var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool"); +var lessConfig4 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool"); var supportsFullBroadcast7 = false; -var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool"); -var logConfig3 = createUnaryKernelConfig(Log); +var lessEqualConfig4 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool"); +var logConfig4 = createUnaryKernelConfig(Log); var supportsFullBroadcast8 = false; -var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool"); +var logicalAndConfig4 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool"); var wasmMax; function setup21(backend2) { wasmMax = backend2.wasm.cwrap(Max, null, [ @@ -61741,7 +68920,7 @@ function setup21(backend2) { "number" ]); } -function max5(args) { +function max6(args) { const { backend: backend2, inputs, attrs } = args; const { reductionIndices: axis, keepDims } = attrs; const { x } = inputs; @@ -61772,14 +68951,14 @@ function max5(args) { } return out; } -var maxConfig3 = { +var maxConfig4 = { kernelName: Max, backendName: "wasm", setupFunc: setup21, - kernelFunc: max5 + kernelFunc: max6 }; var supportsFullBroadcast9 = false; -var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9); +var maximumConfig4 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9); var wasmMaxPool; function setup22(backend2) { wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [ @@ -61802,7 +68981,7 @@ function setup22(backend2) { "number" ]); } -function maxPool4(args) { +function maxPool5(args) { const { inputs, attrs, backend: backend2 } = args; const x = inputs.x; const xId = backend2.dataIdMap.get(x.dataId).id; @@ -61829,17 +69008,17 @@ function maxPool4(args) { wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId); return out; } -var maxPoolConfig3 = { +var maxPoolConfig4 = { kernelName: MaxPool, backendName: "wasm", setupFunc: setup22, - kernelFunc: maxPool4 + kernelFunc: maxPool5 }; var wasmMean; function setup23(backend2) { wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]); } -function mean3(args) { +function mean4(args) { const { backend: backend2, inputs, attrs } = args; const { axis, keepDims } = attrs; const { x } = inputs; @@ -61861,7 +69040,7 @@ function mean3(args) { const reduceSize = util_exports.sizeFromShape(reduceShape); let castedInput = input2; if (input2.dtype !== "float32") { - castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } }); + castedInput = cast6({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } }); inputId = backend2.dataIdMap.get(castedInput.dataId).id; } const out = backend2.makeOutput(outShape, "float32"); @@ -61881,11 +69060,11 @@ function mean3(args) { } return out; } -var meanConfig3 = { +var meanConfig4 = { kernelName: Mean, backendName: "wasm", setupFunc: setup23, - kernelFunc: mean3 + kernelFunc: mean4 }; var wasmMin; function setup24(backend2) { @@ -61896,7 +69075,7 @@ function setup24(backend2) { "number" ]); } -function min5(args) { +function min6(args) { const { backend: backend2, inputs, attrs } = args; const { axis, keepDims } = attrs; const { x } = inputs; @@ -61929,14 +69108,14 @@ function min5(args) { } return out; } -var minConfig3 = { +var minConfig4 = { kernelName: Min, backendName: "wasm", setupFunc: setup24, - kernelFunc: min5 + kernelFunc: min6 }; var supportsFullBroadcast10 = false; -var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10); +var minimumConfig4 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10); var MirrorPaddingMode; (function(MirrorPaddingMode2) { MirrorPaddingMode2[MirrorPaddingMode2["reflect"] = 0] = "reflect"; @@ -61969,15 +69148,15 @@ function mirrorPad3(args) { wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId); return out; } -var mirrorPadConfig3 = { +var mirrorPadConfig4 = { kernelName: MirrorPad, backendName: "wasm", kernelFunc: mirrorPad3, setupFunc: setup25 }; var supportsFullBroadcast11 = true; -var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11); -var negConfig3 = createUnaryKernelConfig(Neg); +var multiplyConfig4 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11); +var negConfig4 = createUnaryKernelConfig(Neg); function parseResultStruct(backend2, resOffset) { const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4); const pSelectedIndices = result[0]; @@ -62010,7 +69189,7 @@ function kernelFunc(args) { const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices); return selectedIndicesTensor; } -var nonMaxSuppressionV3Config3 = { +var nonMaxSuppressionV3Config4 = { kernelName: NonMaxSuppressionV3, backendName: "wasm", setupFunc: setup26, @@ -62070,14 +69249,14 @@ function kernelFunc2(args) { const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores); return [selectedIndicesTensor, selectedScoresTensor]; } -var nonMaxSuppressionV5Config3 = { +var nonMaxSuppressionV5Config4 = { kernelName: NonMaxSuppressionV5, backendName: "wasm", setupFunc: setup28, kernelFunc: kernelFunc2 }; var supportsFullBroadcast12 = false; -var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool"); +var notEqualConfig4 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool"); var wasmOneHot; function setup29(backend2) { wasmOneHot = backend2.wasm.cwrap(OneHot, null, [ @@ -62105,23 +69284,23 @@ var oneHotConfig3 = { setupFunc: setup29, kernelFunc: oneHot4 }; -function onesLike4(args) { +function onesLike5(args) { const { inputs: { x }, backend: backend2 } = args; const out = backend2.makeOutput(x.shape, x.dtype); const outVals = backend2.typedArrayFromHeap(out); outVals.fill(1); return out; } -var onesLikeConfig3 = { +var onesLikeConfig4 = { kernelName: OnesLike, backendName: "wasm", - kernelFunc: onesLike4 + kernelFunc: onesLike5 }; -function pack3(args) { +function pack4(args) { const { inputs, backend: backend2, attrs } = args; const { axis } = attrs; if (inputs.length === 1) { - return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); + return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } }); } const shape = inputs[0].shape; const dtype = inputs[0].dtype; @@ -62131,18 +69310,18 @@ function pack3(args) { }); const intermediateTensorInfos = []; const expandedTensors = inputs.map((t) => { - const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); + const expandedT = expandDims6({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } }); intermediateTensorInfos.push(expandedT); return expandedT; }); - const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); + const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } }); intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId)); return result; } -var packConfig3 = { +var packConfig4 = { kernelName: Pack, backendName: "wasm", - kernelFunc: pack3 + kernelFunc: pack4 }; var wasmPadV2; function setup30(backend2) { @@ -62161,7 +69340,7 @@ function pad2(args) { const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args; const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]); if (util_exports.sizeFromShape(x.shape) === 0) { - return fill4({ + return fill5({ backend: backend2, attrs: { shape: outShape, value: constantValue, dtype: x.dtype } }); @@ -62178,14 +69357,14 @@ function pad2(args) { wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId); return out; } -var padV2Config3 = { +var padV2Config4 = { kernelName: PadV2, backendName: "wasm", kernelFunc: pad2, setupFunc: setup30 }; var supportsFullBroadcast13 = false; -var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13); +var powConfig4 = createBinaryKernelConfig(Pow, supportsFullBroadcast13); var wasmPrelu; function setup31(backend2) { wasmPrelu = backend2.wasm.cwrap(Prelu, null, [ @@ -62194,7 +69373,7 @@ function setup31(backend2) { "number" ]); } -function prelu5(args) { +function prelu6(args) { const { inputs, backend: backend2 } = args; const { x, alpha } = inputs; const xId = backend2.dataIdMap.get(x.dataId).id; @@ -62203,7 +69382,7 @@ function prelu5(args) { const input2 = x; let castedInput = input2; if (input2.dtype !== "float32") { - castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } }); + castedInput = cast6({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } }); inputId = backend2.dataIdMap.get(castedInput.dataId).id; } const out = backend2.makeOutput(x.shape, "float32"); @@ -62214,11 +69393,11 @@ function prelu5(args) { } return out; } -var preluConfig3 = { +var preluConfig4 = { kernelName: Prelu, backendName: "wasm", setupFunc: setup31, - kernelFunc: prelu5 + kernelFunc: prelu6 }; var wasmProd; function setup32(backend2) { @@ -62229,7 +69408,7 @@ function setup32(backend2) { "number" ]); } -function prod4(args) { +function prod5(args) { const { backend: backend2, inputs, attrs } = args; const { axis, keepDims } = attrs; const { x } = inputs; @@ -62263,13 +69442,13 @@ function prod4(args) { } return out; } -var prodConfig3 = { +var prodConfig4 = { kernelName: Prod, backendName: "wasm", setupFunc: setup32, - kernelFunc: prod4 + kernelFunc: prod5 }; -var range5 = (args) => { +var range6 = (args) => { const { backend: backend2, attrs } = args; const { start, stop, step: step5, dtype } = attrs; const values = rangeImpl(start, stop, step5, dtype); @@ -62278,15 +69457,15 @@ var range5 = (args) => { outVals.set(values); return out; }; -var rangeConfig3 = { +var rangeConfig4 = { kernelName: Range, backendName: "wasm", - kernelFunc: range5 + kernelFunc: range6 }; var supportsFullBroadcast14 = true; -var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14); -var reluConfig3 = createUnaryKernelConfig(Relu); -var relu6Config3 = createUnaryKernelConfig(Relu6); +var realDivConfig4 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14); +var reluConfig4 = createUnaryKernelConfig(Relu); +var relu6Config4 = createUnaryKernelConfig(Relu6); var wasmResizeBilinear; function setup33(backend2) { wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [ @@ -62302,7 +69481,7 @@ function setup33(backend2) { "number" ]); } -function resizeBilinear4(args) { +function resizeBilinear5(args) { const { backend: backend2, inputs, attrs } = args; const { images } = inputs; const { alignCorners, halfPixelCenters, size } = attrs; @@ -62312,7 +69491,7 @@ function resizeBilinear4(args) { let xData = backend2.dataIdMap.get(images.dataId); let castedData; if (xData.dtype !== "float32") { - castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } }); + castedData = cast6({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } }); xData = backend2.dataIdMap.get(castedData.dataId); } const xId = xData.id; @@ -62327,11 +69506,11 @@ function resizeBilinear4(args) { } return out; } -var resizeBilinearConfig3 = { +var resizeBilinearConfig4 = { kernelName: ResizeBilinear, backendName: "wasm", setupFunc: setup33, - kernelFunc: resizeBilinear4 + kernelFunc: resizeBilinear5 }; var wasmReverse; function setup34(backend2) { @@ -62350,7 +69529,7 @@ function reverse4(args) { const { dims } = attrs; const axes = util_exports.parseAxisParam(dims, x.shape); if (x.shape.length === 0) { - return identity4({ inputs: { x }, backend: backend2 }); + return identity5({ inputs: { x }, backend: backend2 }); } const out = backend2.makeOutput(x.shape, x.dtype); const xId = backend2.dataIdMap.get(x.dataId).id; @@ -62358,7 +69537,7 @@ function reverse4(args) { const axesBytes = new Uint8Array(new Int32Array(axes).buffer); const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer); wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId); - const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 }); + const reshaped = reshape6({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 }); backend2.disposeData(out.dataId); return reshaped; } @@ -62400,14 +69579,14 @@ function rotateWithOffset2(args) { wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId); return out; } -var rotateWithOffsetConfig3 = { +var rotateWithOffsetConfig4 = { kernelName: RotateWithOffset, backendName: "wasm", kernelFunc: rotateWithOffset2, setupFunc: setup35 }; var roundConfig3 = createUnaryKernelConfig(Round); -var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt); +var rsqrtConfig4 = createUnaryKernelConfig(Rsqrt); var wasmScatterNd; function setup36(backend2) { wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [ @@ -62422,7 +69601,7 @@ function setup36(backend2) { "number" ]); } -function scatterNd3(args) { +function scatterNd4(args) { const { backend: backend2, inputs, attrs } = args; const { indices, updates } = inputs; const { shape } = attrs; @@ -62440,11 +69619,11 @@ function scatterNd3(args) { wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId); return out; } -var scatterNdConfig3 = { +var scatterNdConfig4 = { kernelName: ScatterNd, backendName: "wasm", setupFunc: setup36, - kernelFunc: scatterNd3 + kernelFunc: scatterNd4 }; var wasmSelect; function setup37(backend2) { @@ -62456,7 +69635,7 @@ function setup37(backend2) { "number" ]); } -function select4(args) { +function select5(args) { const { inputs, backend: backend2 } = args; const { condition, t, e } = inputs; const conditionId = backend2.dataIdMap.get(condition.dataId).id; @@ -62470,17 +69649,17 @@ function select4(args) { wasmSelect(conditionId, tId, eId, offset, outId); return out; } -var selectConfig3 = { +var selectConfig4 = { kernelName: Select, backendName: "wasm", - kernelFunc: select4, + kernelFunc: select5, setupFunc: setup37 }; var wasmFunc7; function setup38(backend2) { wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]); } -function sigmoid4(args) { +function sigmoid5(args) { const { backend: backend2, inputs: { x } } = args; const xId = backend2.dataIdMap.get(x.dataId).id; const out = backend2.makeOutput(x.shape, x.dtype); @@ -62491,13 +69670,13 @@ function sigmoid4(args) { wasmFunc7(xId, outId); return out; } -var sigmoidConfig3 = { +var sigmoidConfig4 = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: setup38, - kernelFunc: sigmoid4 + kernelFunc: sigmoid5 }; -var sinConfig3 = createUnaryKernelConfig(Sin); +var sinConfig4 = createUnaryKernelConfig(Sin); var wasmFunc8; function setup39(backend2) { wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [ @@ -62507,7 +69686,7 @@ function setup39(backend2) { "number" ]); } -function softmax5(args) { +function softmax6(args) { const { backend: backend2, inputs: { logits }, attrs: { dim } } = args; const xId = backend2.dataIdMap.get(logits.dataId).id; const out = backend2.makeOutput(logits.shape, logits.dtype); @@ -62520,50 +69699,295 @@ function softmax5(args) { wasmFunc8(xId, outId, channels, batch); return out; } -var softmaxConfig3 = { +var softmaxConfig4 = { kernelName: Softmax, backendName: "wasm", setupFunc: setup39, - kernelFunc: softmax5 + kernelFunc: softmax6 }; -function spaceToBatchND4(args) { +function spaceToBatchND5(args) { const { inputs, backend: backend2, attrs } = args; const { x } = inputs; const { blockShape, paddings } = attrs; - const prod5 = util_exports.sizeFromShape(blockShape); + const prod6 = util_exports.sizeFromShape(blockShape); const completePaddings = [[0, 0]]; completePaddings.push(...paddings); for (let i = 1 + blockShape.length; i < x.shape.length; ++i) { completePaddings.push([0, 0]); } - const paddedX = padV2Config3.kernelFunc({ + const paddedX = padV2Config4.kernelFunc({ inputs: { x }, backend: backend2, attrs: { paddings: completePaddings, constantValue: 0 } }); - const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod5, false); + const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false); const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false); - const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod5, false); + const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false); const reshapeInputs = { x: paddedX }; const reshapeAttrs = { shape: reshapedPaddedShape }; - const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); + const paddedXReshaped = reshape6({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs }); const transposeInputs = { x: paddedXReshaped }; const transposeAttrs = { perm: permutedReshapedPaddedPermutation }; - const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); + const paddedXT = transpose5({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs }); const resultReshapeInputs = { x: paddedXT }; const resultReshapeAttrs = { shape: flattenShape }; - const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); + const result = reshape6({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs }); backend2.disposeData(paddedX.dataId); backend2.disposeData(paddedXReshaped.dataId); backend2.disposeData(paddedXT.dataId); return result; } -var spaceToBatchNDConfig3 = { +var spaceToBatchNDConfig4 = { kernelName: SpaceToBatchND, backendName: "wasm", - kernelFunc: spaceToBatchND4 + kernelFunc: spaceToBatchND5 }; -function splitV3(args) { +var wasmSparseFillEmptyRows; +function setup40(backend2) { + wasmSparseFillEmptyRows = backend2.wasm.cwrap("SparseFillEmptyRows", "number", [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + ]); +} +function sparseFillEmptyRows4(args) { + const { backend: backend2, inputs } = args; + const { indices, values, denseShape, defaultValue } = inputs; + const indicesCount = indices.shape[0]; + const rank = indices.shape[1]; + const denseRows = backend2.readSync(denseShape.dataId)[0]; + const maxOutputIndicesShape = [indicesCount + denseRows, rank]; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const valuesId = backend2.dataIdMap.get(values.dataId).id; + const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id; + const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype); + const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id; + const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype); + const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id; + const emptyRowIndicator = backend2.makeOutput([denseRows], "bool"); + const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id; + const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype); + const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 1: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]); + break; + } + case 2: { + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 3: + exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + backend2.disposeData(emptyRowIndicator.dataId); + backend2.disposeData(reverseIndexMap.dataId); + throw new Error(exceptionMessage); + } + let resizedIndices = outputIndices; + let resizedValues = outputValues; + if (outputRows !== maxOutputIndicesShape[0]) { + resizedIndices = slice5({ + inputs: { x: outputIndices }, + attrs: { begin: 0, size: [outputRows, rank] }, + backend: backend2 + }); + resizedValues = slice5({ + inputs: { x: outputValues }, + attrs: { begin: 0, size: outputRows }, + backend: backend2 + }); + backend2.disposeData(outputIndices.dataId); + backend2.disposeData(outputValues.dataId); + } + return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap]; +} +var sparseFillEmptyRowsConfig3 = { + kernelName: SparseFillEmptyRows, + backendName: "wasm", + setupFunc: setup40, + kernelFunc: sparseFillEmptyRows4 +}; +var wasmSparseReshape; +function setup41(backend2) { + wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number" + ]); +} +function sparseReshape4(args) { + const { backend: backend2, inputs } = args; + const { inputIndices, inputShape, newShape } = inputs; + if (inputIndices.shape.length !== 2) { + throw new Error(`Input indices should be a matrix but received shape + ${inputIndices.shape}`); + } + if (inputShape.shape.length !== 1) { + throw new Error(`Input shape should be a vector but received shape + ${inputShape.shape}`); + } + if (newShape.shape.length !== 1) { + throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`); + } + const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id; + const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id; + const newShapeId = backend2.dataIdMap.get(newShape.dataId).id; + const nnz = inputIndices.shape[0]; + const outputRank = util_exports.sizeFromShape(newShape.shape); + const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype); + const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id; + const outputShape = backend2.makeOutput([outputRank], newShape.dtype); + const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id; + const exceptionValues = backend2.makeOutput([3], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage(); + break; + case 3: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues); + break; + } + case 4: { + const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId)); + exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues); + break; + } + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(newIndices.dataId); + backend2.disposeData(outputShape.dataId); + throw new Error(exceptionMessage); + } + return [newIndices, outputShape]; +} +var sparseReshapeConfig3 = { + kernelName: SparseReshape, + backendName: "wasm", + setupFunc: setup41, + kernelFunc: sparseReshape4 +}; +var wasmSparseSegmentReduction; +function setup42(backend2) { + wasmSparseSegmentReduction = backend2.wasm.cwrap("SparseSegmentReduction", null, [ + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number", + "number" + ]); +} +function sparseSegmentReduction(args, isMean) { + const { backend: backend2, inputs } = args; + const { data, indices, segmentIds } = inputs; + const numIndices = indices.shape[0]; + const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0]; + const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0; + const outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + const outputShape = data.shape.slice(); + outputShape[0] = outputRows; + const dataId = backend2.dataIdMap.get(data.dataId).id; + const indicesId = backend2.dataIdMap.get(indices.dataId).id; + const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id; + const output = backend2.makeOutput(outputShape, data.dtype); + const outputId = backend2.dataIdMap.get(output.dataId).id; + const exceptionValues = backend2.makeOutput([4], "int32"); + const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id; + wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0); + const exceptionValuesArray = backend2.readSync(exceptionValues.dataId); + let exceptionMessage; + switch (exceptionValuesArray[0]) { + case 0: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage(); + break; + } + case 1: { + exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage(); + break; + } + case 2: + exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]); + break; + case 3: + exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]); + break; + default: + exceptionMessage = ""; + } + backend2.disposeData(exceptionValues.dataId); + if (exceptionMessage) { + backend2.disposeData(output.dataId); + throw new Error(exceptionMessage); + } + return output; +} +function sparseSegmentMean4(args) { + return sparseSegmentReduction(args, true); +} +var sparseSegmentMeanConfig3 = { + kernelName: SparseSegmentMean, + backendName: "wasm", + setupFunc: setup42, + kernelFunc: sparseSegmentMean4 +}; +function sparseSegmentSum4(args) { + return sparseSegmentReduction(args, false); +} +var sparseSegmentSumConfig3 = { + kernelName: SparseSegmentSum, + backendName: "wasm", + setupFunc: setup42, + kernelFunc: sparseSegmentSum4 +}; +function splitV4(args) { const { inputs, attrs, backend: backend2 } = args; const { x } = inputs; const { numOrSizeSplits, axis } = attrs; @@ -62574,22 +69998,22 @@ function splitV3(args) { return splitSizes.map((s) => { const xSliceSize = [...size]; xSliceSize[$axis] = s; - const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 }); + const xSlice = slice5({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 }); begin[$axis] += s; return xSlice; }); } -var splitVConfig3 = { +var splitVConfig4 = { kernelName: SplitV, backendName: "wasm", - kernelFunc: splitV3 + kernelFunc: splitV4 }; -var sqrtConfig3 = createUnaryKernelConfig(Sqrt); -var squareConfig3 = createUnaryKernelConfig(Square); +var sqrtConfig4 = createUnaryKernelConfig(Sqrt); +var squareConfig4 = createUnaryKernelConfig(Square); var supportsFullBroadcast15 = true; -var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15); +var squaredDifferenceConfig4 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15); var wasmStep; -function setup40(backend2) { +function setup43(backend2) { wasmStep = backend2.wasm.cwrap(Step, null, [ "number", "number", @@ -62610,11 +70034,11 @@ function step4(args) { var stepConfig3 = { kernelName: Step, backendName: "wasm", - setupFunc: setup40, + setupFunc: setup43, kernelFunc: step4 }; var wasmStridedSlice; -function setup41(backend2) { +function setup44(backend2) { wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [ "number", "array", @@ -62628,19 +70052,37 @@ function setup41(backend2) { "number" ]); } -function stridedSlice4(args) { +function stridedSlice5(args) { const { backend: backend2, inputs, attrs } = args; const { x } = inputs; - const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs; - const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); + const { + begin, + end, + strides, + beginMask, + endMask, + ellipsisMask, + newAxisMask, + shrinkAxisMask + } = attrs; + const { + finalShapeSparse, + finalShape, + isIdentity, + sliceDim0, + isSimpleSlice, + begin: $begin, + end: $end, + strides: $strides + } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); let result; if (isIdentity) { - result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); + result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } }); } else if (sliceDim0 || isSimpleSlice) { util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`); const size = slice_util_exports.computeOutShape($begin, $end, $strides); - const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); - result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); + const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } }); + result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } }); backend2.disposeData(sliced.dataId); } else { const out = backend2.makeOutput(finalShapeSparse, "float32"); @@ -62653,21 +70095,21 @@ function stridedSlice4(args) { const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer); const outId = backend2.dataIdMap.get(out.dataId).id; wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId); - result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } }); + result = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } }); backend2.disposeData(out.dataId); } return result; } -var stridedSliceConfig3 = { +var stridedSliceConfig4 = { kernelName: StridedSlice, backendName: "wasm", - setupFunc: setup41, - kernelFunc: stridedSlice4 + setupFunc: setup44, + kernelFunc: stridedSlice5 }; var supportsFullBroadcast16 = true; -var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16); +var subConfig4 = createBinaryKernelConfig(Sub, supportsFullBroadcast16); var wasmSum; -function setup42(backend2) { +function setup45(backend2) { wasmSum = backend2.wasm.cwrap(Sum, null, [ "number", "number", @@ -62675,7 +70117,7 @@ function setup42(backend2) { "number" ]); } -function sum5(args) { +function sum6(args) { const { backend: backend2, inputs, attrs } = args; const { axis, keepDims } = attrs; const { x } = inputs; @@ -62709,16 +70151,16 @@ function sum5(args) { } return out; } -var sumConfig3 = { +var sumConfig4 = { kernelName: Sum, backendName: "wasm", - setupFunc: setup42, - kernelFunc: sum5 + setupFunc: setup45, + kernelFunc: sum6 }; var tanConfig3 = createUnaryKernelConfig(Tan); -var tanhConfig3 = createUnaryKernelConfig(Tanh); +var tanhConfig4 = createUnaryKernelConfig(Tanh); var wasmTile; -function setup43(backend2) { +function setup46(backend2) { wasmTile = backend2.wasm.cwrap(Tile, null, [ "number", "array", @@ -62728,7 +70170,7 @@ function setup43(backend2) { "number" ]); } -function tile5(args) { +function tile6(args) { const { inputs, backend: backend2, attrs } = args; const { x } = inputs; const xId = backend2.dataIdMap.get(x.dataId).id; @@ -62744,14 +70186,14 @@ function tile5(args) { wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId); return out; } -var tileConfig3 = { +var tileConfig4 = { kernelName: Tile, backendName: "wasm", - setupFunc: setup43, - kernelFunc: tile5 + setupFunc: setup46, + kernelFunc: tile6 }; var wasmTopK; -function setup44(backend2) { +function setup47(backend2) { wasmTopK = backend2.wasm.cwrap(TopK, null, [ "number", "array", @@ -62777,14 +70219,14 @@ var topk2 = ({ inputs, backend: backend2, attrs }) => { wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId); return [outValues, outIndices]; }; -var topKConfig3 = { +var topKConfig4 = { kernelName: TopK, backendName: "wasm", - setupFunc: setup44, + setupFunc: setup47, kernelFunc: topk2 }; var wasmTransform; -function setup45(backend2) { +function setup48(backend2) { wasmTransform = backend2.wasm.cwrap(Transform, null, [ "number", "number", @@ -62803,7 +70245,7 @@ function setup45(backend2) { "number" ]); } -function transform4(args) { +function transform5(args) { const { backend: backend2, inputs, attrs } = args; const { image: image3, transforms } = inputs; const { interpolation, fillMode, fillValue, outputShape } = attrs; @@ -62844,13 +70286,13 @@ function transform4(args) { wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, strides, image3.shape.length - 1, interpolationModeId, fillModeId, fillValue, outId); return out; } -var transformConfig3 = { +var transformConfig4 = { kernelName: Transform, backendName: "wasm", - setupFunc: setup45, - kernelFunc: transform4 + setupFunc: setup48, + kernelFunc: transform5 }; -function unpack3(args) { +function unpack4(args) { const { inputs, backend: backend2, attrs } = args; const { value } = inputs; let { axis } = attrs; @@ -62872,130 +70314,134 @@ function unpack3(args) { size[axis] = 1; for (let i = 0; i < outs.length; i++) { begin[axis] = i; - outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 }); + outs[i] = slice5({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 }); } return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape })); } -var unpackConfig3 = { +var unpackConfig4 = { kernelName: Unpack, backendName: "wasm", - kernelFunc: unpack3 + kernelFunc: unpack4 }; -function zerosLike4(args) { +function zerosLike5(args) { const { inputs: { x }, backend: backend2 } = args; const out = backend2.makeOutput(x.shape, x.dtype); const outVals = backend2.typedArrayFromHeap(out); outVals.fill(0); return out; } -var zerosLikeConfig3 = { +var zerosLikeConfig4 = { kernelName: ZerosLike, backendName: "wasm", - kernelFunc: zerosLike4 + kernelFunc: zerosLike5 }; -var kernelConfigs3 = [ - absConfig3, - addConfig3, - addNConfig3, +var kernelConfigs4 = [ + absConfig4, + addConfig4, + addNConfig4, allConfig3, anyConfig3, - argMaxConfig3, - avgPoolConfig3, - batchMatMulConfig3, - batchToSpaceNDConfig3, - castConfig3, - ceilConfig3, - clipByValueConfig2, - concatConfig3, - conv2DConfig3, - conv2DBackpropInputConfig3, - cosConfig3, - coshConfig3, - cropAndResizeConfig3, + argMaxConfig4, + avgPoolConfig4, + batchMatMulConfig4, + batchToSpaceNDConfig4, + castConfig4, + ceilConfig4, + clipByValueConfig3, + concatConfig4, + conv2DConfig4, + conv2DBackpropInputConfig4, + cosConfig4, + coshConfig4, + cropAndResizeConfig4, cumsumConfig3, - depthToSpaceConfig3, - depthwiseConv2dNativeConfig3, - eluConfig3, - equalConfig3, - expConfig3, - expandDimsConfig3, - fillConfig3, - flipLeftRightConfig3, - floorConfig3, - floorDivConfig3, + depthToSpaceConfig4, + depthwiseConv2dNativeConfig4, + eluConfig4, + equalConfig4, + expConfig4, + expandDimsConfig4, + fillConfig4, + flipLeftRightConfig4, + floorConfig4, + floorDivConfig4, fusedMatMulConfig, - fusedBatchNormConfig, - fusedConv2DConfig3, - fusedDepthwiseConv2DConfig3, - gatherNdConfig3, - gatherV2Config3, - greaterConfig3, - greaterEqualConfig3, - identityConfig3, + fusedBatchNormConfig2, + fusedConv2DConfig4, + fusedDepthwiseConv2DConfig4, + gatherNdConfig4, + gatherV2Config4, + greaterConfig4, + greaterEqualConfig4, + identityConfig4, leakyReluConfig3, - lessConfig3, - lessEqualConfig3, - logConfig3, - logicalAndConfig3, - maxConfig3, - maximumConfig3, - maxPoolConfig3, - meanConfig3, - minConfig3, - minimumConfig3, - mirrorPadConfig3, - multiplyConfig3, - negConfig3, - nonMaxSuppressionV3Config3, + lessConfig4, + lessEqualConfig4, + logConfig4, + logicalAndConfig4, + maxConfig4, + maximumConfig4, + maxPoolConfig4, + meanConfig4, + minConfig4, + minimumConfig4, + mirrorPadConfig4, + multiplyConfig4, + negConfig4, + nonMaxSuppressionV3Config4, nonMaxSuppressionV4Config3, - nonMaxSuppressionV5Config3, - notEqualConfig3, + nonMaxSuppressionV5Config4, + notEqualConfig4, oneHotConfig3, - onesLikeConfig3, - packConfig3, - padV2Config3, - powConfig3, - preluConfig3, - prodConfig3, - rangeConfig3, - realDivConfig3, - reluConfig3, - relu6Config3, - reshapeConfig3, - resizeBilinearConfig3, + onesLikeConfig4, + packConfig4, + padV2Config4, + powConfig4, + preluConfig4, + prodConfig4, + rangeConfig4, + realDivConfig4, + reluConfig4, + relu6Config4, + reshapeConfig4, + resizeBilinearConfig4, reverseConfig3, - rotateWithOffsetConfig3, - rsqrtConfig3, + rotateWithOffsetConfig4, + rsqrtConfig4, roundConfig3, - scatterNdConfig3, - selectConfig3, - sigmoidConfig3, - sinConfig3, - sliceConfig3, - softmaxConfig3, - spaceToBatchNDConfig3, - splitVConfig3, - sqrtConfig3, - squareConfig3, - squaredDifferenceConfig3, + scatterNdConfig4, + selectConfig4, + sigmoidConfig4, + sinConfig4, + sliceConfig4, + softmaxConfig4, + spaceToBatchNDConfig4, + sparseFillEmptyRowsConfig3, + sparseReshapeConfig3, + sparseSegmentMeanConfig3, + sparseSegmentSumConfig3, + splitVConfig4, + sqrtConfig4, + squareConfig4, + squaredDifferenceConfig4, stepConfig3, - stridedSliceConfig3, - subConfig3, - sumConfig3, + stridedSliceConfig4, + subConfig4, + sumConfig4, tanConfig3, - tanhConfig3, - tileConfig3, - topKConfig3, - transformConfig3, - transposeConfig3, - unpackConfig3, - zerosLikeConfig3 + tanhConfig4, + tileConfig4, + topKConfig4, + transformConfig4, + transposeConfig4, + unpackConfig4, + zerosLikeConfig4 ]; -for (const kernelConfig of kernelConfigs3) { +for (const kernelConfig of kernelConfigs4) { registerKernel(kernelConfig); } -var ENV4 = env(); -ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([ +var ENV6 = env(); +ENV6.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([ 0, 97, 115, @@ -63026,8 +70472,8 @@ ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new 26, 11 ]))); -ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { - if (ENV4.get("IS_NODE")) { +ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => { + if (ENV6.get("IS_NODE")) { return false; } try { @@ -63120,12 +70566,18 @@ var BackendWasm = class extends KernelBackend { async read(dataId) { return this.readSync(dataId); } - readSync(dataId) { + readSync(dataId, start, end) { const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId); if (dtype === "string") { - return stringBytes; + if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) { + return stringBytes; + } + return stringBytes.slice(start, end); } - const bytes = this.wasm.HEAPU8.slice(memoryOffset, memoryOffset + util_exports.sizeFromShape(shape) * util_exports.bytesPerElement(dtype)); + start = start || 0; + end = end || util_exports.sizeFromShape(shape); + const bytesPerElement2 = util_exports.bytesPerElement(dtype); + const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2); return typedArrayFromBuffer(bytes.buffer, dtype); } disposeData(dataId, force = false) { @@ -63347,29 +70799,22 @@ function getThreadsCount() { } return actualThreadsCount; } -var version8 = "3.11.0"; +var version7 = "0.0.0"; var WASM_PRIORITY = 2; registerBackend("wasm", async () => { const { wasm } = await init(); return new BackendWasm(wasm); }, WASM_PRIORITY); -var version9 = "3.11.0"; -var version22 = "3.11.0"; -var version32 = "3.11.0"; -var version42 = "3.11.0"; -var version52 = "3.11.0"; -var version62 = "3.11.0"; -var version72 = "3.11.0"; -var version82 = "3.11.0"; -var version92 = { - tfjs: version9, - "tfjs-core": version22, - "tfjs-data": version32, - "tfjs-layers": version42, - "tfjs-converter": version52, - "tfjs-backend-cpu": version62, - "tfjs-backend-webgl": version72, - "tfjs-backend-wasm": version82 +var externalVersion = "3.11.0-20211201"; +var version8 = { + tfjs: externalVersion, + "tfjs-core": externalVersion, + "tfjs-data": externalVersion, + "tfjs-layers": externalVersion, + "tfjs-converter": externalVersion, + "tfjs-backend-cpu": externalVersion, + "tfjs-backend-webgl": externalVersion, + "tfjs-backend-wasm": externalVersion }; // src/draw/index.ts @@ -63423,7 +70868,7 @@ __export(utils_exports, { isTensor4D: () => isTensor4D, isValidNumber: () => isValidNumber, isValidProbablitiy: () => isValidProbablitiy, - range: () => range6, + range: () => range7, round: () => round5 }); @@ -63481,9 +70926,9 @@ function computeReshapedDimensions({ width, height }, inputSize) { return new Dimensions(Math.round(width * scale3), Math.round(height * scale3)); } function getCenterPoint(pts) { - return pts.reduce((sum6, pt) => sum6.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); + return pts.reduce((sum7, pt) => sum7.add(pt), new Point(0, 0)).div(new Point(pts.length, pts.length)); } -function range6(num, start, step5) { +function range7(num, start, step5) { return Array(num).fill(0).map((_, i) => start + i * step5); } function isValidNumber(num) { @@ -63803,10 +71248,10 @@ function iou(box1, box2, isIOU = true) { function minBbox(pts) { const xs = pts.map((pt) => pt.x); const ys = pts.map((pt) => pt.y); - const minX = xs.reduce((min6, x) => x < min6 ? x : min6, Infinity); - const minY = ys.reduce((min6, y) => y < min6 ? y : min6, Infinity); - const maxX = xs.reduce((max6, x) => max6 < x ? x : max6, 0); - const maxY = ys.reduce((max6, y) => max6 < y ? y : max6, 0); + const minX = xs.reduce((min7, x) => x < min7 ? x : min7, Infinity); + const minY = ys.reduce((min7, y) => y < min7 ? y : min7, Infinity); + const maxX = xs.reduce((max7, x) => max7 < x ? x : max7, 0); + const maxY = ys.reduce((max7, y) => max7 < y ? y : max7, 0); return new BoundingBox(minX, minY, maxX, maxY); } @@ -63877,7 +71322,7 @@ function shuffleArray(inputArray) { } // src/ops/index.ts -function sigmoid5(x) { +function sigmoid6(x) { return 1 / (1 + Math.exp(-x)); } function inverseSigmoid(x) { @@ -64570,7 +72015,7 @@ var NetInput = class { return this._inputSize; } get reshapedInputDimensions() { - return range6(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx)); + return range7(this.batchSize, 0, 1).map((_, batchIdx) => this.getReshapedInputDimensions(batchIdx)); } getInput(batchIdx) { return this.canvases[batchIdx] || this.imageTensors[batchIdx]; @@ -64595,7 +72040,7 @@ var NetInput = class { toBatchTensor(inputSize, isCenterInputs = true) { this._inputSize = inputSize; return tidy(() => { - const inputTensors = range6(this.batchSize, 0, 1).map((batchIdx) => { + const inputTensors = range7(this.batchSize, 0, 1).map((batchIdx) => { const input2 = this.getInput(batchIdx); if (input2 instanceof Tensor) { let imgTensor = isTensor4D(input2) ? input2 : expandDims(input2); @@ -65449,7 +72894,7 @@ function drawFaceLandmarks(canvasArg, faceLandmarks) { } // package.json -var version6 = "1.5.8"; +var version9 = "1.5.8"; // src/xception/extractParams.ts function extractorsFactory2(extractWeights, paramMappings) { @@ -65495,7 +72940,7 @@ function extractParams3(weights, numMainBlocks) { reduction_block_1: entry_flow_reduction_block_1 }; const middle_flow = {}; - range6(numMainBlocks, 0, 1).forEach((idx) => { + range7(numMainBlocks, 0, 1).forEach((idx) => { middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`); }); const exit_flow_reduction_block = extractReductionBlockParams(128, 256, "exit_flow/reduction_block"); @@ -65554,7 +72999,7 @@ function extractParamsFromWeightMap3(weightMap, numMainBlocks) { reduction_block_1: entry_flow_reduction_block_1 }; const middle_flow = {}; - range6(numMainBlocks, 0, 1).forEach((idx) => { + range7(numMainBlocks, 0, 1).forEach((idx) => { middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`); }); const exit_flow_reduction_block = extractReductionBlockParams("exit_flow/reduction_block"); @@ -65603,7 +73048,7 @@ var TinyXception = class extends NeuralNetwork { let out = relu(conv(normalized, params.entry_flow.conv_in, [2, 2])); out = reductionBlock(out, params.entry_flow.reduction_block_0, false); out = reductionBlock(out, params.entry_flow.reduction_block_1); - range6(this._numMainBlocks, 0, 1).forEach((idx) => { + range7(this._numMainBlocks, 0, 1).forEach((idx) => { out = mainBlock(out, params.middle_flow[`main_block_${idx}`]); }); out = reductionBlock(out, params.exit_flow.reduction_block); @@ -66823,8 +74268,8 @@ function validateConfig(config) { // src/tinyYolov2/leaky.ts function leaky(x) { return tidy(() => { - const min6 = mul(x, scalar(0.10000000149011612)); - return add2(relu(sub(x, min6)), min6); + const min7 = mul(x, scalar(0.10000000149011612)); + return add2(relu(sub(x, min7)), min7); }); } @@ -66854,10 +74299,10 @@ function depthwiseSeparableConv2(x, params) { function extractorsFactory7(extractWeights, paramMappings) { const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings); function extractBatchNormParams(size, mappedPrefix) { - const sub4 = tensor1d(extractWeights(size)); + const sub5 = tensor1d(extractWeights(size)); const truediv = tensor1d(extractWeights(size)); paramMappings.push({ paramPath: `${mappedPrefix}/sub` }, { paramPath: `${mappedPrefix}/truediv` }); - return { sub: sub4, truediv }; + return { sub: sub5, truediv }; } function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) { const conv3 = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`); @@ -66938,9 +74383,9 @@ function extractParams7(weights, config, boxEncodingSize, filterSizes) { function extractorsFactory8(weightMap, paramMappings) { const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings); function extractBatchNormParams(prefix) { - const sub4 = extractWeightEntry(`${prefix}/sub`, 1); + const sub5 = extractWeightEntry(`${prefix}/sub`, 1); const truediv = extractWeightEntry(`${prefix}/truediv`, 1); - return { sub: sub4, truediv }; + return { sub: sub5, truediv }; } function extractConvParams(prefix) { const filters = extractWeightEntry(`${prefix}/filters`, 4); @@ -67137,10 +74582,10 @@ var _TinyYolov2Base = class extends NeuralNetwork { for (let row = 0; row < numCells; row++) { for (let col = 0; col < numCells; col++) { for (let anchor = 0; anchor < numBoxes; anchor++) { - const score = sigmoid5(scoresData[row][col][anchor][0]); + const score = sigmoid6(scoresData[row][col][anchor][0]); if (!scoreThreshold || score > scoreThreshold) { - const ctX = (col + sigmoid5(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; - const ctY = (row + sigmoid5(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; + const ctX = (col + sigmoid6(boxesData[row][col][anchor][0])) / numCells * correctionFactorX; + const ctY = (row + sigmoid6(boxesData[row][col][anchor][1])) / numCells * correctionFactorY; const widthLocal = Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x / numCells * correctionFactorX; const heightLocal = Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y / numCells * correctionFactorY; const x = ctX - widthLocal / 2; @@ -67169,7 +74614,7 @@ var _TinyYolov2Base = class extends NeuralNetwork { return Array(this.config.classes.length).fill(0).map((_, i) => classesData[row][col][anchor][i]).map((classScore, label) => ({ classScore, label - })).reduce((max6, curr) => max6.classScore > curr.classScore ? max6 : curr); + })).reduce((max7, curr) => max7.classScore > curr.classScore ? max7 : curr); } }; var TinyYolov2Base = _TinyYolov2Base; @@ -67679,7 +75124,7 @@ function resizeResults(results, dimensions) { // src/index.ts var node = typeof process !== "undefined"; var browser = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined"; -var version7 = { faceapi: version6, node, browser }; +var version10 = { faceapi: version9, node, browser }; if (browser) { ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false); ENV.set("WEBGL_CPU_FORWARD", true); @@ -67798,7 +75243,7 @@ export { resizeResults, resolveInput, shuffleArray, - sigmoid5 as sigmoid, + sigmoid6 as sigmoid, ssdMobilenetv1, tfjs_esm_exports as tf, tinyFaceDetector, @@ -67806,7 +75251,7 @@ export { toNetInput, utils_exports as utils, validateConfig, - version7 as version + version10 as version }; /** * @license @@ -67933,6 +75378,22 @@ export { * https://opensource.org/licenses/MIT. * ============================================================================= */ +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use backend file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. @@ -67997,6 +75458,22 @@ export { * limitations under the License. * ============================================================================= */ +/** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map index 963c7a7..42bdb2f 100644 --- a/dist/face-api.esm.js.map +++ b/dist/face-api.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js", "../node_modules/.pnpm/node-fetch@2.6.6/node_modules/node-fetch/browser.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js", "(disabled):crypto", "../node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js", 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4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11\r\n ])), {}).exports;\r\n} catch (e) {\r\n // no wasm support :(\r\n}\r\n\r\n/**\r\n * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.\r\n * See the from* functions below for more convenient ways of constructing Longs.\r\n * @exports Long\r\n * @class A Long class for representing a 64 bit two's-complement integer value.\r\n * @param {number} low The low (signed) 32 bits of the long\r\n * @param {number} high The high (signed) 32 bits of the long\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @constructor\r\n */\r\nfunction Long(low, high, unsigned) {\r\n\r\n /**\r\n * The low 32 bits as a signed value.\r\n * @type {number}\r\n */\r\n this.low = low | 0;\r\n\r\n /**\r\n * The high 32 bits as a signed value.\r\n * @type {number}\r\n */\r\n this.high = high | 0;\r\n\r\n /**\r\n * Whether unsigned or not.\r\n * @type {boolean}\r\n */\r\n this.unsigned = !!unsigned;\r\n}\r\n\r\n// The internal representation of a long is the two given signed, 32-bit values.\r\n// We use 32-bit pieces because these are the size of integers on which\r\n// Javascript performs bit-operations. For operations like addition and\r\n// multiplication, we split each number into 16 bit pieces, which can easily be\r\n// multiplied within Javascript's floating-point representation without overflow\r\n// or change in sign.\r\n//\r\n// In the algorithms below, we frequently reduce the negative case to the\r\n// positive case by negating the input(s) and then post-processing the result.\r\n// Note that we must ALWAYS check specially whether those values are MIN_VALUE\r\n// (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as\r\n// a positive number, it overflows back into a negative). Not handling this\r\n// case would often result in infinite recursion.\r\n//\r\n// Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*\r\n// methods on which they depend.\r\n\r\n/**\r\n * An indicator used to reliably determine if an object is a Long or not.\r\n * @type {boolean}\r\n * @const\r\n * @private\r\n */\r\nLong.prototype.__isLong__;\r\n\r\nObject.defineProperty(Long.prototype, \"__isLong__\", { value: true });\r\n\r\n/**\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n * @inner\r\n */\r\nfunction isLong(obj) {\r\n return (obj && obj[\"__isLong__\"]) === true;\r\n}\r\n\r\n/**\r\n * Tests if the specified object is a Long.\r\n * @function\r\n * @param {*} obj Object\r\n * @returns {boolean}\r\n */\r\nLong.isLong = isLong;\r\n\r\n/**\r\n * A cache of the Long representations of small integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar INT_CACHE = {};\r\n\r\n/**\r\n * A cache of the Long representations of small unsigned integer values.\r\n * @type {!Object}\r\n * @inner\r\n */\r\nvar UINT_CACHE = {};\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromInt(value, unsigned) {\r\n var obj, cachedObj, cache;\r\n if (unsigned) {\r\n value >>>= 0;\r\n if (cache = (0 <= value && value < 256)) {\r\n cachedObj = UINT_CACHE[value];\r\n if (cachedObj)\r\n return cachedObj;\r\n }\r\n obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\r\n if (cache)\r\n UINT_CACHE[value] = obj;\r\n return obj;\r\n } else {\r\n value |= 0;\r\n if (cache = (-128 <= value && value < 128)) {\r\n cachedObj = INT_CACHE[value];\r\n if (cachedObj)\r\n return cachedObj;\r\n }\r\n obj = fromBits(value, value < 0 ? -1 : 0, false);\r\n if (cache)\r\n INT_CACHE[value] = obj;\r\n return obj;\r\n }\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given 32 bit integer value.\r\n * @function\r\n * @param {number} value The 32 bit integer in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromInt = fromInt;\r\n\r\n/**\r\n * @param {number} value\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromNumber(value, unsigned) {\r\n if (isNaN(value))\r\n return unsigned ? UZERO : ZERO;\r\n if (unsigned) {\r\n if (value < 0)\r\n return UZERO;\r\n if (value >= TWO_PWR_64_DBL)\r\n return MAX_UNSIGNED_VALUE;\r\n } else {\r\n if (value <= -TWO_PWR_63_DBL)\r\n return MIN_VALUE;\r\n if (value + 1 >= TWO_PWR_63_DBL)\r\n return MAX_VALUE;\r\n }\r\n if (value < 0)\r\n return fromNumber(-value, unsigned).neg();\r\n return fromBits((value % TWO_PWR_32_DBL) | 0, (value / TWO_PWR_32_DBL) | 0, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.\r\n * @function\r\n * @param {number} value The number in question\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromNumber = fromNumber;\r\n\r\n/**\r\n * @param {number} lowBits\r\n * @param {number} highBits\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromBits(lowBits, highBits, unsigned) {\r\n return new Long(lowBits, highBits, unsigned);\r\n}\r\n\r\n/**\r\n * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is\r\n * assumed to use 32 bits.\r\n * @function\r\n * @param {number} lowBits The low 32 bits\r\n * @param {number} highBits The high 32 bits\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromBits = fromBits;\r\n\r\n/**\r\n * @function\r\n * @param {number} base\r\n * @param {number} exponent\r\n * @returns {number}\r\n * @inner\r\n */\r\nvar pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)\r\n\r\n/**\r\n * @param {string} str\r\n * @param {(boolean|number)=} unsigned\r\n * @param {number=} radix\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromString(str, unsigned, radix) {\r\n if (str.length === 0)\r\n throw Error('empty string');\r\n if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\r\n return ZERO;\r\n if (typeof unsigned === 'number') {\r\n // For goog.math.long compatibility\r\n radix = unsigned,\r\n unsigned = false;\r\n } else {\r\n unsigned = !! unsigned;\r\n }\r\n radix = radix || 10;\r\n if (radix < 2 || 36 < radix)\r\n throw RangeError('radix');\r\n\r\n var p;\r\n if ((p = str.indexOf('-')) > 0)\r\n throw Error('interior hyphen');\r\n else if (p === 0) {\r\n return fromString(str.substring(1), unsigned, radix).neg();\r\n }\r\n\r\n // Do several (8) digits each time through the loop, so as to\r\n // minimize the calls to the very expensive emulated div.\r\n var radixToPower = fromNumber(pow_dbl(radix, 8));\r\n\r\n var result = ZERO;\r\n for (var i = 0; i < str.length; i += 8) {\r\n var size = Math.min(8, str.length - i),\r\n value = parseInt(str.substring(i, i + size), radix);\r\n if (size < 8) {\r\n var power = fromNumber(pow_dbl(radix, size));\r\n result = result.mul(power).add(fromNumber(value));\r\n } else {\r\n result = result.mul(radixToPower);\r\n result = result.add(fromNumber(value));\r\n }\r\n }\r\n result.unsigned = unsigned;\r\n return result;\r\n}\r\n\r\n/**\r\n * Returns a Long representation of the given string, written using the specified radix.\r\n * @function\r\n * @param {string} str The textual representation of the Long\r\n * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {number=} radix The radix in which the text is written (2-36), defaults to 10\r\n * @returns {!Long} The corresponding Long value\r\n */\r\nLong.fromString = fromString;\r\n\r\n/**\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val\r\n * @param {boolean=} unsigned\r\n * @returns {!Long}\r\n * @inner\r\n */\r\nfunction fromValue(val, unsigned) {\r\n if (typeof val === 'number')\r\n return fromNumber(val, unsigned);\r\n if (typeof val === 'string')\r\n return fromString(val, unsigned);\r\n // Throws for non-objects, converts non-instanceof Long:\r\n return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned);\r\n}\r\n\r\n/**\r\n * Converts the specified value to a Long using the appropriate from* function for its type.\r\n * @function\r\n * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {!Long}\r\n */\r\nLong.fromValue = fromValue;\r\n\r\n// NOTE: the compiler should inline these constant values below and then remove these variables, so there should be\r\n// no runtime penalty for these.\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_16_DBL = 1 << 16;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24_DBL = 1 << 24;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\r\n\r\n/**\r\n * @type {number}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\r\n\r\n/**\r\n * @type {!Long}\r\n * @const\r\n * @inner\r\n */\r\nvar TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ZERO = fromInt(0);\r\n\r\n/**\r\n * Signed zero.\r\n * @type {!Long}\r\n */\r\nLong.ZERO = ZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UZERO = fromInt(0, true);\r\n\r\n/**\r\n * Unsigned zero.\r\n * @type {!Long}\r\n */\r\nLong.UZERO = UZERO;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar ONE = fromInt(1);\r\n\r\n/**\r\n * Signed one.\r\n * @type {!Long}\r\n */\r\nLong.ONE = ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar UONE = fromInt(1, true);\r\n\r\n/**\r\n * Unsigned one.\r\n * @type {!Long}\r\n */\r\nLong.UONE = UONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar NEG_ONE = fromInt(-1);\r\n\r\n/**\r\n * Signed negative one.\r\n * @type {!Long}\r\n */\r\nLong.NEG_ONE = NEG_ONE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_VALUE = fromBits(0xFFFFFFFF|0, 0x7FFFFFFF|0, false);\r\n\r\n/**\r\n * Maximum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_VALUE = MAX_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF|0, 0xFFFFFFFF|0, true);\r\n\r\n/**\r\n * Maximum unsigned value.\r\n * @type {!Long}\r\n */\r\nLong.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\r\n\r\n/**\r\n * @type {!Long}\r\n * @inner\r\n */\r\nvar MIN_VALUE = fromBits(0, 0x80000000|0, false);\r\n\r\n/**\r\n * Minimum signed value.\r\n * @type {!Long}\r\n */\r\nLong.MIN_VALUE = MIN_VALUE;\r\n\r\n/**\r\n * @alias Long.prototype\r\n * @inner\r\n */\r\nvar LongPrototype = Long.prototype;\r\n\r\n/**\r\n * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.\r\n * @returns {number}\r\n */\r\nLongPrototype.toInt = function toInt() {\r\n return this.unsigned ? this.low >>> 0 : this.low;\r\n};\r\n\r\n/**\r\n * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).\r\n * @returns {number}\r\n */\r\nLongPrototype.toNumber = function toNumber() {\r\n if (this.unsigned)\r\n return ((this.high >>> 0) * TWO_PWR_32_DBL) + (this.low >>> 0);\r\n return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\r\n};\r\n\r\n/**\r\n * Converts the Long to a string written in the specified radix.\r\n * @param {number=} radix Radix (2-36), defaults to 10\r\n * @returns {string}\r\n * @override\r\n * @throws {RangeError} If `radix` is out of range\r\n */\r\nLongPrototype.toString = function toString(radix) {\r\n radix = radix || 10;\r\n if (radix < 2 || 36 < radix)\r\n throw RangeError('radix');\r\n if (this.isZero())\r\n return '0';\r\n if (this.isNegative()) { // Unsigned Longs are never negative\r\n if (this.eq(MIN_VALUE)) {\r\n // We need to change the Long value before it can be negated, so we remove\r\n // the bottom-most digit in this base and then recurse to do the rest.\r\n var radixLong = fromNumber(radix),\r\n div = this.div(radixLong),\r\n rem1 = div.mul(radixLong).sub(this);\r\n return div.toString(radix) + rem1.toInt().toString(radix);\r\n } else\r\n return '-' + this.neg().toString(radix);\r\n }\r\n\r\n // Do several (6) digits each time through the loop, so as to\r\n // minimize the calls to the very expensive emulated div.\r\n var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned),\r\n rem = this;\r\n var result = '';\r\n while (true) {\r\n var remDiv = rem.div(radixToPower),\r\n intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0,\r\n digits = intval.toString(radix);\r\n rem = remDiv;\r\n if (rem.isZero())\r\n return digits + result;\r\n else {\r\n while (digits.length < 6)\r\n digits = '0' + digits;\r\n result = '' + digits + result;\r\n }\r\n }\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as a signed integer.\r\n * @returns {number} Signed high bits\r\n */\r\nLongPrototype.getHighBits = function getHighBits() {\r\n return this.high;\r\n};\r\n\r\n/**\r\n * Gets the high 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned high bits\r\n */\r\nLongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\r\n return this.high >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as a signed integer.\r\n * @returns {number} Signed low bits\r\n */\r\nLongPrototype.getLowBits = function getLowBits() {\r\n return this.low;\r\n};\r\n\r\n/**\r\n * Gets the low 32 bits as an unsigned integer.\r\n * @returns {number} Unsigned low bits\r\n */\r\nLongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\r\n return this.low >>> 0;\r\n};\r\n\r\n/**\r\n * Gets the number of bits needed to represent the absolute value of this Long.\r\n * @returns {number}\r\n */\r\nLongPrototype.getNumBitsAbs = function getNumBitsAbs() {\r\n if (this.isNegative()) // Unsigned Longs are never negative\r\n return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\r\n var val = this.high != 0 ? this.high : this.low;\r\n for (var bit = 31; bit > 0; bit--)\r\n if ((val & (1 << bit)) != 0)\r\n break;\r\n return this.high != 0 ? bit + 33 : bit + 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isZero = function isZero() {\r\n return this.high === 0 && this.low === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eqz = LongPrototype.isZero;\r\n\r\n/**\r\n * Tests if this Long's value is negative.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isNegative = function isNegative() {\r\n return !this.unsigned && this.high < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is positive.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isPositive = function isPositive() {\r\n return this.unsigned || this.high >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is odd.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isOdd = function isOdd() {\r\n return (this.low & 1) === 1;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is even.\r\n * @returns {boolean}\r\n */\r\nLongPrototype.isEven = function isEven() {\r\n return (this.low & 1) === 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.equals = function equals(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n if (this.unsigned !== other.unsigned && (this.high >>> 31) === 1 && (other.high >>> 31) === 1)\r\n return false;\r\n return this.high === other.high && this.low === other.low;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.eq = LongPrototype.equals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.notEquals = function notEquals(other) {\r\n return !this.eq(/* validates */ other);\r\n};\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.neq = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ne = LongPrototype.notEquals;\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThan = function lessThan(other) {\r\n return this.comp(/* validates */ other) < 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lt = LongPrototype.lessThan;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\r\n return this.comp(/* validates */ other) <= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.lte = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.le = LongPrototype.lessThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThan = function greaterThan(other) {\r\n return this.comp(/* validates */ other) > 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gt = LongPrototype.greaterThan;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\r\n return this.comp(/* validates */ other) >= 0;\r\n};\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.gte = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {boolean}\r\n */\r\nLongPrototype.ge = LongPrototype.greaterThanOrEqual;\r\n\r\n/**\r\n * Compares this Long's value with the specified's.\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n * if the given one is greater\r\n */\r\nLongPrototype.compare = function compare(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n if (this.eq(other))\r\n return 0;\r\n var thisNeg = this.isNegative(),\r\n otherNeg = other.isNegative();\r\n if (thisNeg && !otherNeg)\r\n return -1;\r\n if (!thisNeg && otherNeg)\r\n return 1;\r\n // At this point the sign bits are the same\r\n if (!this.unsigned)\r\n return this.sub(other).isNegative() ? -1 : 1;\r\n // Both are positive if at least one is unsigned\r\n return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1;\r\n};\r\n\r\n/**\r\n * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.\r\n * @function\r\n * @param {!Long|number|string} other Other value\r\n * @returns {number} 0 if they are the same, 1 if the this is greater and -1\r\n * if the given one is greater\r\n */\r\nLongPrototype.comp = LongPrototype.compare;\r\n\r\n/**\r\n * Negates this Long's value.\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.negate = function negate() {\r\n if (!this.unsigned && this.eq(MIN_VALUE))\r\n return MIN_VALUE;\r\n return this.not().add(ONE);\r\n};\r\n\r\n/**\r\n * Negates this Long's value. This is an alias of {@link Long#negate}.\r\n * @function\r\n * @returns {!Long} Negated Long\r\n */\r\nLongPrototype.neg = LongPrototype.negate;\r\n\r\n/**\r\n * Returns the sum of this and the specified Long.\r\n * @param {!Long|number|string} addend Addend\r\n * @returns {!Long} Sum\r\n */\r\nLongPrototype.add = function add(addend) {\r\n if (!isLong(addend))\r\n addend = fromValue(addend);\r\n\r\n // Divide each number into 4 chunks of 16 bits, and then sum the chunks.\r\n\r\n var a48 = this.high >>> 16;\r\n var a32 = this.high & 0xFFFF;\r\n var a16 = this.low >>> 16;\r\n var a00 = this.low & 0xFFFF;\r\n\r\n var b48 = addend.high >>> 16;\r\n var b32 = addend.high & 0xFFFF;\r\n var b16 = addend.low >>> 16;\r\n var b00 = addend.low & 0xFFFF;\r\n\r\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n c00 += a00 + b00;\r\n c16 += c00 >>> 16;\r\n c00 &= 0xFFFF;\r\n c16 += a16 + b16;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c32 += a32 + b32;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c48 += a48 + b48;\r\n c48 &= 0xFFFF;\r\n return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long.\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.subtract = function subtract(subtrahend) {\r\n if (!isLong(subtrahend))\r\n subtrahend = fromValue(subtrahend);\r\n return this.add(subtrahend.neg());\r\n};\r\n\r\n/**\r\n * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.\r\n * @function\r\n * @param {!Long|number|string} subtrahend Subtrahend\r\n * @returns {!Long} Difference\r\n */\r\nLongPrototype.sub = LongPrototype.subtract;\r\n\r\n/**\r\n * Returns the product of this and the specified Long.\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.multiply = function multiply(multiplier) {\r\n if (this.isZero())\r\n return ZERO;\r\n if (!isLong(multiplier))\r\n multiplier = fromValue(multiplier);\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n var low = wasm.mul(this.low,\r\n this.high,\r\n multiplier.low,\r\n multiplier.high);\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n if (multiplier.isZero())\r\n return ZERO;\r\n if (this.eq(MIN_VALUE))\r\n return multiplier.isOdd() ? MIN_VALUE : ZERO;\r\n if (multiplier.eq(MIN_VALUE))\r\n return this.isOdd() ? MIN_VALUE : ZERO;\r\n\r\n if (this.isNegative()) {\r\n if (multiplier.isNegative())\r\n return this.neg().mul(multiplier.neg());\r\n else\r\n return this.neg().mul(multiplier).neg();\r\n } else if (multiplier.isNegative())\r\n return this.mul(multiplier.neg()).neg();\r\n\r\n // If both longs are small, use float multiplication\r\n if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\r\n return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\r\n\r\n // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.\r\n // We can skip products that would overflow.\r\n\r\n var a48 = this.high >>> 16;\r\n var a32 = this.high & 0xFFFF;\r\n var a16 = this.low >>> 16;\r\n var a00 = this.low & 0xFFFF;\r\n\r\n var b48 = multiplier.high >>> 16;\r\n var b32 = multiplier.high & 0xFFFF;\r\n var b16 = multiplier.low >>> 16;\r\n var b00 = multiplier.low & 0xFFFF;\r\n\r\n var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\r\n c00 += a00 * b00;\r\n c16 += c00 >>> 16;\r\n c00 &= 0xFFFF;\r\n c16 += a16 * b00;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c16 += a00 * b16;\r\n c32 += c16 >>> 16;\r\n c16 &= 0xFFFF;\r\n c32 += a32 * b00;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c32 += a16 * b16;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c32 += a00 * b32;\r\n c48 += c32 >>> 16;\r\n c32 &= 0xFFFF;\r\n c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\r\n c48 &= 0xFFFF;\r\n return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.\r\n * @function\r\n * @param {!Long|number|string} multiplier Multiplier\r\n * @returns {!Long} Product\r\n */\r\nLongPrototype.mul = LongPrototype.multiply;\r\n\r\n/**\r\n * Returns this Long divided by the specified. The result is signed if this Long is signed or\r\n * unsigned if this Long is unsigned.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.divide = function divide(divisor) {\r\n if (!isLong(divisor))\r\n divisor = fromValue(divisor);\r\n if (divisor.isZero())\r\n throw Error('division by zero');\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n // guard against signed division overflow: the largest\r\n // negative number / -1 would be 1 larger than the largest\r\n // positive number, due to two's complement.\r\n if (!this.unsigned &&\r\n this.high === -0x80000000 &&\r\n divisor.low === -1 && divisor.high === -1) {\r\n // be consistent with non-wasm code path\r\n return this;\r\n }\r\n var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\r\n this.low,\r\n this.high,\r\n divisor.low,\r\n divisor.high\r\n );\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n if (this.isZero())\r\n return this.unsigned ? UZERO : ZERO;\r\n var approx, rem, res;\r\n if (!this.unsigned) {\r\n // This section is only relevant for signed longs and is derived from the\r\n // closure library as a whole.\r\n if (this.eq(MIN_VALUE)) {\r\n if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\r\n return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE\r\n else if (divisor.eq(MIN_VALUE))\r\n return ONE;\r\n else {\r\n // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.\r\n var halfThis = this.shr(1);\r\n approx = halfThis.div(divisor).shl(1);\r\n if (approx.eq(ZERO)) {\r\n return divisor.isNegative() ? ONE : NEG_ONE;\r\n } else {\r\n rem = this.sub(divisor.mul(approx));\r\n res = approx.add(rem.div(divisor));\r\n return res;\r\n }\r\n }\r\n } else if (divisor.eq(MIN_VALUE))\r\n return this.unsigned ? UZERO : ZERO;\r\n if (this.isNegative()) {\r\n if (divisor.isNegative())\r\n return this.neg().div(divisor.neg());\r\n return this.neg().div(divisor).neg();\r\n } else if (divisor.isNegative())\r\n return this.div(divisor.neg()).neg();\r\n res = ZERO;\r\n } else {\r\n // The algorithm below has not been made for unsigned longs. It's therefore\r\n // required to take special care of the MSB prior to running it.\r\n if (!divisor.unsigned)\r\n divisor = divisor.toUnsigned();\r\n if (divisor.gt(this))\r\n return UZERO;\r\n if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true\r\n return UONE;\r\n res = UZERO;\r\n }\r\n\r\n // Repeat the following until the remainder is less than other: find a\r\n // floating-point that approximates remainder / other *from below*, add this\r\n // into the result, and subtract it from the remainder. It is critical that\r\n // the approximate value is less than or equal to the real value so that the\r\n // remainder never becomes negative.\r\n rem = this;\r\n while (rem.gte(divisor)) {\r\n // Approximate the result of division. This may be a little greater or\r\n // smaller than the actual value.\r\n approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\r\n\r\n // We will tweak the approximate result by changing it in the 48-th digit or\r\n // the smallest non-fractional digit, whichever is larger.\r\n var log2 = Math.ceil(Math.log(approx) / Math.LN2),\r\n delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48),\r\n\r\n // Decrease the approximation until it is smaller than the remainder. Note\r\n // that if it is too large, the product overflows and is negative.\r\n approxRes = fromNumber(approx),\r\n approxRem = approxRes.mul(divisor);\r\n while (approxRem.isNegative() || approxRem.gt(rem)) {\r\n approx -= delta;\r\n approxRes = fromNumber(approx, this.unsigned);\r\n approxRem = approxRes.mul(divisor);\r\n }\r\n\r\n // We know the answer can't be zero... and actually, zero would cause\r\n // infinite recursion since we would make no progress.\r\n if (approxRes.isZero())\r\n approxRes = ONE;\r\n\r\n res = res.add(approxRes);\r\n rem = rem.sub(approxRem);\r\n }\r\n return res;\r\n};\r\n\r\n/**\r\n * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Quotient\r\n */\r\nLongPrototype.div = LongPrototype.divide;\r\n\r\n/**\r\n * Returns this Long modulo the specified.\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.modulo = function modulo(divisor) {\r\n if (!isLong(divisor))\r\n divisor = fromValue(divisor);\r\n\r\n // use wasm support if present\r\n if (wasm) {\r\n var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\r\n this.low,\r\n this.high,\r\n divisor.low,\r\n divisor.high\r\n );\r\n return fromBits(low, wasm.get_high(), this.unsigned);\r\n }\r\n\r\n return this.sub(this.div(divisor).mul(divisor));\r\n};\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.mod = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.\r\n * @function\r\n * @param {!Long|number|string} divisor Divisor\r\n * @returns {!Long} Remainder\r\n */\r\nLongPrototype.rem = LongPrototype.modulo;\r\n\r\n/**\r\n * Returns the bitwise NOT of this Long.\r\n * @returns {!Long}\r\n */\r\nLongPrototype.not = function not() {\r\n return fromBits(~this.low, ~this.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise AND of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.and = function and(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise OR of this Long and the specified.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.or = function or(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns the bitwise XOR of this Long and the given one.\r\n * @param {!Long|number|string} other Other Long\r\n * @returns {!Long}\r\n */\r\nLongPrototype.xor = function xor(other) {\r\n if (!isLong(other))\r\n other = fromValue(other);\r\n return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftLeft = function shiftLeft(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n if ((numBits &= 63) === 0)\r\n return this;\r\n else if (numBits < 32)\r\n return fromBits(this.low << numBits, (this.high << numBits) | (this.low >>> (32 - numBits)), this.unsigned);\r\n else\r\n return fromBits(0, this.low << (numBits - 32), this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shl = LongPrototype.shiftLeft;\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRight = function shiftRight(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n if ((numBits &= 63) === 0)\r\n return this;\r\n else if (numBits < 32)\r\n return fromBits((this.low >>> numBits) | (this.high << (32 - numBits)), this.high >> numBits, this.unsigned);\r\n else\r\n return fromBits(this.high >> (numBits - 32), this.high >= 0 ? 0 : -1, this.unsigned);\r\n};\r\n\r\n/**\r\n * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr = LongPrototype.shiftRight;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount.\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\r\n if (isLong(numBits))\r\n numBits = numBits.toInt();\r\n numBits &= 63;\r\n if (numBits === 0)\r\n return this;\r\n else {\r\n var high = this.high;\r\n if (numBits < 32) {\r\n var low = this.low;\r\n return fromBits((low >>> numBits) | (high << (32 - numBits)), high >>> numBits, this.unsigned);\r\n } else if (numBits === 32)\r\n return fromBits(high, 0, this.unsigned);\r\n else\r\n return fromBits(high >>> (numBits - 32), 0, this.unsigned);\r\n }\r\n};\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shru = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.\r\n * @function\r\n * @param {number|!Long} numBits Number of bits\r\n * @returns {!Long} Shifted Long\r\n */\r\nLongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\r\n\r\n/**\r\n * Converts this Long to signed.\r\n * @returns {!Long} Signed long\r\n */\r\nLongPrototype.toSigned = function toSigned() {\r\n if (!this.unsigned)\r\n return this;\r\n return fromBits(this.low, this.high, false);\r\n};\r\n\r\n/**\r\n * Converts this Long to unsigned.\r\n * @returns {!Long} Unsigned long\r\n */\r\nLongPrototype.toUnsigned = function toUnsigned() {\r\n if (this.unsigned)\r\n return this;\r\n return fromBits(this.low, this.high, true);\r\n};\r\n\r\n/**\r\n * Converts this Long to its byte representation.\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {!Array.} Byte representation\r\n */\r\nLongPrototype.toBytes = function toBytes(le) {\r\n return le ? this.toBytesLE() : this.toBytesBE();\r\n};\r\n\r\n/**\r\n * Converts this Long to its little endian byte representation.\r\n * @returns {!Array.} Little endian byte representation\r\n */\r\nLongPrototype.toBytesLE = function toBytesLE() {\r\n var hi = this.high,\r\n lo = this.low;\r\n return [\r\n lo & 0xff,\r\n lo >>> 8 & 0xff,\r\n lo >>> 16 & 0xff,\r\n lo >>> 24 ,\r\n hi & 0xff,\r\n hi >>> 8 & 0xff,\r\n hi >>> 16 & 0xff,\r\n hi >>> 24\r\n ];\r\n};\r\n\r\n/**\r\n * Converts this Long to its big endian byte representation.\r\n * @returns {!Array.} Big endian byte representation\r\n */\r\nLongPrototype.toBytesBE = function toBytesBE() {\r\n var hi = this.high,\r\n lo = this.low;\r\n return [\r\n hi >>> 24 ,\r\n hi >>> 16 & 0xff,\r\n hi >>> 8 & 0xff,\r\n hi & 0xff,\r\n lo >>> 24 ,\r\n lo >>> 16 & 0xff,\r\n lo >>> 8 & 0xff,\r\n lo & 0xff\r\n ];\r\n};\r\n\r\n/**\r\n * Creates a Long from its byte representation.\r\n * @param {!Array.} bytes Byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @param {boolean=} le Whether little or big endian, defaults to big endian\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytes = function fromBytes(bytes, unsigned, le) {\r\n return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);\r\n};\r\n\r\n/**\r\n * Creates a Long from its little endian byte representation.\r\n * @param {!Array.} bytes Little endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesLE = function fromBytesLE(bytes, unsigned) {\r\n return new Long(\r\n bytes[0] |\r\n bytes[1] << 8 |\r\n bytes[2] << 16 |\r\n bytes[3] << 24,\r\n bytes[4] |\r\n bytes[5] << 8 |\r\n bytes[6] << 16 |\r\n bytes[7] << 24,\r\n unsigned\r\n );\r\n};\r\n\r\n/**\r\n * Creates a Long from its big endian byte representation.\r\n * @param {!Array.} bytes Big endian byte representation\r\n * @param {boolean=} unsigned Whether unsigned or not, defaults to signed\r\n * @returns {Long} The corresponding Long value\r\n */\r\nLong.fromBytesBE = function fromBytesBE(bytes, unsigned) {\r\n return new Long(\r\n bytes[4] << 24 |\r\n bytes[5] << 16 |\r\n bytes[6] << 8 |\r\n bytes[7],\r\n bytes[0] << 24 |\r\n bytes[1] << 16 |\r\n bytes[2] << 8 |\r\n bytes[3],\r\n unsigned\r\n );\r\n};\r\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];function warnOnce(text){if(!warnOnce.shown)warnOnce.shown={};if(!warnOnce.shown[text]){warnOnce.shown[text]=1;err(text)}}var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function 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i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"I\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={10520:function(){throw\"Canceled!\"},10538:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";var pthread=PThread.pthreads[pthread_ptr];if(pthread){GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},runExitHandlersAndDeinitThread:function(tb,exitCode){Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0)},threadExit:function(exitCode){var tb=_pthread_self();if(tb){PThread.runExitHandlersAndDeinitThread(tb,exitCode);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlersAndDeinitThread(_pthread_self(),-1);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function __pthread_testcancel_js(){if(!ENVIRONMENT_IS_PTHREAD)return;var tb=_pthread_self();if(!tb)return;var cancelDisabled=Atomics.load(GROWABLE_HEAP_U32(),tb+56>>2);if(cancelDisabled)return;var canceled=Atomics.load(GROWABLE_HEAP_U32(),tb+0>>2);if(canceled==2)throw\"Canceled!\"}function _emscripten_check_blocking_allowed(){if(ENVIRONMENT_IS_NODE)return;if(ENVIRONMENT_IS_WORKER)return;warnOnce(\"Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread\")}function __emscripten_do_pthread_join(thread,status,block){if(!thread){err(\"pthread_join attempted on a null thread pointer!\");return ERRNO_CODES.ESRCH}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self()==thread){err(\"PThread \"+thread+\" is attempting to join to itself!\");return ERRNO_CODES.EDEADLK}else if(!ENVIRONMENT_IS_PTHREAD&&_emscripten_main_browser_thread_id()==thread){err(\"Main thread \"+thread+\" is attempting to join to itself!\");return ERRNO_CODES.EDEADLK}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_join attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var detached=Atomics.load(GROWABLE_HEAP_U32(),thread+64>>2);if(detached){err(\"Attempted to join thread \"+thread+\", which was already detached!\");return ERRNO_CODES.EINVAL}if(block){_emscripten_check_blocking_allowed()}for(;;){var threadStatus=Atomics.load(GROWABLE_HEAP_U32(),thread+0>>2);if(threadStatus==1){var threadExitCode=Atomics.load(GROWABLE_HEAP_U32(),thread+4>>2);if(status)GROWABLE_HEAP_I32()[status>>2]=threadExitCode;Atomics.store(GROWABLE_HEAP_U32(),thread+64>>2,1);if(!ENVIRONMENT_IS_PTHREAD)cleanupThread(thread);else postMessage({\"cmd\":\"cleanupThread\",\"thread\":thread});return 0}if(!block){return ERRNO_CODES.EBUSY}__pthread_testcancel_js();if(!ENVIRONMENT_IS_PTHREAD)_emscripten_main_thread_process_queued_calls();_emscripten_futex_wait(thread+0,threadStatus,ENVIRONMENT_IS_PTHREAD?100:1)}}function _pthread_join(thread,status){return __emscripten_do_pthread_join(thread,status,true)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"A\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"l\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"z\":_pthread_cleanup_pop,\"k\":_pthread_cleanup_push,\"h\":_pthread_create,\"m\":_pthread_join,\"t\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init_with_threads_count=Module[\"_init_with_threads_count\"]=function(){return(_init_with_threads_count=Module[\"_init_with_threads_count\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _get_threads_count=Module[\"_get_threads_count\"]=function(){return(_get_threads_count=Module[\"_get_threads_count\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _All=Module[\"_All\"]=function(){return(_All=Module[\"_All\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _Any=Module[\"_Any\"]=function(){return(_Any=Module[\"_Any\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Cosh=Module[\"_Cosh\"]=function(){return(_Cosh=Module[\"_Cosh\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _Elu=Module[\"_Elu\"]=function(){return(_Elu=Module[\"_Elu\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var 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74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write,\"h\":_pthread_create,\"g\":_pthread_join,\"i\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var 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stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"Ya\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Backend, DataId} from '../tensor';\nimport {BackendValues, DataType} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n kernelMs: number|{error: string};\n getExtraProfileInfo?(): string; // a field for additional timing information\n // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): BackendValues;\n disposeData(dataId: DataId, force?: boolean): boolean;\n write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n move(\n dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n refCount: number): void;\n memory(): {unreliable: boolean;}; // Backend-specific information.\n /** Returns number of data ids currently in the storage. */\n numDataIds(): number;\n refCount(dataId: DataId): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n private data = new WeakMap();\n private dataIdsCount = 0;\n\n constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n get(dataId: DataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n\n set(dataId: DataId, value: T): void {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n\n has(dataId: DataId): boolean {\n return this.data.has(dataId);\n }\n\n delete(dataId: DataId): boolean {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n\n numDataIds(): number {\n return this.dataIdsCount;\n }\n}\n\nexport interface DataMover {\n /**\n * To be called by backends whenever they see a dataId that they don't own.\n * Upon calling this method, the mover will fetch the tensor from another\n * backend and register it with the current active backend.\n */\n moveData(backend: KernelBackend, dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n // check if backend timer is available\n timerAvailable(): boolean;\n time(f: () => void): Promise;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, Backend, BackendTimer {\n refCount(dataId: DataId): number {\n return notYetImplemented('refCount');\n }\n incRef(dataId: DataId): void {\n return notYetImplemented('incRef');\n }\n timerAvailable(): boolean {\n return true;\n }\n time(f: () => void): Promise {\n return notYetImplemented('time');\n }\n read(dataId: object): Promise {\n return notYetImplemented('read');\n }\n readSync(dataId: object): BackendValues {\n return notYetImplemented('readSync');\n }\n numDataIds(): number {\n return notYetImplemented('numDataIds');\n }\n disposeData(dataId: object, force?: boolean): boolean {\n return notYetImplemented('disposeData');\n }\n write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n return notYetImplemented('write');\n }\n move(\n dataId: DataId, values: BackendValues, shape: number[], dtype: DataType,\n refCount: number): void {\n return notYetImplemented('move');\n }\n memory(): {unreliable: boolean; reasons?: string[]} {\n return notYetImplemented('memory');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision(): 16|32 {\n return notYetImplemented('floatPrecision');\n }\n /** Returns the smallest representable number. */\n epsilon(): number {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n }\n dispose(): void {\n return notYetImplemented('dispose');\n }\n}\n\nfunction notYetImplemented(kernelName: string): never {\n throw new Error(\n `'${kernelName}' not yet implemented or not found in the registry. ` +\n `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n Float32Array): void {\n let counter = array.length;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element with it\n swap(array, counter, index);\n }\n}\n\n/**\n * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1,2,3,4,5];\n * const b = [11,22,33,44,55];\n * tf.util.shuffleCombo(a, b);\n * console.log(a, b);\n * ```\n *\n * @param array The first array to shuffle in-place.\n * @param array2 The second array to shuffle in-place with the same permutation\n * as the first array.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function shuffleCombo(\n // tslint:disable-next-line:no-any\n array: any[]|Uint32Array|Int32Array|Float32Array,\n // tslint:disable-next-line:no-any\n array2: any[]|Uint32Array|Int32Array|Float32Array): void {\n if (array.length !== array2.length) {\n throw new Error(\n `Array sizes must match to be shuffled together ` +\n `First array length was ${array.length}` +\n `Second array length was ${array2.length}`);\n }\n let counter = array.length;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element of each array with it\n swap(array, counter, index);\n swap(array2, counter, index);\n }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n return val % 2 === 0 ? val : val + 1;\n}\n\nexport function swap(\n object: {[index: number]: T}, left: number, right: number) {\n const temp = object[left];\n object[left] = object[right];\n object[right] = temp;\n}\n\nexport function sum(arr: number[]): number {\n let sum = 0;\n for (let i = 0; i < arr.length; i++) {\n sum += arr[i];\n }\n return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n const r = Math.random();\n return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\n\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n * error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr: boolean, msg: () => string) {\n if (!expr) {\n throw new Error(typeof msg === 'string' ? msg : msg());\n }\n}\n\nexport function assertShapesMatch(\n shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n assert(\n arraysEqual(shapeA, shapeB),\n () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n assert(\n a != null,\n () => `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten().\n/**\n * Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n * @param arr The nested array to flatten.\n * @param result The destination array which holds the elements.\n * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n * to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function\nflatten|TypedArray>(\n arr: T|RecursiveArray, result: T[] = [], skipTypedArray = false): T[] {\n if (result == null) {\n result = [];\n }\n if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], result, skipTypedArray);\n }\n } else {\n result.push(arr as T);\n }\n return result;\n}\n\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape: number[]): number {\n if (shape.length === 0) {\n // Scalar.\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\n\nexport function isInt(a: number): boolean {\n return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n // tslint:disable-next-line:no-any\n if ((Math as any).tanh != null) {\n // tslint:disable-next-line:no-any\n return (Math as any).tanh(x);\n }\n if (x === Infinity) {\n return 1;\n } else if (x === -Infinity) {\n return -1;\n } else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\n\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n: number): Uint32Array {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n if (size <= a.length) {\n return a;\n }\n return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n checkFn: () => boolean, delayFn = (counter: number) => 0,\n maxCounter?: number): Promise {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n\n tryCount++;\n\n const nextBackoff = delayFn(tryCount);\n\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n setTimeout(tryFn, nextBackoff);\n };\n\n tryFn();\n });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n shape: number[], size: number): number[] {\n let shapeProd = 1;\n let implicitIdx = -1;\n\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n } else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(\n `Shapes can only have 1 implicit size. ` +\n `Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n } else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n\n if (shapeProd === 0) {\n throw Error(\n `Cannot infer the missing size in [${shape}] when ` +\n `there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(\n `The implicit shape can't be a fractional number. ` +\n `Got ${size} / ${shapeProd}`);\n }\n\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\n\nexport function parseAxisParam(\n axis: number|number[], shape: number[]): number[] {\n const rank = shape.length;\n\n // Normalize input\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n // Check for valid range\n assert(\n axis.every(ax => ax >= -rank && ax < rank),\n () =>\n `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n `got axis ${axis}`);\n\n // Check for only integers\n assert(\n axis.every(ax => isInt(ax)),\n () => `All values in axis param must be integers but ` +\n `got axis ${axis}`);\n\n // Handle negative axis.\n return axis.map(a => a < 0 ? rank + a : a);\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n {newShape: number[], keptDims: number[]} {\n const newShape: number[] = [];\n const keptDims: number[] = [];\n const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n const axes = (axis == null || isEmptyArray) ?\n null :\n parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(\n `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values as DataTypeMap[D];\n}\n\nexport function getArrayFromDType(\n dtype: D, size: number): DataTypeMap[D] {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n } else if (dtype === 'int32') {\n values = new Int32Array(size);\n } else if (dtype === 'bool') {\n values = new Uint8Array(size);\n } else if (dtype === 'string') {\n values = new Array<'string'>(size);\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values as DataTypeMap[D];\n}\n\nexport function checkConversionForErrors(\n vals: DataTypeMap[D]|number[], dtype: D): void {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\n\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype: DataType): boolean {\n return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n dtype === 'int32' || dtype === 'string';\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n if (newType === 'complex64') {\n return false;\n }\n if (newType === 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'bool' && oldType === 'bool') {\n return false;\n }\n return true;\n}\n\nexport function isTypedArray(a: {}):\n a is Float32Array|Int32Array|Uint8Array|Uint8ClampedArray {\n return a instanceof Float32Array || a instanceof Int32Array ||\n a instanceof Uint8Array || a instanceof Uint8ClampedArray;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n if (dtype === 'float32' || dtype === 'int32') {\n return 4;\n } else if (dtype === 'complex64') {\n return 8;\n } else if (dtype === 'bool') {\n return 1;\n } else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: Uint8Array[]): number {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach(x => bytes += x.length);\n return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return 'float32';\n } else if (values instanceof Int32Array\n || values instanceof Uint8Array\n || values instanceof Uint8ClampedArray) {\n return 'int32';\n } else if (isNumber(values)) {\n return 'float32';\n } else if (isString(values)) {\n return 'string';\n } else if (isBoolean(values)) {\n return 'bool';\n }\n return 'float32';\n}\n\nexport function isFunction(f: Function) {\n return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n\n // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n // strides.\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\n\nfunction createNestedArray(\n offset: number, shape: number[], a: TypedArray, isComplex = false) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0] * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n } else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n }\n }\n return ret;\n}\n\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(\n shape: number[], a: TypedArray, isComplex = false) {\n if (shape.length === 0) {\n // Scalar type should return a single number.\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n if (size === 0) {\n // A tensor with shape zero should be turned into empty list.\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size ${a.length}${\n isComplex ? ' for a complex tensor' : ''}.`);\n }\n\n return createNestedArray(0, shape, a, isComplex);\n}\n\nexport function makeOnesTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n const array = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array.length; i++) {\n array[i] = 1;\n }\n return array;\n}\n\nexport function makeZerosTypedArray(\n size: number, dtype: D): DataTypeMap[D] {\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(size) as DataTypeMap[D];\n } else if (dtype === 'int32') {\n return new Int32Array(size) as DataTypeMap[D];\n } else if (dtype === 'bool') {\n return new Uint8Array(size) as DataTypeMap[D];\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(\n shape: number[], dtype: D) {\n const size = shape.reduce((prev, curr) => prev * curr, 1);\n if (dtype == null || dtype === 'float32') {\n return toNestedArray(shape, new Float32Array(size));\n } else if (dtype === 'int32') {\n return toNestedArray(shape, new Int32Array(size));\n } else if (dtype === 'bool') {\n return toNestedArray(shape, new Uint8Array(size));\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\nexport function assertNonNegativeIntegerDimensions(shape: number[]) {\n shape.forEach(dimSize => {\n assert(\n Number.isInteger(dimSize) && dimSize >= 0,\n () =>\n `Tensor must have a shape comprised of positive integers but got ` +\n `shape [${shape}].`);\n });\n}\n\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(\n locs: number[], rank: number, strides: number[]): number {\n if (rank === 0) {\n return 0;\n } else if (rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += strides[i] * locs[i];\n }\n return index;\n}\n\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(\n index: number, rank: number, strides: number[]): number[] {\n if (rank === 0) {\n return [];\n } else if (rank === 1) {\n return [index];\n }\n const locs: number[] = new Array(rank);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / strides[i]);\n index -= locs[i] * strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n}\n\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object: any): object is Promise {\n // We chose to not use 'obj instanceOf Promise' for two reasons:\n // 1. It only reliably works for es6 Promise, not other Promise\n // implementations.\n // 2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n // the async calls, so it is possible the obj (patched) is comparing to a\n // pre-patched Promise.\n return object && object.then && typeof object.then === 'function';\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.warn(...msg);\n }\n}\n\nexport function log(...msg: Array<{}>): void {\n if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) {\n console.log(...msg);\n }\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Platform} from './platforms/platform';\nimport {isPromise} from './util_base';\nimport * as log from './log';\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n\ntype FlagValue = number|boolean;\ntype FlagEvaluationFn = (() => FlagValue)|(() => Promise);\nexport type Flags = {\n [featureName: string]: FlagValue\n};\nexport type FlagRegistryEntry = {\n evaluationFn: FlagEvaluationFn;\n setHook?: (value: FlagValue) => void;\n};\n\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n private flags: Flags = {};\n private flagRegistry: {[flagName: string]: FlagRegistryEntry} = {};\n\n private urlFlags: Flags = {};\n\n platformName: string;\n platform: Platform;\n\n // Jasmine spies on this in 'environment_test.ts'\n getQueryParams = getQueryParams;\n\n // tslint:disable-next-line: no-any\n constructor(public global: any) {\n this.populateURLFlags();\n }\n\n setPlatform(platformName: string, platform: Platform) {\n if (this.platform != null) {\n log.warn(\n `Platform ${this.platformName} has already been set. ` +\n `Overwriting the platform with ${platform}.`);\n }\n this.platformName = platformName;\n this.platform = platform;\n }\n\n registerFlag(\n flagName: string, evaluationFn: FlagEvaluationFn,\n setHook?: (value: FlagValue) => void) {\n this.flagRegistry[flagName] = {evaluationFn, setHook};\n\n // Override the flag value from the URL. This has to happen here because the\n // environment is initialized before flags get registered.\n if (this.urlFlags[flagName] != null) {\n const flagValue = this.urlFlags[flagName];\n log.warn(\n `Setting feature override from URL ${flagName}: ${flagValue}.`);\n this.set(flagName, flagValue);\n }\n }\n\n async getAsync(flagName: string): Promise {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n\n this.flags[flagName] = await this.evaluateFlag(flagName);\n return this.flags[flagName];\n }\n\n get(flagName: string): FlagValue {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n\n const flagValue = this.evaluateFlag(flagName);\n if (isPromise(flagValue)) {\n throw new Error(\n `Flag ${flagName} cannot be synchronously evaluated. ` +\n `Please use getAsync() instead.`);\n }\n\n this.flags[flagName] = flagValue as number | boolean;\n\n return this.flags[flagName];\n }\n\n getNumber(flagName: string): number {\n return this.get(flagName) as number;\n }\n\n getBool(flagName: string): boolean {\n return this.get(flagName) as boolean;\n }\n\n getFlags(): Flags {\n return this.flags;\n }\n // For backwards compatibility.\n get features(): Flags {\n return this.flags;\n }\n\n set(flagName: string, value: FlagValue): void {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(\n `Cannot set flag ${flagName} as it has not been registered.`);\n }\n this.flags[flagName] = value;\n if (this.flagRegistry[flagName].setHook != null) {\n this.flagRegistry[flagName].setHook(value);\n }\n }\n\n private evaluateFlag(flagName: string): FlagValue|Promise {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(\n `Cannot evaluate flag '${flagName}': no evaluation function found.`);\n }\n return this.flagRegistry[flagName].evaluationFn();\n }\n\n setFlags(flags: Flags) {\n this.flags = Object.assign({}, flags);\n }\n\n reset() {\n this.flags = {};\n this.urlFlags = {};\n this.populateURLFlags();\n }\n\n private populateURLFlags(): void {\n if (typeof this.global === 'undefined' ||\n typeof this.global.location === 'undefined' ||\n typeof this.global.location.search === 'undefined') {\n return;\n }\n\n const urlParams = this.getQueryParams(this.global.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n keyValues.forEach(keyValue => {\n const [key, value] = keyValue.split(':') as [string, string];\n this.urlFlags[key] = parseValue(key, value);\n });\n }\n }\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join('=');\n });\n return params;\n}\n\nfunction decodeParam(\n params: {[key: string]: string}, name: string, value?: string) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n\nfunction parseValue(flagName: string, value: string): FlagValue {\n value = value.toLowerCase();\n if (value === 'true' || value === 'false') {\n return value === 'true';\n } else if (`${+ value}` === value) {\n return +value;\n }\n throw new Error(\n `Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n return ENV;\n}\n\nexport let ENV: Environment = null;\nexport function setEnvironmentGlobal(environment: Environment) {\n ENV = environment;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace: {_tfGlobals: Map};\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace(): {_tfGlobals: Map} {\n if (globalNameSpace == null) {\n // tslint:disable-next-line:no-any\n let ns: any;\n if (typeof (window) !== 'undefined') {\n ns = window;\n } else if (typeof (global) !== 'undefined') {\n ns = global;\n } else if (typeof (process) !== 'undefined') {\n ns = process;\n } else if (typeof (self) !== 'undefined') {\n ns = self;\n } else {\n throw new Error('Could not find a global object');\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\n\n// tslint:disable-next-line:no-any\nfunction getGlobalMap(): Map {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = new Map();\n }\n return ns._tfGlobals;\n}\n\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n * the first time it is fetched.\n */\nexport function getGlobal(key: string, init: () => T): T {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n } else {\n const singleton = init();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Allow UpperCamelCase variable names\n// tslint:disable: variable-name\n// Unfortunately just enabling PascalCase per file (tslint:enable:\n// allow-pascal-case) doesn't work.\nimport {NamedTensorInfoMap, TensorInfo} from './kernel_registry';\nimport {ExplicitPadding} from './ops/conv_util';\nimport {Activation} from './ops/fused_types';\nimport {DataType, PixelData} from './types';\n\nexport const Abs = 'Abs';\nexport type AbsInputs = UnaryInputs;\n\nexport const Acos = 'Acos';\nexport type AcosInputs = UnaryInputs;\n\nexport const Acosh = 'Acosh';\nexport type AcoshInputs = UnaryInputs;\n\nexport const Add = 'Add';\nexport type AddInputs = BinaryInputs;\n\nexport const AddN = 'AddN';\nexport type AddNInputs = TensorInfo[];\n\nexport const All = 'All';\nexport type AllInputs = Pick;\nexport interface AllAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Any = 'Any';\nexport type AnyInputs = Pick;\nexport interface AnyAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const ArgMax = 'ArgMax';\nexport type ArgMaxInputs = Pick;\nexport interface ArgMaxAttrs {\n axis: number;\n}\n\nexport const ArgMin = 'ArgMin';\nexport type ArgMinInputs = Pick;\nexport interface ArgMinAttrs {\n axis: number;\n}\n\nexport const Asin = 'Asin';\nexport type AsinInputs = UnaryInputs;\n\nexport const Asinh = 'Asinh';\nexport type AsinhInputs = UnaryInputs;\n\nexport const Atan = 'Atan';\nexport type AtanInputs = UnaryInputs;\n\nexport const Atanh = 'Atanh';\nexport type AtanhInputs = UnaryInputs;\n\nexport const Atan2 = 'Atan2';\nexport type Atan2Inputs = BinaryInputs;\n\nexport const AvgPool = 'AvgPool';\nexport type AvgPoolInputs = Pick;\nexport interface AvgPoolAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const AvgPoolGrad = 'AvgPoolGrad';\nexport type AvgPoolGradInputs = Pick;\nexport interface AvgPoolGradAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n}\n\nexport const AvgPool3D = 'AvgPool3D';\nexport type AvgPool3DInputs = Pick;\nexport interface AvgPool3DAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n dataFormat: 'NDHWC'|'NCDHW';\n}\n\nexport const AvgPool3DGrad = 'AvgPool3DGrad';\nexport type AvgPool3DGradInputs = Pick;\nexport interface AvgPool3DGradAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const BatchMatMul = 'BatchMatMul';\nexport type BatchMatMulInputs = Pick;\nexport interface BatchMatMulAttrs {\n transposeA: boolean;\n transposeB: boolean;\n}\n\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport type BatchToSpaceNDInputs = Pick;\nexport interface BatchToSpaceNDAttrs {\n blockShape: number[];\n crops: number[][];\n}\n\nexport type BinaryInputs = Pick;\n\nexport const Bincount = 'Bincount';\nexport type BincountInputs = Pick;\nexport interface BincountAttrs {\n size: number;\n}\n\nexport const BroadcastTo = 'BroadcastTo';\nexport type BroadcastToInputs = Pick;\nexport interface BroadCastToAttrs {\n shape: number[];\n inputShape: number[]; // for gradient\n}\n\nexport const BroadcastArgs = 'BroadcastArgs';\nexport type BroadcastArgsInputs = Pick;\n\nexport const Cast = 'Cast';\nexport type CastInputs = UnaryInputs;\nexport interface CastAttrs {\n dtype: DataType;\n}\n\nexport const Ceil = 'Ceil';\nexport type CeilInputs = UnaryInputs;\n\nexport const ClipByValue = 'ClipByValue';\nexport type ClipByValueInputs = UnaryInputs;\nexport interface ClipByValueAttrs {\n clipValueMin: number;\n clipValueMax: number;\n}\n\nexport const Complex = 'Complex';\nexport type ComplexInputs = Pick;\n\nexport const ComplexAbs = 'ComplexAbs';\nexport type ComplexAbsInputs = UnaryInputs;\n\nexport const Concat = 'Concat';\nexport type ConcatInputs = TensorInfo[];\nexport interface ConcatAttrs {\n axis: number;\n}\n\nexport const Conv2D = 'Conv2D';\nexport type Conv2DInputs = Pick;\nexport interface Conv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport type Conv2DBackpropFilterInputs = Pick;\nexport interface Conv2DBackpropFilterAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dimRoundingMode?: 'floor'|'round'|'ceil';\n filterShape: [number, number, number, number];\n}\n\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport type Conv2DBackpropInputInputs = Pick;\nexport interface Conv2DBackpropInputAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dimRoundingMode?: 'floor'|'round'|'ceil';\n inputShape: [number, number, number, number];\n}\n\nexport const Conv3D = 'Conv3D';\nexport type Conv3DInputs = Pick;\nexport interface Conv3DAttrs {\n strides: [number, number, number]|number;\n pad: 'valid'|'same';\n dataFormat: 'NDHWC'|'NCDHW';\n dilations: [number, number, number]|number;\n}\n\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport type Conv3DBackpropFilterV2Inputs = Pick;\n\nexport interface Conv3DBackpropFilterV2Attrs {\n strides: [number, number, number]|number;\n pad: 'valid'|'same';\n filterShape: [number, number, number, number, number];\n}\n\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport type Conv3DBackpropInputV2Inputs =\n Pick;\nexport interface Conv3DBackpropInputV2Attrs {\n strides: [number, number, number]|number;\n pad: 'valid'|'same';\n inputShape: [number, number, number, number, number];\n}\n\nexport const Cos = 'Cos';\nexport type CosInputs = UnaryInputs;\n\nexport const Cosh = 'Cosh';\nexport type CoshInputs = UnaryInputs;\n\nexport const Cumsum = 'Cumsum';\nexport type CumsumInputs = Pick;\nexport interface CumsumAttrs {\n axis: number;\n exclusive: boolean;\n reverse: boolean;\n}\n\nexport const CropAndResize = 'CropAndResize';\nexport type CropAndResizeInputs =\n Pick;\nexport interface CropAndResizeAttrs {\n cropSize: [number, number];\n method: 'bilinear'|'nearest';\n extrapolationValue: number;\n}\n\nexport const DenseBincount = 'DenseBincount';\nexport type DenseBincountInputs = Pick;\nexport interface DenseBincountAttrs {\n size: number;\n binaryOutput?: boolean;\n}\n\nexport const DepthToSpace = 'DepthToSpace';\nexport type DepthToSpaceInputs = Pick;\nexport interface DepthToSpaceAttrs {\n blockSize: number;\n dataFormat: 'NHWC'|'NCHW';\n}\n\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport type DepthwiseConv2dNativeInputs =\n Pick;\nexport interface DepthwiseConv2dNativeAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const DepthwiseConv2dNativeBackpropFilter =\n 'DepthwiseConv2dNativeBackpropFilter';\nexport type DepthwiseConv2dNativeBackpropFilterInputs =\n Pick;\nexport interface DepthwiseConv2dNativeBackpropFilterAttrs {\n strides: [number, number]|number;\n dilations: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n filterShape: [number, number, number, number];\n}\n\nexport const DepthwiseConv2dNativeBackpropInput =\n 'DepthwiseConv2dNativeBackpropInput';\nexport type DepthwiseConv2dNativeBackpropInputInputs =\n Pick;\nexport interface DepthwiseConv2dNativeBackpropInputAttrs {\n strides: [number, number]|number;\n dilations: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n inputShape: [number, number, number, number];\n}\n\nexport const Diag = 'Diag';\nexport type DiagInputs = Pick;\n\nexport const Dilation2D = 'Dilation2D';\nexport type Dilation2DInputs = Pick;\nexport interface Dilation2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n dilations: [number, number]|number;\n}\n\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport type Dilation2DBackpropInputInputs =\n Pick;\n\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport type Dilation2DBackpropFilterInputs =\n Pick;\n\nexport const RealDiv = 'RealDiv';\nexport type RealDivInputs = BinaryInputs;\n\nexport const Einsum = 'Einsum';\nexport type EinsumInputs = TensorInfo[];\nexport interface EinsumAttrs {\n equation: string;\n}\n\nexport const Elu = 'Elu';\nexport type EluInputs = Pick;\n\nexport const EluGrad = 'EluGrad';\nexport type EluGradInputs = Pick;\n\nexport const Erf = 'Erf';\nexport type ErfInputs = UnaryInputs;\n\nexport const Equal = 'Equal';\nexport type EqualInputs = BinaryInputs;\n\nexport const Exp = 'Exp';\nexport type ExpInputs = UnaryInputs;\n\nexport const ExpandDims = 'ExpandDims';\nexport type ExpandDimsInputs = Pick;\nexport interface ExpandDimsAttrs {\n dim: number;\n}\n\nexport const Expm1 = 'Expm1';\nexport type Expm1Inputs = UnaryInputs;\n\nexport const FFT = 'FFT';\nexport type FFTInputs = Pick;\n\nexport const Fill = 'Fill';\nexport interface FillAttrs {\n shape: number[];\n value: number|string;\n dtype: DataType;\n}\n\nexport const FlipLeftRight = 'FlipLeftRight';\nexport type FlipLeftRightInputs = Pick;\n\nexport const Floor = 'Floor';\nexport type FloorInputs = UnaryInputs;\n\nexport const FloorDiv = 'FloorDiv';\nexport type FloorDivInputs = BinaryInputs;\n\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport type FusedBatchNormInputs =\n Pick;\nexport interface FusedBatchNormAttrs {\n varianceEpsilon: number;\n}\n\nexport const GatherV2 = 'GatherV2';\nexport type GatherV2Inputs = Pick;\nexport interface GatherV2Attrs {\n axis: number;\n batchDims: number;\n}\n\nexport const GatherNd = 'GatherNd';\nexport type GatherNdInputs = Pick;\n\nexport const Greater = 'Greater';\nexport type GreaterInputs = BinaryInputs;\n\nexport const GreaterEqual = 'GreaterEqual';\nexport type GreaterEqualInputs = BinaryInputs;\n\nexport const Identity = 'Identity';\nexport type IdentityInputs = Pick;\n\nexport const IFFT = 'IFFT';\nexport type IFFTInputs = Pick;\n\nexport const Imag = 'Imag';\nexport type ImagInputs = Pick;\n\nexport const IsFinite = 'IsFinite';\nexport type IsFiniteInputs = UnaryInputs;\n\nexport const IsInf = 'IsInf';\nexport type IsInfInputs = UnaryInputs;\n\nexport const IsNan = 'IsNan';\nexport type IsNanInputs = UnaryInputs;\n\nexport const LeakyRelu = 'LeakyRelu';\nexport type LeakyReluInputs = Pick;\nexport interface LeakyReluAttrs {\n alpha: number;\n}\n\nexport const Less = 'Less';\nexport type LessInputs = BinaryInputs;\n\nexport const LessEqual = 'LessEqual';\nexport type LessEqualInputs = BinaryInputs;\n\nexport const LinSpace = 'LinSpace';\nexport interface LinSpaceAttrs {\n start: number;\n stop: number;\n num: number;\n}\nexport const Log = 'Log';\nexport type LogInputs = UnaryInputs;\n\nexport const Log1p = 'Log1p';\nexport type Log1pInputs = UnaryInputs;\n\nexport const LogicalAnd = 'LogicalAnd';\nexport type LogicalAndInputs = BinaryInputs;\n\nexport const LogicalNot = 'LogicalNot';\nexport type LogicalNotInputs = Pick;\n\nexport const LogicalOr = 'LogicalOr';\nexport type LogicalOrInputs = BinaryInputs;\n\nexport const LogSoftmax = 'LogSoftmax';\nexport type LogSoftmaxInputs = Pick;\nexport interface LogSoftmaxAttrs {\n axis: number;\n}\n\nexport const LRN = 'LRN';\nexport type LRNInputs = Pick;\nexport interface LRNAttrs {\n depthRadius: number;\n bias: number;\n alpha: number;\n beta: number;\n}\n\nexport const LRNGrad = 'LRNGrad';\nexport type LRNGradInputs = Pick;\nexport interface LRNGradAttrs {\n depthRadius: number;\n bias: number;\n alpha: number;\n beta: number;\n}\n\nexport const Max = 'Max';\nexport type MaxInputs = Pick;\nexport interface MaxAttrs {\n reductionIndices: number|number[];\n keepDims: boolean;\n}\n\nexport const Maximum = 'Maximum';\nexport type MaximumInputs = BinaryInputs;\n\nexport const MaxPool = 'MaxPool';\nexport type MaxPoolInputs = Pick;\nexport interface MaxPoolAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolGrad = 'MaxPoolGrad';\nexport type MaxPoolGradInputs = Pick;\nexport interface MaxPoolGradAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3D = 'MaxPool3D';\nexport type MaxPool3DInputs = Pick;\nexport interface MaxPool3DAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dataFormat: 'NDHWC'|'NCDHW';\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3DGrad = 'MaxPool3DGrad';\nexport type MaxPool3DGradInputs =\n Pick;\nexport interface MaxPool3DGradAttrs {\n filterSize: [number, number, number]|number;\n strides: [number, number, number]|number;\n pad: 'valid'|'same'|number;\n dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport type MaxPoolWithArgmaxInputs = Pick;\nexport interface MaxPoolWithArgmaxAttrs {\n filterSize: [number, number]|number;\n strides: [number, number]|number;\n pad: 'valid'|'same'|number;\n includeBatchInIndex: boolean;\n}\n\nexport const Mean = 'Mean';\nexport type MeanInputs = Pick;\nexport interface MeanAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Min = 'Min';\nexport type MinInputs = Pick;\nexport interface MinAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Minimum = 'Minimum';\nexport type MinimumInputs = BinaryInputs;\n\nexport const MirrorPad = 'MirrorPad';\nexport type MirrorPadInputs = Pick;\nexport interface MirrorPadAttrs {\n paddings: Array<[number, number]>;\n mode: 'reflect'|'symmetric';\n}\n\nexport const Mod = 'Mod';\nexport type ModInputs = BinaryInputs;\n\nexport const Multinomial = 'Multinomial';\nexport type MultinomialInputs = Pick;\nexport interface MultinomialAttrs {\n numSamples: number;\n seed: number;\n normalized: boolean;\n}\n\nexport const Multiply = 'Multiply';\nexport type MultiplyInputs = BinaryInputs;\n\nexport const Neg = 'Neg';\nexport type NegInputs = UnaryInputs;\n\nexport const NotEqual = 'NotEqual';\nexport type NotEqualInputs = BinaryInputs;\n\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport type NonMaxSuppressionV3Inputs =\n Pick;\nexport interface NonMaxSuppressionV3Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n}\n\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport type NonMaxSuppressionV4Inputs =\n Pick;\nexport interface NonMaxSuppressionV4Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n padToMaxOutputSize: boolean;\n}\n\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport type NonMaxSuppressionV5Inputs =\n Pick;\nexport interface NonMaxSuppressionV5Attrs {\n maxOutputSize: number;\n iouThreshold: number;\n scoreThreshold: number;\n softNmsSigma: number;\n}\n\nexport const OnesLike = 'OnesLike';\nexport type OnesLikeInputs = UnaryInputs;\n\nexport const OneHot = 'OneHot';\nexport type OneHotInputs = Pick;\nexport interface OneHotAttrs {\n depth: number;\n onValue: number;\n offValue: number;\n}\n\nexport const Pack = 'Pack';\nexport type PackInputs = TensorInfo[];\nexport interface PackAttrs {\n axis: number;\n}\n\nexport const PadV2 = 'PadV2';\nexport type PadV2Inputs = Pick;\nexport interface PadV2Attrs {\n paddings: Array<[number, number]>;\n constantValue: number;\n}\n\nexport const Pool = 'Pool';\nexport type PoolInputs = Pick;\n\nexport const Pow = 'Pow';\nexport type PowInputs = BinaryInputs;\n\nexport const Prelu = 'Prelu';\nexport type PreluInputs = Pick;\n\nexport const Prod = 'Prod';\nexport type ProdInputs = Pick;\nexport interface ProdAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const Range = 'Range';\nexport interface RangeAttrs {\n start: number;\n stop: number;\n step: number;\n dtype: 'float32'|'int32';\n}\n\nexport const Real = 'Real';\nexport type RealInputs = Pick;\n\nexport const Reciprocal = 'Reciprocal';\nexport type ReciprocalInputs = UnaryInputs;\n\nexport const Relu = 'Relu';\nexport type ReluInputs = Pick;\n\nexport const Reshape = 'Reshape';\nexport type ReshapeInputs = Pick;\nexport interface ReshapeAttrs {\n shape: number[];\n}\n\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport type ResizeNearestNeighborInputs = Pick;\nexport interface ResizeNearestNeighborAttrs {\n alignCorners: boolean;\n halfPixelCenters: boolean;\n size: [number, number];\n}\n\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport type ResizeNearestNeighborGradInputs =\n Pick;\nexport type ResizeNearestNeighborGradAttrs = ResizeNearestNeighborAttrs;\n\nexport const ResizeBilinear = 'ResizeBilinear';\nexport type ResizeBilinearInputs = Pick;\nexport interface ResizeBilinearAttrs {\n alignCorners: boolean;\n halfPixelCenters: boolean;\n size: [number, number];\n}\n\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport type ResizeBilinearGradInputs = Pick;\nexport type ResizeBilinearGradAttrs = ResizeBilinearAttrs;\n\nexport const Relu6 = 'Relu6';\nexport type Relu6Inputs = Pick;\n\nexport const Reverse = 'Reverse';\nexport type ReverseInputs = Pick;\nexport interface ReverseAttrs {\n dims: number|number[];\n}\n\nexport const Round = 'Round';\nexport type RoundInputs = UnaryInputs;\n\nexport const Rsqrt = 'Rsqrt';\nexport type RsqrtInputs = UnaryInputs;\n\nexport const ScatterNd = 'ScatterNd';\nexport type ScatterNdInputs = Pick;\nexport interface ScatterNdAttrs {\n shape: number[];\n}\n\nexport const Select = 'Select';\nexport type SelectInputs = Pick;\n\nexport const Selu = 'Selu';\nexport type SeluInputs = Pick;\n\nexport const Slice = 'Slice';\nexport type SliceInputs = Pick;\nexport interface SliceAttrs {\n begin: number|number[];\n size: number|number[];\n}\nexport const Sin = 'Sin';\nexport type SinInputs = UnaryInputs;\n\nexport const Sinh = 'Sinh';\nexport type SinhInputs = UnaryInputs;\n\nexport const Sign = 'Sign';\nexport type SignInputs = UnaryInputs;\n\nexport const Sigmoid = 'Sigmoid';\nexport type SigmoidInputs = UnaryInputs;\n\nexport const Softplus = 'Softplus';\nexport type SoftplusInputs = UnaryInputs;\n\nexport const Sqrt = 'Sqrt';\nexport type SqrtInputs = UnaryInputs;\n\nexport const Sum = 'Sum';\nexport type SumInputs = Pick;\nexport interface SumAttrs {\n axis: number|number[];\n keepDims: boolean;\n}\n\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport type SpaceToBatchNDInputs = Pick;\nexport interface SpaceToBatchNDAttrs {\n blockShape: number[];\n paddings: number[][];\n}\n\nexport const SplitV = 'SplitV';\nexport type SplitVInputs = Pick;\nexport interface SplitVAttrs {\n numOrSizeSplits: number[]|number;\n axis: number;\n}\n\nexport const Softmax = 'Softmax';\nexport type SoftmaxInputs = Pick;\nexport interface SoftmaxAttrs {\n dim: number;\n}\n\nexport const SparseFillEmptyRows = 'SparseFillEmptyRows';\nexport type SparseFillEmptyRowsInputs =\n Pick;\n\nexport const SparseReshape = 'SparseReshape';\nexport type SparseReshapeInputs =\n Pick;\n\nexport const SparseSegmentMean = 'SparseSegmentMean';\nexport type SparseSegmentMeanInputs =\n Pick;\n\nexport const SparseSegmentSum = 'SparseSegmentSum';\nexport type SparseSegmentSumInputs =\n Pick;\n\nexport const SparseToDense = 'SparseToDense';\nexport type SparseToDenseInputs =\n Pick;\nexport interface SparseToDenseAttrs {\n outputShape: number[];\n}\n\nexport const SquaredDifference = 'SquaredDifference';\nexport type SquaredDifferenceInputs = BinaryInputs;\n\nexport const Square = 'Square';\nexport type SquareInputs = Pick;\n\nexport const StridedSlice = 'StridedSlice';\nexport type StridedSliceInputs = Pick;\nexport interface StridedSliceAttrs {\n begin: number[];\n end: number[];\n strides: number[];\n beginMask: number;\n endMask: number;\n ellipsisMask: number;\n newAxisMask: number;\n shrinkAxisMask: number;\n}\n\nexport const StringNGrams = 'StringNGrams';\nexport type StringNGramsInputs = Pick;\nexport interface StringNGramsAttrs {\n separator: string;\n nGramWidths: number[];\n leftPad: string;\n rightPad: string;\n padWidth: number;\n preserveShortSequences: boolean;\n}\n\nexport const StringSplit = 'StringSplit';\nexport type StringSplitInputs = Pick;\nexport interface StringSplitAttrs {\n skipEmpty: boolean;\n}\n\nexport const StringToHashBucketFast = 'StringToHashBucketFast';\nexport type StringToHashBucketFastInputs = Pick;\nexport interface StringToHashBucketFastAttrs {\n numBuckets: number;\n}\n\nexport const Sub = 'Sub';\nexport type SubInputs = BinaryInputs;\n\nexport const Tan = 'Tan';\nexport type TanInputs = UnaryInputs;\n\nexport const Tanh = 'Tanh';\nexport type TanhInputs = UnaryInputs;\n\nexport const Tile = 'Tile';\nexport type TileInputs = Pick;\nexport interface TileAttrs {\n reps: number[];\n}\n\nexport const TopK = 'TopK';\nexport type TopKInputs = Pick;\nexport interface TopKAttrs {\n k: number;\n sorted: boolean;\n}\n\nexport const Transform = 'Transform';\nexport type TransformInputs = Pick;\nexport interface TransformAttrs {\n interpolation: 'nearest'|'bilinear';\n fillMode: 'constant'|'reflect'|'wrap'|'nearest';\n fillValue: number;\n outputShape?: [number, number];\n}\n\nexport const Transpose = 'Transpose';\nexport type TransposeInputs = Pick;\nexport interface TransposeAttrs {\n perm: number[];\n}\n\nexport const Unique = 'Unique';\nexport type UniqueInputs = Pick;\nexport interface UniqueAttrs {\n axis: number;\n}\n\nexport type UnaryInputs = Pick;\n\nexport const Unpack = 'Unpack';\nexport type UnpackInputs = Pick;\nexport interface UnpackAttrs {\n axis: number;\n}\n\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport type UnsortedSegmentSumInputs =\n Pick;\nexport interface UnsortedSegmentSumAttrs {\n numSegments: number;\n}\n\nexport const ZerosLike = 'ZerosLike';\nexport type ZerosLikeInputs = UnaryInputs;\n\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport type StepInputs = UnaryInputs;\nexport interface StepAttrs {\n alpha: number;\n}\n\nexport const FromPixels = 'FromPixels';\nexport interface FromPixelsInputs {\n pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n HTMLVideoElement|ImageBitmap;\n}\nexport interface FromPixelsAttrs {\n numChannels: number;\n}\n\nexport const RotateWithOffset = 'RotateWithOffset';\nexport type RotateWithOffsetInputs = Pick;\nexport interface RotateWithOffsetAttrs {\n radians: number;\n fillValue: number|[number, number, number];\n center: number|[number, number];\n}\n\nexport const _FusedMatMul = '_FusedMatMul';\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulInputs extends NamedTensorInfoMap {\n a: TensorInfo;\n b: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulAttrs {\n transposeA: boolean;\n transposeB: boolean;\n activation: Activation;\n leakyreluAlpha?: number;\n}\n\nexport const FusedConv2D = 'FusedConv2D';\nexport interface FusedConv2DInputs extends NamedTensorInfoMap {\n x: TensorInfo;\n filter: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\nexport interface FusedConv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode: 'floor'|'round'|'ceil';\n activation: Activation;\n leakyreluAlpha?: number;\n}\n\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\nexport interface FusedDepthwiseConv2DInputs extends NamedTensorInfoMap {\n x: TensorInfo;\n filter: TensorInfo;\n bias?: TensorInfo;\n preluActivationWeights?: TensorInfo;\n}\nexport interface FusedDepthwiseConv2DAttrs {\n strides: [number, number]|number;\n pad: 'valid'|'same'|number|ExplicitPadding;\n dataFormat: 'NHWC'|'NCHW';\n dilations: [number, number]|number;\n dimRoundingMode: 'floor'|'round'|'ceil';\n activation: Activation;\n leakyreluAlpha?: number;\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from './environment';\nimport {getGlobal} from './global_util';\nimport * as log from './log';\nimport {NamedGradientMap} from './tape';\nimport {Tensor} from './tensor';\nimport {DataType, RecursiveArray} from './types';\n\nconst kernelRegistry =\n getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry =\n getGlobal('gradRegistry', () => new Map());\n\nexport type DataId = object;\n\ntype AttributeValue =\n number|number[]|boolean|boolean[]|string|string[]|NamedAttrMap;\n\n/** These are extra non-tensor/primitive params passed to kernel functions. */\nexport type Attribute = AttributeValue|RecursiveArray;\n\n/** Specifies the code to run when executing a kernel. */\nexport type KernelFunc = (params: {\n inputs: NamedTensorInfoMap,\n backend: {},\n attrs?: NamedAttrMap,\n}) => TensorInfo|TensorInfo[];\n\n/** The function to run when computing a gradient during backprop. */\nexport type GradFunc =\n (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>\n NamedGradientMap;\n\n/** Function that gets called after the backend initializes. */\nexport type KernelSetupFunc = (backend: {}) => void;\n/** Function that gets called right before the backend is disposed. */\nexport type KernelDisposeFunc = KernelSetupFunc;\n\n/** Config object for registering a kernel in the global registry. */\nexport interface KernelConfig {\n kernelName: string;\n backendName: string;\n kernelFunc: KernelFunc;\n setupFunc?: KernelSetupFunc;\n disposeFunc?: KernelDisposeFunc;\n}\n\n/** Config object for registering a gradient in the global registry. */\nexport interface GradConfig {\n kernelName: string;\n inputsToSave?: string[];\n // When saveAllInputs is true, all inputs will be saved. Only use this flag\n // if inputs is an array of Tensors.\n saveAllInputs?: boolean;\n outputsToSave?: boolean[];\n gradFunc: GradFunc;\n}\n\n/** Holds metadata for a given tensor. */\nexport interface TensorInfo {\n dataId: DataId;\n shape: number[];\n dtype: DataType;\n}\n\nexport interface NamedTensorInfoMap {\n [name: string]: TensorInfo|undefined;\n}\n\nexport interface NamedAttrMap {\n [name: string]: Attribute;\n}\n\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(\n kernelName: string, backendName: string): KernelConfig {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\n\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName: string): GradConfig {\n return gradRegistry.get(kernelName);\n}\n\nexport function getKernelsForBackend(backendName: string): KernelConfig[] {\n const it = kernelRegistry.entries();\n const result: KernelConfig[] = [];\n\n while (true) {\n const {done, value} = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend, ] = key.split('_');\n if (backend === backendName) {\n result.push(config);\n }\n }\n return result;\n}\n\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config: KernelConfig) {\n const {kernelName, backendName} = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n log.warn(\n `The kernel '${kernelName}' for backend ` +\n `'${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\n\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config: GradConfig) {\n const {kernelName} = config;\n\n if (gradRegistry.has(kernelName)) {\n // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n // to debug mode.\n if (env().getBool('DEBUG')) {\n log.warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\n\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(\n kernelName: string, backendName: string): void {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(\n `The kernel '${kernelName}' for backend ` +\n `'${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\n\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName: string): void {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(\n `The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\n\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(\n registeredBackendName: string, newBackendName: string): void {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach(kernelConfig => {\n const newKernelConfig =\n Object.assign({}, kernelConfig, {backendName: newBackendName});\n registerKernel(newKernelConfig);\n });\n}\n\nfunction makeKey(kernelName: string, backendName: string) {\n return `${backendName}_${kernelName}`;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\nimport {BackendValues, DataType, TensorLike, TypedArray} from './types';\nimport * as base from './util_base';\nexport * from './util_base';\nexport * from './hash_util';\n\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(\n value: DataType, dtype: DataType): BackendValues {\n if (dtype === 'string') {\n return encodeString(value);\n }\n\n return toTypedArray([value], dtype);\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n return (a instanceof Float32Array && dtype === 'float32') ||\n (a instanceof Int32Array && dtype === 'int32') ||\n (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function toTypedArray(a: TensorLike, dtype: DataType): TypedArray {\n if (dtype === 'string') {\n throw new Error('Cannot convert a string[] to a TypedArray');\n }\n if (Array.isArray(a)) {\n a = base.flatten(a);\n }\n\n if (env().getBool('DEBUG')) {\n base.checkConversionForErrors(a as number[], dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a as TypedArray;\n }\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(a as number[]);\n } else if (dtype === 'int32') {\n return new Int32Array(a as number[]);\n } else if (dtype === 'bool') {\n const bool = new Uint8Array((a as number[]).length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round((a as number[])[i]) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n } else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now(): number {\n return env().platform.now();\n}\n\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(\n path: string, requestInits?: RequestInit): Promise {\n return env().platform.fetch(path, requestInits);\n}\n\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s: string, encoding = 'utf-8'): Uint8Array {\n encoding = encoding || 'utf-8';\n return env().platform.encode(s, encoding);\n}\n\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {\n encoding = encoding || 'utf-8';\n return env().platform.decode(bytes, encoding);\n}\n", "/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Workaround for allowing cjs module to be included in bundle created by\n// rollup.\nimport * as LongExports from 'long';\n// tslint:disable-next-line\nconst Long: LongExports.LongConstructor =\n // tslint:disable-next-line\n (LongExports as any).default || LongExports;\n\nexport function hexToLong(hex: string): Long {\n return Long.fromString(hex, true, 16);\n}\n\n// Some primes between 2^63 and 2^64 for various uses.\n// Hex 0xc3a5c85c97cb3127\nconst k0: Long = hexToLong('c3a5c85c97cb3127');\n// Hex 0xb492b66fbe98f273\nconst k1: Long = hexToLong('b492b66fbe98f273');\n// Hex 0x9ae16a3b2f90404f\nconst k2: Long = hexToLong('9ae16a3b2f90404f');\n\nfunction shiftMix(val: Long): Long {\n return val.xor(val.shru(47));\n}\n\nfunction fetch(s: Uint8Array, offset: number, numBytes: number): Long {\n const bytes = s.slice(offset, offset + numBytes);\n return Long.fromBytes(Array.from(bytes), true, true);\n}\n\nfunction fetch64(s: Uint8Array, offset: number): Long {\n return fetch(s, offset, 8);\n}\n\nfunction fetch32(s: Uint8Array, offset: number): Long {\n return fetch(s, offset, 4);\n}\n\nfunction rotate64(val: Long, shift: number): Long {\n // Avoid shifting by 64: doing so yields an undefined result.\n return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\n\nfunction hashLen16(u: Long, v: Long, mul = hexToLong('9ddfea08eb382d69')) {\n // Murmur-inspired hashing.\n let a = u.xor(v).mul(mul);\n a = a.xor(a.shru(47));\n let b = v.xor(a).mul(mul);\n b = b.xor(b.shru(47));\n b = b.mul(mul);\n return b;\n}\n\n// Return a 16-byte hash for 48 bytes. Quick and dirty.\n// Callers do best to use \"random-looking\" values for a and b.\nfunction weakHashLen32WithSeeds(\n w: Long, x: Long, y: Long, z: Long, a: Long, b: Long) {\n a = a.add(w);\n b = rotate64(b.add(a).add(z), 21);\n const c = a;\n a = a.add(x);\n a = a.add(y);\n b = b.add(rotate64(a, 44));\n return [a.add(z), b.add(c)];\n}\n\nfunction weakHashLen32WithSeedsStr(\n s: Uint8Array, offset: number, a: Long, b: Long) {\n return weakHashLen32WithSeeds(\n fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16),\n fetch64(s, offset + 24), a, b);\n}\n\nfunction hashLen0to16(s: Uint8Array, len = s.length): Long {\n if (len >= 8) {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).add(k2);\n const b = fetch64(s, len - 8);\n const c = rotate64(b, 37).mul(mul).add(a);\n const d = rotate64(a, 25).add(b).mul(mul);\n return hashLen16(c, d, mul);\n }\n if (len >= 4) {\n const mul = k2.add(len * 2);\n const a = fetch32(s, 0);\n return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);\n }\n if (len > 0) {\n const a = s[0];\n const b = s[len >> 1];\n const c = s[len - 1];\n const y = a + (b << 8);\n const z = len + (c << 2);\n return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n }\n return k2;\n}\n\nfunction hashLen17to32(s: Uint8Array, len = s.length): Long {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k1);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul);\n const d = fetch64(s, len - 16).mul(k2);\n return hashLen16(\n rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d),\n a.add(rotate64(b.add(k2), 18)).add(c), mul);\n}\n\nfunction hashLen33to64(s: Uint8Array, len = s.length): Long {\n const mul = k2.add(len * 2);\n const a = fetch64(s, 0).mul(k2);\n const b = fetch64(s, 8);\n const c = fetch64(s, len - 8).mul(mul);\n const d = fetch64(s, len - 16).mul(k2);\n const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);\n const e = fetch64(s, 16).mul(mul);\n const f = fetch64(s, 24);\n const g = y.add(fetch64(s, len - 32)).mul(mul);\n const h = z.add(fetch64(s, len - 24)).mul(mul);\n return hashLen16(\n rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h),\n e.add(rotate64(f.add(a), 18)).add(g), mul);\n}\n\nexport function fingerPrint64(s: Uint8Array, len = s.length): Long {\n const seed: Long = Long.fromNumber(81, true);\n if (len <= 32) {\n if (len <= 16) {\n return hashLen0to16(s, len);\n } else {\n return hashLen17to32(s, len);\n }\n } else if (len <= 64) {\n return hashLen33to64(s, len);\n }\n\n // For strings over 64 bytes we loop. Internal state consists of\n // 56 bytes: v, w, x, y, and z.\n let x = seed;\n let y = seed.mul(k1).add(113);\n\n let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n let v = [Long.UZERO, Long.UZERO];\n let w = [Long.UZERO, Long.UZERO];\n x = x.mul(k2).add(fetch64(s, 0));\n\n let offset = 0;\n // Set end so that after the loop we have 1 to 64 bytes left to process.\n const end = ((len - 1) >> 6) * 64;\n const last64 = end + ((len - 1) & 63) - 63;\n\n do {\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);\n x = x.xor(w[1]);\n y = y.add(v[0]).add(fetch64(s, offset + 40));\n z = rotate64(z.add(w[0]), 33).mul(k1);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(\n s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n\n [z, x] = [x, z];\n offset += 64;\n } while (offset !== end);\n const mul = k1.add(z.and(0xff).shl(1));\n // Point to the last 64 bytes of input.\n offset = last64;\n\n w[0] = w[0].add((len - 1) & 63);\n v[0] = v[0].add(w[0]);\n w[0] = w[0].add(v[0]);\n\n x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);\n y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);\n x = x.xor(w[1].mul(9));\n y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));\n z = rotate64(z.add(w[0]), 33).mul(mul);\n v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));\n w = weakHashLen32WithSeedsStr(\n s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n\n [z, x] = [x, z];\n\n return hashLen16(\n hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z),\n hashLen16(v[1], w[1], mul).add(x), mul);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer, BackendTimingInfo} from './backends/backend';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {DataType, DataTypeMap, TypedArray} from './types';\nimport * as util from './util';\n\nexport type KernelProfile = {\n kernelName: string,\n outputs: Tensor[],\n inputs: NamedTensorMap,\n timeMs: Promise,\n extraInfo: Promise\n};\n\nexport class Profiler {\n constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n\n profileKernel(kernelName: string, inputs: NamedTensorMap, f: () => Tensor[]):\n KernelProfile {\n let outputs: Tensor[];\n const holdResultWrapperFn = () => {\n outputs = f();\n };\n let timer: Promise;\n const start = util.now();\n if (this.backendTimer.timerAvailable()) {\n timer = this.backendTimer.time(holdResultWrapperFn);\n } else {\n holdResultWrapperFn();\n for (const output of outputs) {\n output.dataSync();\n }\n timer = Promise.resolve({kernelMs: util.now() - start});\n }\n if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {\n for (let i = 0; i < outputs.length; i++) {\n const output = outputs[i];\n // Dangling promise here because we don't want to propagate up\n // asynchronicity.\n output.data().then(tensorVals => {\n checkComputationForErrors(tensorVals, output.dtype, kernelName);\n });\n }\n }\n\n const kernelProfile = {\n kernelName,\n outputs,\n inputs,\n timeMs: timer.then(timing => timing.kernelMs),\n extraInfo: timer.then(\n timing => timing.getExtraProfileInfo != null ?\n timing.getExtraProfileInfo() :\n '')\n };\n return kernelProfile;\n }\n\n logKernelProfile(kernelProfile: KernelProfile): void {\n const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;\n\n outputs.forEach(result => {\n Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n this.logger.logKernelProfile(\n kernelName, result, valueContainer[0], valueContainer[1], inputs,\n valueContainer[2]);\n });\n });\n }\n}\n\nexport function checkComputationForErrors(\n vals: DataTypeMap[D], dtype: D, kernelName: string): boolean {\n if (dtype !== 'float32') {\n // Only floating point computations will generate NaN values\n return false;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i] as number;\n if (isNaN(num) || !isFinite(num)) {\n // Throwing custom exception so behavior is testable.\n console.warn(`Found ${num} in the result of '${kernelName}'`);\n return true;\n }\n }\n return false;\n}\n\nexport class Logger {\n logKernelProfile(\n name: string, result: Tensor, vals: TypedArray,\n timeMs: number|{error: string}, inputs: NamedTensorMap,\n extraInfo?: string) {\n const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n timeMs['error'];\n const paddedName = util.rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = util.rightPad(result.shape.toString(), 14);\n let inputShapesDescription = '';\n\n for (const name in inputs) {\n const input = inputs[name];\n if (input != null) {\n // The input might be a non-tensor (e.g HTMLImageElement), in which case\n // we claim the output shape as input shape.\n const inputShape = input.shape || result.shape;\n const inputRank = inputShape.length;\n inputShapesDescription +=\n `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n }\n }\n\n console.log(\n `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n inputShapesDescription}\\t%c${extraInfo}`,\n 'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n 'color: green', 'color: steelblue');\n }\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n id: number;\n kernelName: string;\n outputs: Tensor[];\n inputs: NamedTensorMap;\n // Optional params, defined only for ops with gradient impl.\n gradient?: (dys: Tensor[]) => NamedGradientMap;\n saved?: Tensor[];\n}\n\nexport type NamedGradientMap = {\n [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n // Forward pass to compute all the nodes and Tensors that are transitively a\n // function of x.\n const tensorsFromX: {[tensorId: number]: boolean} = {};\n const nodesFromX: {[nodeId: number]: boolean} = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input = nodeInputs[inputName];\n\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input.id]) {\n node.outputs.forEach(output => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n\n if (anyInputFromX) {\n break;\n }\n }\n }\n\n // Backward pass to find all of the nodes and Tensors that lead to y.\n const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY: {[nodeId: number]: boolean} = {};\n\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n\n // If any of the outputs lead to y, mark all of the inputs as leading to y.\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n\n // Return the paths that come from x and lead to y.\n const filteredTape: TapeNode[] = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n // Prune the inputs from the node that aren't a function of x.\n const prunedInputs: {[inputName: string]: Tensor} = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n\n // Copy the node and overwrite inputsAndArgs to the pruned version.\n const prunedNode = Object.assign({}, node);\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n\n filteredTape.push(prunedNode);\n }\n }\n\n return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n filteredTape: TapeNode[], tidy: (f: Function) => Tensor,\n add: (a: Tensor, b: Tensor) => Tensor) {\n // Walk the tape backward and keep a map of Tensor to its gradient.\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n\n const dys: Tensor[] = [];\n node.outputs.forEach(o => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n } else {\n // This particular output is not in the back-propagation subgraph, so it\n // does not affect the final output, thus we put null for its dy.\n dys.push(null);\n }\n });\n\n if (node.gradient == null) {\n throw new Error(\n `Cannot compute gradient: gradient function not found ` +\n `for ${node.kernelName}.`);\n }\n\n // Backprop dy through this node and accumulate gradients over the inputs.\n const inputGradients = node.gradient(dys);\n\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(\n `Cannot backprop through input ${inputName}. ` +\n `Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n\n // Call the gradient function.\n const dx = tidy(() => inputGradients[inputName]());\n if (dx.dtype !== 'float32') {\n throw new Error(\n `Error in gradient for op ${\n node.kernelName}. The gradient of input ` +\n `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!util.arraysEqual(dx.shape, x.shape)) {\n throw new Error(\n `Error in gradient for op ${\n node.kernelName}. The gradient of input ` +\n `'${inputName}' has shape '${dx.shape}', which does not match ` +\n `the shape of the input '${x.shape}'`);\n }\n\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n } else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n curGradient.dispose();\n }\n }\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n verbose: boolean) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = ['Tensor'];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map(l => ' ' + l).join('\\n'));\n return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[]): number[] {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples =\n dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(\n padPerCol[j],\n valToString(valuesOrTuples[offset + j], 0, dtype).length);\n }\n }\n }\n return padPerCol;\n}\n\nfunction valToString(\n val: number|string|[number, number], pad: number, dtype: DataType) {\n let valStr: string;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n } else if (isString(val)) {\n valStr = `'${val}'`;\n } else if (dtype === 'bool') {\n valStr = boolNumToString(val);\n } else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n\n return rightPad(valStr, pad);\n}\n\nfunction boolNumToString(v: number): string {\n return v === 0 ? 'false' : 'true';\n}\n\nfunction subTensorToString(\n vals: TypedArray|string[], shape: number[], dtype: DataType,\n strides: number[], padPerCol: number[], isLast = true): string[] {\n const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === 'complex64') {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0, dtype)];\n }\n if (dtype === 'bool') {\n return [boolNumToString(vals[0] as number)];\n }\n return [vals[0].toString()];\n }\n\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n let firstVals = Array.from(\n vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice(\n (size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement,\n size * storagePerElement));\n if (dtype === 'complex64') {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n '[' +\n firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ', ..., ' +\n lastVals\n .map(\n (x, i) => valToString(\n x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n .join(', ') +\n ']'\n ];\n }\n const displayVals: Array =\n dtype === 'complex64' ? createComplexTuples(vals) :\n Array.from(vals);\n\n return [\n '[' +\n displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ']'\n ];\n }\n\n // The array is rank 2 or more.\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines: string[] = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n false /* isLast */));\n }\n lines.push('...');\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n } else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(\n vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n i === size - 1 /* isLast */));\n }\n }\n const sep = rank === 2 ? ',' : '';\n lines[0] = '[' + lines[0] + sep;\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = ' ' + lines[i] + sep;\n }\n let newLineSep = ',\\n';\n for (let i = 2; i < rank; i++) {\n newLineSep += '\\n';\n }\n lines[lines.length - 1] =\n ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n TypedArray): Array<[number, number]> {\n const complexTuples: Array<[number, number]> = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n }\n return complexTuples;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getGlobal} from './global_util';\nimport {tensorToString} from './tensor_format';\nimport {ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides, toNestedArray} from './util';\n\nexport interface TensorData {\n dataId?: DataId;\n values?: DataTypeMap[D];\n}\n\n// This interface mimics KernelBackend (in backend.ts), which would create a\n// circular dependency if imported.\nexport interface Backend {}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n size: number;\n shape: ShapeMap[R];\n strides: number[];\n values: DataTypeMap[D];\n\n constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n this.shape = shape.slice() as ShapeMap[R];\n this.size = util.sizeFromShape(shape);\n\n if (values != null) {\n const n = values.length;\n util.assert(\n n === this.size,\n () => `Length of values '${n}' does not match the size ` +\n `inferred by the shape '${this.size}'.`);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `complex64 dtype TensorBuffers are not supported. Please create ` +\n `a TensorBuffer for the real and imaginary parts separately and ` +\n `call tf.complex(real, imag).`);\n }\n this.values = values || util.getArrayFromDType(dtype, this.size);\n this.strides = computeStrides(shape);\n }\n\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n set(value: SingleValueMap[D], ...locs: number[]): void {\n if (locs.length === 0) {\n locs = [0];\n }\n util.assert(\n locs.length === this.rank,\n () => `The number of provided coordinates (${locs.length}) must ` +\n `match the rank (${this.rank})`);\n\n const index = this.locToIndex(locs);\n this.values[index] = value as number;\n }\n\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n get(...locs: number[]): SingleValueMap[D] {\n if (locs.length === 0) {\n locs = [0];\n }\n let i = 0;\n for (const loc of locs) {\n if (loc < 0 || loc >= this.shape[i]) {\n const msg = `Requested out of range element at ${locs}. ` +\n ` Buffer shape=${this.shape}`;\n throw new Error(msg);\n }\n i++;\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.values[index] as SingleValueMap[D];\n }\n\n locToIndex(locs: number[]): number {\n if (this.rank === 0) {\n return 0;\n } else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n\n indexToLoc(index: number): number[] {\n if (this.rank === 0) {\n return [];\n } else if (this.rank === 1) {\n return [index];\n }\n const locs: number[] = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n\n get rank() {\n return this.shape.length;\n }\n\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n toTensor(): Tensor {\n return trackerFn().makeTensor(this.values, this.shape, this.dtype) as\n Tensor;\n }\n}\n\nexport interface TensorTracker {\n makeTensor(\n values: DataValues, shape: number[], dtype: DataType,\n backend?: Backend): Tensor;\n makeVariable(\n initialValue: Tensor, trainable?: boolean, name?: string,\n dtype?: DataType): Variable;\n incRef(a: Tensor, backend: Backend): void;\n disposeTensor(t: Tensor): void;\n disposeVariable(v: Variable): void;\n read(dataId: DataId): Promise;\n readSync(dataId: DataId): BackendValues;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n cast(x: T, dtype: DataType): T;\n buffer(\n shape: ShapeMap[R], dtype: D,\n values?: DataTypeMap[D]): TensorBuffer;\n print(x: T, verbose: boolean): void;\n clone(x: T): T;\n // TODO(yassogba) bring reshape back?\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn: (msg: string) => void = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n opHandler = handler;\n}\n\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn: (msg: string) => void) {\n deprecationWarningFn = fn;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object; // object instead of {} to force non-primitive.\n\n// Declare this namespace to make Tensor class augmentation work in google3.\nexport declare namespace Tensor {}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * For performance reasons, functions that create tensors do not necessarily\n * perform a copy of the data passed to them (e.g. if the data is passed as a\n * `Float32Array`), and changes to the data will change the tensor. This is not\n * a feature and is not supported. To avoid this behavior, use the tensor before\n * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n /** Unique id of this tensor. */\n readonly id: number;\n /**\n * Id of the bucket holding the data for this tensor. Multiple arrays can\n * point to the same bucket (e.g. when calling array.reshape()).\n */\n dataId: DataId;\n /** The shape of the tensor. */\n readonly shape: ShapeMap[R];\n /** Number of elements in the tensor. */\n readonly size: number;\n /** The data type for the array. */\n readonly dtype: DataType;\n /** The rank type for the array (see `Rank` enum). */\n readonly rankType: R;\n\n /** Whether this tensor has been globally kept. */\n kept = false;\n /** The id of the scope this tensor is being tracked in. */\n scopeId: number;\n\n /**\n * Number of elements to skip in each dimension when indexing. See\n * https://docs.scipy.org/doc/numpy/reference/generated/\\\n * numpy.ndarray.strides.html\n */\n readonly strides: number[];\n\n constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {\n this.shape = shape.slice() as ShapeMap[R];\n this.dtype = dtype || 'float32';\n this.size = util.sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId;\n this.id = id;\n this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n }\n\n get rank(): number {\n return this.shape.length;\n }\n\n /**\n * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async buffer(): Promise> {\n const vals = await this.data();\n return opHandler.buffer(this.shape, this.dtype as D, vals);\n }\n\n /**\n * Returns a `tf.TensorBuffer` that holds the underlying data.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n bufferSync(): TensorBuffer {\n return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async array(): Promise {\n const vals = await this.data();\n return toNestedArray(this.shape, vals, this.dtype === 'complex64') as\n ArrayMap[R];\n }\n\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n arraySync(): ArrayMap[R] {\n return toNestedArray(\n this.shape, this.dataSync(), this.dtype === 'complex64') as\n ArrayMap[R];\n }\n\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n * promise of `TypedArray` that resolves when the computation has finished.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async data(): Promise {\n this.throwIfDisposed();\n const data = trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n const bytes = await data as Uint8Array[];\n try {\n return bytes.map(b => util.decodeString(b)) as DataTypeMap[D];\n } catch {\n throw new Error(\n 'Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data as Promise;\n }\n\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n * UI thread until the values are ready, which can cause performance issues.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataSync(): DataTypeMap[D] {\n this.throwIfDisposed();\n const data = trackerFn().readSync(this.dataId);\n if (this.dtype === 'string') {\n try {\n return (data as Uint8Array[]).map(b => util.decodeString(b)) as\n DataTypeMap[D];\n } catch {\n throw new Error(\n 'Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data as DataTypeMap[D];\n }\n\n /** Returns the underlying bytes of the tensor's data. */\n async bytes(): Promise {\n this.throwIfDisposed();\n const data = await trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n return data as Uint8Array[];\n } else {\n return new Uint8Array((data as TypedArray).buffer);\n }\n }\n\n /**\n * Disposes `tf.Tensor` from memory.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dispose(): void {\n if (this.isDisposed) {\n return;\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n\n protected isDisposedInternal = false;\n get isDisposed(): boolean {\n return this.isDisposedInternal;\n }\n\n throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n print(verbose = false): void {\n return opHandler.print(this, verbose);\n }\n\n /**\n * Returns a copy of the tensor. See `tf.clone` for details.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n clone(this: T): T {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n\n /**\n * Returns a human-readable description of the tensor. Useful for logging.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n toString(verbose = false): string {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n\n cast(dtype: DataType): T {\n this.throwIfDisposed();\n return opHandler.cast(this as T, dtype);\n }\n variable(trainable = true, name?: string, dtype?: DataType): Variable {\n this.throwIfDisposed();\n return trackerFn().makeVariable(this, trainable, name, dtype) as\n Variable;\n }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance: Tensor) => {\n // Implementation note: we should use properties of the object that will be\n // defined before the constructor body has finished executing (methods).\n // This is because when this code is transpiled by babel, babel will call\n // classCallCheck before the constructor body is run.\n // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n return !!instance && instance.data != null && instance.dataSync != null &&\n instance.throwIfDisposed != null;\n }\n});\n\nexport function getGlobalTensorClass() {\n // Use getGlobal so that we can augment the Tensor class across package\n // boundaries becase the node resolution alg may result in different modules\n // being returned for this file depending on the path they are loaded from.\n return getGlobal('Tensor', () => {\n return Tensor;\n });\n}\n\n// Global side effect. Cache global reference to Tensor class\ngetGlobalTensorClass();\n\nexport interface NumericTensor extends Tensor {\n dtype: NumericDataType;\n dataSync(): DataTypeMap[D];\n data(): Promise;\n}\n\nexport interface StringTensor extends Tensor {\n dtype: 'string';\n dataSync(): DataTypeMap[D];\n data(): Promise;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n name: string;\n\n constructor(\n initialValue: Tensor, public trainable: boolean, name: string,\n tensorId: number) {\n super(\n initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n this.name = name;\n }\n\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n assign(newValue: Tensor): void {\n if (newValue.dtype !== this.dtype) {\n throw new Error(\n `dtype of the new value (${newValue.dtype}) and ` +\n `previous value (${this.dtype}) must match`);\n }\n if (!util.arraysEqual(newValue.shape, this.shape)) {\n throw new Error(\n `shape of the new value (${newValue.shape}) and ` +\n `previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().incRef(this, null /* backend */);\n }\n\n dispose(): void {\n trackerFn().disposeVariable(this);\n this.isDisposedInternal = true;\n }\n}\n\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance: Variable) => {\n return instance instanceof Tensor && instance.assign != null &&\n instance.assign instanceof Function;\n }\n});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch(a: T, b: T): [T, T] {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n assert(\n a.dtype === b.dtype,\n () => `The dtypes of the first(${a.dtype}) and` +\n ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n return tensorList.some(x => x.id === tensor.id);\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n * is safe to pass any object here, except that `Promise`s are not\n * supported.\n * @returns An array of `Tensors` found within the passed object. If the\n * argument is simply a `Tensor', a list containing that `Tensor` is\n * returned. If the object is not a `Tensor` or does not\n * contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n const list: Tensor[] = [];\n const seen = new Set<{}|void>();\n walkTensorContainer(result, list, seen);\n return list;\n}\n\nfunction walkTensorContainer(\n container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n // Iteration over keys works also for arrays.\n const iterable = container as TensorContainerArray;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n return Array.isArray(obj) || typeof obj === 'object';\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n R0: number[];\n R1: [number];\n R2: [number, number];\n R3: [number, number, number];\n R4: [number, number, number, number];\n R5: [number, number, number, number, number];\n R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n R0: number;\n R1: number[];\n R2: number[][];\n R3: number[][][];\n R4: number[][][][];\n R5: number[][][][][];\n R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n float32: Float32Array;\n int32: Int32Array;\n bool: Uint8Array;\n complex64: Float32Array;\n string: string[];\n}\n\nexport interface SingleValueMap {\n bool: boolean;\n int32: number;\n float32: number;\n complex64: number;\n string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\n/** Tensor data used in tensor creation and user-facing API. */\nexport type DataValues = DataTypeMap[DataType];\n/** The underlying tensor data that gets stored in a backend. */\nexport type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];\n\nexport enum Rank {\n R0 = 'R0',\n R1 = 'R1',\n R2 = 'R2',\n R3 = 'R3',\n R4 = 'R4',\n R5 = 'R5',\n R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray =\n T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray {\n [index: number]: T|RecursiveArray;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'int32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n 'float32' = 'float32',\n 'int32' = 'int32',\n 'bool' = 'bool',\n 'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n 'float32' = 'float32',\n 'int32' = 'float32',\n 'bool' = 'float32',\n 'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n 'float32' = 'complex64',\n 'int32' = 'complex64',\n 'bool' = 'complex64',\n 'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n TypedArray|number|boolean|string|RecursiveArray|\n RecursiveArray|RecursiveArray|Uint8Array[];\nexport type ScalarLike = number|boolean|string|Uint8Array;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|\n string[]|string[][]|Uint8Array[]|Uint8Array[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D =\n TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|\n string[][][][][]|Uint8Array[]|Uint8Array[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D =\n TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|\n string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];\n\n/** Type for representing image data in Uint8Array type. */\nexport interface PixelData {\n width: number;\n height: number;\n data: Uint8Array;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './backends/backend';\nimport {Environment, setEnvironmentGlobal} from './environment';\nimport {getGlobalNamespace} from './global_util';\nimport {Add, Cast, Identity} from './kernel_names';\nimport {getGradient, getKernel, getKernelsForBackend, GradFunc, NamedAttrMap, TensorInfo} from './kernel_registry';\nimport {KernelProfile, Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';\nimport {DataId, setTensorTracker, Tensor, TensorTracker, Variable} from './tensor';\nimport {GradSaveFunc, NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\nimport {BackendValues, DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\nimport * as log from './log';\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc = (backend: KernelBackend, save?: GradSaveFunc) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,..., save?: Function) => {\n * value: Tensor,\n * gradFunc: (dy: Tensor, saved?: NamedTensorMap) => Tensor | Tensor[]\n * }\n */\nexport type CustomGradientFunc =\n (...inputs: Array) => {\n value: T;\n gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[];\n };\n\nexport type MemoryInfo = {\n numTensors: number; numDataBuffers: number; numBytes: number;\n unreliable?: boolean; reasons: string[];\n};\n\ntype KernelInfo = {\n name: string; bytesAdded: number; totalBytesSnapshot: number;\n tensorsAdded: number;\n totalTensorsSnapshot: number;\n inputShapes: number[][];\n outputShapes: number[][];\n kernelTimeMs: number | {error: string} | Promise;\n extraInfo: string | Promise;\n};\n\nexport type ProfileInfo = {\n newBytes: number; newTensors: number; peakBytes: number;\n kernels: KernelInfo[];\n result: TensorContainer;\n kernelNames: string[];\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn = () => T;\n\ninterface ScopeState {\n track: Tensor[];\n name: string;\n id: number;\n}\n\ninterface RegisteredKernelInvocation {\n kernelName: string;\n inputs: I;\n attrs?: NamedAttrMap;\n}\n\ninterface CustomGradKernelInvocation {\n forwardFunc: ForwardFunc;\n backwardsFunc: (dy: T, saved: Tensor[]) => {\n [P in keyof I]: () => I[P]\n };\n inputs: I;\n attrs?: NamedAttrMap;\n}\n\nfunction isRegisteredKernelInvocation(\n kernelInvocation: RegisteredKernelInvocation|\n CustomGradKernelInvocation):\n kernelInvocation is RegisteredKernelInvocation {\n return (kernelInvocation as RegisteredKernelInvocation).kernelName != null;\n}\n\nclass EngineState {\n // Public since optimizers will use it.\n registeredVariables: NamedVariableMap = {};\n\n nextTapeNodeId = 0;\n numBytes = 0;\n numTensors = 0;\n numStringTensors = 0;\n numDataBuffers = 0;\n\n activeTape: TapeNode[];\n // Number of nested tf.grad() statements when computing higher-order\n // gradients. E.g. `1` for first-order gradients and `2` for second-order\n // gradients. Used to track if the tape should be removed after a backprop.\n gradientDepth = 0;\n // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n // off the tape.\n kernelDepth = 0;\n\n // Keep Tensors that parallel the tapes.\n activeScope: ScopeState;\n scopeStack: ScopeState[] = [];\n /**\n * Keeps track of the number of data moves during a kernel execution. We\n * maintain a stack since kernels can call other kernels, recursively.\n */\n numDataMovesStack: number[] = [];\n nextScopeId = 0;\n\n tensorInfo = new WeakMap();\n\n profiling = false;\n activeProfile: ProfileInfo = {\n newBytes: 0,\n newTensors: 0,\n peakBytes: 0,\n kernels: [],\n result: null,\n get kernelNames():\n string[] {\n return Array.from(new Set(this.kernels.map(k => k.name)));\n }\n };\n\n dispose() {\n for (const variableName in this.registeredVariables) {\n this.registeredVariables[variableName].dispose();\n }\n }\n}\n\nexport class Engine implements TensorTracker, DataMover {\n state: EngineState;\n backendName: string;\n registry: {[id: string]: KernelBackend} = {};\n registryFactory: {\n [id: string]: {\n factory: () => KernelBackend | Promise,\n priority: number\n }\n } = {};\n\n private profiler: Profiler;\n private backendInstance: KernelBackend;\n private pendingBackendInit: Promise;\n private pendingBackendInitId = 0;\n\n constructor(public ENV: Environment) {\n this.state = new EngineState();\n }\n\n async ready(): Promise {\n if (this.pendingBackendInit != null) {\n return this.pendingBackendInit.then(() => {});\n }\n if (this.backendInstance != null) {\n return;\n }\n const sortedBackends = this.getSortedBackends();\n\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const success = await this.initializeBackend(backendName).success;\n if (success) {\n await this.setBackend(backendName);\n return;\n }\n }\n\n throw new Error(\n `Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n\n get backend(): KernelBackend {\n if (this.pendingBackendInit != null) {\n throw new Error(\n `Backend '${this.backendName}' has not yet been initialized. Make ` +\n `sure to await tf.ready() or await tf.setBackend() before calling ` +\n `other methods`);\n }\n if (this.backendInstance == null) {\n const {name, asyncInit} = this.initializeBackendsAndReturnBest();\n if (asyncInit) {\n throw new Error(\n `The highest priority backend '${name}' has not yet been ` +\n `initialized. Make sure to await tf.ready() or ` +\n `await tf.setBackend() before calling other methods`);\n }\n this.setBackend(name);\n }\n return this.backendInstance;\n }\n\n backendNames(): string[] {\n return Object.keys(this.registryFactory);\n }\n\n findBackend(backendName: string): KernelBackend {\n if (!(backendName in this.registry)) {\n // If the backend hasn't been initialized but we have a registry entry for\n // it, initialize it and return it.\n if (backendName in this.registryFactory) {\n const {asyncInit} = this.initializeBackend(backendName);\n if (asyncInit) {\n // Backend is not ready yet.\n return null;\n }\n } else {\n return null;\n }\n }\n return this.registry[backendName];\n }\n\n findBackendFactory(backendName: string):\n () => KernelBackend | Promise {\n if (!(backendName in this.registryFactory)) {\n return null;\n }\n return this.registryFactory[backendName].factory;\n }\n\n registerBackend(\n backendName: string,\n factory: () => KernelBackend | Promise,\n priority = 1): boolean {\n if (backendName in this.registryFactory) {\n log.warn(\n `${backendName} backend was already registered. ` +\n `Reusing existing backend factory.`);\n return false;\n }\n this.registryFactory[backendName] = {factory, priority};\n return true;\n }\n\n async setBackend(backendName: string): Promise {\n if (this.registryFactory[backendName] == null) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n this.backendName = backendName;\n if (this.registry[backendName] == null) {\n this.backendInstance = null;\n const {success, asyncInit} = this.initializeBackend(backendName);\n const result = asyncInit ? await success : success;\n if (!result) {\n return false;\n }\n }\n this.backendInstance = this.registry[backendName];\n this.setupRegisteredKernels();\n // Reset the profiler.\n this.profiler = new Profiler(this.backendInstance);\n\n return true;\n }\n\n private setupRegisteredKernels(): void {\n const kernels = getKernelsForBackend(this.backendName);\n kernels.forEach(kernel => {\n if (kernel.setupFunc != null) {\n kernel.setupFunc(this.backendInstance);\n }\n });\n }\n\n private disposeRegisteredKernels(backendName: string): void {\n const kernels = getKernelsForBackend(backendName);\n kernels.forEach(kernel => {\n if (kernel.disposeFunc != null) {\n kernel.disposeFunc(this.registry[backendName]);\n }\n });\n }\n\n /**\n * Initializes a backend by looking up the backend name in the factory\n * registry and calling the factory method. Returns a boolean representing\n * whether the initialization of the backend suceeded. Throws an error if\n * there is no backend in the factory registry.\n */\n private initializeBackend(backendName: string):\n {success: boolean|Promise, asyncInit: boolean} {\n const registryFactoryEntry = this.registryFactory[backendName];\n if (registryFactoryEntry == null) {\n throw new Error(\n `Cannot initialize backend ${backendName}, no registration found.`);\n }\n\n try {\n const backend = registryFactoryEntry.factory();\n /* Test if the factory returns a promise.\n Done in a more liberal way than\n previous 'Promise.resolve(backend)===backend'\n as we needed to account for custom Promise\n implementations (e.g. Angular) */\n if (backend && !(backend instanceof KernelBackend) &&\n typeof backend.then === 'function') {\n const promiseId = ++this.pendingBackendInitId;\n const success =\n backend\n .then(backendInstance => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.registry[backendName] = backendInstance;\n this.pendingBackendInit = null;\n return true;\n })\n .catch(err => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.pendingBackendInit = null;\n log.warn(\n `Initialization of backend ${backendName} failed`);\n log.warn(err.stack || err.message);\n return false;\n });\n this.pendingBackendInit = success;\n return {success, asyncInit: true};\n } else {\n this.registry[backendName] = backend as KernelBackend;\n return {success: true, asyncInit: false};\n }\n } catch (err) {\n log.warn(`Initialization of backend ${backendName} failed`);\n log.warn(err.stack || err.message);\n return {success: false, asyncInit: false};\n }\n }\n\n removeBackend(backendName: string): void {\n if (!(backendName in this.registryFactory)) {\n throw new Error(`${backendName} backend not found in registry`);\n }\n if (this.backendName === backendName && this.pendingBackendInit != null) {\n // There is a pending promise of the backend we want to remove. Make it\n // obsolete.\n this.pendingBackendInitId++;\n }\n\n if (backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n\n delete this.registryFactory[backendName];\n\n // Unset the backend if it is active.\n if (this.backendName === backendName) {\n this.pendingBackendInit = null;\n this.backendName = null;\n this.backendInstance = null;\n }\n }\n\n private getSortedBackends(): string[] {\n if (Object.keys(this.registryFactory).length === 0) {\n throw new Error('No backend found in registry.');\n }\n return Object.keys(this.registryFactory).sort((a: string, b: string) => {\n // Highest priority comes first.\n return this.registryFactory[b].priority -\n this.registryFactory[a].priority;\n });\n }\n\n private initializeBackendsAndReturnBest():\n {name: string, asyncInit: boolean} {\n const sortedBackends = this.getSortedBackends();\n\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const {success, asyncInit} = this.initializeBackend(backendName);\n if (asyncInit || success) {\n return {name: backendName, asyncInit};\n }\n }\n throw new Error(\n `Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n\n moveData(backend: KernelBackend, dataId: DataId) {\n const info = this.state.tensorInfo.get(dataId);\n const srcBackend = info.backend;\n const values = this.readSync(dataId);\n const refCount = srcBackend.refCount(dataId);\n // Delete the tensor from the old backend and move it to the new\n // backend.\n srcBackend.disposeData(dataId, true);\n info.backend = backend;\n backend.move(dataId, values, info.shape, info.dtype, refCount);\n if (this.shouldCheckForMemLeaks()) {\n // Track the number of moves during a kernel execution to correctly\n // detect memory leaks.\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n }\n }\n\n tidy(nameOrFn: string|ScopeFn, fn?: ScopeFn):\n T {\n let name: string = null;\n if (fn == null) {\n // Called with only 1 argument.\n if (typeof nameOrFn !== 'function') {\n throw new Error('Please provide a function to tidy()');\n }\n fn = nameOrFn;\n } else {\n // Called with 2 arguments.\n if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n throw new Error(\n 'When calling with two arguments, the first argument ' +\n 'to tidy() must be a string');\n }\n if (typeof fn !== 'function') {\n throw new Error(\n 'When calling with two arguments, the 2nd argument ' +\n 'to tidy() must be a function');\n }\n name = nameOrFn as string;\n // TODO(nsthorat,smilkov): Do operation logging and performance\n // profiling.\n }\n let result: T;\n return this.scopedRun(\n () => this.startScope(name), () => this.endScope(result), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n return result;\n });\n }\n\n private scopedRun(start: () => void, end: () => void, f: () => T): T {\n start();\n try {\n const res = f();\n end();\n return res;\n } catch (ex) {\n end();\n throw ex;\n }\n }\n\n private static nextTensorId = 0;\n private nextTensorId(): number {\n return Engine.nextTensorId++;\n }\n\n private static nextVariableId = 0;\n private nextVariableId(): number {\n return Engine.nextVariableId++;\n }\n\n /**\n * This method is called instead of the public-facing tensor.clone() when\n * saving a tensor for backwards pass. It makes sure to add the clone\n * operation to the tape regardless of being called inside a kernel\n * execution.\n */\n private clone(x: Tensor): Tensor {\n const y: Tensor = ENGINE.runKernel(Identity, {x} as {} as NamedTensorMap);\n const inputs = {x};\n const grad = (dy: Tensor) => ({\n x: () => {\n const dtype = 'float32';\n const gradInputs = {x: dy};\n const attrs = {dtype};\n\n return ENGINE.runKernel(\n Cast, gradInputs as {} as NamedTensorMap,\n // tslint:disable-next-line: no-unnecessary-type-assertion\n attrs as {} as NamedAttrMap) as Tensor;\n }\n });\n const saved: Tensor[] = [];\n this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n return y;\n }\n\n /**\n * Execute a kernel with the given name and return the output tensor.\n *\n * @param kernelName The name of the kernel to execute.\n * @param inputs A map of input names to tensors.\n * @param attrs A map of attribute names to their values. An attribute is a\n * primitive (non-tensor) input to the kernel.\n * @param inputsToSave A list of tensors, inputs to save for the backprop\n * computation.\n * @param outputsToSave A list of booleans, specifying which output to save\n * for the backprop computation. These are booleans since the output\n * tensors are not visible to the user.\n */\n runKernel(\n kernelName: string, inputs: NamedTensorMap, attrs?: NamedAttrMap): T {\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n const hasKernel = getKernel(kernelName, this.backendName) != null;\n if (!hasKernel) {\n throw new Error(`Kernel '${kernelName}' not registered for backend '${\n this.backendName}'`);\n }\n return this.runKernelFunc({kernelName, inputs, attrs});\n }\n\n private shouldCheckForMemLeaks(): boolean {\n return this.ENV.getBool('IS_TEST');\n }\n\n private checkKernelForMemLeak(\n kernelName: string, numDataIdsBefore: number,\n outInfos: TensorInfo[]): void {\n const numDataIdsAfter = this.backend.numDataIds();\n\n // Count the number of data ids associated with the result of the kernel.\n let numOutputDataIds = 0;\n outInfos.forEach(info => {\n // Complex numbers allocate 3 data ids, one for 'real', one for\n // 'imaginary', and one for the container that holds the former two.\n numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n });\n\n // Account for the number of moves during kernel execution. A \"data move\"\n // can happen in the middle of a kernel execution, placing a new (key,value)\n // pair in the data storage. Since data moves have net zero effect (we\n // always remove the data from the old backend), we have to cancel them out\n // when detecting memory leaks.\n const numMoves =\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n const dataIdsLeaked =\n numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n if (dataIdsLeaked > 0) {\n throw new Error(\n `Backend '${this.backendName}' has an internal memory leak ` +\n `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n }\n }\n\n /**\n * Internal helper method to execute a kernel Func\n *\n * Use `runKernel` to execute kernels from outside of engine.\n */\n private runKernelFunc(\n kernelParams: RegisteredKernelInvocation|\n CustomGradKernelInvocation): T {\n let outputs: Tensor[];\n let saved: Tensor[] = [];\n const isTapeOn = this.isTapeOn();\n\n const startingBytecount = this.state.numBytes;\n const startingNumTensors = this.state.numTensors;\n\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack.push(0);\n }\n\n let kernelFunc: () => Tensor[];\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n\n let out: TensorInfo|TensorInfo[];\n\n const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?\n kernelParams.kernelName :\n this.state.activeScope != null ? this.state.activeScope.name : '';\n\n // Create the kernelFunc from either a registered kernel OR passed in\n // forward/backward functions (used by custom grad). In this context a\n // kernelFunc wraps a kernel implementation with some bookkeeping.\n\n if (isRegisteredKernelInvocation(kernelParams)) {\n const {kernelName, inputs, attrs} = kernelParams;\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n const kernel = getKernel(kernelName, this.backendName);\n util.assert(\n kernel != null,\n () => `Cannot find registered kernel '${kernelName}' for backend '${\n this.backendName}'`);\n\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = kernel.kernelFunc({inputs, attrs, backend: this.backend});\n const outInfos = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n }\n\n const outTensors = outInfos.map((outInfo: TensorInfo|Tensor) => {\n // todo (yassogba) remove this option (Tensor) when node backend\n // methods have been modularized and they all return tensorInfo.\n // TensorInfos do not have a rank attribute.\n if ((outInfo as Tensor).rank != null) {\n return outInfo as Tensor;\n }\n const {dataId, shape, dtype} = outInfo as TensorInfo;\n return this.makeTensorFromDataId(dataId, shape, dtype);\n });\n\n // Save any required inputs and outputs.\n\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since there would be no backprop for these tensors\n // (which would otherwise dispose them).\n if (isTapeOn) {\n const tensorsToSave =\n this.getTensorsForGradient(kernelName, inputs, outTensors);\n saved = this.saveTensorsForBackwardMode(tensorsToSave);\n }\n return outTensors;\n };\n } else {\n const {forwardFunc} = kernelParams;\n // Running a customGrad op.\n const saveFunc: GradSaveFunc = (tensors) => {\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since we would never run backprop, which disposes\n // the kept tensors.\n if (!isTapeOn) {\n return;\n }\n saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n };\n\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n const outs = (Array.isArray(out) ? out : [out]) as Tensor[];\n if (this.shouldCheckForMemLeaks()) {\n // Scope name is used to print a more helpful error message if needed.\n this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n }\n return outs;\n };\n }\n\n //\n // Run the kernelFunc. Optionally profiling it.\n //\n const {inputs, attrs} = kernelParams;\n const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?\n null :\n kernelParams.backwardsFunc;\n\n let kernelProfile: KernelProfile;\n this.scopedRun(\n // Stop recording to a tape when running a kernel.\n () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n outputs = kernelFunc();\n } else {\n kernelProfile = this.profiler.profileKernel(\n kernelOrScopeName, inputs, () => kernelFunc());\n if (this.ENV.getBool('DEBUG')) {\n this.profiler.logKernelProfile(kernelProfile);\n }\n outputs = kernelProfile.outputs;\n }\n });\n\n if (isTapeOn) {\n this.addTapeNode(\n kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n }\n\n if (this.state.profiling) {\n this.state.activeProfile.kernels.push({\n name: kernelOrScopeName,\n bytesAdded: this.state.numBytes - startingBytecount,\n totalBytesSnapshot: this.state.numBytes,\n tensorsAdded: this.state.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.state.numTensors,\n inputShapes: Object.keys(inputs).map(\n key => inputs[key] != null ? inputs[key].shape : null),\n outputShapes: outputs.map(item => item.shape),\n kernelTimeMs: kernelProfile.timeMs,\n extraInfo: kernelProfile.extraInfo\n });\n }\n return (Array.isArray(out) ? outputs : outputs[0]) as T;\n }\n\n /**\n * Saves tensors used in forward mode for use in backward mode.\n *\n * @param tensors the list of tensors to save.\n */\n private saveTensorsForBackwardMode(tensors: Tensor[]): Tensor[] {\n const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n return saved;\n }\n\n /**\n * Returns a list of tensors to save for a given gradient calculation.\n *\n * @param kernelName name of kernel to look up gradient for.\n * @param inputs a map of input tensors.\n * @param outputs an array of output tensors from forward mode of kernel.\n */\n private getTensorsForGradient(\n kernelName: string, inputs: NamedTensorMap,\n outputs: Tensor[]): Tensor[]|null {\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n const inputsToSave: string[] = gradConfig.inputsToSave || [];\n const outputsToSave: boolean[] = gradConfig.outputsToSave || [];\n\n // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n // specified in inputsToSave will be saved.\n let inputTensorsToSave: Tensor[];\n if (gradConfig.saveAllInputs) {\n util.assert(\n Array.isArray(inputs),\n () => 'saveAllInputs is true, expected inputs to be an array.');\n\n inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n } else {\n inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n }\n\n const outputTensorsToSave: Tensor[] =\n outputs.filter((_, i) => outputsToSave[i]);\n\n return inputTensorsToSave.concat(outputTensorsToSave);\n }\n // We return an empty list rather than throw an error because the kernel we\n // are looking up may not actually be relevant to backproping through the\n // overall function\n //\n // See 'does not error if irrelevant (pruned) ops are missing grads' test\n // in gradients_test.ts for an example.\n return [];\n }\n\n /**\n * Internal method used by public APIs for tensor creation. Makes a new\n * tensor with the provided shape, dtype and values. It always\n * creates a new data id and writes the values to the underlying backend.\n */\n makeTensor(\n values: DataValues, shape: number[], dtype: DataType,\n backend?: KernelBackend): Tensor {\n if (values == null) {\n throw new Error('Values passed to engine.makeTensor() are null');\n }\n dtype = dtype || 'float32';\n backend = backend || this.backend;\n let backendVals = values as BackendValues;\n if (dtype === 'string' && util.isString(values[0])) {\n backendVals = (values as string[]).map(d => util.encodeString(d));\n }\n const dataId = backend.write(backendVals, shape, dtype);\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n\n // Count bytes for string tensors.\n if (dtype === 'string') {\n const info = this.state.tensorInfo.get(dataId);\n const newBytes = bytesFromStringArray(backendVals as Uint8Array[]);\n this.state.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n return t;\n }\n\n /**\n * Internal method used by backends. Makes a new tensor\n * that is a wrapper around an existing data id. It doesn't create\n * a new data id, only increments the ref count used in memory tracking.\n */\n makeTensorFromDataId(\n dataId: DataId, shape: number[], dtype: DataType,\n backend?: KernelBackend): Tensor {\n dtype = dtype || 'float32';\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n return t;\n }\n\n makeVariable(\n initialValue: Tensor, trainable = true, name?: string,\n dtype?: DataType): Variable {\n name = name || this.nextVariableId().toString();\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.cast(dtype);\n }\n const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n if (this.state.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.state.registeredVariables[v.name] = v;\n this.incRef(v, this.backend);\n return v;\n }\n\n trackTensor(a: Tensor, backend: KernelBackend): void {\n this.state.numTensors++;\n if (a.dtype === 'string') {\n this.state.numStringTensors++;\n }\n // Bytes for complex numbers are counted by their components. Bytes for\n // string tensors are counted when writing values.\n let bytes = 0;\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n bytes = a.size * util.bytesPerElement(a.dtype);\n }\n this.state.numBytes += bytes;\n\n if (!this.state.tensorInfo.has(a.dataId)) {\n this.state.numDataBuffers++;\n this.state.tensorInfo.set(a.dataId, {\n backend: backend || this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes\n });\n }\n\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n\n // Track the tensor by dataId and increase the refCount for the dataId in the\n // backend.\n // TODO(pyu10055): This is currently used by makeVariable method, to increase\n // refCount on the backend for the dataId. It can potentially be replaced with\n // Identity op indead of calling backend directly.\n incRef(a: Tensor, backend: KernelBackend): void {\n this.trackTensor(a, backend);\n this.backend.incRef(a.dataId);\n }\n\n removeDataId(dataId: DataId, backend: KernelBackend) {\n if (this.state.tensorInfo.has(dataId) &&\n this.state.tensorInfo.get(dataId).backend === backend) {\n this.state.tensorInfo.delete(dataId);\n this.state.numDataBuffers--;\n }\n }\n disposeTensor(a: Tensor): void {\n if (!this.state.tensorInfo.has(a.dataId)) {\n return;\n }\n const info = this.state.tensorInfo.get(a.dataId);\n\n this.state.numTensors--;\n if (a.dtype === 'string') {\n this.state.numStringTensors--;\n this.state.numBytes -= info.bytes;\n }\n // Don't count bytes for complex numbers as they are counted by their\n // components.\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n const bytes = a.size * util.bytesPerElement(a.dtype);\n this.state.numBytes -= bytes;\n }\n\n // Remove the reference to dataId if backend dispose the data successfully\n if (info.backend.disposeData(a.dataId)) {\n this.removeDataId(a.dataId, info.backend);\n }\n\n // TODO(nsthorat): Construct an error and save the stack trace for\n // debugging when in debug mode. Creating a stack trace is too expensive\n // to do unconditionally.\n }\n\n disposeVariables(): void {\n for (const varName in this.state.registeredVariables) {\n const v = this.state.registeredVariables[varName];\n this.disposeVariable(v);\n }\n }\n\n disposeVariable(v: Variable): void {\n this.disposeTensor(v);\n if (this.state.registeredVariables[v.name] != null) {\n delete this.state.registeredVariables[v.name];\n }\n }\n\n memory(): MemoryInfo {\n const info = this.backend.memory() as MemoryInfo;\n info.numTensors = this.state.numTensors;\n info.numDataBuffers = this.state.numDataBuffers;\n info.numBytes = this.state.numBytes;\n if (this.state.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push(\n 'Memory usage by string tensors is approximate ' +\n '(2 bytes per character)');\n }\n return info;\n }\n\n async profile(query: () => (TensorContainer | Promise)):\n Promise {\n this.state.profiling = true;\n\n const startBytes = this.state.numBytes;\n const startNumTensors = this.state.numTensors;\n\n this.state.activeProfile.kernels = [];\n this.state.activeProfile.result = await query();\n\n this.state.profiling = false;\n\n this.state.activeProfile.peakBytes = Math.max(\n ...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n this.state.activeProfile.newTensors =\n this.state.numTensors - startNumTensors;\n for (const kernel of this.state.activeProfile.kernels) {\n kernel.kernelTimeMs = await kernel.kernelTimeMs;\n kernel.extraInfo = await kernel.extraInfo;\n }\n return this.state.activeProfile;\n }\n\n isTapeOn(): boolean {\n return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n }\n\n private addTapeNode(\n kernelName: string, inputs: NamedTensorMap, outputs: Tensor[],\n gradientsFunc: GradFunc, saved: Tensor[], attrs: NamedAttrMap): void {\n const tapeNode: TapeNode =\n {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};\n\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n gradientsFunc = gradConfig.gradFunc;\n }\n if (gradientsFunc != null) {\n tapeNode.gradient = (dys: Tensor[]) => {\n // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n // the backprop graph to the user as null instead of zeros\n dys = dys.map((dy, i) => {\n if (dy == null) {\n const output = outputs[i];\n const vals = util.makeZerosTypedArray(output.size, output.dtype);\n return this.makeTensor(vals, output.shape, output.dtype);\n }\n return dy;\n });\n // Grad functions of ops with single outputs expect a dy, while ops\n // with multiple outputs expect dys (array of dy).\n return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n };\n }\n this.state.activeTape.push(tapeNode);\n }\n\n keep(result: T): T {\n result.kept = true;\n return result;\n }\n\n private startTape() {\n if (this.state.gradientDepth === 0) {\n this.state.activeTape = [];\n }\n this.state.gradientDepth++;\n }\n\n private endTape() {\n this.state.gradientDepth--;\n }\n\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name?: string) {\n const scopeInfo: ScopeState = {\n track: [],\n name: 'unnamed scope',\n id: this.state.nextScopeId++\n };\n if (name) {\n scopeInfo.name = name;\n }\n this.state.scopeStack.push(scopeInfo);\n this.state.activeScope = scopeInfo;\n }\n\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result?: TensorContainer) {\n const tensorsToTrackInParent = getTensorsInContainer(result);\n const tensorsToTrackInParentSet =\n new Set(tensorsToTrackInParent.map(t => t.id));\n\n // Dispose the arrays tracked in this scope.\n for (let i = 0; i < this.state.activeScope.track.length; i++) {\n const tensor = this.state.activeScope.track[i];\n if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n tensor.dispose();\n }\n }\n\n const oldScope = this.state.scopeStack.pop();\n this.state.activeScope = this.state.scopeStack.length === 0 ?\n null :\n this.state.scopeStack[this.state.scopeStack.length - 1];\n\n // Track the current result in the parent scope.\n tensorsToTrackInParent.forEach(tensor => {\n // Only track the tensor if was allocated in the inner scope and is not\n // globally kept.\n if (!tensor.kept && tensor.scopeId === oldScope.id) {\n this.track(tensor);\n }\n });\n }\n\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f`\n * was not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(\n f: () => T, xs: Tensor[], dy?: T,\n allowNoGradients = false): {value: T, grads: Tensor[]} {\n util.assert(\n xs.length > 0, () => 'gradients() received an empty list of xs.');\n if (dy != null && dy.dtype !== 'float32') {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n\n const y = this.scopedRun(\n () => this.startTape(), () => this.endTape(),\n () => this.tidy('forward', f));\n\n util.assert(\n y instanceof Tensor,\n () => 'The result y returned by f() must be a tensor.');\n // Filter out the nodes that don't connect x => y.\n const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error(\n 'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n 'that the f you passed encloses all operations that lead from x ' +\n 'to y.');\n }\n\n return this.tidy('backward', () => {\n const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n // Backprop gradients through the filtered nodes.\n backpropagateGradients(\n accumulatedGradientMap, filteredTape,\n // Pass the tidy function to avoid circular dep with `tape.ts`.\n f => this.tidy(f as ScopeFn),\n // Pass an add function to avoide a circular dep with `tape.ts`.\n add);\n const grads = xs.map(x => accumulatedGradientMap[x.id]);\n\n if (this.state.gradientDepth === 0) {\n // This means that we are not computing higher-order gradients\n // and can clean up the tape.\n this.state.activeTape.forEach(node => {\n for (const tensor of node.saved) {\n tensor.dispose();\n }\n });\n this.state.activeTape = null;\n }\n return {value: y, grads};\n });\n }\n\n customGrad(f: CustomGradientFunc):\n (...args: Array) => T {\n util.assert(\n util.isFunction(f),\n () => 'The f passed in customGrad(f) must be a function.');\n return (...inputs: Tensor[]): T => {\n util.assert(\n inputs.every(t => t instanceof Tensor),\n () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n 'tensors');\n\n let res: {\n value: T,\n gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[],\n };\n const inputMap: NamedTensorMap = {};\n inputs.forEach((input, i) => {\n inputMap[i] = input;\n });\n\n const forwardFunc: ForwardFunc = (_, save) => {\n res = f(...[...inputs, save]);\n util.assert(\n res.value instanceof Tensor,\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.value` is a tensor');\n util.assert(\n util.isFunction(res.gradFunc),\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function.');\n return res.value;\n };\n\n const backwardsFunc = (dy: T, saved: Tensor[]) => {\n const gradRes = res.gradFunc(dy, saved);\n const grads: Tensor[] = Array.isArray(gradRes) ? gradRes : [gradRes];\n util.assert(\n grads.length === inputs.length,\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'the same number of tensors as inputs passed to f(...).');\n util.assert(\n grads.every(t => t instanceof Tensor),\n () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'a list of only tensors.');\n const gradMap: {[key: string]: () => Tensor} = {};\n grads.forEach((grad, i) => {\n gradMap[i] = () => grad;\n });\n return gradMap;\n };\n\n return this.runKernelFunc({\n forwardFunc,\n backwardsFunc,\n inputs: inputMap,\n });\n };\n }\n\n readSync(dataId: DataId): BackendValues {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId: DataId): Promise {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n\n async time(query: () => void): Promise {\n const start = now();\n const timingInfo = await this.backend.time(query) as TimingInfo;\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n private track(result: T): T {\n if (this.state.activeScope != null) {\n result.scopeId = this.state.activeScope.id;\n this.state.activeScope.track.push(result);\n }\n\n return result;\n }\n\n get registeredVariables(): NamedVariableMap {\n return this.state.registeredVariables;\n }\n\n /**\n * Resets the engine state. Removes all backends but does not remove\n * registered backend factories.\n */\n reset(): void {\n // Make any pending promise obsolete.\n this.pendingBackendInitId++;\n\n this.state.dispose();\n this.ENV.reset();\n this.state = new EngineState();\n\n for (const backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n this.backendName = null;\n this.backendInstance = null;\n this.pendingBackendInit = null;\n }\n}\n\nfunction ones(shape: number[]): Tensor {\n const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n return ENGINE.makeTensor(values, shape, 'float32');\n}\n\nexport function getOrMakeEngine(): Engine {\n const ns = getGlobalNamespace() as {} as {_tfengine: Engine};\n if (ns._tfengine == null) {\n const environment = new Environment(ns);\n ns._tfengine = new Engine(environment);\n }\n setEnvironmentGlobal(ns._tfengine.ENV);\n\n // Tell the current tensor interface that the global engine is responsible\n // for tracking.\n setTensorTracker(() => ns._tfengine);\n return ns._tfengine;\n}\n\nexport const ENGINE = getOrMakeEngine();\n\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a: Tensor, b: Tensor): Tensor {\n // We duplicate Add here to avoid a circular dependency with add.ts.\n const inputs = {a, b};\n return ENGINE.runKernel(Add, inputs as {} as NamedTensorMap);\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined(): boolean {\n return typeof navigator !== 'undefined' && navigator != null;\n}\n\nlet isMobileMockValue: boolean | undefined;\n\nexport function mockIsMobile(value: boolean | undefined) {\n isMobileMockValue = value;\n}\n\nexport function isMobile(nav?: Navigator): boolean {\n if (isMobileMockValue !== undefined) {\n return isMobileMockValue;\n }\n if (nav || _isNavigatorDefined()) {\n if (!nav) {\n nav = navigator;\n }\n if (nav.product === 'ReactNative') {\n return true;\n }\n\n // tslint:disable-next-line:no-any\n const a = nav.userAgent || nav.vendor ||\n (typeof window !== 'undefined' ? (window as any).opera : '');\n // Use `navigator.userAgentData.mobile` as fallback.\n if (!a) {\n // tslint:disable-next-line:no-any\n const navAny = nav as any;\n return navAny.userAgentData && navAny.userAgentData.mobile;\n }\n // tslint:disable-next-line:max-line-length\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n .test(a) ||\n // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n .test(a.substr(0, 4));\n }\n return false;\n}\n\nexport function isBrowser(): boolean {\n return (typeof window !== 'undefined' && window.document != null) ||\n //@ts-ignore\n (typeof WorkerGlobalScope !== 'undefined');\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\n\nimport * as device_util from './device_util';\nimport {env} from './environment';\n\nconst ENV = env();\n\n/**\n * This file contains environment-related flag registrations.\n */\n\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n if (debugValue) {\n console.warn(\n 'Debugging mode is ON. The output of every math call will ' +\n 'be downloaded to CPU and checked for NaNs. ' +\n 'This significantly impacts performance.');\n }\n});\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag(\n 'IS_NODE',\n () => (typeof process !== 'undefined') &&\n (typeof process.versions !== 'undefined') &&\n (typeof process.versions.node !== 'undefined'));\n\n/** Whether this browser is Chrome. */\nENV.registerFlag(\n 'IS_CHROME',\n () => typeof navigator !== 'undefined' && navigator != null &&\n navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n /Google Inc/.test(navigator.vendor));\n\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag(\n 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n\n/** Whether to check computation result for errors. */\nENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => true);\n\n/** Whether the backend needs to wrap input to imageBitmap. */\nENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike, dtype?: DataType): number[] {\n let firstElem: typeof val = val;\n\n if (isTypedArray(val)) {\n return dtype === 'string' ? [] : [val.length];\n }\n if (!Array.isArray(val)) {\n return []; // Scalar.\n }\n const shape: number[] = [];\n\n while (Array.isArray(firstElem) ||\n isTypedArray(firstElem) && dtype !== 'string') {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) &&\n env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n deepAssertShapeConsistency(val, shape, []);\n }\n\n return shape;\n}\n\nfunction deepAssertShapeConsistency(\n val: TensorLike, shape: number[], indices: number[]) {\n indices = indices || [];\n if (!(Array.isArray(val)) && !isTypedArray(val)) {\n assert(\n shape.length === 0,\n () => `Element arr[${indices.join('][')}] is a primitive, ` +\n `but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(\n shape.length > 0,\n () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n `but is an array of ${val.length} elements`);\n assert(\n val.length === shape[0],\n () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n `elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\n\nfunction assertDtype(\n expectedDtype: DataType|'numeric'|'string_or_numeric',\n actualDType: DataType, argName: string, functionName: string) {\n if (expectedDtype === 'string_or_numeric') {\n return;\n }\n if (expectedDtype == null) {\n throw new Error(`Expected dtype cannot be null.`);\n }\n if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n expectedDtype === 'numeric' && actualDType === 'string') {\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must ` +\n `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\n\nexport function convertToTensor(\n x: T|TensorLike, argName: string, functionName: string,\n parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T {\n if (x instanceof Tensor) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n // If the user expects a bool/int/float, use that info to update the\n // inferredDtype when it is not a string.\n if (inferredDtype !== 'string' &&\n ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype as DataType;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n if ((x == null) ||\n (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n typeof x !== 'boolean' && typeof x !== 'string')) {\n const type = x == null ? 'null' : (x as {}).constructor.name;\n throw new Error(\n `Argument '${argName}' passed to '${functionName}' must be a ` +\n `Tensor or TensorLike, but got '${type}'`);\n }\n const inferredShape = inferShape(x, inferredDtype);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x] as number[];\n }\n const skipTypedArray = true;\n const values = inferredDtype !== 'string' ?\n toTypedArray(x, inferredDtype as DataType) :\n flatten(x as string[], [], skipTypedArray) as string[];\n return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;\n}\n\nexport function convertToTensorArray(\n arg: Array, argName: string, functionName: string,\n parseAsDtype: DataType|'numeric'|'string_or_numeric' = 'numeric'): T[] {\n if (!Array.isArray(arg)) {\n throw new Error(\n `Argument ${argName} passed to ${functionName} must be a ` +\n '`Tensor[]` or `TensorLike[]`');\n }\n const tensors = arg as T[];\n return tensors.map(\n (t, i) =>\n convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {isPromise} from '../util';\n\nexport const OP_SCOPE_SUFFIX = '__op';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f: {[name: string]: T}): T {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(\n `Please provide an object with a single key ` +\n `(operation name) mapping to a function. Got an object with ` +\n `${keys.length} keys.`);\n }\n\n let opName = keys[0];\n const fn = f[opName];\n\n // Strip the underscore from the end of the function name.\n if (opName.endsWith('_')) {\n opName = opName.substring(0, opName.length - 1);\n }\n\n // add an __op suffix to distinguish ops from kernels in tf.profile\n opName = opName + OP_SCOPE_SUFFIX;\n\n // tslint:disable-next-line:no-any\n const f2 = (...args: any[]) => {\n ENGINE.startScope(opName);\n try {\n const result = fn(...args);\n if (isPromise(result)) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n ENGINE.endScope(result);\n return result;\n } catch (ex) {\n ENGINE.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n // tslint:disable-next-line:no-any\n return f2 as any as T;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Complex, ComplexInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real: T|TensorLike, imag: T|TensorLike): T {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n util.assertShapesMatch(\n $real.shape, $imag.shape,\n `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n\n const inputs: ComplexInputs = {real: $real, imag: $imag};\n return ENGINE.runKernel(Complex, inputs as {} as NamedTensorMap);\n}\n\nexport const complex = op({complex_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {TensorLike, TypedArray} from '../types';\nimport {DataType} from '../types';\nimport {assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray} from '../util';\n\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(\n values: TensorLike, shape: number[], inferredShape: number[],\n dtype?: DataType): Tensor {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(\n `Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error(\n 'values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(\n providedSize === inferredSize,\n () =>\n `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(\n inferredShape[i] === shape[i] || !flatDimsDontMatch,\n () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values] as number[];\n }\n\n shape = shape || inferredShape;\n values = dtype !== 'string' ?\n toTypedArray(values, dtype) :\n flatten(values as string[], [], true) as string[];\n return ENGINE.makeTensor(values as TypedArray, shape, dtype);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`. If the values are strings,\n * they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(\n values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype) as Tensor;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n 'float32': 4,\n 'float16': 2,\n 'int32': 4,\n 'uint16': 2,\n 'uint8': 1,\n 'bool': 1,\n 'complex64': 8\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n /**\n * An ordered list of paths.\n *\n * Paths are intentionally abstract in order to be general. For example, they\n * can be relative URL paths or relative paths on the file system.\n */\n paths: string[];\n\n /**\n * Specifications of the weights stored in the paths.\n */\n weights: WeightsManifestEntry[];\n}\n\n/**\n * Group to which the weight belongs.\n *\n * - 'optimizer': Weight from a stateful optimizer.\n */\nexport type WeightGroup = 'model'|'optimizer';\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n /**\n * Name of the weight, e.g., 'Dense_1/bias'\n */\n name: string;\n\n /**\n * Shape of the weight.\n */\n shape: number[];\n\n /**\n * Data type of the weight.\n */\n dtype: 'float32'|'int32'|'bool'|'string'|'complex64';\n\n /**\n * Type of the weight.\n *\n * Optional.\n *\n * The value 'optimizer' indicates the weight belongs to an optimizer\n * (i.e., used only during model training and not during inference).\n */\n group?: WeightGroup;\n\n /**\n * Information for dequantization of the weight.\n */\n quantization?: {\n scale?: number, // The scaling constant to multiply by.\n min?: number, // The (possibly nudged) minimum weight to add.\n dtype: 'uint16'|'uint8'|'float16' // The dtype of the quantized weights.\n };\n}\n\n/**\n * Options for saving a model.\n * @innamespace io\n */\nexport interface SaveConfig {\n /**\n * Whether to save only the trainable weights of the model, ignoring the\n * non-trainable ones.\n */\n trainableOnly?: boolean;\n\n /**\n * Whether the optimizer will be saved (if exists).\n *\n * Default: `false`.\n */\n includeOptimizer?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n /**\n * Information about the model artifacts saved.\n */\n modelArtifactsInfo: ModelArtifactsInfo;\n\n /**\n * HTTP responses from the server that handled the model-saving request (if\n * any). This is applicable only to server-based saving routes.\n */\n responses?: Response[];\n\n /**\n * Error messages and related data (if any).\n */\n errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n /**\n * Timestamp for when the model is saved.\n */\n dateSaved: Date;\n\n /**\n * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now\n * come in a JSON format and none of our IOHandlers support a non json\n * format. We could conder replacing this with 'Binary' if we want to\n * allow future handlers to save to non json formats (though they will\n * probably want more information than 'Binary').\n * Type of the model topology\n *\n * Type of the model topology\n *\n * Possible values:\n * - JSON: JSON config (human-readable, e.g., Keras JSON).\n * - GraphDef: TensorFlow\n * [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n * protocol buffer (binary).\n */\n modelTopologyType: 'JSON'|'GraphDef';\n\n /**\n * Size of model topology (Keras JSON or GraphDef), in bytes.\n */\n modelTopologyBytes?: number;\n\n /**\n * Size of weight specification or manifest, in bytes.\n */\n weightSpecsBytes?: number;\n\n /**\n * Size of weight value data, in bytes.\n */\n weightDataBytes?: number;\n}\n\n/** Model training configuration. */\nexport declare interface TrainingConfig {\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n // See\n // tslint:disable-next-line:max-line-length\n // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts\n /** Optimizer used for the model training. */\n optimizer_config: {};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n /** Loss function(s) for the model's output(s). */\n loss: string|string[]|{[key: string]: string};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n /** Metric function(s) for the model's output(s). */\n metrics?: string[]|{[key: string]: string};\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n weighted_metrics?: string[];\n\n // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n sample_weight_mode?: string;\n\n loss_weights?: number[]|{[key: string]: number};\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields\n * of this interface are optional, in order to support topology- or weights-only\n * saving and loading.\n *\n * Note this interface is used internally in IOHandlers. For the file format\n * written to disk as `model.json`, see `ModelJSON`.\n */\nexport declare interface ModelArtifacts {\n /**\n * Model topology.\n *\n * For Keras-style `tf.Model`s, this is a JSON object.\n * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n * encoding of the `GraphDef` protocol buffer.\n */\n modelTopology?: {}|ArrayBuffer;\n\n /**\n * Serialized configuration for the model's training.\n */\n trainingConfig?: TrainingConfig;\n\n /**\n * Weight specifications.\n *\n * This corresponds to the weightsData below.\n */\n weightSpecs?: WeightsManifestEntry[];\n\n /**\n * Binary buffer for all weight values concatenated in the order specified\n * by `weightSpecs`.\n */\n weightData?: ArrayBuffer;\n\n /**\n * Hard-coded format name for models saved from TensorFlow.js or converted\n * by TensorFlow.js Converter.\n */\n format?: string;\n\n /**\n * What library is responsible for originally generating this artifact.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n */\n generatedBy?: string;\n\n /**\n * What library or tool is responsible for converting the original model\n * to this format, applicable only if the model is output by a converter.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.\n *\n * A value of `null` means the model artifacts are generated without any\n * conversion process (e.g., saved directly from a TensorFlow.js\n * `tf.LayersModel` instance.)\n */\n convertedBy?: string|null;\n\n /**\n * Inputs and outputs signature for saved model.\n */\n signature?: {};\n\n /**\n * User-defined metadata about the model.\n */\n userDefinedMetadata?: {[key: string]: {}};\n\n /**\n * Initializer for the model.\n */\n modelInitializer?: {};\n}\n\n/**\n * The on-disk format of the `model.json` file.\n *\n * TF.js 1.0 always populates the optional fields when writing model.json.\n * Prior versions did not provide those fields.\n */\nexport declare interface ModelJSON {\n /**\n * Model topology.\n *\n * For Keras-style `tf.Model`s, this is a JSON object.\n * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n * encoding of the `GraphDef` protocol buffer.\n */\n modelTopology: {};\n\n /** Model training configuration. */\n trainingConfig?: TrainingConfig;\n\n /**\n * Weights manifest.\n *\n * The weights manifest consists of an ordered list of weight-manifest\n * groups. Each weight-manifest group consists of a number of weight values\n * stored in a number of paths. See the documentation of\n * `WeightsManifestConfig` for more details.\n */\n weightsManifest: WeightsManifestConfig;\n\n /**\n * Hard-coded format name for models saved from TensorFlow.js or converted\n * by TensorFlow.js Converter.\n */\n format?: string;\n\n /**\n * What library is responsible for originally generating this artifact.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n */\n generatedBy?: string;\n\n /**\n * What library or tool is responsible for converting the original model\n * to this format, applicable only if the model is output by a converter.\n *\n * Used for debugging purposes. E.g., 'TensorFlow.js Converter v1.0.0'.\n *\n * A value of `null` means the model artifacts are generated without any\n * conversion process (e.g., saved directly from a TensorFlow.js\n * `tf.LayersModel` instance.)\n */\n convertedBy?: string|null;\n\n /**\n * Inputs and outputs signature for saved model.\n */\n signature?: {};\n\n /**\n * User-defined metadata about the model.\n */\n userDefinedMetadata?: {[key: string]: {}};\n\n /**\n * Initializer for the model.\n */\n modelInitializer?: {};\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n Promise;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n save?: SaveHandler;\n load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n /**\n * List all models in the model store.\n *\n * @returns A dictionary mapping paths of existing models to their\n * model artifacts info. Model artifacts info include type of the model's\n * topology, byte sizes of the topology, weights, etc.\n */\n listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n /**\n * Remove a model specified by `path`.\n *\n * @param path\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n removeModel(path: string): Promise;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\n/** @innamespace io */\nexport interface LoadOptions {\n /**\n * RequestInit (options) for HTTP requests.\n *\n * For detailed information on the supported fields, see\n * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)\n */\n requestInit?: RequestInit;\n\n /**\n * Progress callback.\n */\n onProgress?: OnProgressCallback;\n\n /**\n * A function used to override the `window.fetch` function.\n */\n fetchFunc?: Function;\n\n /**\n * Strict loading model: whether extraneous weights or missing\n * weights should trigger an `Error`.\n *\n * If `true`, require that the provided weights exactly match those\n * required by the layers. `false` means that both extra weights\n * and missing weights will be silently ignored.\n *\n * Default: `true`.\n */\n strict?: boolean;\n\n /**\n * Path prefix for weight files, by default this is calculated from the\n * path of the model JSON file.\n *\n * For instance, if the path to the model JSON file is\n * `http://localhost/foo/model.json`, then the default path prefix will be\n * `http://localhost/foo/`. If a weight file has the path value\n * `group1-shard1of2` in the weight manifest, then the weight file will be\n * loaded from `http://localhost/foo/group1-shard1of2` by default. However,\n * if you provide a `weightPathPrefix` value of\n * `http://localhost/foo/alt-weights`, then the weight file will be loaded\n * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.\n */\n weightPathPrefix?: string;\n\n /**\n * Whether the module or model is to be loaded from TF Hub.\n *\n * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n * standard model file name and the query parameters.\n *\n * Default: `false`.\n */\n fromTFHub?: boolean;\n\n /**\n * An async function to convert weight file name to URL. The weight file\n * names are stored in model.json's weightsManifest.paths field. By default we\n * consider weight files are colocated with the model.json file. For example:\n * model.json URL: https://www.google.com/models/1/model.json\n * group1-shard1of1.bin url:\n * https://www.google.com/models/1/group1-shard1of1.bin\n *\n * With this func you can convert the weight file name to any URL.\n */\n weightUrlConverter?: (weightFileName: string) => Promise;\n}\n\n/**\n * Additional options for Platform.fetch\n */\nexport interface RequestDetails {\n /**\n * Is this request for a binary file (as opposed to a json file)\n */\n isBinary?: boolean;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {complex} from '../ops/complex';\nimport {tensor} from '../ops/tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, ModelJSON, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n * - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n * concatenated.\n * - An `Array` of `WeightManifestEntry`s, carrying information including\n * tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(\n tensors: NamedTensorMap|NamedTensor[], group?: WeightGroup):\n Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n // TODO(adarob, cais): Support quantization.\n const specs: WeightsManifestEntry[] = [];\n const dataPromises: Array> = [];\n\n const names: string[] = Array.isArray(tensors) ?\n tensors.map(tensor => tensor.name) :\n Object.keys(tensors);\n\n for (let i = 0; i < names.length; ++i) {\n const name = names[i];\n const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n t.dtype !== 'string' && t.dtype !== 'complex64') {\n throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n }\n const spec: WeightsManifestEntry = {name, shape: t.shape, dtype: t.dtype};\n if (t.dtype === 'string') {\n const utf8bytes = new Promise(async resolve => {\n const vals = await t.bytes() as Uint8Array[];\n const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n NUM_BYTES_STRING_LENGTH * vals.length;\n const bytes = new Uint8Array(totalNumBytes);\n let offset = 0;\n for (let i = 0; i < vals.length; i++) {\n const val = vals[i];\n const bytesOfLength =\n new Uint8Array(new Uint32Array([val.length]).buffer);\n bytes.set(bytesOfLength, offset);\n offset += NUM_BYTES_STRING_LENGTH;\n bytes.set(val, offset);\n offset += val.length;\n }\n resolve(bytes);\n });\n dataPromises.push(utf8bytes);\n } else {\n dataPromises.push(t.data());\n }\n if (group != null) {\n spec.group = group;\n }\n specs.push(spec);\n }\n\n const tensorValues = await Promise.all(dataPromises);\n return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n * concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n * whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n * to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n // TODO(adarob, cais): Support quantization.\n const out: NamedTensorMap = {};\n let float16Decode: (buffer: Uint16Array) => Float32Array | undefined;\n let offset = 0;\n for (const spec of specs) {\n const name = spec.name;\n const dtype = spec.dtype;\n const shape = spec.shape;\n const size = sizeFromShape(shape);\n let values: TypedArray|string[]|Uint8Array[];\n\n if ('quantization' in spec) {\n const quantization = spec.quantization;\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n if (!('min' in quantization && 'scale' in quantization)) {\n throw new Error(\n `Weight ${spec.name} with quantization ${quantization.dtype} ` +\n `doesn't have corresponding metadata min and scale.`);\n }\n } else if (quantization.dtype === 'float16') {\n if (dtype !== 'float32') {\n throw new Error(\n `Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n `which only supports weights of type float32 not ${dtype}.`);\n }\n } else {\n throw new Error(\n `Weight ${spec.name} has unknown ` +\n `quantization dtype ${quantization.dtype}. ` +\n `Supported quantization dtypes are: ` +\n `'uint8', 'uint16', and 'float16'.`);\n }\n const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n const byteBuffer =\n buffer.slice(offset, offset + size * quantizationSizeFactor);\n const quantizedArray = (quantization.dtype === 'uint8') ?\n new Uint8Array(byteBuffer) :\n new Uint16Array(byteBuffer);\n if (dtype === 'float32') {\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n values = new Float32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = v * quantization.scale + quantization.min;\n }\n } else if (quantization.dtype === 'float16') {\n if (float16Decode === undefined) {\n float16Decode = getFloat16Decoder();\n }\n values = float16Decode(quantizedArray as Uint16Array);\n } else {\n throw new Error(\n `Unsupported quantization type ${quantization.dtype} ` +\n `for weight type float32.`);\n }\n } else if (dtype === 'int32') {\n if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n throw new Error(\n `Unsupported quantization type ${quantization.dtype} ` +\n `for weight type int32.`);\n }\n values = new Int32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = Math.round(v * quantization.scale + quantization.min);\n }\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * quantizationSizeFactor;\n } else if (dtype === 'string') {\n const size = sizeFromShape(spec.shape);\n values = [];\n for (let i = 0; i < size; i++) {\n const byteLength = new Uint32Array(\n buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n offset += NUM_BYTES_STRING_LENGTH;\n const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n (values as Uint8Array[]).push(bytes);\n offset += byteLength;\n }\n } else {\n const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n if (dtype === 'float32') {\n values = new Float32Array(byteBuffer);\n } else if (dtype === 'int32') {\n values = new Int32Array(byteBuffer);\n } else if (dtype === 'bool') {\n values = new Uint8Array(byteBuffer);\n } else if (dtype === 'complex64') {\n values = new Float32Array(byteBuffer);\n const real = new Float32Array(values.length / 2);\n const image = new Float32Array(values.length / 2);\n for (let i = 0; i < real.length; i++) {\n real[i] = values[i * 2];\n image[i] = values[i * 2 + 1];\n }\n const realTensor = tensor(real, shape, 'float32');\n const imageTensor = tensor(image, shape, 'float32');\n out[name] = complex(realTensor, imageTensor);\n realTensor.dispose();\n imageTensor.dispose();\n } else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * dtypeFactor;\n }\n if (dtype !== 'complex64') {\n out[name] = tensor(values, shape, dtype);\n }\n }\n return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n // TODO(adarob, cais): Support quantization.\n if (xs === null) {\n throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n }\n\n let totalByteLength = 0;\n\n // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n // can have a different byte length from that of the `TypedArray` itself,\n // for example, when the `TypedArray` is created from an offset in an\n // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n // the `TypedArray` in byte length. If an element of `xs` does not show\n // this property, a new `TypedArray` that satisfy this property will be\n // constructed and pushed into `normalizedXs`.\n const normalizedXs: TypedArray[] = [];\n xs.forEach((x: TypedArray) => {\n totalByteLength += x.byteLength;\n // tslint:disable:no-any\n normalizedXs.push(\n x.byteLength === x.buffer.byteLength ? x :\n new (x.constructor as any)(x));\n if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n x as any instanceof Uint8Array)) {\n throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n }\n // tslint:enable:no-any\n });\n\n const y = new Uint8Array(totalByteLength);\n let offset = 0;\n normalizedXs.forEach((x: TypedArray) => {\n y.set(new Uint8Array(x.buffer), offset);\n offset += x.byteLength;\n });\n\n return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n if (useNodeBuffer) {\n return Buffer.byteLength(str);\n }\n return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n if (useNodeBuffer) {\n return Buffer.from(buffer).toString('base64');\n }\n const buf = new Uint8Array(buffer);\n let s = '';\n for (let i = 0, l = buf.length; i < l; i++) {\n s += String.fromCharCode(buf[i]);\n }\n return btoa(s);\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n if (useNodeBuffer) {\n const buf = Buffer.from(str, 'base64');\n return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n }\n const s = atob(str);\n const buffer = new Uint8Array(s.length);\n for (let i = 0; i < s.length; ++i) {\n buffer.set([s.charCodeAt(i)], i);\n }\n return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n if (buffers.length === 1) {\n return buffers[0];\n }\n\n let totalByteLength = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n totalByteLength += buffer.byteLength;\n });\n\n const temp = new Uint8Array(totalByteLength);\n let offset = 0;\n buffers.forEach((buffer: ArrayBuffer) => {\n temp.set(new Uint8Array(buffer), offset);\n offset += buffer.byteLength;\n });\n return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n const SEPARATOR = '/';\n path = path.trim();\n while (path.endsWith(SEPARATOR)) {\n path = path.slice(0, path.length - 1);\n }\n const items = path.split(SEPARATOR);\n return items[items.length - 1];\n}\n\n/**\n * Create `ModelJSON` from `ModelArtifacts`.\n *\n * @param artifacts Model artifacts, describing the model and its weights.\n * @param manifest Weight manifest, describing where the weights of the\n * `ModelArtifacts` are stored, and some metadata about them.\n * @returns Object representing the `model.json` file describing the model\n * artifacts and weights\n */\nexport function getModelJSONForModelArtifacts(\n artifacts: ModelArtifacts, manifest: WeightsManifestConfig): ModelJSON {\n const result: ModelJSON = {\n modelTopology: artifacts.modelTopology,\n format: artifacts.format,\n generatedBy: artifacts.generatedBy,\n convertedBy: artifacts.convertedBy,\n weightsManifest: manifest\n };\n if (artifacts.signature != null) {\n result.signature = artifacts.signature;\n }\n if (artifacts.userDefinedMetadata != null) {\n result.userDefinedMetadata = artifacts.userDefinedMetadata;\n }\n if (artifacts.modelInitializer != null) {\n result.modelInitializer = artifacts.modelInitializer;\n }\n if (artifacts.trainingConfig != null) {\n result.trainingConfig = artifacts.trainingConfig;\n }\n return result;\n}\n\n/**\n * Create `ModelArtifacts` from a JSON file.\n *\n * @param modelJSON Object containing the parsed JSON of `model.json`\n * @param loadWeights Function that takes the JSON file's weights manifest,\n * reads weights from the listed path(s), and returns a Promise of the\n * weight manifest entries along with the weights data.\n * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.\n */\nexport async function getModelArtifactsForJSON(\n modelJSON: ModelJSON,\n loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[\n /* weightSpecs */ WeightsManifestEntry[], /* weightData */ ArrayBuffer\n ]>): Promise {\n const modelArtifacts: ModelArtifacts = {\n modelTopology: modelJSON.modelTopology,\n format: modelJSON.format,\n generatedBy: modelJSON.generatedBy,\n convertedBy: modelJSON.convertedBy\n };\n\n if (modelJSON.trainingConfig != null) {\n modelArtifacts.trainingConfig = modelJSON.trainingConfig;\n }\n if (modelJSON.weightsManifest != null) {\n const [weightSpecs, weightData] =\n await loadWeights(modelJSON.weightsManifest);\n modelArtifacts.weightSpecs = weightSpecs;\n modelArtifacts.weightData = weightData;\n }\n if (modelJSON.signature != null) {\n modelArtifacts.signature = modelJSON.signature;\n }\n if (modelJSON.userDefinedMetadata != null) {\n modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;\n }\n if (modelJSON.modelInitializer != null) {\n modelArtifacts.modelInitializer = modelJSON.modelInitializer;\n }\n\n return modelArtifacts;\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n ModelArtifactsInfo {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('Expected JSON model topology, received ArrayBuffer.');\n }\n\n return {\n dateSaved: new Date(),\n modelTopologyType: 'JSON',\n modelTopologyBytes: modelArtifacts.modelTopology == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n weightDataBytes: modelArtifacts.weightData == null ?\n 0 :\n modelArtifacts.weightData.byteLength,\n };\n}\n\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable(): Uint32Array {\n const convertMantissa = (i: number): number => {\n let m = i << 13;\n let e = 0;\n\n while ((m & 0x00800000) === 0) {\n e -= 0x00800000;\n m <<= 1;\n }\n m &= ~0x00800000;\n e += 0x38800000;\n\n return m | e;\n };\n\n const mantisaTable = new Uint32Array(2048);\n\n mantisaTable[0] = 0;\n for (let i = 1; i < 1024; i++) {\n mantisaTable[i] = convertMantissa(i);\n }\n for (let i = 1024; i < 2048; i++) {\n mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n }\n\n return mantisaTable;\n}\n\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable(): Uint32Array {\n const exponentTable = new Uint32Array(64);\n\n exponentTable[0] = 0;\n exponentTable[31] = 0x47800000;\n exponentTable[32] = 0x80000000;\n exponentTable[63] = 0xc7800000;\n for (let i = 1; i < 31; i++) {\n exponentTable[i] = i << 23;\n }\n for (let i = 33; i < 63; i++) {\n exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n }\n\n return exponentTable;\n}\n\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable(): Uint32Array {\n const offsetTable = new Uint32Array(64);\n\n for (let i = 0; i < 64; i++) {\n offsetTable[i] = 1024;\n }\n offsetTable[0] = offsetTable[32] = 0;\n\n return offsetTable;\n}\n\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n * the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder(): (buffer: Uint16Array) => Float32Array {\n // Algorithm is based off of\n // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n\n // Cache lookup tables\n const mantisaTable = computeFloat16MantisaTable();\n const exponentTable = computeFloat16ExponentTable();\n const offsetTable = computeFloat16OffsetTable();\n\n return (quantizedArray: Uint16Array) => {\n const buffer = new ArrayBuffer(4 * quantizedArray.length);\n const bufferUint32View = new Uint32Array(buffer);\n for (let index = 0; index < quantizedArray.length; index++) {\n const float16Bits = quantizedArray[index];\n const float32Bits =\n mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n exponentTable[float16Bits >> 10];\n bufferUint32View[index] = float32Bits;\n }\n return new Float32Array(buffer);\n };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler, LoadOptions} from './types';\n\nexport type IORouter = (url: string|string[], loadOptions?: LoadOptions) =>\n IOHandler;\n\nexport class IORouterRegistry {\n // Singleton instance.\n private static instance: IORouterRegistry;\n\n private saveRouters: IORouter[];\n private loadRouters: IORouter[];\n\n private constructor() {\n this.saveRouters = [];\n this.loadRouters = [];\n }\n\n private static getInstance(): IORouterRegistry {\n if (IORouterRegistry.instance == null) {\n IORouterRegistry.instance = new IORouterRegistry();\n }\n return IORouterRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerSaveRouter(saveRouter: IORouter) {\n IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n }\n\n /**\n * Register a load-handler router.\n *\n * @param loadRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `load` method defined or `null`.\n */\n static registerLoadRouter(loadRouter: IORouter) {\n IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n }\n\n /**\n * Look up IOHandler for saving, given a URL-like string.\n *\n * @param url\n * @returns If only one match is found, an instance of IOHandler with the\n * `save` method defined. If no match is found, `null`.\n * @throws Error, if more than one match is found.\n */\n static getSaveHandlers(url: string|string[]): IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'save');\n }\n\n /**\n * Look up IOHandler for loading, given a URL-like string.\n *\n * @param url\n * @param loadOptions Optional, custom load options.\n * @returns All valid handlers for `url`, given the currently registered\n * handler routers.\n */\n static getLoadHandlers(url: string|string[], loadOptions?: LoadOptions):\n IOHandler[] {\n return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n }\n\n private static getHandlers(\n url: string|string[], handlerType: 'save'|'load',\n loadOptions?: LoadOptions): IOHandler[] {\n const validHandlers: IOHandler[] = [];\n const routers = handlerType === 'load' ?\n IORouterRegistry.getInstance().loadRouters :\n IORouterRegistry.getInstance().saveRouters;\n routers.forEach(router => {\n const handler = router(url, loadOptions);\n if (handler !== null) {\n validHandlers.push(handler);\n }\n });\n return validHandlers;\n }\n}\n\nexport const registerSaveRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter: IORouter) =>\n IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url: string|string[]) =>\n IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers =\n (url: string|string[], loadOptions?: LoadOptions) =>\n IORouterRegistry.getLoadHandlers(url, loadOptions);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\n\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n// as the type of topology (JSON vs binary), byte size of the topology, byte\n// size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise {\n const idbFactory = getIndexedDBFactory();\n\n return new Promise((resolve, reject) => {\n const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n deleteRequest.onsuccess = () => resolve();\n deleteRequest.onerror = error => reject(error);\n });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Add more info about what IOHandler subtypes are available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'Failed to obtain IndexedDB factory because the current environment' +\n 'is not a web browser.');\n }\n // tslint:disable-next-line:no-any\n const theWindow: any = typeof window === 'undefined' ? self : window;\n const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n theWindow.shimIndexedDB;\n if (factory == null) {\n throw new Error(\n 'The current browser does not appear to support IndexedDB.');\n }\n return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n const db = openRequest.result as IDBDatabase;\n db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n protected readonly indexedDB: IDBFactory;\n protected readonly modelPath: string;\n\n static readonly URL_SCHEME = 'indexeddb://';\n\n constructor(modelPath: string) {\n this.indexedDB = getIndexedDBFactory();\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For IndexedDB, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n // TODO(cais): Support saving GraphDef models.\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n return this.databaseAction(this.modelPath, modelArtifacts) as\n Promise;\n }\n\n async load(): Promise {\n return this.databaseAction(this.modelPath) as Promise;\n }\n\n /**\n * Perform database action to put model artifacts into or read model artifacts\n * from IndexedDB object store.\n *\n * Whether the action is put or get depends on whether `modelArtifacts` is\n * specified. If it is specified, the action will be put; otherwise the action\n * will be get.\n *\n * @param modelPath A unique string path for the model.\n * @param modelArtifacts If specified, it will be the model artifacts to be\n * stored in IndexedDB.\n * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n * of `ModelArtifacts`, if the action is get.\n */\n private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n Promise {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n\n if (modelArtifacts == null) {\n // Read model out from object store.\n const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const getRequest = modelStore.get(this.modelPath);\n getRequest.onsuccess = () => {\n if (getRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${this.modelPath}' ` +\n `in IndexedDB.`));\n } else {\n resolve(getRequest.result.modelArtifacts);\n }\n };\n getRequest.onerror = error => {\n db.close();\n return reject(getRequest.error);\n };\n modelTx.oncomplete = () => db.close();\n } else {\n // Put model into object store.\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n // First, put ModelArtifactsInfo into info store.\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const putInfoRequest =\n infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n let modelTx: IDBTransaction;\n putInfoRequest.onsuccess = () => {\n // Second, put model data into model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const putModelRequest = modelStore.put({\n modelPath: this.modelPath,\n modelArtifacts,\n modelArtifactsInfo\n });\n putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n putModelRequest.onerror = error => {\n // If the put-model request fails, roll back the info entry as\n // well.\n infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const deleteInfoRequest = infoStore.delete(this.modelPath);\n deleteInfoRequest.onsuccess = () => {\n db.close();\n return reject(putModelRequest.error);\n };\n deleteInfoRequest.onerror = error => {\n db.close();\n return reject(putModelRequest.error);\n };\n };\n };\n putInfoRequest.onerror = error => {\n db.close();\n return reject(putInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n }\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n * which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n private indexedDB: IDBFactory;\n\n constructor() {\n this.indexedDB = getIndexedDBFactory();\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n return new Promise<{[path: string]: ModelArtifactsInfo}>(\n (resolve, reject) => {\n const openRequest =\n this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n const store = tx.objectStore(INFO_STORE_NAME);\n // tslint:disable:max-line-length\n // Need to cast `store` as `any` here because TypeScript's DOM\n // library does not have the `getAll()` method even though the\n // method is supported in the latest version of most mainstream\n // browsers:\n // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n // tslint:enable:max-line-length\n // tslint:disable-next-line:no-any\n const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n getAllInfoRequest.onsuccess = () => {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n for (const item of getAllInfoRequest.result) {\n out[item.modelPath] = item.modelArtifactsInfo;\n }\n resolve(out);\n };\n getAllInfoRequest.onerror = error => {\n db.close();\n return reject(getAllInfoRequest.error);\n };\n tx.oncomplete = () => db.close();\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n const getInfoRequest = infoStore.get(path);\n let modelTx: IDBTransaction;\n getInfoRequest.onsuccess = () => {\n if (getInfoRequest.result == null) {\n db.close();\n return reject(new Error(\n `Cannot find model with path '${path}' ` +\n `in IndexedDB.`));\n } else {\n // First, delete the entry in the info store.\n const deleteInfoRequest = infoStore.delete(path);\n const deleteModelData = () => {\n // Second, delete the entry in the model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const deleteModelRequest = modelStore.delete(path);\n deleteModelRequest.onsuccess = () =>\n resolve(getInfoRequest.result.modelArtifactsInfo);\n deleteModelRequest.onerror = error =>\n reject(getInfoRequest.error);\n };\n // Proceed with deleting model data regardless of whether deletion\n // of info data succeeds or not.\n deleteInfoRequest.onsuccess = deleteModelData;\n deleteInfoRequest.onerror = error => {\n deleteModelData();\n db.close();\n return reject(getInfoRequest.error);\n };\n }\n };\n getInfoRequest.onerror = error => {\n db.close();\n return reject(getInfoRequest.error);\n };\n\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n } else {\n modelTx.oncomplete = () => db.close();\n }\n };\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n throw new Error(\n 'purgeLocalStorageModels() cannot proceed because local storage is ' +\n 'unavailable in the current environment.');\n }\n const LS = window.localStorage;\n const purgedModelPaths: string[] = [];\n for (let i = 0; i < LS.length; ++i) {\n const key = LS.key(i);\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n if (key.startsWith(prefix) && key.length > prefix.length) {\n LS.removeItem(key);\n const modelName = getModelPathFromKey(key);\n if (purgedModelPaths.indexOf(modelName) === -1) {\n purgedModelPaths.push(modelName);\n }\n }\n }\n return purgedModelPaths;\n}\n\ntype LocalStorageKeys = {\n /** Key of the localStorage entry storing `ModelArtifactsInfo`. */\n info: string,\n /**\n * Key of the localStorage entry storing the 'modelTopology' key of\n * `model.json`\n */\n topology: string,\n /**\n * Key of the localStorage entry storing the `weightsManifest.weights` entries\n * of `model.json`\n */\n weightSpecs: string,\n /** Key of the localStorage entry storing the weight data in Base64 */\n weightData: string,\n /**\n * Key of the localStorage entry storing the remaining fields of `model.json`\n * @see {@link ModelMetadata}\n */\n modelMetadata: string,\n};\n\ntype ModelMetadata = Omit;\n\nfunction getModelKeys(path: string): LocalStorageKeys {\n return {\n info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n modelMetadata:\n [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\n\nfunction removeItems(keys: LocalStorageKeys): void {\n for (const key of Object.values(keys)) {\n window.localStorage.removeItem(key);\n }\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n const items = key.split(PATH_SEPARATOR);\n if (items.length < 3) {\n throw new Error(`Invalid key format: ${key}`);\n }\n return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n key;\n}\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n protected readonly LS: Storage;\n protected readonly modelPath: string;\n protected readonly keys: LocalStorageKeys;\n\n static readonly URL_SCHEME = 'localstorage://';\n\n constructor(modelPath: string) {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n // TODO(cais): Add more info about what IOHandler subtypes are\n // available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error(\n 'The current environment does not support local storage.');\n }\n this.LS = window.localStorage;\n\n if (modelPath == null || !modelPath) {\n throw new Error(\n 'For local storage, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n this.keys = getModelKeys(this.modelPath);\n }\n\n /**\n * Save model artifacts to browser local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @param modelArtifacts The model artifacts to be stored.\n * @returns An instance of SaveResult.\n */\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const topology = JSON.stringify(modelArtifacts.modelTopology);\n const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n const modelArtifactsInfo: ModelArtifactsInfo =\n getModelArtifactsInfoForJSON(modelArtifacts);\n\n try {\n this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n this.LS.setItem(this.keys.topology, topology);\n this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n this.LS.setItem(\n this.keys.weightData,\n arrayBufferToBase64String(modelArtifacts.weightData));\n\n // Note that JSON.stringify doesn't write out keys that have undefined\n // values, so for some keys, we set undefined instead of a null-ish\n // value.\n const metadata: Required = {\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n signature: modelArtifacts.signature != null ?\n modelArtifacts.signature :\n undefined,\n userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ?\n modelArtifacts.userDefinedMetadata :\n undefined,\n modelInitializer: modelArtifacts.modelInitializer != null ?\n modelArtifacts.modelInitializer :\n undefined,\n trainingConfig: modelArtifacts.trainingConfig != null ?\n modelArtifacts.trainingConfig :\n undefined\n };\n this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));\n\n return {modelArtifactsInfo};\n } catch (err) {\n // If saving failed, clean up all items saved so far.\n removeItems(this.keys);\n\n throw new Error(\n `Failed to save model '${this.modelPath}' to local storage: ` +\n `size quota being exceeded is a possible cause of this failure: ` +\n `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n }\n }\n }\n\n /**\n * Load a model from local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @returns The loaded model (if loading succeeds).\n */\n async load(): Promise {\n const info =\n JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n if (info == null) {\n throw new Error(\n `In local storage, there is no model with name '${this.modelPath}'`);\n }\n\n if (info.modelTopologyType !== 'JSON') {\n throw new Error(\n 'BrowserLocalStorage does not support loading non-JSON model ' +\n 'topology yet.');\n }\n\n const out: ModelArtifacts = {};\n\n // Load topology.\n const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n if (topology == null) {\n throw new Error(\n `In local storage, the topology of model '${this.modelPath}' ` +\n `is missing.`);\n }\n out.modelTopology = topology;\n\n // Load weight specs.\n const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n if (weightSpecs == null) {\n throw new Error(\n `In local storage, the weight specs of model '${this.modelPath}' ` +\n `are missing.`);\n }\n out.weightSpecs = weightSpecs;\n\n // Load meta-data fields.\n const metadataString = this.LS.getItem(this.keys.modelMetadata);\n if (metadataString != null) {\n const metadata = JSON.parse(metadataString) as ModelMetadata;\n out.format = metadata.format;\n out.generatedBy = metadata.generatedBy;\n out.convertedBy = metadata.convertedBy;\n if (metadata.signature != null) {\n out.signature = metadata.signature;\n }\n if (metadata.userDefinedMetadata != null) {\n out.userDefinedMetadata = metadata.userDefinedMetadata;\n }\n if (metadata.modelInitializer != null) {\n out.modelInitializer = metadata.modelInitializer;\n }\n if (metadata.trainingConfig != null) {\n out.trainingConfig = metadata.trainingConfig;\n }\n }\n\n // Load weight data.\n const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n if (weightDataBase64 == null) {\n throw new Error(\n `In local storage, the binary weight values of model ` +\n `'${this.modelPath}' are missing.`);\n }\n out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n return out;\n }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n return browserLocalStorage(\n url.slice(BrowserLocalStorage.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n * - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n * model, such as date saved, type of the topology, size in bytes, etc.\n * - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n * style models, this is a stringized JSON.\n * - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n * model, can be used to decode the saved binary weight values (see\n * item below).\n * - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n * weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n * `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n private readonly LS: Storage;\n\n constructor() {\n assert(\n env().getBool('IS_BROWSER'),\n () => 'Current environment is not a web browser');\n assert(\n typeof window === 'undefined' ||\n typeof window.localStorage !== 'undefined',\n () => 'Current browser does not appear to support localStorage');\n this.LS = window.localStorage;\n }\n\n async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n const out: {[path: string]: ModelArtifactsInfo} = {};\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n for (let i = 0; i < this.LS.length; ++i) {\n const key = this.LS.key(i);\n if (key.startsWith(prefix) && key.endsWith(suffix)) {\n const modelPath = getModelPathFromKey(key);\n out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n }\n }\n return out;\n }\n\n async removeModel(path: string): Promise {\n path = maybeStripScheme(path);\n const keys = getModelKeys(path);\n if (this.LS.getItem(keys.info) == null) {\n throw new Error(`Cannot find model at path '${path}'`);\n }\n const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n removeItems(keys);\n return info;\n }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n * string.\n * - Moving or copying model from one path to another in the same medium or from\n * one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n // Singleton instance.\n private static instance: ModelStoreManagerRegistry;\n\n private managers: {[scheme: string]: ModelStoreManager};\n\n private constructor() {\n this.managers = {};\n }\n\n private static getInstance(): ModelStoreManagerRegistry {\n if (ModelStoreManagerRegistry.instance == null) {\n ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n }\n return ModelStoreManagerRegistry.instance;\n }\n\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerManager(scheme: string, manager: ModelStoreManager) {\n assert(scheme != null, () => 'scheme must not be undefined or null.');\n if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n }\n assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n const registry = ModelStoreManagerRegistry.getInstance();\n assert(\n registry.managers[scheme] == null,\n () => `A model store manager is already registered for scheme '${\n scheme}'.`);\n registry.managers[scheme] = manager;\n }\n\n static getManager(scheme: string): ModelStoreManager {\n const manager = this.getInstance().managers[scheme];\n if (manager == null) {\n throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n }\n return manager;\n }\n\n static getSchemes(): string[] {\n return Object.keys(this.getInstance().managers);\n }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n * Scheme: e.g., 'localstorage' in the example above.\n * Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n throw new Error(\n `The url string provided does not contain a scheme. ` +\n `Supported schemes are: ` +\n `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n }\n return {\n scheme: url.split(URL_SCHEME_SUFFIX)[0],\n path: url.split(URL_SCHEME_SUFFIX)[1],\n };\n}\n\nasync function cloneModelInternal(\n sourceURL: string, destURL: string,\n deleteSource = false): Promise {\n assert(\n sourceURL !== destURL,\n () => `Old path and new path are the same: '${sourceURL}'`);\n\n const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n assert(\n loadHandlers.length > 0,\n () => `Copying failed because no load handler is found for source URL ${\n sourceURL}.`);\n assert(\n loadHandlers.length < 2,\n () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `load handlers for source URL ${sourceURL}.`);\n const loadHandler = loadHandlers[0];\n\n const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n assert(\n saveHandlers.length > 0,\n () => `Copying failed because no save handler is found for destination ` +\n `URL ${destURL}.`);\n assert(\n saveHandlers.length < 2,\n () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `save handlers for destination URL ${destURL}.`);\n const saveHandler = saveHandlers[0];\n\n const sourceScheme = parseURL(sourceURL).scheme;\n const sourcePath = parseURL(sourceURL).path;\n const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n const modelArtifacts = await loadHandler.load();\n\n // If moving within the same storage medium, remove the old model as soon as\n // the loading is done. Without doing this, it is possible that the combined\n // size of the two models will cause the cloning to fail.\n if (deleteSource && sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n const saveResult = await saveHandler.save(modelArtifacts);\n\n // If moving between mediums, the deletion is done after the save succeeds.\n // This guards against the case in which saving to the destination medium\n // fails.\n if (deleteSource && !sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n\n return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n * 'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n const schemes = ModelStoreManagerRegistry.getSchemes();\n const out: {[url: string]: ModelArtifactsInfo} = {};\n for (const scheme of schemes) {\n const schemeOut =\n await ModelStoreManagerRegistry.getManager(scheme).listModels();\n for (const path in schemeOut) {\n const url = scheme + URL_SCHEME_SUFFIX + path;\n out[url] = schemeOut[path];\n }\n }\n return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n * 'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function removeModel(url: string): Promise {\n const schemeAndPath = parseURL(url);\n const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n return manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function copyModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = false;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function moveModel(\n sourceURL: string, destURL: string): Promise {\n const deleteSource = true;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\nimport {BrowserIndexedDB, BrowserIndexedDBManager} from '../io/indexed_db';\nimport {BrowserLocalStorage, BrowserLocalStorageManager} from '../io/local_storage';\nimport {ModelStoreManagerRegistry} from '../io/model_management';\n\nimport {Platform} from './platform';\n\nexport class PlatformBrowser implements Platform {\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n private textEncoder: TextEncoder;\n\n fetch(path: string, init?: RequestInit): Promise {\n return fetch(path, init);\n }\n\n now(): number {\n return performance.now();\n }\n\n encode(text: string, encoding: string): Uint8Array {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(\n `Browser's encoder only supports utf-8, but got ${encoding}`);\n }\n if (this.textEncoder == null) {\n this.textEncoder = new TextEncoder();\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes: Uint8Array, encoding: string): string {\n return new TextDecoder(encoding).decode(bytes);\n }\n}\n\nif (env().get('IS_BROWSER')) {\n env().setPlatform('browser', new PlatformBrowser());\n\n // Register LocalStorage IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n } catch (err) {\n }\n\n // Register IndexedDB IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(\n BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n } catch (err) {\n }\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from '../environment';\n\nimport {Platform} from './platform';\n\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n // tslint:disable-next-line:no-require-imports\n importFetch: () => require('node-fetch')\n};\n\ntype FetchFn = (url: string, init?: RequestInit) => Promise;\nlet systemFetch: FetchFn;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n systemFetch = null;\n}\nexport function setSystemFetch(fetchFn: FetchFn) {\n systemFetch = fetchFn;\n}\nexport function getSystemFetch(): FetchFn {\n return systemFetch;\n}\n\nexport class PlatformNode implements Platform {\n private textEncoder: TextEncoder;\n // tslint:disable-next-line:no-any\n util: any;\n\n constructor() {\n // tslint:disable-next-line:no-require-imports\n this.util = require('util');\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n this.textEncoder = new this.util.TextEncoder();\n }\n\n fetch(path: string, requestInits?: RequestInit): Promise {\n if (env().global.fetch != null) {\n return env().global.fetch(path, requestInits);\n }\n\n if (systemFetch == null) {\n systemFetch = getNodeFetch.importFetch();\n }\n return systemFetch(path, requestInits);\n }\n\n now(): number {\n const time = process.hrtime();\n return time[0] * 1000 + time[1] / 1000000;\n }\n\n encode(text: string, encoding: string): Uint8Array {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(\n `Node built-in encoder only supports utf-8, but got ${encoding}`);\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes: Uint8Array, encoding: string): string {\n if (bytes.length === 0) {\n return '';\n }\n return new this.util.TextDecoder(encoding).decode(bytes);\n }\n}\n\nif (env().get('IS_NODE')) {\n env().setPlatform('node', new PlatformNode());\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, Rank, ShapeMap} from '../types';\nimport * as util from '../util';\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(\n shape: ShapeMap[R], dtype: D = 'float32' as D,\n values?: DataTypeMap[D]): TensorBuffer {\n dtype = dtype || 'float32' as D;\n util.assertNonNegativeIntegerDimensions(shape);\n return new TensorBuffer(shape, dtype, values);\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Cast, CastAttrs, CastInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x: T|TensorLike, dtype: DataType): T {\n const $x = convertToTensor(x, 'x', 'cast');\n\n // Sanity checks.\n if (!util.isValidDtype(dtype)) {\n throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n }\n if (dtype === 'string' && $x.dtype !== 'string' ||\n dtype !== 'string' && $x.dtype === 'string') {\n throw new Error('Only strings can be casted to strings');\n }\n\n const inputs: CastInputs = {x: $x};\n const attrs: CastAttrs = {dtype};\n\n return ENGINE.runKernel(\n Cast, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const cast = op({cast_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Identity, IdentityInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x: T|TensorLike): T {\n const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');\n const inputs: IdentityInputs = {x: $x};\n\n // Note this op is called tf.identity in python. Hence the kernel name used\n // here.\n return ENGINE.runKernel(Identity, inputs as {} as NamedTensorMap);\n}\n\nexport const clone = op({clone_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x: T, verbose = false): void {\n console.log(x.toString(verbose));\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Required side effectful code for tfjs-core\n\n// Set up Engine and ENV\nimport {getOrMakeEngine} from './engine';\ngetOrMakeEngine();\n\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n\n// Set up OpHandler\nimport {buffer} from './ops/buffer';\nimport {cast} from './ops/cast';\nimport {clone} from './ops/clone';\nimport {print} from './ops/print';\nimport {OpHandler, setOpHandler} from './tensor';\nconst opHandler: OpHandler = {\n buffer,\n cast,\n clone,\n print\n};\nsetOpHandler(opHandler);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, http, isHTTPScheme} from './http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsForJSON, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter} from './router_registry';\nimport {IOHandler, LoadHandler, LoadOptions, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, OnProgressCallback, RequestDetails, SaveConfig, SaveHandler, SaveResult, TrainingConfig, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\nexport {\n browserFiles,\n browserHTTPRequest,\n concatenateArrayBuffers,\n decodeWeights,\n encodeWeights,\n fromMemory,\n getLoadHandlers,\n getModelArtifactsForJSON,\n getModelArtifactsInfoForJSON,\n getSaveHandlers,\n http,\n IOHandler,\n isHTTPScheme,\n LoadHandler,\n LoadOptions,\n loadWeights,\n ModelArtifacts,\n ModelArtifactsInfo,\n ModelJSON,\n ModelStoreManager,\n OnProgressCallback,\n registerLoadRouter,\n registerSaveRouter,\n RequestDetails,\n SaveConfig,\n SaveHandler,\n SaveResult,\n TrainingConfig,\n WeightGroup,\n weightsLoaderFactory,\n WeightsManifestConfig,\n WeightsManifestEntry,\n withSaveHandler\n};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {basename, concatenateArrayBuffers, getModelArtifactsForJSON, getModelArtifactsInfoForJSON, getModelJSONForModelArtifacts} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelJSON, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nfunction defer(f: () => T): Promise {\n return new Promise(resolve => setTimeout(resolve)).then(f);\n}\n\nexport class BrowserDownloads implements IOHandler {\n private readonly modelJsonFileName: string;\n private readonly weightDataFileName: string;\n private readonly modelJsonAnchor: HTMLAnchorElement;\n private readonly weightDataAnchor: HTMLAnchorElement;\n\n static readonly URL_SCHEME = 'downloads://';\n\n constructor(fileNamePrefix?: string) {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Provide info on what IOHandlers are available under the\n // current environment.\n throw new Error(\n 'browserDownloads() cannot proceed because the current environment ' +\n 'is not a browser.');\n }\n\n if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n }\n if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n }\n\n this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n this.weightDataFileName =\n fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (typeof (document) === 'undefined') {\n throw new Error(\n 'Browser downloads are not supported in ' +\n 'this environment since `document` is not present');\n }\n const weightsURL = window.URL.createObjectURL(new Blob(\n [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserDownloads.save() does not support saving model topology ' +\n 'in binary formats yet.');\n } else {\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./' + this.weightDataFileName],\n weights: modelArtifacts.weightSpecs\n }];\n const modelJSON: ModelJSON =\n getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n\n const modelJsonURL = window.URL.createObjectURL(\n new Blob([JSON.stringify(modelJSON)], {type: 'application/json'}));\n\n // If anchor elements are not provided, create them without attaching them\n // to parents, so that the downloaded file names can be controlled.\n const jsonAnchor = this.modelJsonAnchor == null ?\n document.createElement('a') :\n this.modelJsonAnchor;\n jsonAnchor.download = this.modelJsonFileName;\n jsonAnchor.href = modelJsonURL;\n // Trigger downloads by evoking a click event on the download anchors.\n // When multiple downloads are started synchronously, Firefox will only\n // save the last one.\n await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n\n if (modelArtifacts.weightData != null) {\n const weightDataAnchor = this.weightDataAnchor == null ?\n document.createElement('a') :\n this.weightDataAnchor;\n weightDataAnchor.download = this.weightDataFileName;\n weightDataAnchor.href = weightsURL;\n await defer(\n () => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n }\n\n return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n }\n }\n}\n\nclass BrowserFiles implements IOHandler {\n private readonly jsonFile: File;\n private readonly weightsFiles: File[];\n\n constructor(files: File[]) {\n if (files == null || files.length < 1) {\n throw new Error(\n `When calling browserFiles, at least 1 file is required, ` +\n `but received ${files}`);\n }\n this.jsonFile = files[0];\n this.weightsFiles = files.slice(1);\n }\n\n async load(): Promise {\n return new Promise((resolve, reject) => {\n const jsonReader = new FileReader();\n jsonReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const modelJSON = JSON.parse((event.target as any).result) as ModelJSON;\n\n const modelTopology = modelJSON.modelTopology;\n if (modelTopology == null) {\n reject(new Error(`modelTopology field is missing from file ${\n this.jsonFile.name}`));\n return;\n }\n\n const weightsManifest = modelJSON.weightsManifest;\n if (weightsManifest == null) {\n reject(new Error(`weightManifest field is missing from file ${\n this.jsonFile.name}`));\n return;\n }\n\n if (this.weightsFiles.length === 0) {\n resolve({modelTopology});\n return;\n }\n\n const modelArtifactsPromise = getModelArtifactsForJSON(\n modelJSON, (weightsManifest) => this.loadWeights(weightsManifest));\n resolve(modelArtifactsPromise);\n };\n\n jsonReader.onerror = error => reject(\n `Failed to read model topology and weights manifest JSON ` +\n `from file '${this.jsonFile.name}'. BrowserFiles supports loading ` +\n `Keras-style tf.Model artifacts only.`);\n jsonReader.readAsText(this.jsonFile);\n });\n }\n\n private loadWeights(weightsManifest: WeightsManifestConfig): Promise<[\n /* weightSpecs */ WeightsManifestEntry[], /* weightData */ ArrayBuffer\n ]> {\n const weightSpecs: WeightsManifestEntry[] = [];\n const paths: string[] = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n paths.push(...entry.paths);\n }\n\n const pathToFile: {[path: string]: File} =\n this.checkManifestAndWeightFiles(weightsManifest);\n\n const promises: Array> =\n paths.map(path => this.loadWeightsFile(path, pathToFile[path]));\n\n return Promise.all(promises).then(\n buffers => [weightSpecs, concatenateArrayBuffers(buffers)]);\n }\n\n private loadWeightsFile(path: string, file: File): Promise {\n return new Promise((resolve, reject) => {\n const weightFileReader = new FileReader();\n weightFileReader.onload = (event: Event) => {\n // tslint:disable-next-line:no-any\n const weightData = (event.target as any).result as ArrayBuffer;\n resolve(weightData);\n };\n weightFileReader.onerror = error =>\n reject(`Failed to weights data from file of path '${path}'.`);\n weightFileReader.readAsArrayBuffer(file);\n });\n }\n\n /**\n * Check the compatibility between weights manifest and weight files.\n */\n private checkManifestAndWeightFiles(manifest: WeightsManifestConfig):\n {[path: string]: File} {\n const basenames: string[] = [];\n const fileNames = this.weightsFiles.map(file => basename(file.name));\n const pathToFile: {[path: string]: File} = {};\n for (const group of manifest) {\n group.paths.forEach(path => {\n const pathBasename = basename(path);\n if (basenames.indexOf(pathBasename) !== -1) {\n throw new Error(\n `Duplicate file basename found in weights manifest: ` +\n `'${pathBasename}'`);\n }\n basenames.push(pathBasename);\n if (fileNames.indexOf(pathBasename) === -1) {\n throw new Error(\n `Weight file with basename '${pathBasename}' is not provided.`);\n } else {\n pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];\n }\n });\n }\n\n if (basenames.length !== this.weightsFiles.length) {\n throw new Error(\n `Mismatch in the number of files in weights manifest ` +\n `(${basenames.length}) and the number of weight files provided ` +\n `(${this.weightsFiles.length}).`);\n }\n return pathToFile;\n }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n } else {\n if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n } else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * // 'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n * `tf.Model`, `fileNamePrefix` should follow either of the following two\n * formats:\n * 1. `null` or `undefined`, in which case the default file\n * names will be used:\n * - 'model.json' for the JSON file containing the model topology and\n * weights manifest.\n * - 'model.weights.bin' for the binary file containing the binary weight\n * values.\n * 2. A single string or an Array of a single string, as the file name prefix.\n * For example, if `'foo'` is provided, the downloaded JSON\n * file and binary weights file will be named 'foo.json' and\n * 'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * // elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (``)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n * [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n * loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n * which an `Array` of `File`s is expected (in that order):\n * - A JSON file containing the model topology and weight manifest.\n * - Optionally, One or more binary files containing the binary weights.\n * These files must have names that match the paths in the `weightsManifest`\n * contained by the aforementioned JSON file, or errors will be thrown\n * during loading. These weights files have the same format as the ones\n * generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n * Python PIP package. If no weights files are provided, only the model\n * topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserFiles(files: File[]): IOHandler {\n return new BrowserFiles(files);\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from '../util';\n\nimport {OnProgressCallback} from './types';\n\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(\n promises: Array>, onProgress: OnProgressCallback,\n startFraction?: number, endFraction?: number) {\n checkPromises(promises);\n startFraction = startFraction == null ? 0 : startFraction;\n endFraction = endFraction == null ? 1 : endFraction;\n checkFraction(startFraction, endFraction);\n let resolvedPromise = 0;\n\n const registerMonitor = (promise: Promise<{}>) => {\n promise.then(value => {\n const fraction = startFraction +\n ++resolvedPromise / promises.length * (endFraction - startFraction);\n // pass fraction as parameter to callback function.\n onProgress(fraction);\n return value;\n });\n return promise;\n };\n\n function checkPromises(promises: Array>): void {\n assert(\n promises != null && Array.isArray(promises) && promises.length > 0,\n () => 'promises must be a none empty array');\n }\n\n function checkFraction(startFraction: number, endFraction: number): void {\n assert(\n startFraction >= 0 && startFraction <= 1,\n () => `Progress fraction must be in range [0, 1], but ` +\n `got startFraction ${startFraction}`);\n assert(\n endFraction >= 0 && endFraction <= 1,\n () => `Progress fraction must be in range [0, 1], but ` +\n `got endFraction ${endFraction}`);\n assert(\n endFraction >= startFraction,\n () => `startFraction must be no more than endFraction, but ` +\n `got startFraction ${startFraction} and endFraction ` +\n `${endFraction}`);\n }\n\n return Promise.all(promises.map(registerMonitor));\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '../environment';\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\nimport {decodeWeights} from './io_utils';\nimport {monitorPromisesProgress} from './progress';\nimport {DTYPE_VALUE_SIZE_MAP, LoadOptions, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n * length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n fetchURLs: string[], loadOptions?: LoadOptions): Promise {\n if (loadOptions == null) {\n loadOptions = {};\n }\n\n const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n loadOptions.fetchFunc;\n\n // Create the requests for all of the weights in parallel.\n const requests = fetchURLs.map(\n fetchURL =>\n fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));\n\n const fetchStartFraction = 0;\n const fetchEndFraction = 0.5;\n\n const responses = loadOptions.onProgress == null ?\n await Promise.all(requests) :\n await monitorPromisesProgress(\n requests, loadOptions.onProgress, fetchStartFraction,\n fetchEndFraction);\n\n const bufferPromises = responses.map(response => response.arrayBuffer());\n\n const bufferStartFraction = 0.5;\n const bufferEndFraction = 1;\n\n const buffers = loadOptions.onProgress == null ?\n await Promise.all(bufferPromises) :\n await monitorPromisesProgress(\n bufferPromises, loadOptions.onProgress, bufferStartFraction,\n bufferEndFraction);\n return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n * Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[],\n requestInit?: RequestInit): Promise {\n // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n // single weight from a group, the whole group will be fetched. At a future\n // date, we should support fetching only the individual shards within a\n // group that are needed to reconstruct the requested weight.\n // TODO(cais): Use `decodeWeights` for implementation.\n\n const fetchWeights = (fetchUrls: string[]) =>\n loadWeightsAsArrayBuffer(fetchUrls, {requestInit});\n const loadWeights = weightsLoaderFactory(fetchWeights);\n\n return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n * filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n * fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n fetchWeightsFunction: (fetchUrls: string[]) => Promise):\n (manifest: WeightsManifestConfig, filePathPrefix?: string,\n weightNames?: string[]) => Promise {\n return async(\n manifest: WeightsManifestConfig, filePathPrefix = '',\n weightNames?: string[]): Promise => {\n // Collect all the groups, weights, and their relative offsets to be\n // fetched.\n const groupIndicesToFetchMap = manifest.map(() => false);\n const groupWeightsToFetch: {\n [group: number]: Array<{\n manifestEntry: WeightsManifestEntry; groupOffset: number;\n sizeBytes: number;\n }>\n } = {};\n const weightsFound =\n weightNames != null ? weightNames.map(() => false) : [];\n const allManifestWeightNames: string[] = [];\n manifest.forEach((manifestGroupConfig, groupIndex) => {\n let groupOffset = 0;\n manifestGroupConfig.weights.forEach(weightsEntry => {\n const rawDtype = ('quantization' in weightsEntry) ?\n weightsEntry.quantization.dtype :\n weightsEntry.dtype;\n\n const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n util.sizeFromShape(weightsEntry.shape);\n\n const enqueueWeightsForFetchingFn = () => {\n groupIndicesToFetchMap[groupIndex] = true;\n if (groupWeightsToFetch[groupIndex] == null) {\n groupWeightsToFetch[groupIndex] = [];\n }\n\n groupWeightsToFetch[groupIndex].push({\n manifestEntry: weightsEntry,\n groupOffset,\n sizeBytes: weightsBytes\n });\n };\n\n if (weightNames != null) {\n weightNames.forEach((weightName, weightIndex) => {\n if (weightName === weightsEntry.name) {\n enqueueWeightsForFetchingFn();\n weightsFound[weightIndex] = true;\n }\n });\n } else {\n enqueueWeightsForFetchingFn();\n }\n\n allManifestWeightNames.push(weightsEntry.name);\n groupOffset += weightsBytes;\n });\n });\n\n if (!weightsFound.every(found => found)) {\n const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n throw new Error(\n `Could not find weights in manifest with names: ` +\n `${weightsNotFound.join(', ')}. \\n` +\n `Manifest JSON has weights with names: ` +\n `${allManifestWeightNames.join(', ')}.`);\n }\n\n // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n // IDs.\n const groupIndicesToFetch =\n groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n if (shouldFetch) {\n accumulator.push(i);\n }\n return accumulator;\n }, []);\n\n const fetchUrls: string[] = [];\n groupIndicesToFetch.forEach(i => {\n manifest[i].paths.forEach(filepath => {\n const fetchUrl = filePathPrefix +\n (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n fetchUrls.push(fetchUrl);\n });\n });\n const buffers = await fetchWeightsFunction(fetchUrls);\n\n const weightsTensorMap: NamedTensorMap = {};\n let bufferIndexOffset = 0;\n groupIndicesToFetch.forEach(i => {\n const numBuffers = manifest[i].paths.length;\n\n let groupBytes = 0;\n for (let i = 0; i < numBuffers; i++) {\n groupBytes += buffers[bufferIndexOffset + i].byteLength;\n }\n\n // Create a buffer for the whole group.\n const groupBuffer = new ArrayBuffer(groupBytes);\n const groupByteBuffer = new Uint8Array(groupBuffer);\n let groupBufferOffset = 0;\n for (let i = 0; i < numBuffers; i++) {\n const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n groupByteBuffer.set(buffer, groupBufferOffset);\n groupBufferOffset += buffer.byteLength;\n }\n\n const weightsEntries = groupWeightsToFetch[i];\n weightsEntries.forEach(weightsEntry => {\n const byteBuffer = groupBuffer.slice(\n weightsEntry.groupOffset,\n weightsEntry.groupOffset + weightsEntry.sizeBytes);\n const nameToTensorMap =\n decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n for (const name in nameToTensorMap) {\n weightsTensorMap[name] = nameToTensorMap[name];\n }\n });\n\n bufferIndexOffset += numBuffers;\n });\n\n return weightsTensorMap;\n };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsForJSON, getModelArtifactsInfoForJSON, getModelJSONForModelArtifacts} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadOptions, ModelArtifacts, ModelJSON, OnProgressCallback, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest implements IOHandler {\n protected readonly path: string;\n protected readonly requestInit: RequestInit;\n\n private readonly fetch: Function;\n private readonly weightUrlConverter: (weightName: string) => Promise;\n\n readonly DEFAULT_METHOD = 'POST';\n\n static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n private readonly weightPathPrefix: string;\n private readonly onProgress: OnProgressCallback;\n\n constructor(path: string, loadOptions?: LoadOptions) {\n if (loadOptions == null) {\n loadOptions = {};\n }\n this.weightPathPrefix = loadOptions.weightPathPrefix;\n this.onProgress = loadOptions.onProgress;\n this.weightUrlConverter = loadOptions.weightUrlConverter;\n\n if (loadOptions.fetchFunc != null) {\n assert(\n typeof loadOptions.fetchFunc === 'function',\n () => 'Must pass a function that matches the signature of ' +\n '`fetch` (see ' +\n 'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n this.fetch = loadOptions.fetchFunc;\n } else {\n this.fetch = env().platform.fetch;\n }\n\n assert(\n path != null && path.length > 0,\n () => 'URL path for http must not be null, undefined or ' +\n 'empty.');\n\n if (Array.isArray(path)) {\n assert(\n path.length === 2,\n () => 'URL paths for http must have a length of 2, ' +\n `(actual length is ${path.length}).`);\n }\n this.path = path;\n\n if (loadOptions.requestInit != null &&\n loadOptions.requestInit.body != null) {\n throw new Error(\n 'requestInit is expected to have no pre-existing body, but has one.');\n }\n this.requestInit = loadOptions.requestInit || {};\n }\n\n async save(modelArtifacts: ModelArtifacts): Promise {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error(\n 'BrowserHTTPRequest.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n\n const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n init.body = new FormData();\n\n const weightsManifest: WeightsManifestConfig = [{\n paths: ['./model.weights.bin'],\n weights: modelArtifacts.weightSpecs,\n }];\n const modelTopologyAndWeightManifest: ModelJSON =\n getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n\n init.body.append(\n 'model.json',\n new Blob(\n [JSON.stringify(modelTopologyAndWeightManifest)],\n {type: JSON_TYPE}),\n 'model.json');\n\n if (modelArtifacts.weightData != null) {\n init.body.append(\n 'model.weights.bin',\n new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}),\n 'model.weights.bin');\n }\n\n const response = await this.fetch(this.path, init);\n\n if (response.ok) {\n return {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n responses: [response],\n };\n } else {\n throw new Error(\n `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n `${response.status}.`);\n }\n }\n\n /**\n * Load model artifacts via HTTP request(s).\n *\n * See the documentation to `tf.io.http` for details on the saved\n * artifacts.\n *\n * @returns The loaded model artifacts (if loading succeeds).\n */\n async load(): Promise {\n const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n\n if (!modelConfigRequest.ok) {\n throw new Error(\n `Request to ${this.path} failed with status code ` +\n `${modelConfigRequest.status}. Please verify this URL points to ` +\n `the model JSON of the model to load.`);\n }\n let modelJSON: ModelJSON;\n try {\n modelJSON = await modelConfigRequest.json();\n } catch (e) {\n let message = `Failed to parse model JSON of response from ${this.path}.`;\n // TODO(nsthorat): Remove this after some time when we're comfortable that\n // .pb files are mostly gone.\n if (this.path.endsWith('.pb')) {\n message += ' Your path contains a .pb file extension. ' +\n 'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n 'in favor of .json models. You can re-convert your Python ' +\n 'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n 'or you can convert your.pb models with the \\'pb2json\\'' +\n 'NPM script in the tensorflow/tfjs-converter repository.';\n } else {\n message += ' Please make sure the server is serving valid ' +\n 'JSON for this request.';\n }\n throw new Error(message);\n }\n\n // We do not allow both modelTopology and weightsManifest to be missing.\n const modelTopology = modelJSON.modelTopology;\n const weightsManifest = modelJSON.weightsManifest;\n if (modelTopology == null && weightsManifest == null) {\n throw new Error(\n `The JSON from HTTP path ${this.path} contains neither model ` +\n `topology or manifest for weights.`);\n }\n\n return getModelArtifactsForJSON(\n modelJSON, (weightsManifest) => this.loadWeights(weightsManifest));\n }\n\n private async loadWeights(weightsManifest: WeightsManifestConfig):\n Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n const [prefix, suffix] = parseUrl(weightPath);\n const pathPrefix = this.weightPathPrefix || prefix;\n\n const weightSpecs = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n }\n\n const fetchURLs: string[] = [];\n const urlPromises: Array> = [];\n for (const weightsGroup of weightsManifest) {\n for (const path of weightsGroup.paths) {\n if (this.weightUrlConverter != null) {\n urlPromises.push(this.weightUrlConverter(path));\n } else {\n fetchURLs.push(pathPrefix + path + suffix);\n }\n }\n }\n\n if (this.weightUrlConverter) {\n fetchURLs.push(...await Promise.all(urlPromises));\n }\n\n const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n requestInit: this.requestInit,\n fetchFunc: this.fetch,\n onProgress: this.onProgress\n });\n return [weightSpecs, concatenateArrayBuffers(buffers)];\n }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n const lastSlash = url.lastIndexOf('/');\n const lastSearchParam = url.lastIndexOf('?');\n const prefix = url.substring(0, lastSlash);\n const suffix =\n lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRouter: IORouter =\n (url: string, loadOptions?: LoadOptions) => {\n if (typeof fetch === 'undefined' &&\n (loadOptions == null || loadOptions.fetchFunc == null)) {\n // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n // an environment that is not the browser or node they have to setup a\n // global fetch polyfill.\n return null;\n } else {\n let isHTTP = true;\n if (Array.isArray(url)) {\n isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n } else {\n isHTTP = isHTTPScheme(url);\n }\n if (isHTTP) {\n return http(url, loadOptions);\n }\n }\n return null;\n };\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n * - A JSON file consisting of `modelTopology` and `weightsManifest`.\n * - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n * 'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n * Can be an absolute HTTP path (e.g.,\n * 'http://localhost:8000/model-upload)') or a relative path (e.g.,\n * './model-upload').\n * @param requestInit Request configurations to be used when sending\n * HTTP request to server using `fetch`. It can contain fields such as\n * `method`, `credentials`, `headers`, `mode`, etc. See\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n * for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n * following fields:\n * - weightPathPrefix Optional, this specifies the path prefix for weight\n * files, by default this is calculated from the path param.\n * - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n * the `fetch` from node-fetch can be used here.\n * - onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function http(path: string, loadOptions?: LoadOptions): IOHandler {\n return new HTTPRequest(path, loadOptions);\n}\n\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(\n path: string, loadOptions?: LoadOptions): IOHandler {\n return http(path, loadOptions);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, TrainingConfig, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n constructor(private readonly modelArtifacts?: ModelArtifacts) {}\n\n async load(): Promise {\n return this.modelArtifacts;\n }\n}\n\nclass PassthroughSaver implements IOHandler {\n constructor(\n private readonly saveHandler:\n (artifacts: ModelArtifacts) => Promise) {}\n\n async save(modelArtifacts: ModelArtifacts) {\n return this.saveHandler(modelArtifacts);\n }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n * modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n * the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n * names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n * concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler {\n if (arguments.length === 1) {\n const isModelArtifacts =\n (modelArtifacts as ModelArtifacts).modelTopology != null ||\n (modelArtifacts as ModelArtifacts).weightSpecs != null;\n if (isModelArtifacts) {\n return new PassthroughLoader(modelArtifacts as ModelArtifacts);\n } else {\n // Legacy support: with only modelTopology.\n // TODO(cais): Remove this deprecated API.\n console.warn(\n 'Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({modelTopology: modelArtifacts as {}});\n }\n } else {\n // Legacy support.\n // TODO(cais): Remove this deprecated API.\n console.warn(\n 'Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({\n modelTopology: modelArtifacts as {},\n weightSpecs,\n weightData,\n trainingConfig\n });\n }\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n * // ... do something with the artifacts ...\n * return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n * `SaveResult`.\n */\nexport function withSaveHandler(\n saveHandler: (artifacts: ModelArtifacts) =>\n Promise): IOHandler {\n return new PassthroughSaver(saveHandler);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Exports under the tf.math.* namespace.\n */\n\nimport {confusionMatrix} from './ops/confusion_matrix';\n\nexport {confusionMatrix};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print(); // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(\n a: Tensor|TensorLike, b: Tensor|TensorLike, transposeA = false,\n transposeB = false): T {\n let $a = convertToTensor(a, 'a', 'matMul');\n let $b = convertToTensor(b, 'b', 'matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n\n const inputs: BatchMatMulInputs = {a: $a, b: $b};\n const attrs: BatchMatMulAttrs = {transposeA, transposeB};\n\n return ENGINE.runKernel(\n BatchMatMul, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const matMul = op({matMul_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {OneHot, OneHotAttrs, OneHotInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(\n indices: Tensor|TensorLike, depth: number, onValue = 1,\n offValue = 0): Tensor {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n\n const inputs: OneHotInputs = {indices: $indices};\n const attrs: OneHotAttrs = {depth, onValue, offValue};\n\n return ENGINE.runKernel(\n OneHot, inputs as unknown as NamedTensorMap,\n attrs as unknown as NamedAttrMap);\n}\n\nexport const oneHot = op({oneHot_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Transpose, TransposeAttrs, TransposeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x: T|TensorLike, perm?: number[]): T {\n const $x = convertToTensor(x, 'x', 'transpose');\n\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n util.assert(\n $x.rank === perm.length,\n () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n util.assert(\n axis >= 0 && axis < $x.rank,\n () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n\n if ($x.rank <= 1) {\n return $x.clone();\n }\n\n const inputs: TransposeInputs = {x: $x};\n const attrs: TransposeAttrs = {perm};\n\n return ENGINE.runKernel(\n Transpose, inputs as {} as NamedTensorMap, attrs as {} as NamedAttrMap);\n}\n\nexport const transpose = op({transpose_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {matMul} from './mat_mul';\nimport {oneHot} from './one_hot';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * // [0, 1, 1],\n * // [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n * for the classes. The shape is `[numExamples]`, where\n * `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n * 0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n * Its value must be larger than the largest element in `labels` and\n * `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n * row `r` and column `c` is the number of times examples of actual class\n * `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(\n labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n numClasses: number): Tensor2D {\n const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n const $predictions =\n convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n util.assert(\n numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n () => `If provided, numClasses must be a positive integer, ` +\n `but got ${numClasses}`);\n util.assert(\n $labels.rank === 1,\n () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n util.assert(\n $predictions.rank === 1,\n () => `Expected the rank of predictions to be 1, ` +\n `but got ${$predictions.rank}`);\n util.assert(\n $labels.shape[0] === $predictions.shape[0],\n () => `Mismatch in the number of examples: ` +\n `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n `Labels and predictions should have the same number of elements.`);\n util.assert(\n numClasses > 0 && Number.isInteger(numClasses),\n () => `numClasses is required to be a positive integer, but got ` +\n `${numClasses}`);\n // TODO(cais): In the future, if oneHot supports tensors inputs for\n // `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;\n const oneHotPredictions =\n oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;\n const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);\n const product: Tensor2D = matMul(oneHotLabelsT, oneHotPredictions);\n return cast(product, 'int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {env} from '../environment';\nimport {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '../kernel_names';\nimport {getKernel, NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor2D, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {PixelData, TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {tensor3d} from './tensor3d';\n\nlet fromPixels2DContext: CanvasRenderingContext2D;\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @returns A Tensor3D with the shape `[height, width, numChannels]`.\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(\n pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n HTMLVideoElement|ImageBitmap,\n numChannels = 3): Tensor3D {\n // Sanity checks.\n if (numChannels > 4) {\n throw new Error(\n 'Cannot construct Tensor with more than 4 channels from pixels.');\n }\n if (pixels == null) {\n throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n }\n let isPixelData = false;\n let isImageData = false;\n let isVideo = false;\n let isImage = false;\n let isCanvasLike = false;\n let isImageBitmap = false;\n if ((pixels as PixelData).data instanceof Uint8Array) {\n isPixelData = true;\n } else if (\n typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n isImageData = true;\n } else if (\n typeof (HTMLVideoElement) !== 'undefined' &&\n pixels instanceof HTMLVideoElement) {\n isVideo = true;\n } else if (\n typeof (HTMLImageElement) !== 'undefined' &&\n pixels instanceof HTMLImageElement) {\n isImage = true;\n // tslint:disable-next-line: no-any\n } else if ((pixels as any).getContext != null) {\n isCanvasLike = true;\n } else if (\n typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap) {\n isImageBitmap = true;\n } else {\n throw new Error(\n 'pixels passed to tf.browser.fromPixels() must be either an ' +\n `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n `in browser, or OffscreenCanvas, ImageData in webworker` +\n ` or {data: Uint32Array, width: number, height: number}, ` +\n `but was ${(pixels as {}).constructor.name}`);\n }\n if (isVideo) {\n const HAVE_CURRENT_DATA_READY_STATE = 2;\n if (isVideo &&\n (pixels as HTMLVideoElement).readyState <\n HAVE_CURRENT_DATA_READY_STATE) {\n throw new Error(\n 'The video element has not loaded data yet. Please wait for ' +\n '`loadeddata` event on the