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conv_backprop_gpu_depthwise.ts
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/**
* @license
* Copyright 2018 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.
* =============================================================================
*/
import {backend_util} from '@tensorflow/tfjs-core';
import {GPGPUProgram} from './gpgpu_math';
export class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {
variableNames = ['x', 'dy'];
outputShape: number[];
userCode: string;
constructor(convInfo: backend_util.Conv2DInfo) {
this.outputShape = convInfo.filterShape;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const channelMul = convInfo.outChannels / convInfo.inChannels;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${channelMul} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${convInfo.batchSize}; b++) {
for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
int xR = wR + yR * ${strideHeight} - ${padTop};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
int xC = wC + yC * ${strideWidth} - ${padLeft};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`;
}
}
export class DepthwiseConv2DDerInputProgram implements GPGPUProgram {
variableNames = ['dy', 'W'];
outputShape: number[];
userCode: string;
constructor(convInfo: backend_util.Conv2DInfo) {
this.outputShape = convInfo.inShape;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padTop = filterHeight - 1 - convInfo.padInfo.top;
const padLeft = filterWidth - 1 - convInfo.padInfo.left;
const channelMul = convInfo.outChannels / convInfo.inChannels;
this.userCode = `
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${filterHeight}; wR++) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${filterHeight} - 1 - wR;
for (int wC = 0; wC < ${filterWidth}; wC++) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${filterWidth} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${channelMul}; dm++) {
int d2 = d1 * ${channelMul} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`;
}
}