"use strict";
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var conv_1 = require("../ops/conv");
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var conv_util = require("../ops/conv_util");
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var operation_1 = require("../ops/operation");
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var tensor_util_1 = require("../tensor_util");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var binary_ops_1 = require("./binary_ops");
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var broadcast_util = require("./broadcast_util");
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var conv_2 = require("./conv");
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var fused_util_1 = require("./fused_util");
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var matmul_1 = require("./matmul");
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var relu_ops_1 = require("./relu_ops");
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// Returns gradient for fused activation.
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var getFusedDyActivation = function (dy, y, activation) {
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if (activation == null || activation === 'linear') {
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return dy;
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}
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if (activation === 'relu') {
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return dy.mul(y.step());
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}
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throw new Error("Gradient for activation " + activation + " has not been " +
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"implemented yet.");
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};
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// Returns gradient for fused bias.
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var getFusedBiasGradient = function (bias, dyActivation) {
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var res = dyActivation;
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var reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape(bias.shape);
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};
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var applyActivation = function (x, activation, preluActivationWeights) {
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if (activation === 'linear') {
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return x;
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}
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else if (activation === 'relu') {
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return relu_ops_1.relu(x);
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}
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else if (activation === 'elu') {
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return relu_ops_1.elu(x);
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}
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else if (activation === 'relu6') {
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return relu_ops_1.relu6(x);
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}
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else if (activation === 'prelu') {
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return relu_ops_1.prelu(x, preluActivationWeights);
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}
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throw new Error("Unknown fused activation " + activation + ".");
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};
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/**
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* Computes the dot product of two matrices with optional activation and bias.
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*
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* ```js
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* const a = tf.tensor2d([-1, -2], [1, 2]);
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* const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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* const bias = tf.tensor2d([1, 2], [1, 2]);
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*
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* tf.fused.matMul({a, b, bias, activation: 'relu'}).print();
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* ```
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*
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* @param obj An object with the following properties:
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* - `a` First matrix in dot product operation.
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* - `b` Second matrix in dot product operation.
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* - `transposeA` If true, `a` is transposed before multiplication.
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* - `transposeB` If true, `b` is transposed before multiplication.
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* - `bias` Matrix to be added to the result.
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* - `activation` Name of activation kernel (defaults to `linear`).
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* - `preluActivationWeights` Tensor of prelu weights.
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*/
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function fusedMatMul_(_a) {
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var _b;
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var a = _a.a, b = _a.b, _c = _a.transposeA, transposeA = _c === void 0 ? false : _c, _d = _a.transposeB, transposeB = _d === void 0 ? false : _d, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights;
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if (fused_util_1.shouldFuse(engine_1.ENGINE.state.gradientDepth, activation) === false) {
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var result = matmul_1.matMul(a, b, transposeA, transposeB);
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if (bias != null) {
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result = binary_ops_1.add(result, bias);
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}
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return applyActivation(result, activation, preluActivationWeights);
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}
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'fused matMul');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'fused matMul');
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_b = tensor_util_1.makeTypesMatch($a, $b), $a = _b[0], $b = _b[1];
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var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
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var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
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var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
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var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
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var outerDimsA = $a.shape.slice(0, -2);
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var outerDimsB = $b.shape.slice(0, -2);
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var batchDimA = util.sizeFromShape(outerDimsA);
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var batchDimB = util.sizeFromShape(outerDimsB);
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util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, function () {
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return "Error in fused matMul: inputs must have the same rank of at least " +
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("2, got ranks " + $a.rank + " and " + $b.rank + ".");
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});
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util.assert(util.arraysEqual(outerDimsA, outerDimsB), function () { return "Error in fused matMul: outer dimensions (" + outerDimsA + ") and (" +
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(outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
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($b.shape + " must match."); });
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util.assert(innerShapeA === innerShapeB, function () { return "Error in fused matMul: inner shapes (" + innerShapeA + ") and (" +
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(innerShapeB + ") of Tensors with shapes " + $a.shape + " and ") +
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($b.shape + " and transposeA=" + transposeA) +
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(" and transposeB=" + transposeB + " must match."); });
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var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
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var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
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$a.as3D(batchDimA, outerShapeA, innerShapeA);
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var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
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$b.as3D(batchDimB, innerShapeB, outerShapeB);
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var $bias;
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if (bias != null) {
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$bias = tensor_util_env_1.convertToTensor(bias, 'bias', 'fused matMul');
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$bias = tensor_util_1.makeTypesMatch($bias, $a)[0];
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broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);
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}
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var $preluActivationWeights;
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if (preluActivationWeights != null) {
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$preluActivationWeights = tensor_util_env_1.convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');
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}
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var grad = function (dy, saved) {
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var a3D = saved[0], b3D = saved[1], y = saved[2];
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var dyActivation = getFusedDyActivation(dy, y, activation);
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var biasGradient = {};
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if (bias != null) {
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biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
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}
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if (!transposeA && !transposeB) {
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return Object.assign({
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a: function () { return dyActivation.matMul(b3D, false, true); },
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b: function () { return a3D.matMul(dyActivation, true, false); }
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}, biasGradient);
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}
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else if (!transposeA && transposeB) {
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return Object.assign({
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a: function () { return dyActivation.matMul(b3D, false, false); },
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b: function () { return dyActivation.matMul(a3D, true, false); }
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}, biasGradient);
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}
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else if (transposeA && !transposeB) {
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return Object.assign({
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a: function () { return b3D.matMul(dyActivation, false, true); },
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b: function () { return a3D.matMul(dyActivation, false, false); }
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}, biasGradient);
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}
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else {
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return Object.assign({
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a: function () { return b3D.matMul(dyActivation, true, true); },
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b: function () { return dyActivation.matMul(a3D, true, true); }
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}, biasGradient);
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}
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};
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var inputs = { a: a3D, b: b3D };
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if (bias != null) {
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inputs.bias = $bias;
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}
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if (preluActivationWeights != null) {
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inputs.preluActivationWeights = $preluActivationWeights;
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}
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var inputsToSave = [a3D, b3D];
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var outputsToSave = [true];
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var y = backend.fusedBatchMatMul({
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a: a3D,
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b: b3D,
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transposeA: transposeA,
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transposeB: transposeB,
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bias: $bias,
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activation: activation,
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preluActivationWeights: $preluActivationWeights
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});
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save([a3D, b3D, y]);
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return y;
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}, inputs, grad, '_FusedMatMul', { transposeA: transposeA, transposeB: transposeB, activation: activation }, inputsToSave, outputsToSave);
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return res.reshape(outShape);
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}
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/**
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* Computes a 2D convolution over the input x, optionally fused with adding a
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* bias and applying an activation.
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*
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* ```js
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* const inputDepth = 2;
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* const inShape = [2, 2, 2, inputDepth];
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* const outputDepth = 2;
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* const fSize = 1;
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* const pad = 0;
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* const strides = 1;
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*
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* const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
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* 16], inShape);
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* const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
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* outputDepth]);
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*
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* tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
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* dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
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* ```
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*
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* @param obj An object with the following properties:
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* @param x The input tensor, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter, rank 4, of shape
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* `[filterHeight, filterWidth, inDepth, outDepth]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid` output will be smaller than input if filter is larger
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* than 1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
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* https://www.tensorflow.org/api_guides/python/nn#Convolution)
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* @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels]. Only "NHWC" is currently supported.
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
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* in which we sample input values across the height and width dimensions
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* in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
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* number, then `dilationHeight == dilationWidth`. If it is greater than
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* 1, then all values of `strides` must be 1.
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* @param dimRoundingMode The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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* @param bias Tensor to be added to the result.
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* @param activation Name of activation kernel (defaults to `linear`) to be
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* applied
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* after biasAdd.
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* @param preluActivationWeights Tensor of prelu weights to be applied as part
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* of a `prelu` activation, typically the same shape as `x`.
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*/
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function fusedConv2d_(_a) {
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var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights;
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activation = activation || 'linear';
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if (fused_util_1.shouldFuse(engine_1.ENGINE.state.gradientDepth, activation) === false) {
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var result = conv_2.conv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
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if (bias != null) {
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result = binary_ops_1.add(result, bias);
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}
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return applyActivation(result, activation, preluActivationWeights);
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}
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2d');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2d');
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var x4D = $x;
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var reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
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}
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util.assert(x4D.rank === 4, function () { return "Error in fused conv2d: input must be rank 4, but got rank " +
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(x4D.rank + "."); });
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util.assert($filter.rank === 4, function () { return "Error in fused conv2d: filter must be rank 4, but got rank " +
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($filter.rank + "."); });
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in fused conv2d: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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util.assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
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("input depth for filter " + $filter.shape[2] + "."); });
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
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("Got strides " + strides + " and dilations '" + dilations + "'"); });
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util.assert(dataFormat === 'NHWC', function () { return "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported."; });
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var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
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var $bias;
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if (bias != null) {
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$bias = tensor_util_env_1.convertToTensor(bias, 'bias', 'fused conv2d');
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$bias = tensor_util_1.makeTypesMatch($bias, $x)[0];
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broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
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}
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var $preluActivationWeights;
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if (preluActivationWeights != null) {
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$preluActivationWeights = tensor_util_env_1.convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');
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}
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var grad = function (dy, saved) {
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var _a = saved, $filter = _a[0], x4D = _a[1], y = _a[2];
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var dyActivation = getFusedDyActivation(dy, y, activation);
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util.assert(conv_util.tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused conv2D: ' +
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"dilation rates greater than 1 " +
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("are not yet supported in gradients. Got dilations '" + dilations + "'"); });
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var biasGradient = {};
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if (bias != null) {
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biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
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}
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return Object.assign({
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x: function () {
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return conv_1.conv2dDerInput(x4D.shape, dyActivation, $filter, strides, pad);
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},
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filter: function () {
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return conv_1.conv2dDerFilter(x4D, dyActivation, $filter.shape, strides, pad);
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}
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}, biasGradient);
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};
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var inputs = { x: x4D, filter: $filter };
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if (bias != null) {
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inputs.bias = $bias;
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}
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if (preluActivationWeights != null) {
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inputs.preluActivationWeights = $preluActivationWeights;
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}
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var inputsToSave = [$filter, x4D];
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var outputsToSave = [true]; // Save the only output.
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.fusedConv2d({
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input: x4D,
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filter: $filter,
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convInfo: convInfo,
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bias: $bias,
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activation: activation,
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preluActivationWeights: $preluActivationWeights
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});
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save([$filter, x4D, res]);
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return res;
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}, inputs, grad, 'FusedConv2D', { convInfo: convInfo, activation: activation }, inputsToSave, outputsToSave);
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
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}
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return res;
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}
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/**
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* Computes depthwise 2D convolution, optionally fused with adding a
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* bias and applying an activation.
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*
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* Given a 4D `input` array and a `filter` array of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
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* `inChannels` convolutional filters of depth 1, this op applies a
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* different filter to each input channel (expanding from 1 channel to
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* `channelMultiplier` channels for each), then concatenates the results
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* together. The output has `inChannels * channelMultiplier` channels.
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*
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* See
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* [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
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* https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
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* for more details.
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*
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* @param obj An object with the following properties:
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* @param x The input tensor, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter tensor, rank 4, of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`. If strides is a single number, then `strideHeight ==
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* strideWidth`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
|
* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
|
* - For more info, see this guide:
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* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
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* https://www.tensorflow.org/api_guides/python/nn#Convolution)
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
|
* in which we sample input values across the height and width dimensions
|
* in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
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* number, then `dilationHeight == dilationWidth`. If it is greater than
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* 1, then all values of `strides` must be 1.
|
* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
|
* "NHWC". Specify the data format of the input and output data. With the
|
* default format "NHWC", the data is stored in the order of: [batch,
|
* height, width, channels]. Only "NHWC" is currently supported.
|
* @param dimRoundingMode The rounding mode used when computing output
|
* dimensions if pad is a number. If none is provided, it will not round
|
* and error if the output is of fractional size.
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* @param bias Tensor to be added to the result.
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* @param activation Name of activation kernel (defaults to `linear`).
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* @param preluActivationWeights Tensor of prelu weights to be applied as part
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* of a `prelu` activation, typically the same shape as `x`.
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*/
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function fusedDepthwiseConv2d_(_a) {
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var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _b = _a.dataFormat, dataFormat = _b === void 0 ? 'NHWC' : _b, _c = _a.dilations, dilations = _c === void 0 ? [1, 1] : _c, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _d = _a.activation, activation = _d === void 0 ? 'linear' : _d, preluActivationWeights = _a.preluActivationWeights;
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if (fused_util_1.shouldFuse(engine_1.ENGINE.state.gradientDepth, activation) === false) {
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var result = conv_2.depthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
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if (bias != null) {
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result = binary_ops_1.add(result, bias);
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}
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return applyActivation(result, activation, preluActivationWeights);
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}
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'depthwiseConv2d');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'depthwiseConv2d');
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var x4D = $x;
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var reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
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}
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util.assert(x4D.rank === 4, function () { return "Error in fused depthwiseConv2d: input must be rank 4, but got " +
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("rank " + x4D.rank + "."); });
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util.assert($filter.rank === 4, function () { return "Error in fused depthwiseConv2d: filter must be rank 4, " +
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("but got rank " + $filter.rank + "."); });
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util.assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in fused depthwiseConv2d: number of input channels " +
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("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
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("filter " + $filter.shape[2] + "."); });
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if (dilations == null) {
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dilations = [1, 1];
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}
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), function () {
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return 'Error in fused depthwiseConv2d: Either strides or dilations must ' +
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("be 1. Got strides " + strides + " and dilations '" + dilations + "'");
|
});
|
if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in fused depthwiseConv2d: pad must be an integer when " +
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("using dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
|
}
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var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
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var $bias;
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if (bias != null) {
|
$bias = tensor_util_env_1.convertToTensor(bias, 'bias', 'fused conv2d');
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$bias = tensor_util_1.makeTypesMatch($bias, $x)[0];
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broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
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}
|
var $preluActivationWeights;
|
if (preluActivationWeights != null) {
|
$preluActivationWeights = tensor_util_env_1.convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
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}
|
var grad = function (dy, saved) {
|
util.assert(conv_util.tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
|
"greater than 1 are not yet supported. Got dilations " +
|
("'" + dilations + "'"); });
|
var $filter = saved[0], x4D = saved[1], y = saved[2];
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var dyActivation = getFusedDyActivation(dy, y, activation);
|
var biasGradient = {};
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if (bias != null) {
|
biasGradient = { bias: function () { return getFusedBiasGradient($bias, dyActivation); } };
|
}
|
return Object.assign({
|
x: function () { return conv_1.depthwiseConv2dDerInput(x4D.shape, dyActivation, $filter, convInfo); },
|
filter: function () { return conv_1.depthwiseConv2dDerFilter(x4D, dyActivation, $filter.shape, convInfo); },
|
}, biasGradient);
|
};
|
var inputs = { x: x4D, filter: $filter };
|
if (bias != null) {
|
inputs.bias = $bias;
|
}
|
if (preluActivationWeights != null) {
|
inputs.preluActivationWeights = $preluActivationWeights;
|
}
|
var inputsToSave = [$filter, x4D];
|
var outputsToSave = [true];
|
var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.fusedDepthwiseConv2D({
|
input: x4D,
|
filter: $filter,
|
convInfo: convInfo,
|
bias: $bias,
|
activation: activation,
|
preluActivationWeights: $preluActivationWeights
|
});
|
save([$filter, x4D, res]);
|
return res;
|
}, inputs, grad, 'FusedDepthwiseConv2D', { convInfo: convInfo, activation: activation }, inputsToSave, outputsToSave);
|
if (reshapedTo4D) {
|
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
|
}
|
return res;
|
}
|
exports.matMul = operation_1.op({ fusedMatMul_: fusedMatMul_ });
|
exports.conv2d = operation_1.op({ fusedConv2d_: fusedConv2d_ });
|
exports.depthwiseConv2d = operation_1.op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ });
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//# sourceMappingURL=fused_ops.js.map
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