@tensorflow/tfjs-core/src/backends/webgl/conv_gpu_depthwise.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2017 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.
 * =============================================================================
 */

import {Conv2DInfo} from '../../ops/conv_util';
import {GPGPUProgram} from './gpgpu_math';

export class DepthwiseConv2DProgram implements GPGPUProgram {
  variableNames = ['x', 'W'];
  outputShape: number[];
  userCode: string;

  constructor(
      convInfo: Conv2DInfo, addBias = false, activation: string = null,
      hasPreluActivation = false) {
    this.outputShape = convInfo.outShape;

    const xNumRows = convInfo.inHeight;
    const xNumCols = convInfo.inWidth;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const channelMul = convInfo.outChannels / convInfo.inChannels;

    let activationSnippet = '', applyActivationSnippet = '';
    if (activation) {
      if (hasPreluActivation) {
        activationSnippet = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
      } else {
        activationSnippet = `
          float activation(float x) {
            ${activation}
          }
        `;
      }

      applyActivationSnippet = `result = activation(result);`;
    }

    const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';
    if (addBias) {
      this.variableNames.push('bias');
    }

    if (hasPreluActivation) {
      this.variableNames.push('preluActivationWeights');
    }

    this.userCode = `
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${channelMul};
        int q = d2 - d1 * ${channelMul};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${xNumRows}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${xNumCols}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
  }
}