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// Copyright 2014 BVLC and contributors.
#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"
namespace caffe {
template <typename TypeParam>
class MaxPoolingDropoutTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
MaxPoolingDropoutTest()
: blob_bottom_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()) {}
virtual void SetUp() {
Caffe::set_random_seed(1703);
blob_bottom_->Reshape(2, 3, 6, 5);
// fill the values
FillerParameter filler_param;
filler_param.set_value(1.);
ConstantFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
blob_bottom_vec_.push_back(blob_bottom_);
blob_top_vec_.push_back(blob_top_);
}
virtual ~MaxPoolingDropoutTest() { 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_;
};
TYPED_TEST_CASE(MaxPoolingDropoutTest, TestDtypesAndDevices);
TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
pooling_param->set_stride(2);
PoolingLayer<Dtype> max_layer(layer_param);
max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
DropoutLayer<Dtype> dropout_layer(layer_param);
dropout_layer.SetUp(this->blob_top_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(MaxPoolingDropoutTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
pooling_param->set_stride(2);
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
const Dtype* top_data = this->blob_top_->cpu_data();
Dtype sum = 0.;
for (int i = 0; i < this->blob_top_->count(); ++i) {
sum += top_data[i];
}
EXPECT_EQ(sum, this->blob_top_->count());
// Dropout in-place
DropoutLayer<Dtype> dropout_layer(layer_param);
dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
sum = 0.;
Dtype scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
top_data = this->blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
sum += top_data[i];
}
EXPECT_GE(sum, 0);
EXPECT_LE(sum, this->blob_top_->count()*scale);
}
TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
typedef typename TypeParam::Dtype Dtype;
Caffe::set_phase(Caffe::TRAIN);
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
pooling_param->set_stride(2);
PoolingLayer<Dtype> layer(layer_param);
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_));
const Dtype* bottom_diff = this->blob_bottom_->cpu_diff();
Dtype sum = 0.;
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
sum += bottom_diff[i];
}
EXPECT_EQ(sum, this->blob_top_->count());
// Dropout in-place
DropoutLayer<Dtype> dropout_layer(layer_param);
dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
dropout_layer.Backward(this->blob_top_vec_, propagate_down,
&(this->blob_top_vec_));
layer.Backward(this->blob_top_vec_, propagate_down,
&(this->blob_bottom_vec_));
Dtype sum_with_dropout = 0.;
bottom_diff = this->blob_bottom_->cpu_diff();
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
sum_with_dropout += bottom_diff[i];
}
EXPECT_GE(sum_with_dropout, sum);
}
} // namespace caffe
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