fix for wasm compatibility
Vladimir Mandic
parent
4f17fe476b
commit
7e1de2e57b
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@ -1,6 +1,6 @@
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{
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"name": "@vladmandic/face-api",
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"version": "0.7.4",
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"version": "0.8.1",
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"description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
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"main": "dist/face-api.cjs",
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"module": "dist/face-api.esm.js",
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@ -15,7 +15,7 @@ export class FaceFeatureExtractor extends NeuralNetwork {
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throw new Error('FaceFeatureExtractor - load model before inference');
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}
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return tf.tidy(() => {
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const batchTensor = input.toBatchTensor(112, true);
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const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
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const meanRgb = [122.782, 117.001, 104.298];
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const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
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let out = denseBlock4(normalized, params.dense0, true);
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@ -1 +1 @@
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{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
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{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
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@ -15,7 +15,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork {
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throw new Error('TinyFaceFeatureExtractor - load model before inference');
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}
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return tf.tidy(() => {
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const batchTensor = input.toBatchTensor(112, true);
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const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
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const meanRgb = [122.782, 117.001, 104.298];
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const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
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let out = denseBlock3(normalized, params.dense0, true);
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@ -1 +1 @@
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{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
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{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
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@ -15,10 +15,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
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});
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const batchSize = inputDimensions.length;
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return tf.tidy(() => {
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const createInterleavedTensor = (fillX, fillY) => tf.stack([
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tf.fill([68], fillX),
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tf.fill([68], fillY)
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], 1).as2D(1, 136).as1D();
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const createInterleavedTensor = (fillX, fillY) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();
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const getPadding = (batchIdx, cond) => {
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const { width, height } = inputDimensions[batchIdx];
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return cond(width, height) ? Math.abs(width - height) / 2 : 0;
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@ -26,7 +23,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
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const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
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const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
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const landmarkTensors = output
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.mul(tf.fill([batchSize, 136], inputSize))
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.mul(tf.fill([batchSize, 136], inputSize, 'float32'))
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.sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx)))))
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.div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
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return landmarkTensors;
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@ -1 +1 @@
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{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAe,KAAK,EAAE,MAAM,YAAY,CAAC;AAChD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AAEzD,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,MAAM,EAAE,MAAM,UAAU,CAAC;AAElC,MAAM,OAAgB,qBAGpB,SAAQ,aAA+B;IAEhC,WAAW,CAAC,MAAmB,EAAE,SAAiB,EAAE,kBAAiC;QAE1F,MAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE;YACnE,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,MAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,uBAAuB,GAAG,CAAC,KAAa,EAAE,KAAa,EAAE,EAAE,CAC/D,EAAE,CAAC,KAAK,CAAC;gBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;gBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;aACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE,CAAA;YAE3B,MAAM,UAAU,GAAG,CAAC,QAAgB,EAAE,IAAuC,EAAU,EAAE;gBACvF,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,eAAe,CAAC,QAAQ,CAAC,CAAA;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAC/E,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAE/E,MAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;iBACzC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,YAAY,CAAC,KAAe;QACjC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,GAAG,GAAG,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,IAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,CAAC,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,eAAe,CAAC,KAAgB;QAC3C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,CAC9C,CAAA;QAED,MAAM,iBAAiB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,KAAK,EAAE,cAAc,EAAE,QAAQ,EAAE,EAAE;YACjC,MAAM,cAAc,GAAG,KAAK,CAAC,IAAI,CAAC,MAAM,cAAc,CAAC,IAAI,EAAE,CAAC,CAAA;YAC9D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAC1D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAE3D,OAAO,IAAI,eAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,KAAK,CAAC,OAAO,CAAC,CAAC,CAAW,EAAE,OAAO,CAAC,CAAC,CAAW,CAAC,CAAC,EACtF;gBACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;gBACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;aACzC,CACF,CAAA;QACH,CAAC,CACF,CAAC,CAAA;QAEF,eAAe,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;QAEzC,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC,CAAC,iBAAsC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAoB,CAAC;IAClH,CAAC;IAES,wBAAwB;QAChC,OAAO,GAAG,CAAA;IACZ,CAAC;CACF"}
|
||||
{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAe,KAAK,EAAE,MAAM,YAAY,CAAC;AAChD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AAEzD,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,MAAM,EAAE,MAAM,UAAU,CAAC;AAElC,MAAM,OAAgB,qBAGpB,SAAQ,aAA+B;IAEhC,WAAW,CAAC,MAAmB,EAAE,SAAiB,EAAE,kBAAiC;QAE1F,MAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE;YACnE,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,MAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,uBAAuB,GAAG,CAAC,KAAa,EAAE,KAAa,EAAE,EAAE,CAC/D,EAAE,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,SAAS,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE,CAAA;YAErG,MAAM,UAAU,GAAG,CAAC,QAAgB,EAAE,IAAuC,EAAU,EAAE;gBACvF,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,eAAe,CAAC,QAAQ,CAAC,CAAA;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAC/E,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAE/E,MAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;iBACpD,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,YAAY,CAAC,KAAe;QACjC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,GAAG,GAAG,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,IAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,CAAC,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,eAAe,CAAC,KAAgB;QAC3C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,CAC9C,CAAA;QAED,MAAM,iBAAiB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,KAAK,EAAE,cAAc,EAAE,QAAQ,EAAE,EAAE;YACjC,MAAM,cAAc,GAAG,KAAK,CAAC,IAAI,CAAC,MAAM,cAAc,CAAC,IAAI,EAAE,CAAC,CAAA;YAC9D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAC1D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAE3D,OAAO,IAAI,eAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,KAAK,CAAC,OAAO,CAAC,CAAC,CAAW,EAAE,OAAO,CAAC,CAAC,CAAW,CAAC,CAAC,EACtF;gBACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;gBACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;aACzC,CACF,CAAA;QACH,CAAC,CACF,CAAC,CAAA;QAEF,eAAe,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;QAEzC,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC,CAAC,iBAAsC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAoB,CAAC;IAClH,CAAC;IAES,wBAAwB;QAChC,OAAO,GAAG,CAAA;IACZ,CAAC;CACF"}
|
||||
|
|
@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
|
|||
export function normalize(x, meanRgb) {
|
||||
return tf.tidy(() => {
|
||||
const [r, g, b] = meanRgb;
|
||||
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r);
|
||||
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g);
|
||||
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b);
|
||||
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');
|
||||
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');
|
||||
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');
|
||||
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
|
||||
return tf.sub(x, avg_rgb);
|
||||
});
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
|
||||
{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
|
||||
|
|
@ -19,7 +19,7 @@ export function padToSquare(imgTensor, isCenterImage = false) {
|
|||
const createPaddingTensor = (paddingAmount) => {
|
||||
const paddingTensorShape = imgTensor.shape.slice();
|
||||
paddingTensorShape[paddingAxis] = paddingAmount;
|
||||
return tf.fill(paddingTensorShape, 0);
|
||||
return tf.fill(paddingTensorShape, 0, 'float32');
|
||||
};
|
||||
const paddingTensorAppend = createPaddingTensor(paddingAmount);
|
||||
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD;;;;;;;GAOG;AACH,MAAM,UAAU,WAAW,CACzB,SAAsB,EACtB,gBAAyB,KAAK;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,MAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,MAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,MAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,MAAM,mBAAmB,GAAG,CAAC,aAAqB,EAAa,EAAE;YAC/D,MAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,CAAA;YAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,kBAAkB,EAAE,CAAC,CAAC,CAAA;QACvC,CAAC,CAAA;QAED,MAAM,mBAAmB,GAAG,mBAAmB,CAAC,aAAa,CAAC,CAAA;QAC9D,MAAM,sBAAsB,GAAG,OAAO,GAAI,mBAAmB,CAAC,KAAK,CAAC,WAAW,CAAY,CAAA;QAE3F,MAAM,oBAAoB,GAAG,aAAa,IAAI,sBAAsB;YAClE,CAAC,CAAC,mBAAmB,CAAC,sBAAsB,CAAC;YAC7C,CAAC,CAAC,IAAI,CAAA;QAER,MAAM,cAAc,GAAG;YACrB,oBAAoB;YACpB,SAAS;YACT,mBAAmB;SACpB;aACE,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;YACjB,uDAAuD;aACtD,GAAG,CAAC,CAAC,CAAY,EAAE,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAkB,CAAA;QAChE,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAE/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
|
||||
{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD;;;;;;;GAOG;AACH,MAAM,UAAU,WAAW,CACzB,SAAsB,EACtB,gBAAyB,KAAK;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,MAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,MAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,MAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,MAAM,mBAAmB,GAAG,CAAC,aAAqB,EAAa,EAAE;YAC/D,MAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,CAAA;YAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,kBAAkB,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAClD,CAAC,CAAA;QAED,MAAM,mBAAmB,GAAG,mBAAmB,CAAC,aAAa,CAAC,CAAA;QAC9D,MAAM,sBAAsB,GAAG,OAAO,GAAI,mBAAmB,CAAC,KAAK,CAAC,WAAW,CAAY,CAAA;QAE3F,MAAM,oBAAoB,GAAG,aAAa,IAAI,sBAAsB;YAClE,CAAC,CAAC,mBAAmB,CAAC,sBAAsB,CAAC;YAC7C,CAAC,CAAC,IAAI,CAAA;QAER,MAAM,cAAc,GAAG;YACrB,oBAAoB;YACpB,SAAS;YACT,mBAAmB;SACpB;aACE,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;YACjB,uDAAuD;aACtD,GAAG,CAAC,CAAC,CAAY,EAAE,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAkB,CAAA;QAChE,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAE/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
|
||||
|
|
@ -35,7 +35,7 @@ export class TinyXception extends NeuralNetwork {
|
|||
throw new Error('TinyXception - load model before inference');
|
||||
}
|
||||
return tf.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
|
||||
let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
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|
||||
{"version":3,"file":"TinyXception.js","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAc,sBAAsB,EAAE,MAAM,WAAW,CAAC;AAC/D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,SAAS,IAAI,CAAC,CAAc,EAAE,MAAkB,EAAE,MAAwB;IACxE,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,MAAM,EAAE,MAAM,CAAC,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;AAC1E,CAAC;AAED,SAAS,cAAc,CAAC,CAAc,EAAE,MAA4B,EAAE,kBAA2B,IAAI;IACnG,IAAI,GAAG,GAAG,eAAe,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1C,GAAG,GAAG,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACjE,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAG,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC3E,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;IAC7C,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,EAAG,MAAM,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1D,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,SAAS,SAAS,CAAC,CAAc,EAAE,MAAuB;IACxD,IAAI,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC5E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,OAAO,YAAa,SAAQ,aAAiC;IAIjE,YAAY,aAAqB;QAC/B,KAAK,CAAC,cAAc,CAAC,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;SAC9D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,EAAE,MAAM,CAAC,UAAU,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACtE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,EAAE,KAAK,CAAC,CAAA;YACrE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,CAAC,CAAA;YAE9D,KAAK,CAAC,IAAI,CAAC,cAAc,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;gBAC/C,GAAG,GAAG,SAAS,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAC,CAAA;YAEF,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,eAAe,CAAC,CAAA;YAC3D,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACnF,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,qBAAqB,CAAA;IAC9B,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACnE,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACpD,CAAC;CACF"}
|
||||
File diff suppressed because one or more lines are too long
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File diff suppressed because one or more lines are too long
|
|
@ -440,9 +440,9 @@ const tf2 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
|
|||
function normalize(x, meanRgb) {
|
||||
return tf2.tidy(() => {
|
||||
const [r, g, b] = meanRgb;
|
||||
const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r);
|
||||
const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g);
|
||||
const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b);
|
||||
const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32");
|
||||
const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32");
|
||||
const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32");
|
||||
const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
|
||||
return tf2.sub(x, avg_rgb);
|
||||
});
|
||||
|
|
@ -462,7 +462,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
|
|||
const createPaddingTensor = (paddingAmount2) => {
|
||||
const paddingTensorShape = imgTensor.shape.slice();
|
||||
paddingTensorShape[paddingAxis] = paddingAmount2;
|
||||
return tf3.fill(paddingTensorShape, 0);
|
||||
return tf3.fill(paddingTensorShape, 0, "float32");
|
||||
};
|
||||
const paddingTensorAppend = createPaddingTensor(paddingAmount);
|
||||
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
|
||||
|
|
@ -1716,7 +1716,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
|
|||
throw new Error("FaceFeatureExtractor - load model before inference");
|
||||
}
|
||||
return tf15.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255));
|
||||
let out = denseBlock4(normalized, params.dense0, true);
|
||||
|
|
@ -2153,7 +2153,7 @@ class TinyXception extends NeuralNetwork {
|
|||
throw new Error("TinyXception - load model before inference");
|
||||
}
|
||||
return tf19.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256));
|
||||
let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
|
||||
|
|
@ -2325,17 +2325,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
|
|||
});
|
||||
const batchSize = inputDimensions.length;
|
||||
return tf21.tidy(() => {
|
||||
const createInterleavedTensor = (fillX, fillY) => tf21.stack([
|
||||
tf21.fill([68], fillX),
|
||||
tf21.fill([68], fillY)
|
||||
], 1).as2D(1, 136).as1D();
|
||||
const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D();
|
||||
const getPadding = (batchIdx, cond) => {
|
||||
const {width, height} = inputDimensions[batchIdx];
|
||||
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
|
||||
};
|
||||
const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
|
||||
const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
|
||||
const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize)).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
|
||||
const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
|
||||
return landmarkTensors;
|
||||
});
|
||||
}
|
||||
|
|
@ -2430,7 +2427,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
|
|||
throw new Error("TinyFaceFeatureExtractor - load model before inference");
|
||||
}
|
||||
return tf22.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255));
|
||||
let out = denseBlock3(normalized, params.dense0, true);
|
||||
|
|
@ -4292,7 +4289,7 @@ function resizeResults(results, dimensions) {
|
|||
}
|
||||
|
||||
// package.json
|
||||
var version = "0.7.4";
|
||||
var version = "0.8.1";
|
||||
|
||||
// src/index.ts
|
||||
__export(exports, {
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -447,9 +447,9 @@ import {
|
|||
function normalize(x, meanRgb) {
|
||||
return tidy(() => {
|
||||
const [r, g, b] = meanRgb;
|
||||
const avg_r = fill([...x.shape.slice(0, 3), 1], r);
|
||||
const avg_g = fill([...x.shape.slice(0, 3), 1], g);
|
||||
const avg_b = fill([...x.shape.slice(0, 3), 1], b);
|
||||
const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32");
|
||||
const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32");
|
||||
const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32");
|
||||
const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
|
||||
return sub(x, avg_rgb);
|
||||
});
|
||||
|
|
@ -474,7 +474,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
|
|||
const createPaddingTensor = (paddingAmount2) => {
|
||||
const paddingTensorShape = imgTensor.shape.slice();
|
||||
paddingTensorShape[paddingAxis] = paddingAmount2;
|
||||
return fill2(paddingTensorShape, 0);
|
||||
return fill2(paddingTensorShape, 0, "float32");
|
||||
};
|
||||
const paddingTensorAppend = createPaddingTensor(paddingAmount);
|
||||
const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
|
||||
|
|
@ -1762,6 +1762,7 @@ function extractParamsFromWeigthMap(weightMap) {
|
|||
// src/faceFeatureExtractor/FaceFeatureExtractor.ts
|
||||
import {
|
||||
avgPool,
|
||||
cast as cast3,
|
||||
scalar,
|
||||
tidy as tidy9
|
||||
} from "@tensorflow/tfjs/dist/tf.es2017.js";
|
||||
|
|
@ -1775,7 +1776,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
|
|||
throw new Error("FaceFeatureExtractor - load model before inference");
|
||||
}
|
||||
return tidy9(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = cast3(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
|
||||
let out = denseBlock4(normalized, params.dense0, true);
|
||||
|
|
@ -2194,6 +2195,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
|
|||
// src/xception/TinyXception.ts
|
||||
import {
|
||||
add as add5,
|
||||
cast as cast4,
|
||||
conv2d as conv2d3,
|
||||
maxPool,
|
||||
relu as relu3,
|
||||
|
|
@ -2229,7 +2231,7 @@ class TinyXception extends NeuralNetwork {
|
|||
throw new Error("TinyXception - load model before inference");
|
||||
}
|
||||
return tidy13(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = cast4(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(scalar2(256));
|
||||
let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2]));
|
||||
|
|
@ -2411,17 +2413,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
|
|||
});
|
||||
const batchSize = inputDimensions.length;
|
||||
return tidy15(() => {
|
||||
const createInterleavedTensor = (fillX, fillY) => stack2([
|
||||
fill3([68], fillX),
|
||||
fill3([68], fillY)
|
||||
], 1).as2D(1, 136).as1D();
|
||||
const createInterleavedTensor = (fillX, fillY) => stack2([fill3([68], fillX, "float32"), fill3([68], fillY, "float32")], 1).as2D(1, 136).as1D();
|
||||
const getPadding = (batchIdx, cond) => {
|
||||
const {width, height} = inputDimensions[batchIdx];
|
||||
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
|
||||
};
|
||||
const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
|
||||
const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
|
||||
const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize)).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
|
||||
const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize, "float32")).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
|
||||
return landmarkTensors;
|
||||
});
|
||||
}
|
||||
|
|
@ -2507,6 +2506,7 @@ function extractParamsTiny(weights) {
|
|||
// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
|
||||
import {
|
||||
avgPool as avgPool3,
|
||||
cast as cast5,
|
||||
scalar as scalar3,
|
||||
tidy as tidy16
|
||||
} from "@tensorflow/tfjs/dist/tf.es2017.js";
|
||||
|
|
@ -2520,7 +2520,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
|
|||
throw new Error("TinyFaceFeatureExtractor - load model before inference");
|
||||
}
|
||||
return tidy16(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true);
|
||||
const batchTensor = cast5(input.toBatchTensor(112, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(scalar3(255));
|
||||
let out = denseBlock3(normalized, params.dense0, true);
|
||||
|
|
@ -2804,7 +2804,7 @@ function residualDown(x, params) {
|
|||
|
||||
// src/faceRecognitionNet/FaceRecognitionNet.ts
|
||||
import {
|
||||
cast as cast3,
|
||||
cast as cast6,
|
||||
matMul as matMul2,
|
||||
maxPool as maxPool2,
|
||||
scalar as scalar4,
|
||||
|
|
@ -2821,7 +2821,7 @@ class FaceRecognitionNet extends NeuralNetwork {
|
|||
throw new Error("FaceRecognitionNet - load model before inference");
|
||||
}
|
||||
return tidy18(() => {
|
||||
const batchTensor = cast3(input.toBatchTensor(150, true), "float32");
|
||||
const batchTensor = cast6(input.toBatchTensor(150, true), "float32");
|
||||
const meanRgb = [122.782, 117.001, 104.298];
|
||||
const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
|
||||
let out = convDown(normalized, params.conv32_down);
|
||||
|
|
@ -3445,7 +3445,7 @@ class SsdMobilenetv1Options {
|
|||
|
||||
// src/ssdMobilenetv1/SsdMobilenetv1.ts
|
||||
import {
|
||||
cast as cast4,
|
||||
cast as cast7,
|
||||
mul as mul3,
|
||||
scalar as scalar6,
|
||||
sub as sub3,
|
||||
|
|
@ -3461,7 +3461,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
|
|||
throw new Error("SsdMobilenetv1 - load model before inference");
|
||||
}
|
||||
return tidy24(() => {
|
||||
const batchTensor = cast4(input.toBatchTensor(512, false), "float32");
|
||||
const batchTensor = cast7(input.toBatchTensor(512, false), "float32");
|
||||
const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1));
|
||||
const features = mobileNetV1(x, params.mobilenetv1);
|
||||
const {
|
||||
|
|
@ -3791,7 +3791,7 @@ class TinyYolov2Options {
|
|||
|
||||
// src/tinyYolov2/TinyYolov2Base.ts
|
||||
import {
|
||||
cast as cast5,
|
||||
cast as cast8,
|
||||
maxPool as maxPool3,
|
||||
scalar as scalar8,
|
||||
softmax as softmax3,
|
||||
|
|
@ -3853,7 +3853,7 @@ class TinyYolov2Base extends NeuralNetwork {
|
|||
throw new Error("TinyYolov2 - load model before inference");
|
||||
}
|
||||
return tidy28(() => {
|
||||
let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32");
|
||||
let batchTensor = cast8(input.toBatchTensor(inputSize, false), "float32");
|
||||
batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
|
||||
batchTensor = batchTensor.div(scalar8(256));
|
||||
return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
|
||||
|
|
@ -4481,7 +4481,7 @@ function resizeResults(results, dimensions) {
|
|||
}
|
||||
|
||||
// package.json
|
||||
var version = "0.7.4";
|
||||
var version = "0.8.1";
|
||||
|
||||
// src/index.ts
|
||||
import * as tf42 from "@tensorflow/tfjs/dist/tf.es2017.js";
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -23,7 +23,7 @@ export class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorPara
|
|||
}
|
||||
|
||||
return tf.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true)
|
||||
const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
|
||||
const meanRgb = [122.782, 117.001, 104.298]
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
|
||||
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtra
|
|||
}
|
||||
|
||||
return tf.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true)
|
||||
const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
|
||||
const meanRgb = [122.782, 117.001, 104.298]
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
|
||||
|
||||
|
|
|
|||
|
|
@ -26,10 +26,7 @@ export abstract class FaceLandmark68NetBase<
|
|||
|
||||
return tf.tidy(() => {
|
||||
const createInterleavedTensor = (fillX: number, fillY: number) =>
|
||||
tf.stack([
|
||||
tf.fill([68], fillX),
|
||||
tf.fill([68], fillY)
|
||||
], 1).as2D(1, 136).as1D()
|
||||
tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()
|
||||
|
||||
const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {
|
||||
const { width, height } = inputDimensions[batchIdx]
|
||||
|
|
@ -39,7 +36,7 @@ export abstract class FaceLandmark68NetBase<
|
|||
const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)
|
||||
|
||||
const landmarkTensors = output
|
||||
.mul(tf.fill([batchSize, 136], inputSize))
|
||||
.mul(tf.fill([batchSize, 136], inputSize, 'float32'))
|
||||
.sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
|
||||
createInterleavedTensor(
|
||||
getPaddingX(batchIdx),
|
||||
|
|
|
|||
|
|
@ -3,9 +3,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
|
|||
export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
|
||||
return tf.tidy(() => {
|
||||
const [r, g, b] = meanRgb
|
||||
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)
|
||||
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)
|
||||
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)
|
||||
const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')
|
||||
const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')
|
||||
const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')
|
||||
const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
|
||||
|
||||
return tf.sub(x, avg_rgb)
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ export function padToSquare(
|
|||
const createPaddingTensor = (paddingAmount: number): tf.Tensor => {
|
||||
const paddingTensorShape = imgTensor.shape.slice()
|
||||
paddingTensorShape[paddingAxis] = paddingAmount
|
||||
return tf.fill(paddingTensorShape, 0)
|
||||
return tf.fill(paddingTensorShape, 0, 'float32')
|
||||
}
|
||||
|
||||
const paddingTensorAppend = createPaddingTensor(paddingAmount)
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ export class TinyXception extends NeuralNetwork<TinyXceptionParams> {
|
|||
}
|
||||
|
||||
return tf.tidy(() => {
|
||||
const batchTensor = input.toBatchTensor(112, true)
|
||||
const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
|
||||
const meanRgb = [122.782, 117.001, 104.298]
|
||||
const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue