void ConvBaseProjection::reshape(int batchSize) {
size_t width = calOutputSize();
CHECK_EQ(width, out_->value->getWidth());
CHECK_EQ(calInputSize(), in_->value->getWidth());
reshapeTensorDesc(batchSize);
bool useDilation = false;
if (dilationH_ > 1 || dilationW_ > 1) {
useDilation = true;
}
hl_conv_workspace(imageDesc_,
outputDesc_,
filterDesc_,
convDesc_,
&fwdAlgo_,
&fwdLimitBytes_,
&bwdDataAlgo_,
&bwdDataLimitBytes_,
&bwdFilterAlgo_,
&bwdFilterLimitBytes_,
useDilation);
size_t maxWorkSpace = 0;
maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_);
maxWorkSpace = std::max(maxWorkSpace, bwdFilterLimitBytes_);
workSpaceInBytes_ = maxWorkSpace;
VLOG(3) << getName() << " Fwd / BwdData / BwdFilter algo: " << fwdAlgo_
<< " / " << bwdDataAlgo_ << " / " << bwdFilterAlgo_;
}
void CudnnConvLayer::forward(PassType passType) {
Layer::forward(passType);
int batchSize = getInput(0).getBatchSize();
resetOutput(batchSize, calOutputSize());
for (size_t i = 0; i != inputLayers_.size(); ++i) {
projections_[i]->forward(&getInput(i), &getOutput(), passType);
}
if (biases_) {
REGISTER_TIMER_INFO("CudnnConvBiasTimer", getName().c_str());
int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
hl_tensor_reshape(outputDesc_, batchSize, numFilters_ / groups_[0],
outputH_[0], outputW_[0], numFilters_ * outputH_[0] * outputW_[0],
outputH_[0] * outputW_[0], outputW_[0], 1);
outputOffset_ = getOutputValue()->getWidth() / groups_[0];
for (int g = 0; g < groups_[0]; ++g) {
real *biasData = biases_->getW()->getData() + biasOffset_ * g;
real *outData = getOutputValue()->getData() + outputOffset_ * g;
hl_convolution_forward_add_bias(biasDesc_, biasData,
outputDesc_, outData);
}
}
forwardActivation();
}
