void conv_im2col_gpu(const Dtype* data, Dtype* col_buff){
if (!force_nd_im2col&&num_spatial_axes == 2){
im2col_gpu(data, conv_in_channels, conv_input_shape.cpu_data()[1], conv_input_shape.cpu_data()[2],
kernel_shape.cpu_data()[0], kernel_shape.cpu_data()[1], pad.cpu_data()[0], pad.cpu_data()[1],
stride.cpu_data()[0], stride.cpu_data()[1], col_buff);
}
}
void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = (*top)[0]->mutable_gpu_data();
for (int n = 0; n < bottom[0]->num(); ++n) {
im2col_gpu(bottom_data + bottom[0]->offset(n), CHANNELS_, HEIGHT_,
WIDTH_, KSIZE_, STRIDE_, top_data + (*top)[0]->offset(n));
}
}
inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
im2col_gpu(data, conv_in_channels_,
conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
pad_.cpu_data()[0], pad_.cpu_data()[1],
stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff);
} else {
im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
kernel_shape_.gpu_data(), pad_.gpu_data(),
stride_.gpu_data(), col_buff);
}
}
void
TiedConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype> *> &bottom,
vector<Blob<Dtype> *> *top) {
const Dtype *weight = this->blobs_[0]->gpu_data();
const int weight_offset = M_ * K_;
for (int i = 0; i < num_in_; ++i) {
//-----Same concept as Forward_gpu of convolutionlayer-----
const Dtype *bottom_data = bottom[i]->gpu_data();
const int col_offset = K_ * N_[i];
const int top_offset = M_ * N_[i];
Dtype *top_data = (*top)[i]->mutable_gpu_data();
Dtype *col_data = this->col_buffers_[i]->mutable_gpu_data();
for (int n = 0; n < num_; ++n) {
// First, im2col
im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_[i],
width_[i], kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_,
stride_w_, col_data);
// Second, innerproduct with groups.
for (int g = 0; g < group_; ++g) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_[i], K_,
(Dtype)1., weight + weight_offset * g,
col_data + col_offset * g, (Dtype)0.,
top_data + (*top)[i]->offset(n) + top_offset * g);
}
// third, add bias
if (bias_term_) {
caffe_gpu_gemm<Dtype>(
CblasNoTrans, CblasNoTrans, num_output_, N_[i], 1, (Dtype)1.,
this->blobs_[1]->gpu_data(),
reinterpret_cast<const Dtype *>(bias_multipliers_[i]->gpu_data()),
(Dtype)1., top_data + (*top)[i]->offset(n));
}
}
//---------------------------------------------------------
}
// montage(this->blobs_[0].get(), "tconv" +
// boost::lexical_cast<std::string>(M_));
}
inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
im2col_gpu(data, bottom_offset_, conv_in_channels_, conv_in_height_,
conv_in_width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_,
stride_w_, col_buff, 0);
}
inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
filter_stride_h_, filter_stride_w_, col_buff);
}
void
TiedConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype> *> &top,
const vector<bool> &propagate_down,
vector<Blob<Dtype> *> *bottom) {
const Dtype *weight = NULL;
Dtype *weight_diff = NULL;
if (this->param_propagate_down_[0]) {
weight = this->blobs_[0]->gpu_data();
weight_diff = this->blobs_[0]->mutable_gpu_diff();
// Init weight diffs to all 0s.
caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
}
Dtype *bias_diff = NULL;
if (bias_term_ && this->param_propagate_down_[1]) {
bias_diff = this->blobs_[1]->mutable_gpu_diff();
caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
}
const int weight_offset = M_ * K_;
for (int i = 0; i < num_in_; ++i) {
//-----Same concept as Backward_cpu of convolutionlayer-----
const Dtype* top_diff = NULL;
// Bias gradient if necessary
if (bias_term_ && this->param_propagate_down_[1]) {
top_diff = top[i]->gpu_diff();
for (int n = 0; n < num_; ++n) {
caffe_gpu_gemv<Dtype>(
CblasNoTrans, num_output_, N_[i], 1., top_diff + top[i]->offset(n),
reinterpret_cast<const Dtype *>(bias_multipliers_[i]->gpu_data()),
1., bias_diff);
}
}
if (this->param_propagate_down_[0] || propagate_down[i]) {
if (!top_diff) {
top_diff = top[i]->gpu_diff();
}
Dtype* col_data = this->col_buffers_[i]->mutable_gpu_data();
const Dtype* bottom_data = (*bottom)[i]->gpu_data();
Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
const int col_offset = K_ * N_[i];
const int top_offset = M_ * N_[i];
for (int n = 0; n < num_; ++n) {
// Since we saved memory in the forward pass by not storing all col data,
// we will need to recompute them.
im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_[i],
width_[i], kernel_h_, kernel_w_, pad_h_, pad_w_,
stride_h_, stride_w_, col_data);
// gradient w.r.t. weight. Note that we will accumulate diffs.
if (this->param_propagate_down_[0]) {
for (int g = 0; g < group_; ++g) {
caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_[i],
(Dtype)1.,
top_diff + top[i]->offset(n) + top_offset * g,
col_data + col_offset * g, (Dtype)1.,
weight_diff + weight_offset * g);
}
}
// gradient w.r.t. bottom data, if necessary
if (propagate_down[i]) {
if (weight == NULL) {
weight = this->blobs_[0]->gpu_data();
}
for (int g = 0; g < group_; ++g) {
caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_[i], M_,
(Dtype)1., weight + weight_offset * g,
top_diff + top[i]->offset(n) + top_offset * g,
(Dtype)0., col_data + col_offset * g);
}
// col2im back to the data
col2im_gpu(col_data, channels_, height_[i], width_[i], kernel_h_,
kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
bottom_diff + (*bottom)[i]->offset(n));
}
}
}
// montage_channels(this->blobs_[0].get(),
// boost::lexical_cast<std::string>(M_) + " tconv bprop " +
// boost::lexical_cast<std::string>(i) , true);
//// make sure to give back the pointer to gpu after visualization
// weight_diff = this->blobs_[0]->mutable_gpu_diff();
} // end for each input
// montage_channels(this->blobs_[0].get(), "final tconv bprop " +
// boost::lexical_cast<std::string>(M_), true);
// cv::waitKey(0);
}
