/**
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* @license
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* Copyright 2017 Google Inc. 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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import {Conv2DInfo} from '../../ops/conv_util';
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import {GPGPUProgram} from './gpgpu_math';
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export class DepthwiseConv2DProgram implements GPGPUProgram {
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variableNames = ['x', 'W'];
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outputShape: number[];
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userCode: string;
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constructor(
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convInfo: Conv2DInfo, addBias = false, activation: string = null,
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hasPreluActivation = false) {
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this.outputShape = convInfo.outShape;
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const xNumRows = convInfo.inHeight;
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const xNumCols = convInfo.inWidth;
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const padTop = convInfo.padInfo.top;
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const padLeft = convInfo.padInfo.left;
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const strideHeight = convInfo.strideHeight;
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const strideWidth = convInfo.strideWidth;
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const dilationHeight = convInfo.dilationHeight;
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const dilationWidth = convInfo.dilationWidth;
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const filterHeight = convInfo.filterHeight;
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const filterWidth = convInfo.filterWidth;
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const channelMul = convInfo.outChannels / convInfo.inChannels;
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let activationSnippet = '', applyActivationSnippet = '';
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if (activation) {
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if (hasPreluActivation) {
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activationSnippet = `float activation(float a) {
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float b = getPreluActivationWeightsAtOutCoords();
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${activation}
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}`;
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} else {
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activationSnippet = `
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float activation(float x) {
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${activation}
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}
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`;
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}
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applyActivationSnippet = `result = activation(result);`;
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}
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const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
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if (addBias) {
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this.variableNames.push('bias');
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}
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if (hasPreluActivation) {
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this.variableNames.push('preluActivationWeights');
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}
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this.userCode = `
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${activationSnippet}
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const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
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const ivec2 pads = ivec2(${padTop}, ${padLeft});
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void main() {
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ivec4 coords = getOutputCoords();
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int batch = coords.x;
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ivec2 xRCCorner = coords.yz * strides - pads;
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int d2 = coords.w;
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int d1 = d2 / ${channelMul};
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int q = d2 - d1 * ${channelMul};
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int xRCorner = xRCCorner.x;
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int xCCorner = xRCCorner.y;
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// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
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// ? = to be determined. : = across all values in that axis.
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float dotProd = 0.0;
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// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
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for (int wR = 0; wR < ${filterHeight}; wR++) {
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int xR = xRCorner + wR * ${dilationHeight};
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if (xR < 0 || xR >= ${xNumRows}) {
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continue;
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}
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for (int wC = 0; wC < ${filterWidth}; wC++) {
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int xC = xCCorner + wC * ${dilationWidth};
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if (xC < 0 || xC >= ${xNumCols}) {
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continue;
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}
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float xVal = getX(batch, xR, xC, d1);
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float wVal = getW(wR, wC, d1, q);
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dotProd += xVal * wVal;
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}
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}
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float result = dotProd;
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${addBiasSnippet}
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${applyActivationSnippet}
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setOutput(result);
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}
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`;
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}
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}
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