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// Copyright 2014 BVLC and contributors.
#include <algorithm>
#include <cstring>
#include <vector>
#include "cuda_runtime.h"
#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_gradient_check_util.hpp"
#include "caffe/test/test_caffe_main.hpp"
using std::min;
namespace caffe {
extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
template <typename Dtype>
class StochasticPoolingLayerTest : public ::testing::Test {
protected:
StochasticPoolingLayerTest()
: blob_bottom_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()) {}
virtual void SetUp() {
Caffe::set_random_seed(1701);
blob_bottom_->Reshape(2, 3, 6, 5);
// fill the values
FillerParameter filler_param;
filler_param.set_min(0.1);
filler_param.set_max(1.);
UniformFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_);
}
virtual ~StochasticPoolingLayerTest() {
delete blob_bottom_; delete blob_top_;
}
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
typedef ::testing::Types<float, double> Dtypes;
TYPED_TEST_CASE(StochasticPoolingLayerTest, Dtypes);
TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
PoolingLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
EXPECT_EQ(this->blob_top_->height(), 3);
EXPECT_EQ(this->blob_top_->width(), 2);
}
TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
Caffe::set_mode(Caffe::GPU);
Caffe::set_phase(Caffe::TRAIN);
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
layer_param.set_pool(LayerParameter_PoolMethod_STOCHASTIC);
PoolingLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
// Check if the output is correct - it should do random sampling
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
const TypeParam* top_data = this->blob_top_->cpu_data();
TypeParam total = 0;
for (int n = 0; n < this->blob_top_->num(); ++n) {
for (int c = 0; c < this->blob_top_->channels(); ++c) {
for (int ph = 0; ph < this->blob_top_->height(); ++ph) {
for (int pw = 0; pw < this->blob_top_->width(); ++pw) {
TypeParam pooled = top_data[this->blob_top_->offset(n, c, ph, pw)];
total += pooled;
int hstart = ph * 2;
int hend = min(hstart + 3, this->blob_bottom_->height());
int wstart = pw * 2;
int wend = min(wstart + 3, this->blob_bottom_->width());
bool has_equal = false;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
has_equal |= (pooled == bottom_data[this->blob_bottom_->
offset(n, c, h, w)]);
}
}
EXPECT_TRUE(has_equal);
}
}
}
}
// When we are doing stochastic pooling, the average we get should be higher
// than the simple data average since we are weighting more on higher-valued
// ones.
EXPECT_GE(total / this->blob_top_->count(), 0.55);
}
TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
Caffe::set_mode(Caffe::GPU);
Caffe::set_phase(Caffe::TEST);
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
layer_param.set_pool(LayerParameter_PoolMethod_STOCHASTIC);
PoolingLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
// Check if the output is correct - it should do random sampling
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
const TypeParam* top_data = this->blob_top_->cpu_data();
for (int n = 0; n < this->blob_top_->num(); ++n) {
for (int c = 0; c < this->blob_top_->channels(); ++c) {
for (int ph = 0; ph < this->blob_top_->height(); ++ph) {
for (int pw = 0; pw < this->blob_top_->width(); ++pw) {
TypeParam pooled = top_data[this->blob_top_->offset(n, c, ph, pw)];
int hstart = ph * 2;
int hend = min(hstart + 3, this->blob_bottom_->height());
int wstart = pw * 2;
int wend = min(wstart + 3, this->blob_bottom_->width());
bool smaller_than_max = false;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
smaller_than_max |= (pooled <= bottom_data[this->blob_bottom_->
offset(n, c, h, w)]);
}
}
EXPECT_TRUE(smaller_than_max);
}
}
}
}
}
TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
Caffe::set_mode(Caffe::GPU);
Caffe::set_phase(Caffe::TRAIN);
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
layer_param.set_pool(LayerParameter_PoolMethod_STOCHASTIC);
PoolingLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-4, 1e-2);
// it is too expensive to call curand multiple times, so we don't do an
// exhaustive gradient check.
checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
&(this->blob_top_vec_));
}
} // namespace caffe
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