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// Copyright 2013 Yangqing Jia
#include <vector>
#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/util/im2col.hpp"
#include "caffe/filler.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
CHECK_EQ(bottom.size(), 1) << "Conv Layer takes a single blob as input.";
CHECK_EQ(top->size(), 1) << "Conv Layer takes a single blob as output.";
KSIZE_ = this->layer_param_.kernelsize();
STRIDE_ = this->layer_param_.stride();
GROUP_ = this->layer_param_.group();
PAD_ = this->layer_param_.pad();
NUM_ = bottom[0]->num();
CHANNELS_ = bottom[0]->channels();
HEIGHT_ = bottom[0]->height();
WIDTH_ = bottom[0]->width();
NUM_OUTPUT_ = this->layer_param_.num_output();
CHECK_GT(NUM_OUTPUT_, 0);
CHECK_EQ(CHANNELS_ % GROUP_, 0);
// The im2col result buffer would only hold one image at a time to avoid
// overly large memory usage.
int height_out = (HEIGHT_ + 2 * PAD_ - KSIZE_) / STRIDE_ + 1;
int width_out = (WIDTH_ + 2 * PAD_ - KSIZE_) / STRIDE_ + 1;
col_buffer_.Reshape(1, CHANNELS_ * KSIZE_ * KSIZE_, height_out, width_out);
// Set the parameters
CHECK_EQ(NUM_OUTPUT_ % GROUP_, 0)
<< "Number of output should be multiples of group.";
biasterm_ = this->layer_param_.biasterm();
// Figure out the dimensions for individual gemms.
M_ = NUM_OUTPUT_ / GROUP_;
K_ = CHANNELS_ * KSIZE_ * KSIZE_ / GROUP_;
N_ = height_out * width_out;
(*top)[0]->Reshape(bottom[0]->num(), NUM_OUTPUT_, height_out, width_out);
// Check if we need to set up the weights
if (this->blobs_.size() > 0) {
LOG(INFO) << "Skipping parameter initialization";
} else {
if (biasterm_) {
this->blobs_.resize(2);
} else {
this->blobs_.resize(1);
}
// Intialize the weight
this->blobs_[0].reset(
new Blob<Dtype>(NUM_OUTPUT_, CHANNELS_ / GROUP_, KSIZE_, KSIZE_));
// fill the weights
shared_ptr<Filler<Dtype> > weight_filler(
GetFiller<Dtype>(this->layer_param_.weight_filler()));
weight_filler->Fill(this->blobs_[0].get());
// If necessary, intiialize and fill the bias term
if (biasterm_) {
this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, NUM_OUTPUT_));
shared_ptr<Filler<Dtype> > bias_filler(
GetFiller<Dtype>(this->layer_param_.bias_filler()));
bias_filler->Fill(this->blobs_[1].get());
}
}
// Set up the bias filler
if (biasterm_) {
bias_multiplier_.reset(new SyncedMemory(N_ * sizeof(Dtype)));
Dtype* bias_multiplier_data =
reinterpret_cast<Dtype*>(bias_multiplier_->mutable_cpu_data());
for (int i = 0; i < N_; ++i) {
bias_multiplier_data[i] = 1.;
}
}
}
template <typename Dtype>
Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = (*top)[0]->mutable_cpu_data();
Dtype* col_data = col_buffer_.mutable_cpu_data();
const Dtype* weight = this->blobs_[0]->cpu_data();
int weight_offset = M_ * K_;
int col_offset = K_ * N_;
int top_offset = M_ * N_;
for (int n = 0; n < NUM_; ++n) {
// First, im2col
im2col_cpu(bottom_data + bottom[0]->offset(n), CHANNELS_, HEIGHT_,
WIDTH_, KSIZE_, PAD_, STRIDE_, col_data);
// Second, innerproduct with groups
for (int g = 0; g < GROUP_; ++g) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
(Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
(Dtype)0., top_data + (*top)[0]->offset(n) + top_offset * g);
}
// third, add bias
if (biasterm_) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, NUM_OUTPUT_,
N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
(Dtype)1., top_data + (*top)[0]->offset(n));
}
}
return Dtype(0.);
}
template <typename Dtype>
void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
const Dtype* top_diff = top[0]->cpu_diff();
const Dtype* weight = this->blobs_[0]->cpu_data();
Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
const Dtype* bottom_data = (*bottom)[0]->cpu_data();
Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
Dtype* col_data = col_buffer_.mutable_cpu_data();
Dtype* col_diff = col_buffer_.mutable_cpu_diff();
// bias gradient if necessary
Dtype* bias_diff = NULL;
if (biasterm_) {
bias_diff = this->blobs_[1]->mutable_cpu_diff();
memset(bias_diff, 0, sizeof(Dtype) * this->blobs_[1]->count());
for (int n = 0; n < NUM_; ++n) {
caffe_cpu_gemv<Dtype>(CblasNoTrans, NUM_OUTPUT_, N_,
1., top_diff + top[0]->offset(n),
reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), 1.,
bias_diff);
}
}
int weight_offset = M_ * K_;
int col_offset = K_ * N_;
int top_offset = M_ * N_;
memset(weight_diff, 0, sizeof(Dtype) * this->blobs_[0]->count());
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_cpu(bottom_data + (*bottom)[0]->offset(n), CHANNELS_, HEIGHT_,
WIDTH_, KSIZE_, PAD_, STRIDE_, col_data);
// gradient w.r.t. weight. Note that we will accumulate diffs.
for (int g = 0; g < GROUP_; ++g) {
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
(Dtype)1., top_diff + top[0]->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) {
for (int g = 0; g < GROUP_; ++g) {
caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
(Dtype)1., weight + weight_offset * g,
top_diff + top[0]->offset(n) + top_offset * g,
(Dtype)0., col_diff + col_offset * g);
}
// col2im back to the data
col2im_cpu(col_diff, CHANNELS_, HEIGHT_, WIDTH_, KSIZE_, PAD_, STRIDE_,
bottom_diff + (*bottom)[0]->offset(n));
}
}
}
INSTANTIATE_CLASS(ConvolutionLayer);
} // namespace caffe
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