Paddle/paddle/gserver/layers/ConvBaseLayerCpu.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.

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. */


#include "paddle/utils/Logging.h"
#include "ConvBaseLayerCpu.h"
namespace paddle {

bool ConvBaseLayerCpu::init(const LayerMap &layerMap,
                           const ParameterMap &parameterMap) {
  /* Initialize the basic convolutional parent class */
  ConvBaseLayer::init(layerMap, parameterMap);

  int channel;
  /* Initialize the projection */
  for (auto &inputConfig : config_.inputs()) {
    const ConvConfig &conf = inputConfig.conv_conf();
    subM_.push_back(numFilters_ / conf.groups());
    subN_.push_back(conf.output_x() * conf.output_x());
    channel = isConv_ ? conf.channels() : numFilters_;
    subK_.push_back(channel * conf.filter_size() * conf.filter_size() /
                    conf.groups());
    /* Consistent caffe mode for multiple input */
    caffeMode_ = conf.caffe_mode();
  }

  return true;
}

void ConvBaseLayerCpu::resetExpandInput(size_t height, size_t width) {
  Matrix::resizeOrCreate(expandInput_, height, width, false, useGpu_);
}

void ConvBaseLayerCpu::addSharedBias() {
  size_t mapW = getSize() / numFilters_;
  size_t mapH = getOutputValue()->getElementCnt() / mapW;
  MatrixPtr out =
      Matrix::create(getOutputValue()->getData(), mapH, mapW, false, useGpu_);

  Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);

  out->transpose(transOutValue_, false);  // false means no memory allocation
  transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
                          numFilters_);

  MatrixPtr bias =
      Matrix::create(biases_->getW()->getData(), 1,
                     biases_->getW()->getElementCnt(), false, useGpu_);
  transOutValue_->addBias(*bias, 1.0f);

  transOutValue_->reshape(mapW, mapH);
  transOutValue_->transpose(out, false);  // false means no memory allocation

  out->clear();
  bias->clear();
}

void ConvBaseLayerCpu::addUnsharedBias() {
  MatrixPtr outValue = getOutputValue();
  MatrixPtr bias =
      Matrix::create(biases_->getW()->getData(), 1,
                     biases_->getW()->getElementCnt(), false, useGpu_);
  outValue->addBias(*bias, 1.0f);
}


void ConvBaseLayerCpu::expandOneFrame(MatrixPtr image, size_t startIdx,
                                     int inIdx) {
  int channel = isConv_ ? channels_[inIdx] : numFilters_;

  resetExpandInput(subK_[inIdx] * groups_[inIdx], subN_[inIdx]);
  real *imgData = image->getData() + startIdx * image->getWidth();
  MatrixPtr imageTmp = Matrix::create(
      imgData, 1, imgSizeH_[inIdx] * imgSizeW_[inIdx] * channel, false,
      useGpu_);
  expandInput_->convExpand(*imageTmp, imgSizeH_[inIdx], imgSizeW_[inIdx],
                           channel, filterSize_[inIdx],
                           filterSize_[inIdx], stride_[inIdx], stride_[inIdx],
                           padding_[inIdx], padding_[inIdx],
                           outputH_[inIdx], outputW_[inIdx]);
  imageTmp->clear();
}

void ConvBaseLayerCpu::expandFwdOnce(MatrixPtr image, MatrixPtr out,
                                     int inIdx, int startIdx) {
  int subM = subM_[inIdx];
  int subN = subN_[inIdx];
  int subK = subK_[inIdx];

  expandOneFrame(image, startIdx, inIdx);

  int nf = isConv_ ? numFilters_ : channels_[inIdx];

  real *outData =
      out->getData() + startIdx * subN * nf;

  real *wgtData = weights_[inIdx]->getW()->getData();
  real *expInData = expandInput_->getData();
  for (int g = 0; g < groups_[inIdx]; ++g) {
    MatrixPtr A =
        Matrix::create(wgtData, subK, subM, true, useGpu_);  // mark transpose
    MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_);
    MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_);
    C->mul(A, B, 1, 1);

    A->clear();
    B->clear();
    C->clear();
    wgtData += subK * subM;
    expInData += subK * subN;
    outData += subM * subN;
  }
}

void ConvBaseLayerCpu::bpropActs(MatrixPtr image, MatrixPtr out, int inpIdx) {
  int channel = isConv_ ? channels_[inpIdx] : numFilters_;

  int subM = subM_[inpIdx];
  int subN = subN_[inpIdx];
  int subK = subK_[inpIdx];
  size_t batchSize = image->getHeight();
  MatrixPtr tgtGrad = out;

  /* reset the expand-grad memory */
  resetExpandInput(subK * groups_[inpIdx], subN);

  real *localGradData = image->getData();
  real *tgtGradData = tgtGrad->getData();
  for (size_t n = 0; n < batchSize; n++) {
    real *wgtData = weights_[inpIdx]->getW()->getData();
    real *expandInData = expandInput_->getData();

    for (int g = 0; g < groups_[inpIdx]; g++) {
      // create temporary matrix
      MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_);
      MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_);
      MatrixPtr A = Matrix::create(wgtData, subK, subM, false, useGpu_);
      C->mul(A, B);  // mul

      // clear the temporary matrix
      A->clear();
      B->clear();
      C->clear();

      expandInData += subK * subN;
      localGradData += subM * subN;
      wgtData += subK * subM;
    }

    // shrink one frame outGrad
    MatrixPtr oneGradTmp = Matrix::create(
        expandInput_->getData(), subK * groups_[inpIdx], subN, false, useGpu_);
    MatrixPtr vTmp = Matrix::create(
        tgtGradData, 1,
        imgSizeH_[inpIdx] * imgSizeW_[inpIdx] * channel, false,
        useGpu_);
    vTmp->convShrink(*oneGradTmp, imgSizeH_[inpIdx], imgSizeW_[inpIdx],
                     channel, filterSize_[inpIdx],
                     filterSize_[inpIdx], stride_[inpIdx], stride_[inpIdx],
                     padding_[inpIdx], padding_[inpIdx],
                     outputH_[inpIdx], outputW_[inpIdx], 1.0f, 1.0f);
    vTmp->clear();
    oneGradTmp->clear();

    // move the data-pointer
    tgtGradData += imgSizeH_[inpIdx] * imgSizeW_[inpIdx] * channel;
  }
}

void ConvBaseLayerCpu::bpropWeights(MatrixPtr image, MatrixPtr out,
                                    int inpIdx) {
  MatrixPtr weightGrad = weights_[inpIdx]->getWGrad();

  int subM = subM_[inpIdx];
  int subN = subN_[inpIdx];
  int subK = subK_[inpIdx];
  size_t batchSize = image->getHeight();
  resetExpandInput(subK * groups_[inpIdx], subN);

  real *gradData = out->getData();

  for (size_t n = 0; n < batchSize; n++) {  // frame by frame
    // expand
    expandOneFrame(image, n, inpIdx);
    real *wGradData = weightGrad->getData();
    real *expandInData = expandInput_->getData();

    // expand-mul one-group by one
    for (int g = 0; g < groups_[inpIdx]; g++) {
      MatrixPtr A = Matrix::create(expandInData, subK, subN, false, useGpu_);
      MatrixPtr B = Matrix::create(gradData, subM, subN, true, useGpu_);
      MatrixPtr C = Matrix::create(wGradData, subK, subM, false, useGpu_);
      C->mul(A, B, 1, 1);

      A->clear();
      B->clear();
      C->clear();
      gradData += subM * subN;
      wGradData += subK * subM;
      expandInData += subK * subN;
    }
  }
}

void ConvBaseLayerCpu::bpropSharedBias(MatrixPtr biases, MatrixPtr v) {
  size_t mapW = getSize() / numFilters_;
  size_t mapH = v->getElementCnt() / mapW;
  MatrixPtr vTmp = Matrix::create(v->getData(), mapH, mapW, false, useGpu_);

  Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);

  vTmp->transpose(transOutValue_, false);  // false means no memory allocation
  transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
                          numFilters_);
  biases->collectBias(*transOutValue_, 1.0f);
}

void ConvBaseLayerCpu::bpropBiases(MatrixPtr v) {
  MatrixPtr biases =
      Matrix::create(biases_->getWGrad()->getData(), 1,
                     biases_->getWGrad()->getElementCnt(), false, useGpu_);
  if (sharedBiases_) {
    bpropSharedBias(biases, v);
  } else {
    biases->collectBias(*v, 1.0f);
  }
  biases->clear();
}

}  // namespace paddle