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#include <algorithm>
#include <cstring>
#include <vector>
#include "gtest/gtest.h"
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"
using std::min;
using std::max;
namespace caffe {
template <typename TypeParam>
class LRNLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
LRNLayerTest()
: epsilon_(Dtype(1e-5)),
blob_bottom_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()) {}
virtual void SetUp() {
Caffe::set_random_seed(1701);
blob_bottom_->Reshape(2, 7, 3, 3);
// fill the values
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_);
}
virtual ~LRNLayerTest() { delete blob_bottom_; delete blob_top_; }
void ReferenceLRNForward(const Blob<Dtype>& blob_bottom,
const LayerParameter& layer_param, Blob<Dtype>* blob_top);
Dtype epsilon_;
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
template <typename TypeParam>
void LRNLayerTest<TypeParam>::ReferenceLRNForward(
const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,
Blob<Dtype>* blob_top) {
typedef typename TypeParam::Dtype Dtype;
blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
blob_bottom.height(), blob_bottom.width());
Dtype* top_data = blob_top->mutable_cpu_data();
LRNParameter lrn_param = layer_param.lrn_param();
Dtype alpha = lrn_param.alpha();
Dtype beta = lrn_param.beta();
int size = lrn_param.local_size();
switch (lrn_param.norm_region()) {
case LRNParameter_NormRegion_ACROSS_CHANNELS:
for (int n = 0; n < blob_bottom.num(); ++n) {
for (int c = 0; c < blob_bottom.channels(); ++c) {
for (int h = 0; h < blob_bottom.height(); ++h) {
for (int w = 0; w < blob_bottom.width(); ++w) {
int c_start = c - (size - 1) / 2;
int c_end = min(c_start + size, blob_bottom.channels());
c_start = max(c_start, 0);
Dtype scale = 1.;
for (int i = c_start; i < c_end; ++i) {
Dtype value = blob_bottom.data_at(n, i, h, w);
scale += value * value * alpha / size;
}
*(top_data + blob_top->offset(n, c, h, w)) =
blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
}
}
}
}
break;
case LRNParameter_NormRegion_WITHIN_CHANNEL:
for (int n = 0; n < blob_bottom.num(); ++n) {
for (int c = 0; c < blob_bottom.channels(); ++c) {
for (int h = 0; h < blob_bottom.height(); ++h) {
int h_start = h - (size - 1) / 2;
int h_end = min(h_start + size, blob_bottom.height());
h_start = max(h_start, 0);
for (int w = 0; w < blob_bottom.width(); ++w) {
Dtype scale = 1.;
int w_start = w - (size - 1) / 2;
int w_end = min(w_start + size, blob_bottom.width());
w_start = max(w_start, 0);
for (int nh = h_start; nh < h_end; ++nh) {
for (int nw = w_start; nw < w_end; ++nw) {
Dtype value = blob_bottom.data_at(n, c, nh, nw);
scale += value * value * alpha / (size * size);
}
}
*(top_data + blob_top->offset(n, c, h, w)) =
blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
}
}
}
}
break;
default:
LOG(FATAL) << "Unknown normalization region.";
}
}
TYPED_TEST_CASE(LRNLayerTest, TestDtypesAndDevices);
TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
LRNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_->num(), 2);
EXPECT_EQ(this->blob_top_->channels(), 7);
EXPECT_EQ(this->blob_top_->height(), 3);
EXPECT_EQ(this->blob_top_->width(), 3);
}
TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
LRNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
Blob<Dtype> top_reference;
this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
&top_reference);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
this->epsilon_);
}
}
TYPED_TEST(LRNLayerTest, TestForwardAcrossChannelsLargeRegion) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_lrn_param()->set_local_size(15);
LRNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
Blob<Dtype> top_reference;
this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
&top_reference);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
this->epsilon_);
}
}
TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
LRNLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
for (int i = 0; i < this->blob_top_->count(); ++i) {
this->blob_top_->mutable_cpu_diff()[i] = 1.;
}
vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
layer.Backward(this->blob_top_vec_, propagate_down,
this->blob_bottom_vec_);
// for (int i = 0; i < this->blob_bottom_->count(); ++i) {
// std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
// << std::endl;
// }
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(LRNLayerTest, TestGradientAcrossChannelsLargeRegion) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_lrn_param()->set_local_size(15);
LRNLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
for (int i = 0; i < this->blob_top_->count(); ++i) {
this->blob_top_->mutable_cpu_diff()[i] = 1.;
}
vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
layer.Backward(this->blob_top_vec_, propagate_down,
this->blob_bottom_vec_);
// for (int i = 0; i < this->blob_bottom_->count(); ++i) {
// std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
// << std::endl;
// }
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_lrn_param()->set_norm_region(
LRNParameter_NormRegion_WITHIN_CHANNEL);
layer_param.mutable_lrn_param()->set_local_size(3);
LRNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_->num(), 2);
EXPECT_EQ(this->blob_top_->channels(), 7);
EXPECT_EQ(this->blob_top_->height(), 3);
EXPECT_EQ(this->blob_top_->width(), 3);
}
TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_lrn_param()->set_norm_region(
LRNParameter_NormRegion_WITHIN_CHANNEL);
layer_param.mutable_lrn_param()->set_local_size(3);
LRNLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
Blob<Dtype> top_reference;
this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
&top_reference);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
this->epsilon_);
}
}
TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.mutable_lrn_param()->set_norm_region(
LRNParameter_NormRegion_WITHIN_CHANNEL);
layer_param.mutable_lrn_param()->set_local_size(3);
LRNLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
for (int i = 0; i < this->blob_top_->count(); ++i) {
this->blob_top_->mutable_cpu_diff()[i] = 1.;
}
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
} // namespace caffe
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