handsfree
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A Face Pointer and Pose Estimator for interacting with pages, desktops, robots, and more via gestures
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JavaScript
/**
* @license
* Copyright 2020 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.
* =============================================================================
*/
(function (global, factory) {
typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('path'), require('fs')) :
typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', 'path', 'fs'], factory) :
(global = global || self, factory((global.tf = global.tf || {}, global.tf.wasm = global.tf.wasm || {}), global.tf, global.path, global.fs));
}(this, (function (exports, tfjsCore, path, fs) { 'use strict';
path = path && path.hasOwnProperty('default') ? path['default'] : path;
fs = fs && fs.hasOwnProperty('default') ? fs['default'] : fs;
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
// This enum must align with the enum defined in cc/backend.h.
var CppDType;
(function (CppDType) {
CppDType[CppDType["float32"] = 0] = "float32";
CppDType[CppDType["int32"] = 1] = "int32";
CppDType[CppDType["bool"] = 2] = "bool";
CppDType[CppDType["string"] = 3] = "string";
CppDType[CppDType["complex64"] = 4] = "complex64";
})(CppDType || (CppDType = {}));
// Must match enum in cc/fusable_activations.h.
var FusableActivation;
(function (FusableActivation) {
FusableActivation[FusableActivation["linear"] = 0] = "linear";
FusableActivation[FusableActivation["relu"] = 1] = "relu";
FusableActivation[FusableActivation["relu6"] = 2] = "relu6";
FusableActivation[FusableActivation["prelu"] = 3] = "prelu";
})(FusableActivation || (FusableActivation = {}));
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmFusedMatMul;
function setup(backend) {
wasmFusedMatMul = backend.wasm.cwrap(tfjsCore._FusedMatMul, null /* void */, [
'number',
'array',
'number',
'number',
'array',
'number',
'number',
'number',
'number',
'number',
'number',
'number' // out_id
]);
}
function fusedBatchMatMul(args) {
const { inputs, backend, attrs } = args;
const { a, b, bias, preluActivationWeights } = inputs;
if (a.dtype !== 'float32' || b.dtype !== 'float32') {
throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);
}
const { transposeA, transposeB, activation } = attrs;
const aId = backend.dataIdMap.get(a.dataId).id;
const bId = backend.dataIdMap.get(b.dataId).id;
let biasId = 0;
if (bias != null) {
const biasData = backend.dataIdMap.get(bias.dataId);
if (biasData.shape.length !== 1) {
throw new Error(`_FusedMatMul only supports rank-1 bias but got ` +
`rank ${biasData.shape.length}.`);
}
biasId = biasData.id;
}
const preluActivationWeightsId = preluActivationWeights == null ?
0 :
backend.dataIdMap.get(preluActivationWeights.dataId).id;
const fusedActivation = FusableActivation[activation];
if (fusedActivation == null) {
throw new Error(`${activation} activation not yet supported for FusedConv2D ` +
`in the wasm backend.`);
}
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 = backend.makeOutput([batchDim, leftDim, rightDim], a.dtype);
const outId = backend.dataIdMap.get(out.dataId).id;
const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, outId);
return out;
}
const fusedMatMulConfig = {
kernelName: tfjsCore._FusedMatMul,
backendName: 'wasm',
setupFunc: setup,
kernelFunc: fusedBatchMatMul
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
function createUnaryKernelConfig(kernelName) {
let wasmFunc;
function setupFunc(backend) {
wasmFunc =
backend.wasm.cwrap(kernelName, null /* void */, ['number', 'number']);
}
function kernelFunc(args) {
const { backend, inputs: { x } } = args;
const xId = backend.dataIdMap.get(x.dataId).id;
const out = backend.makeOutput(x.shape, x.dtype);
const outId = backend.dataIdMap.get(out.dataId).id;
// Short-circuit zero-sized tensors.
if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
return out;
}
wasmFunc(xId, outId);
return out;
}
return { kernelName, backendName: 'wasm', setupFunc, kernelFunc };
}
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
const absConfig = createUnaryKernelConfig(tfjsCore.Abs);
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
function createBinaryKernelConfig(kernelName, supportsFullBroadcast, dtype) {
let wasmFunc;
function setupFunc(backend) {
wasmFunc = backend.wasm.cwrap(kernelName, null /* void */, [
'number',
'array',
'number',
'number',
'array',
'number',
'number',
'number' // out_id
]);
}
function kernelFunc(args) {
const { backend, inputs } = args;
const { a, b } = inputs;
const aId = backend.dataIdMap.get(a.dataId).id;
const bId = backend.dataIdMap.get(b.dataId).id;
const outputType = dtype != null ? dtype : a.dtype;
const newShape = tfjsCore.backend_util.assertAndGetBroadcastShape(a.shape, b.shape);
const out = backend.makeOutput(newShape, outputType);
// Short-circuit zero-sized tensors.
if (tfjsCore.util.sizeFromShape(newShape) === 0) {
return out;
}
const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
const outId = backend.dataIdMap.get(out.dataId).id;
const kernelFunc = () => wasmFunc(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);
// Currently only some float operations support full broadcast.
if (supportsFullBroadcast && a.dtype === 'float32') {
kernelFunc();
return out;
}
const aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(a.shape, newShape);
const bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(b.shape, newShape);
const loopsOverAllOfA = aBroadcastDims.every((v, i) => v === i);
const loopsOverAllOfB = bBroadcastDims.every((v, i) => v === i);
if (loopsOverAllOfA && loopsOverAllOfB) {
kernelFunc();
return out;
}
else {
throw new Error(`Broadcasting along outer dims is not yet ` +
`supported for ${a.dtype} ${kernelName}.`);
}
}
return { kernelName, backendName: 'wasm', setupFunc, kernelFunc };
}
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
const supportsFullBroadcast = true;
const addConfig = createBinaryKernelConfig(tfjsCore.Add, supportsFullBroadcast);
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmFunc;
function setupFunc(backend) {
wasmFunc = backend.wasm.cwrap(tfjsCore.AddN, null /* void */, [
'array',
'number',
'number',
'number',
]);
}
function addn(args) {
const { inputs, backend } = args;
const out = backend.makeOutput(inputs[0].shape, inputs[0].dtype);
// Short-circuit zero-sized tensors.
if (tfjsCore.util.sizeFromShape(out.shape) === 0) {
return out;
}
const inputIds = inputs.map(x => backend.dataIdMap.get(x.dataId).id);
const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
const outId = backend.dataIdMap.get(out.dataId).id;
wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
return out;
}
const addNConfig = {
kernelName: tfjsCore.AddN,
backendName: 'wasm',
setupFunc,
kernelFunc: addn,
};
/**
* @license
* Copyright 2020 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.
* =============================================================================
*/
function identity(args) {
const { inputs: { x }, backend } = args;
const out = backend.makeOutput(x.shape, x.dtype);
const inVals = backend.typedArrayFromHeap(x);
const outVals = backend.typedArrayFromHeap(out);
outVals.set(inVals);
return out;
}
const identityConfig = {
kernelName: tfjsCore.Identity,
backendName: 'wasm',
kernelFunc: identity,
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmTranspose;
function setup$1(backend) {
wasmTranspose = backend.wasm.cwrap(tfjsCore.Transpose, null /* void */, [
'number',
'array',
'number',
'number',
'number',
'array',
'number',
]);
}
function transpose(args) {
const { inputs, backend, attrs } = args;
// Reduce any dimensions with size one. Lower-rank transpose kernel performs
// better due to simpler memory access pattern.
const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
let permIsNoOp = true;
for (let i = 0; i < perm.length; i++) {
if (perm[i] !== i) {
permIsNoOp = false;
}
}
const outShape = computeOutShape(inputs.x.shape, attrs.perm);
const x = {
dataId: inputs.x.dataId,
shape: reducedShape,
dtype: inputs.x.dtype
};
if (permIsNoOp) {
const cloned = identity({ inputs, backend });
cloned.shape = outShape;
return cloned;
}
const out = backend.makeOutput(outShape, x.dtype);
const xId = backend.dataIdMap.get(x.dataId).id;
const outId = backend.dataIdMap.get(out.dataId).id;
const permBytes = new Uint8Array(new Int32Array(perm).buffer);
const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);
return out;
}
function computeOutShape(inShape, perm) {
const outShape = new Array(inShape.length);
for (let i = 0; i < outShape.length; i++) {
outShape[i] = inShape[perm[i]];
}
return outShape;
}
function removeOneSizeDims(shape, perm) {
const newShape = [];
const newPerm = [];
for (let i = 0; i < shape.length; ++i) {
if (shape[i] !== 1) {
newShape.push(shape[i]);
}
if (shape[perm[i]] !== 1) {
newPerm.push(perm[i]);
}
}
for (let i = 0; i < newPerm.length; ++i) {
let minValIdx = -1;
for (let j = 0; j < newPerm.length; ++j) {
if (newPerm[j] >= i &&
(minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
minValIdx = j;
}
}
newPerm[minValIdx] = i;
}
return [newShape, newPerm];
}
const transposeConfig = {
kernelName: tfjsCore.Transpose,
backendName: 'wasm',
kernelFunc: transpose,
setupFunc: setup$1,
};
/**
* @license
* Copyright 2020 Google Inc. 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.
* =============================================================================
*/
/**
* Compute permutation axes and do a transpose if necessary.
*
* Used by reduction ops.
* @param x input TensorInfo
* @param axis reduction axes
* @param backend wasm backend instance
*/
function permuteAxesAndTranspose(x, axis, backend) {
const xShape = x.shape;
const xRank = x.shape.length;
const originalAxes = tfjsCore.util.parseAxisParam(axis, xShape);
let axes = originalAxes;
const permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, xRank);
let xTransposed = null;
let inputWasTransposed = false;
if (permutedAxes != null) {
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = xShape[permutedAxes[i]];
}
axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, xRank);
xTransposed =
transpose({ inputs: { x }, attrs: { perm: permutedAxes }, backend });
const xId = backend.dataIdMap.get(x.dataId).id;
const transposedId = backend.dataIdMap.get(xTransposed.dataId).id;
if (transposedId !== xId) {
inputWasTransposed = true;
}
}
return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };
}
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmFunc$1;
function setup$2(backend) {
wasmFunc$1 = backend.wasm.cwrap(tfjsCore.ArgMax, null /* void */, [
'number',
'number',
'number',
'number',
'number' // out_id
]);
}
function argmax(args) {
const { backend, inputs, attrs } = args;
const { axis } = attrs;
const { x } = inputs;
const xId = backend.dataIdMap.get(x.dataId).id;
let inputId = xId;
let input = x;
const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend);
if (inputWasTransposed) {
const transposedId = backend.dataIdMap.get(transposed.dataId).id;
if (transposedId !== xId) {
// transpose was not a no-op. We will need to dispose of this
// once we are done.
input = transposed;
inputId = transposedId;
}
}
const outShape = input.shape.slice(0, -1);
const out = backend.makeOutput(outShape, 'int32');
const outId = backend.dataIdMap.get(out.dataId).id;
const outerSize = tfjsCore.util.sizeFromShape(out.shape);
const innerSize = input.shape[axes[0]];
wasmFunc$1(inputId, CppDType[input.dtype], outerSize, innerSize, outId);
if (inputWasTransposed) {
// dispose of the transposed tensor.
backend.disposeData(transposed.dataId);
}
return out;
}
const argMaxConfig = {
kernelName: tfjsCore.ArgMax,
backendName: 'wasm',
kernelFunc: argmax,
setupFunc: setup$2
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmAvgPool;
function setup$3(backend) {
wasmAvgPool = backend.wasm.cwrap(tfjsCore.AvgPool, null /* void */, [
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
]);
}
function avgPool(args) {
const { inputs, attrs, backend } = args;
const x = inputs.x;
const xId = backend.dataIdMap.get(x.dataId).id;
const { filterSize, strides, pad, dimRoundingMode } = attrs;
const convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const channels = convInfo.inChannels;
if (convInfo.dataFormat !== 'channelsLast') {
throw new Error(`wasm backend does not support dataFormat:'` +
`${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
throw new Error(`was backend only supports average pooling with dilation = [1, 1], ` +
`got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
}
const out = backend.makeOutput(convInfo.outShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);
return out;
}
const avgPoolConfig = {
kernelName: tfjsCore.AvgPool,
backendName: 'wasm',
setupFunc: setup$3,
kernelFunc: avgPool
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmBatchMatMul;
function setup$4(backend) {
wasmBatchMatMul = backend.wasm.cwrap(tfjsCore.BatchMatMul, null /* void */, [
'number',
'array',
'number',
'number',
'array',
'number',
'number',
'number',
'number' // out_id
]);
}
function batchMatMul(args) {
const { inputs, backend, attrs } = args;
const { a, b } = inputs;
if (a.dtype !== 'float32' || b.dtype !== 'float32') {
throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
}
const { transposeA, transposeB } = attrs;
const aId = backend.dataIdMap.get(a.dataId).id;
const bId = backend.dataIdMap.get(b.dataId).id;
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 = backend.makeOutput([batchDim, leftDim, rightDim], a.dtype);
const outId = backend.dataIdMap.get(out.dataId).id;
const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);
const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
wasmBatchMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, outId);
return out;
}
const batchMatMulConfig = {
kernelName: tfjsCore.BatchMatMul,
backendName: 'wasm',
setupFunc: setup$4,
kernelFunc: batchMatMul
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
function cast(args) {
const { inputs: { x }, attrs: { dtype }, backend } = args;
const out = backend.makeOutput(x.shape, dtype);
const inVals = backend.typedArrayFromHeap(x);
const outVals = backend.typedArrayFromHeap(out);
outVals.set(inVals);
return out;
}
const castConfig = {
kernelName: tfjsCore.Cast,
backendName: 'wasm',
kernelFunc: cast,
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmClip;
function setup$5(backend) {
wasmClip = backend.wasm.cwrap(tfjsCore.ClipByValue, null /* void */, [
'number',
'number',
'number',
'number' // out_id
]);
}
function clip(args) {
const { inputs, backend, attrs } = args;
const { x } = inputs;
const { clipValueMin, clipValueMax } = attrs;
const xId = backend.dataIdMap.get(x.dataId).id;
const out = backend.makeOutput(x.shape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
wasmClip(xId, clipValueMin, clipValueMax, outId);
return out;
}
const clipByValueConfig = {
kernelName: tfjsCore.ClipByValue,
backendName: 'wasm',
setupFunc: setup$5,
kernelFunc: clip
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
function concat(args) {
const { inputs, backend } = args;
const axis = tfjsCore.util.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
const outShape = tfjsCore.backend_util.computeOutShape(inputs.map(t => t.shape), axis);
const out = backend.makeOutput(outShape, inputs[0].dtype);
const batchDim = tfjsCore.util.sizeFromShape(inputs[0].shape.slice(0, axis));
let sumInnerDims = 0;
const innerDims = inputs.map(input => {
const innerDim = tfjsCore.util.sizeFromShape(input.shape.slice(axis));
sumInnerDims += innerDim;
return innerDim;
});
const inVals = inputs.map(input => backend.typedArrayFromHeap(input));
const outVals = backend.typedArrayFromHeap(out);
for (let b = 0; b < batchDim; b++) {
let outOffset = b * sumInnerDims;
for (let i = 0; i < inVals.length; i++) {
const innerDim = innerDims[i];
const inOffset = b * innerDim;
const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
outVals.set(vals, outOffset);
outOffset += innerDim;
}
}
return out;
}
const concatConfig = {
kernelName: tfjsCore.Concat,
backendName: 'wasm',
kernelFunc: concat,
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmConv2d;
function setup$6(backend) {
wasmConv2d = backend.wasm.cwrap(tfjsCore.Conv2D, null /* void */, [
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
]);
}
function conv2d(args) {
const { inputs, attrs, backend } = args;
const { x, filter } = inputs;
const xId = backend.dataIdMap.get(x.dataId).id;
const filterId = backend.dataIdMap.get(filter.dataId).id;
const { strides, dilations, pad, dimRoundingMode, dataFormat } = attrs;
const $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
if (convInfo.dataFormat !== 'channelsLast') {
throw new Error(`wasm backend Conv2D does not support dataFormat:'` +
`${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend.makeOutput(convInfo.outShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
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;
}
const conv2DConfig = {
kernelName: tfjsCore.Conv2D,
backendName: 'wasm',
setupFunc: setup$6,
kernelFunc: conv2d
};
/**
* @license
* Copyright 2020 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.
* =============================================================================
*/
let wasmConv2DBackpropInput;
function setup$7(backend) {
wasmConv2DBackpropInput = backend.wasm.cwrap(tfjsCore.Conv2DBackpropInput, null, [
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
]);
}
function conv2DBackpropInput(args) {
const { backend, inputs, attrs } = args;
const { dy, filter } = inputs;
const { strides, pad, dataFormat, dimRoundingMode, inputShape } = attrs;
const dilations = 1;
const $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat);
const convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);
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';
const dxStrides = tfjsCore.util.computeStrides(convInfo.inShape);
const dyStrides = tfjsCore.util.computeStrides(dy.shape);
const [fltS0, fltS1, fltS2] = tfjsCore.util.computeStrides(filter.shape);
const xBatchStride = dxStrides[0];
const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
const xColStride = isChannelsLast ? dxStrides[2] : 1;
const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
const yBatchStride = dyStrides[0];
const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
const yColStride = isChannelsLast ? dyStrides[2] : 1;
const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
const out = backend.makeOutput(convInfo.inShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
const dyId = backend.dataIdMap.get(dy.dataId).id;
const filterId = backend.dataIdMap.get(filter.dataId).id;
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;
}
const conv2DBackpropInputConfig = {
kernelName: tfjsCore.Conv2DBackpropInput,
backendName: 'wasm',
setupFunc: setup$7,
kernelFunc: conv2DBackpropInput
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
const cosConfig = createUnaryKernelConfig(tfjsCore.Cos);
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
// Must match enum in CropAndResize.cc
var InterpolationMethod;
(function (InterpolationMethod) {
InterpolationMethod[InterpolationMethod["bilinear"] = 0] = "bilinear";
InterpolationMethod[InterpolationMethod["nearest"] = 1] = "nearest";
})(InterpolationMethod || (InterpolationMethod = {}));
let wasmCropAndResize;
function setup$8(backend) {
wasmCropAndResize = backend.wasm.cwrap(tfjsCore.CropAndResize, null /*void*/, [
'number',
'number',
'number',
'number',
'array',
'number',
'number',
'number',
'number',
'number' // out id
]);
}
function cropAndResize(args) {
const { backend, inputs, attrs } = args;
const { method, extrapolationValue, cropSize } = attrs;
const { image, boxes, boxInd } = inputs;
const numBoxes = boxes.shape[0];
const [cropHeight, cropWidth] = cropSize;
const outShape = [numBoxes, cropHeight, cropWidth, image.shape[3]];
let imagesData = backend.dataIdMap.get(image.dataId);
let castedData;
if (image.dtype !== 'float32') {
castedData = cast({ backend, inputs: { x: image }, attrs: { dtype: 'float32' } });
imagesData = backend.dataIdMap.get(castedData.dataId);
}
const imagesId = imagesData.id;
const boxesId = backend.dataIdMap.get(boxes.dataId).id;
const boxIndId = backend.dataIdMap.get(boxInd.dataId).id;
const out = backend.makeOutput(outShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
const imagesShapeBytes = new Uint8Array(new Int32Array(image.shape).buffer);
wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
if (castedData != null) {
backend.disposeData(castedData.dataId);
}
return out;
}
const cropAndResizeConfig = {
kernelName: tfjsCore.CropAndResize,
backendName: 'wasm',
setupFunc: setup$8,
kernelFunc: cropAndResize
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
let wasmDepthwiseConv2d;
function setup$9(backend) {
wasmDepthwiseConv2d =
backend.wasm.cwrap(tfjsCore.DepthwiseConv2dNative, null /* void */, [
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
'number',
]);
}
function depthwiseConv2d(args) {
const { inputs, attrs, backend } = args;
const { x, filter } = inputs;
const xId = backend.dataIdMap.get(x.dataId).id;
const filterId = backend.dataIdMap.get(filter.dataId).id;
const { strides, dilations, pad, dimRoundingMode } = attrs;
const $dilations = dilations == null ? [1, 1] : dilations;
const convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const padTop = convInfo.padInfo.top;
const padRight = convInfo.padInfo.right;
const padBottom = convInfo.padInfo.bottom;
const padLeft = convInfo.padInfo.left;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const inputChannels = convInfo.inChannels;
const outputChannels = convInfo.outChannels;
const isSamePad = convInfo.padInfo.type === 'SAME' ? 1 : 0;
if (convInfo.dataFormat !== 'channelsLast') {
throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'` +
`${convInfo.dataFormat}'. Please use 'channelsLast'.`);
}
const out = backend.makeOutput(convInfo.outShape, 'float32');
const outId = backend.dataIdMap.get(out.dataId).id;
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;
}
const depthwiseConv2DNativeConfig = {
kernelName: tfjsCore.DepthwiseConv2dNative,
backendName: 'wasm',
setupFunc: setup$9,
kernelFunc: depthwiseConv2d
};
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
const supportsFullBroadcast$1 = true;
const divConfig = createBinaryKernelConfig(tfjsCore.Div, supportsFullBroadcast$1);
/**
* @license
* Copyright 2020 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.
* =============================================================================
*/
const supportsFullBroadcast$2 = false;
const equalConfig = createBinaryKernelConfig(tfjsCore.Equal, supportsFullBroadcast$2, 'bool');
/**
* @license
* Copyright 2019 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.
* ==================