Caffe学習ノート(4)layerをカスタマイズしてテストする
例:sinレイヤを追加する
主なプロセス:
include/caffe/layers/your_layer.hpp
src/caffe/layers/your_layer.cpp
およびsrc/caffe/layers/your_layer.cu
[オプション]test/test_your_layer.cpp
ヘッダファイル定義
caffe_root/include/caffe/layers/
ディレクトリの下にヘッダファイルsin_layer.hpp
を作成し、以下の内容を追加します.#ifndef CAFFE_SIN_LAYER_HPP_
#define CAFFE_SIN_LAYER_HPP_
#include
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/layers/neuron_layer.hpp"
namespace caffe {
template <typename Dtype>
class SinLayer : public NeuronLayer<Dtype> {
public:
explicit SinLayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param) {}
#
virtual inline const char* type() const { return "Sin"; }
# ,
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
};
} // namespace caffe
#endif // CAFFE_SIN_LAYER_HPP_
Layer定義
CPUバージョン:.cpp
caffe_root/src/caffe/layers/
ディレクトリの下に配置されます.// Sin neuron activation function layer.
// Adapted from TanH layer which was adapted from the ReLU layer code written by Yangqing Jia
#include
#include "caffe/layers/sin_layer.hpp"
namespace caffe {
template <typename Dtype>
void SinLayer<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();
const int count = bottom[0]->count();
for (int i = 0; i < count; ++i) {
top_data[i] = sin(bottom_data[i]);
}
}
template <typename Dtype>
void SinLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom)
{
if (propagate_down[0]) {
const Dtype* bottom_data = bottom[0]->cpu_data();
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
const int count = bottom[0]->count();
Dtype bottom_datum;
for (int i = 0; i < count; ++i) {
bottom_datum = bottom_data[i];
bottom_diff[i] = top_diff[i] * cos(bottom_datum);
}
}
}
#ifdef CPU_ONLY
STUB_GPU(SinLayer);
#endif
INSTANTIATE_CLASS(SinLayer);
REGISTER_LAYER_CLASS(Sin);
} // namespace caffe
GPUバージョン:.cu
caffe_root/src/caffe/layers/
ディレクトリの下に配置されます.// Sin neuron activation function layer.
// Adapted from TanH layer which was adapted from the ReLU layer code written by Yangqing Jia
#include
#include "caffe/layers/sin_layer.hpp"
namespace caffe {
template <typename Dtype>
__global__ void SinForward(const int n, const Dtype* in, Dtype* out) {
CUDA_KERNEL_LOOP(index, n) {
out[index] = sin(in[index]);
}
}
template <typename Dtype>
void SinLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
const int count = bottom[0]->count();
// NOLINT_NEXT_LINE(whitespace/operators)
SinForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, top_data);
CUDA_POST_KERNEL_CHECK;
}
template <typename Dtype>
__global__ void SinBackward(const int n, const Dtype* in_diff,
const Dtype* out_data, Dtype* out_diff) {
CUDA_KERNEL_LOOP(index, n) {
Dtype sinx = out_data[index];
out_diff[index] = in_diff[index] * cos(sinx);
}
}
template <typename Dtype>
void SinLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) {
if (propagate_down[0]) {
const Dtype* bottom_data = bottom[0]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
const int count = bottom[0]->count();
// NOLINT_NEXT_LINE(whitespace/operators)
SinBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, top_diff, bottom_data, bottom_diff);
CUDA_POST_KERNEL_CHECK;
}
}
INSTANTIATE_LAYER_GPU_FUNCS(SinLayer);
} // namespace caffe
テストレイヤー
定義されたsin layerが有効かどうかをテストするためのテストファイルを追加し、
caffe_root/src/caffe/test/
ディレクトリの下にテストファイルtest_sin_layer.cpp
を追加します.#include
#include
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
// inclued the layer that we are testing!
#include "caffe/layers/sin_layer.hpp"
namespace caffe {
template <typename TypeParam>
class SinLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
SinLayerTest()
: blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
blob_top_(new Blob<Dtype>())
{
Caffe::set_random_seed(1701);
FillerParameter filler_param;
blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_);
}
virtual ~SinLayerTest() { delete blob_bottom_; delete blob_top_; }
// test forward process
void TestForward(Dtype filler_std)
{
FillerParameter filler_param;
filler_param.set_std(filler_std);
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
LayerParameter layer_param;
SinLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
const Dtype* bottom_data = this->blob_bottom_->cpu_data();
const Dtype* top_data = this->blob_top_->cpu_data();
const Dtype min_precision = 1e-5;
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
Dtype expected_value = sin(bottom_data[i]);
Dtype precision = std::max(
Dtype(std::abs(expected_value * Dtype(1e-4))), min_precision);
EXPECT_NEAR(expected_value, top_data[i], precision);
}
}
// test backward process
void TestBackward(Dtype filler_std)
{
FillerParameter filler_param;
filler_param.set_std(filler_std);
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
LayerParameter layer_param;
SinLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-4, 1e-2, 1701);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
// test type(in this case SinLayerTest)
TYPED_TEST_CASE(SinLayerTest, TestDtypesAndDevices);
// test sin
TYPED_TEST(SinLayerTest, TestSin) {
this->TestForward(1.0);
}
// test calculating the gradient correctly when backpropagating
TYPED_TEST(SinLayerTest, TestSinGradient) {
this->TestBackward(1.0);
}
} // namespace caffe
テストの実行:buildフォルダの下
cmake ..
make all -j8
make test
make runtest GTEST_FILTER='SinLayerTest/*'
テストのクラス名は対応するtestファイルで探します