src/neuralnet/neuron_layer/cudnn_lrn.cc

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#include "singa/neuralnet/neuron_layer.h"

namespace singa {
CudnnLRNLayer::~CudnnLRNLayer() {
  if (has_init_cudnn_) {
    cudnnDestroyLRNDescriptor(norm_desc_);
  }
}

void CudnnLRNLayer::InitCudnn() {
  mode_ = CUDNN_LRN_CROSS_CHANNEL_DIM1;
  CudnnBase::InitCudnn();
  CHECK_CUDNN(cudnnCreateLRNDescriptor(&norm_desc_));
  CHECK_CUDNN(cudnnSetLRNDescriptor(norm_desc_,
        lsize_,
        alpha_,
        beta_,
        knorm_));
  CHECK_CUDNN(cudnnSetTensor4dDescriptor(src_desc_,
      CUDNN_TENSOR_NCHW,
      CUDNN_DATA_FLOAT,
      batchsize_,
      channels_,
      height_,
      width_));
  CHECK_CUDNN(cudnnSetTensor4dDescriptor(my_desc_,
      CUDNN_TENSOR_NCHW,
      CUDNN_DATA_FLOAT,
      batchsize_,
      channels_,
      height_,
      width_));
}
void CudnnLRNLayer::ComputeFeature(int flag, const vector<Layer*>& srclayers) {
  if (!has_init_cudnn_)
    InitCudnn();
  float alpha = 1.0f, beta = 0.0f;
  CHECK_CUDNN(cudnnLRNCrossChannelForward(handle_,
      norm_desc_,
      mode_,
      &alpha,
      src_desc_,
      srclayers[0]->data(this).gpu_data(),
      &beta,
      my_desc_,
      data_.mutable_gpu_data()));
}
void CudnnLRNLayer::ComputeGradient(int flag, const vector<Layer*>& srclayers) {
  float alpha = 1.0f, beta = 0.0f;
  CHECK_CUDNN(cudnnLRNCrossChannelBackward(handle_,
        norm_desc_,
        mode_,
        &alpha,
        my_desc_,
        data_.gpu_data(),
        my_desc_,
        grad_.gpu_data(),
        src_desc_,
        srclayers[0]->data(this).gpu_data(),
        &beta,
        src_desc_,
        srclayers[0]->mutable_grad(this)->mutable_gpu_data()));
}


}  // namespace singa