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#include <cstring>
#include <limits>
#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"
namespace caffe {
template <typename TypeParam>
class FilterLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
FilterLayerTest()
: blob_bottom_data_(new Blob<Dtype>(4, 3, 6, 4)),
blob_bottom_labels_(new Blob<Dtype>(4, 1, 1, 1)),
blob_bottom_selector_(new Blob<Dtype>(4, 1, 1, 1)),
blob_top_data_(new Blob<Dtype>()),
blob_top_labels_(new Blob<Dtype>()) {}
virtual void SetUp() {
// fill the values
Caffe::set_random_seed(1890);
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
// fill the selector blob
Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data();
bottom_data_selector_[0] = 0;
bottom_data_selector_[1] = 1;
bottom_data_selector_[2] = 1;
bottom_data_selector_[3] = 0;
// fill the other bottom blobs
filler.Fill(blob_bottom_data_);
for (int i = 0; i < blob_bottom_labels_->count(); ++i) {
blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
}
blob_bottom_vec_.push_back(blob_bottom_data_);
blob_bottom_vec_.push_back(blob_bottom_labels_);
blob_bottom_vec_.push_back(blob_bottom_selector_);
blob_top_vec_.push_back(blob_top_data_);
blob_top_vec_.push_back(blob_top_labels_);
}
virtual ~FilterLayerTest() {
delete blob_bottom_data_;
delete blob_bottom_labels_;
delete blob_bottom_selector_;
delete blob_top_data_;
delete blob_top_labels_;
}
Blob<Dtype>* const blob_bottom_data_;
Blob<Dtype>* const blob_bottom_labels_;
Blob<Dtype>* const blob_bottom_selector_;
// blobs for the top of FilterLayer
Blob<Dtype>* const blob_top_data_;
Blob<Dtype>* const blob_top_labels_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices);
TYPED_TEST(FilterLayerTest, TestReshape) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
FilterLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
// In the test first and last items should have been filtered
// so we just expect 2 remaining items
EXPECT_EQ(this->blob_top_data_->shape(0), 2);
EXPECT_EQ(this->blob_top_labels_->shape(0), 2);
EXPECT_GT(this->blob_bottom_data_->shape(0),
this->blob_top_data_->shape(0));
EXPECT_GT(this->blob_bottom_labels_->shape(0),
this->blob_top_labels_->shape(0));
for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) {
EXPECT_EQ(this->blob_bottom_labels_->shape(i),
this->blob_top_labels_->shape(i));
}
}
TYPED_TEST(FilterLayerTest, TestForward) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
FilterLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0),
this->blob_bottom_labels_->data_at(1, 0, 0, 0));
EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0),
this->blob_bottom_labels_->data_at(2, 0, 0, 0));
int dim = this->blob_top_data_->count() /
this->blob_top_data_->shape(0);
const Dtype* top_data = this->blob_top_data_->cpu_data();
const Dtype* bottom_data = this->blob_bottom_data_->cpu_data();
// selector is 0 1 1 0, so we need to compare bottom(1,c,h,w)
// with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w)
bottom_data += dim; // bottom(1,c,h,w)
for (size_t n = 0; n < dim; n++)
EXPECT_EQ(top_data[n], bottom_data[n]);
bottom_data += dim; // bottom(2,c,h,w)
top_data += dim; // top(1,c,h,w)
for (size_t n = 0; n < dim; n++)
EXPECT_EQ(top_data[n], bottom_data[n]);
}
TYPED_TEST(FilterLayerTest, TestGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
FilterLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
// check only input 0 (data) because labels and selector
// don't need backpropagation
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_, 0);
}
} // namespace caffe
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