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#include <vector>
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/im2col.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
ConvolutionParameter conv_param = this->layer_param_.convolution_param();
CHECK(!conv_param.has_kernel_size() !=
!(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
<< "Filter size is kernel_size OR kernel_h and kernel_w; not both";
CHECK(conv_param.has_kernel_size() ||
(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
<< "For non-square filters both kernel_h and kernel_w are required.";
CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
&& conv_param.has_pad_w())
|| (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
<< "pad is pad OR pad_h and pad_w are required.";
CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
&& conv_param.has_stride_w())
|| (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
<< "Stride is stride OR stride_h and stride_w are required.";
if (conv_param.has_kernel_size()) {
kernel_h_ = kernel_w_ = conv_param.kernel_size();
} else {
kernel_h_ = conv_param.kernel_h();
kernel_w_ = conv_param.kernel_w();
}
CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
if (!conv_param.has_pad_h()) {
pad_h_ = pad_w_ = conv_param.pad();
} else {
pad_h_ = conv_param.pad_h();
pad_w_ = conv_param.pad_w();
}
if (!conv_param.has_stride_h()) {
stride_h_ = stride_w_ = conv_param.stride();
} else {
stride_h_ = conv_param.stride_h();
stride_w_ = conv_param.stride_w();
}
}
template <typename Dtype>
void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
<< "corresponding to (num, channels, height, width)";
channels_ = bottom[0]->channels();
height_ = bottom[0]->height();
width_ = bottom[0]->width();
top[0]->Reshape(
bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
(height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
(width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
}
template <typename Dtype>
void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = top[0]->mutable_cpu_data();
for (int n = 0; n < bottom[0]->num(); ++n) {
im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
stride_h_, stride_w_, top_data + top[0]->offset(n));
}
}
template <typename Dtype>
void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
for (int n = 0; n < top[0]->num(); ++n) {
col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
kernel_h_, kernel_w_, pad_h_, pad_w_,
stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
}
}
#ifdef CPU_ONLY
STUB_GPU(Im2colLayer);
#endif
INSTANTIATE_CLASS(Im2colLayer);
REGISTER_LAYER_CLASS(Im2col);
} // namespace caffe
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