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test_pooling_layer.cpp
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1182 lines (1146 loc) · 49.5 KB
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#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"
namespace caffe {
template <typename TypeParam>
class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
typedef typename TypeParam::Dtype Dtype;
protected:
PoolingLayerTest()
: blob_bottom_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()),
blob_top_mask_(new Blob<Dtype>()) {}
virtual void SetUp() {
Caffe::set_random_seed(1701);
blob_bottom_->Reshape(2, 3, 6, 5);
// 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 ~PoolingLayerTest() {
delete blob_bottom_;
delete blob_top_;
delete blob_top_mask_;
}
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
Blob<Dtype>* const blob_top_mask_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
// Test for 2x 2 square pooling layer
void TestForwardSquare() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 3, 5);
// Input: 2x 2 channels of:
// [1 2 5 2 3]
// [9 4 1 4 8]
// [1 2 5 2 3]
for (int i = 0; i < 15 * num * channels; i += 15) {
blob_bottom_->mutable_cpu_data()[i + 0] = 1;
blob_bottom_->mutable_cpu_data()[i + 1] = 2;
blob_bottom_->mutable_cpu_data()[i + 2] = 5;
blob_bottom_->mutable_cpu_data()[i + 3] = 2;
blob_bottom_->mutable_cpu_data()[i + 4] = 3;
blob_bottom_->mutable_cpu_data()[i + 5] = 9;
blob_bottom_->mutable_cpu_data()[i + 6] = 4;
blob_bottom_->mutable_cpu_data()[i + 7] = 1;
blob_bottom_->mutable_cpu_data()[i + 8] = 4;
blob_bottom_->mutable_cpu_data()[i + 9] = 8;
blob_bottom_->mutable_cpu_data()[i + 10] = 1;
blob_bottom_->mutable_cpu_data()[i + 11] = 2;
blob_bottom_->mutable_cpu_data()[i + 12] = 5;
blob_bottom_->mutable_cpu_data()[i + 13] = 2;
blob_bottom_->mutable_cpu_data()[i + 14] = 3;
}
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 2);
EXPECT_EQ(blob_top_->width(), 4);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 2);
EXPECT_EQ(blob_top_mask_->width(), 4);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [9 5 5 8]
// [9 5 5 8]
for (int i = 0; i < 8 * num * channels; i += 8) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 9);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 8);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 9);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 8);
}
if (blob_top_vec_.size() > 1) {
// Expected mask output: 2x 2 channels of:
// [5 2 2 9]
// [5 12 12 9]
for (int i = 0; i < 8 * num * channels; i += 8) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 5);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 2);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 2);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 9);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 5);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 9);
}
}
}
// Test for 3x 2 rectangular pooling layer with kernel_h > kernel_w
void TestForwardRectHigh() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(3);
pooling_param->set_kernel_w(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 6, 6);
// Input: 2x 2 channels of:
// [35 1 6 26 19 24]
// [ 3 32 7 21 23 25]
// [31 9 2 22 27 20]
// [ 8 28 33 17 10 15]
// [30 5 34 12 14 16]
// [ 4 36 29 13 18 11]
// (this is generated by magic(6) in MATLAB)
for (int i = 0; i < 36 * num * channels; i += 36) {
blob_bottom_->mutable_cpu_data()[i + 0] = 35;
blob_bottom_->mutable_cpu_data()[i + 1] = 1;
blob_bottom_->mutable_cpu_data()[i + 2] = 6;
blob_bottom_->mutable_cpu_data()[i + 3] = 26;
blob_bottom_->mutable_cpu_data()[i + 4] = 19;
blob_bottom_->mutable_cpu_data()[i + 5] = 24;
blob_bottom_->mutable_cpu_data()[i + 6] = 3;
blob_bottom_->mutable_cpu_data()[i + 7] = 32;
blob_bottom_->mutable_cpu_data()[i + 8] = 7;
blob_bottom_->mutable_cpu_data()[i + 9] = 21;
blob_bottom_->mutable_cpu_data()[i + 10] = 23;
blob_bottom_->mutable_cpu_data()[i + 11] = 25;
blob_bottom_->mutable_cpu_data()[i + 12] = 31;
blob_bottom_->mutable_cpu_data()[i + 13] = 9;
blob_bottom_->mutable_cpu_data()[i + 14] = 2;
blob_bottom_->mutable_cpu_data()[i + 15] = 22;
blob_bottom_->mutable_cpu_data()[i + 16] = 27;
blob_bottom_->mutable_cpu_data()[i + 17] = 20;
blob_bottom_->mutable_cpu_data()[i + 18] = 8;
blob_bottom_->mutable_cpu_data()[i + 19] = 28;
blob_bottom_->mutable_cpu_data()[i + 20] = 33;
blob_bottom_->mutable_cpu_data()[i + 21] = 17;
blob_bottom_->mutable_cpu_data()[i + 22] = 10;
blob_bottom_->mutable_cpu_data()[i + 23] = 15;
blob_bottom_->mutable_cpu_data()[i + 24] = 30;
blob_bottom_->mutable_cpu_data()[i + 25] = 5;
blob_bottom_->mutable_cpu_data()[i + 26] = 34;
blob_bottom_->mutable_cpu_data()[i + 27] = 12;
blob_bottom_->mutable_cpu_data()[i + 28] = 14;
blob_bottom_->mutable_cpu_data()[i + 29] = 16;
blob_bottom_->mutable_cpu_data()[i + 30] = 4;
blob_bottom_->mutable_cpu_data()[i + 31] = 36;
blob_bottom_->mutable_cpu_data()[i + 32] = 29;
blob_bottom_->mutable_cpu_data()[i + 33] = 13;
blob_bottom_->mutable_cpu_data()[i + 34] = 18;
blob_bottom_->mutable_cpu_data()[i + 35] = 11;
}
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 4);
EXPECT_EQ(blob_top_->width(), 5);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 4);
EXPECT_EQ(blob_top_mask_->width(), 5);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [35 32 26 27 27]
// [32 33 33 27 27]
// [31 34 34 27 27]
// [36 36 34 18 18]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 35);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 8], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 9], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 10], 31);
EXPECT_EQ(blob_top_->cpu_data()[i + 11], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 13], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 14], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 15], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 17], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 18], 18);
EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
}
if (blob_top_vec_.size() > 1) {
// [ 1 8 4 17 17]
// [ 8 21 21 17 17]
// [13 27 27 17 17]
// [32 32 27 35 35]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 0);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 8], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 9], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 34);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
}
}
}
// Test for rectangular pooling layer with kernel_w > kernel_h
void TestForwardRectWide() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(2);
pooling_param->set_kernel_w(3);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 6, 6);
// Input: 2x 2 channels of:
// [35 1 6 26 19 24]
// [ 3 32 7 21 23 25]
// [31 9 2 22 27 20]
// [ 8 28 33 17 10 15]
// [30 5 34 12 14 16]
// [ 4 36 29 13 18 11]
// (this is generated by magic(6) in MATLAB)
for (int i = 0; i < 36 * num * channels; i += 36) {
blob_bottom_->mutable_cpu_data()[i + 0] = 35;
blob_bottom_->mutable_cpu_data()[i + 1] = 1;
blob_bottom_->mutable_cpu_data()[i + 2] = 6;
blob_bottom_->mutable_cpu_data()[i + 3] = 26;
blob_bottom_->mutable_cpu_data()[i + 4] = 19;
blob_bottom_->mutable_cpu_data()[i + 5] = 24;
blob_bottom_->mutable_cpu_data()[i + 6] = 3;
blob_bottom_->mutable_cpu_data()[i + 7] = 32;
blob_bottom_->mutable_cpu_data()[i + 8] = 7;
blob_bottom_->mutable_cpu_data()[i + 9] = 21;
blob_bottom_->mutable_cpu_data()[i + 10] = 23;
blob_bottom_->mutable_cpu_data()[i + 11] = 25;
blob_bottom_->mutable_cpu_data()[i + 12] = 31;
blob_bottom_->mutable_cpu_data()[i + 13] = 9;
blob_bottom_->mutable_cpu_data()[i + 14] = 2;
blob_bottom_->mutable_cpu_data()[i + 15] = 22;
blob_bottom_->mutable_cpu_data()[i + 16] = 27;
blob_bottom_->mutable_cpu_data()[i + 17] = 20;
blob_bottom_->mutable_cpu_data()[i + 18] = 8;
blob_bottom_->mutable_cpu_data()[i + 19] = 28;
blob_bottom_->mutable_cpu_data()[i + 20] = 33;
blob_bottom_->mutable_cpu_data()[i + 21] = 17;
blob_bottom_->mutable_cpu_data()[i + 22] = 10;
blob_bottom_->mutable_cpu_data()[i + 23] = 15;
blob_bottom_->mutable_cpu_data()[i + 24] = 30;
blob_bottom_->mutable_cpu_data()[i + 25] = 5;
blob_bottom_->mutable_cpu_data()[i + 26] = 34;
blob_bottom_->mutable_cpu_data()[i + 27] = 12;
blob_bottom_->mutable_cpu_data()[i + 28] = 14;
blob_bottom_->mutable_cpu_data()[i + 29] = 16;
blob_bottom_->mutable_cpu_data()[i + 30] = 4;
blob_bottom_->mutable_cpu_data()[i + 31] = 36;
blob_bottom_->mutable_cpu_data()[i + 32] = 29;
blob_bottom_->mutable_cpu_data()[i + 33] = 13;
blob_bottom_->mutable_cpu_data()[i + 34] = 18;
blob_bottom_->mutable_cpu_data()[i + 35] = 11;
}
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 5);
EXPECT_EQ(blob_top_->width(), 4);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 5);
EXPECT_EQ(blob_top_mask_->width(), 4);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [35 32 26 26]
// [32 32 27 27]
// [33 33 33 27]
// [34 34 34 17]
// [36 36 34 18]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 35);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 8], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 9], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 10], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 11], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 13], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 14], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 15], 17);
EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 17], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 18], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
}
if (blob_top_vec_.size() > 1) {
// [ 1 8 4 4]
// [ 8 8 17 17]
// [21 21 21 17]
// [27 27 27 22]
// [32 32 27 35]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 0);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 8], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 9], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 21);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
}
}
}
};
TYPED_TEST_CASE(PoolingLayerTest, TestDtypesAndDevices);
TYPED_TEST(PoolingLayerTest, 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> 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(PoolingLayerTest, TestSetupPadded) {
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);
pooling_param->set_pad(1);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
PoolingLayer<Dtype> 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(), 4);
EXPECT_EQ(this->blob_top_->width(), 3);
}
TYPED_TEST(PoolingLayerTest, TestSetupGlobalPooling) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_global_pooling(true);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
PoolingLayer<Dtype> 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(), 1);
EXPECT_EQ(this->blob_top_->width(), 1);
}
/*
TYPED_TEST(PoolingLayerTest, PrintBackward) {
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
layer_param.set_pool(LayerParameter_PoolMethod_MAX);
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_);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
cout << "bottom data " << i << " " << this->blob_bottom_->cpu_data()[i] << endl;
}
for (int i = 0; i < this->blob_top_->count(); ++i) {
cout << "top data " << i << " " << this->blob_top_->cpu_data()[i] << endl;
}
for (int i = 0; i < this->blob_top_->count(); ++i) {
this->blob_top_->mutable_cpu_diff()[i] = i;
}
layer.Backward(this->blob_top_vec_, true, this->blob_bottom_vec_);
for (int i = 0; i < this->blob_bottom_->count(); ++i) {
cout << "bottom diff " << i << " " << this->blob_bottom_->cpu_diff()[i] << endl;
}
}
*/
TYPED_TEST(PoolingLayerTest, TestForwardMax) {
this->TestForwardSquare();
this->TestForwardRectHigh();
this->TestForwardRectWide();
}
TYPED_TEST(PoolingLayerTest, TestForwardMaxTopMask) {
this->blob_top_vec_.push_back(this->blob_top_mask_);
this->TestForwardSquare();
this->TestForwardRectHigh();
this->TestForwardRectWide();
}
TYPED_TEST(PoolingLayerTest, TestGradientMax) {
typedef typename TypeParam::Dtype Dtype;
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(kernel_h);
pooling_param->set_kernel_w(kernel_w);
pooling_param->set_stride(2);
pooling_param->set_pad(1);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
PoolingLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-4, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
}
}
TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
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);
pooling_param->set_pad(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
this->blob_bottom_->Reshape(1, 1, 3, 3);
// Input:
// [ 1 2 4 ]
// [ 2 3 2 ]
// [ 4 2 1 ]
this->blob_bottom_->mutable_cpu_data()[0] = 1;
this->blob_bottom_->mutable_cpu_data()[1] = 2;
this->blob_bottom_->mutable_cpu_data()[2] = 4;
this->blob_bottom_->mutable_cpu_data()[3] = 2;
this->blob_bottom_->mutable_cpu_data()[4] = 3;
this->blob_bottom_->mutable_cpu_data()[5] = 2;
this->blob_bottom_->mutable_cpu_data()[6] = 4;
this->blob_bottom_->mutable_cpu_data()[7] = 2;
this->blob_bottom_->mutable_cpu_data()[8] = 1;
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_->num(), 1);
EXPECT_EQ(this->blob_top_->channels(), 1);
EXPECT_EQ(this->blob_top_->height(), 3);
EXPECT_EQ(this->blob_top_->width(), 3);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
Dtype epsilon = 1e-8;
// Output:
// [ 1 4 4 ]
// [ 4 4 4 ]
// [ 4 4 1 ]
EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
}
TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
typedef typename TypeParam::Dtype Dtype;
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(kernel_h);
pooling_param->set_kernel_w(kernel_w);
pooling_param->set_stride(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
this->blob_top_vec_.push_back(this->blob_top_mask_);
PoolingLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-4, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
this->blob_top_vec_.pop_back();
}
}
}
TYPED_TEST(PoolingLayerTest, TestForwardAve) {
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(1);
pooling_param->set_pad(1);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
this->blob_bottom_->Reshape(1, 1, 3, 3);
FillerParameter filler_param;
filler_param.set_value(Dtype(2));
ConstantFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
PoolingLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(this->blob_top_->num(), 1);
EXPECT_EQ(this->blob_top_->channels(), 1);
EXPECT_EQ(this->blob_top_->height(), 3);
EXPECT_EQ(this->blob_top_->width(), 3);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
Dtype epsilon = 1e-5;
EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0 , epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
}
TYPED_TEST(PoolingLayerTest, TestGradientAve) {
typedef typename TypeParam::Dtype Dtype;
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(kernel_h);
pooling_param->set_kernel_w(kernel_w);
pooling_param->set_stride(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
PoolingLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
}
}
TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
typedef typename TypeParam::Dtype Dtype;
for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(kernel_h);
pooling_param->set_kernel_w(kernel_w);
pooling_param->set_stride(2);
pooling_param->set_pad(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
PoolingLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
}
}
#ifdef USE_CUDNN
template <typename Dtype>
class CuDNNPoolingLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNPoolingLayerTest()
: blob_bottom_(new Blob<Dtype>()),
blob_top_(new Blob<Dtype>()),
blob_top_mask_(new Blob<Dtype>()) {}
virtual void SetUp() {
Caffe::set_random_seed(1701);
blob_bottom_->Reshape(2, 3, 6, 5);
// 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 ~CuDNNPoolingLayerTest() {
delete blob_bottom_;
delete blob_top_;
delete blob_top_mask_;
}
Blob<Dtype>* const blob_bottom_;
Blob<Dtype>* const blob_top_;
Blob<Dtype>* const blob_top_mask_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
// Test for 2x 2 square pooling layer
void TestForwardSquare() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 3, 5);
// Input: 2x 2 channels of:
// [1 2 5 2 3]
// [9 4 1 4 8]
// [1 2 5 2 3]
for (int i = 0; i < 15 * num * channels; i += 15) {
blob_bottom_->mutable_cpu_data()[i + 0] = 1;
blob_bottom_->mutable_cpu_data()[i + 1] = 2;
blob_bottom_->mutable_cpu_data()[i + 2] = 5;
blob_bottom_->mutable_cpu_data()[i + 3] = 2;
blob_bottom_->mutable_cpu_data()[i + 4] = 3;
blob_bottom_->mutable_cpu_data()[i + 5] = 9;
blob_bottom_->mutable_cpu_data()[i + 6] = 4;
blob_bottom_->mutable_cpu_data()[i + 7] = 1;
blob_bottom_->mutable_cpu_data()[i + 8] = 4;
blob_bottom_->mutable_cpu_data()[i + 9] = 8;
blob_bottom_->mutable_cpu_data()[i + 10] = 1;
blob_bottom_->mutable_cpu_data()[i + 11] = 2;
blob_bottom_->mutable_cpu_data()[i + 12] = 5;
blob_bottom_->mutable_cpu_data()[i + 13] = 2;
blob_bottom_->mutable_cpu_data()[i + 14] = 3;
}
CuDNNPoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 2);
EXPECT_EQ(blob_top_->width(), 4);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 2);
EXPECT_EQ(blob_top_mask_->width(), 4);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [9 5 5 8]
// [9 5 5 8]
for (int i = 0; i < 8 * num * channels; i += 8) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 9);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 8);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 9);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 5);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 8);
}
if (blob_top_vec_.size() > 1) {
// Expected mask output: 2x 2 channels of:
// [5 2 2 9]
// [5 12 12 9]
for (int i = 0; i < 8 * num * channels; i += 8) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 5);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 2);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 2);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 9);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 5);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 9);
}
}
}
// Test for 3x 2 rectangular pooling layer with kernel_h > kernel_w
void TestForwardRectHigh() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(3);
pooling_param->set_kernel_w(2);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 6, 6);
// Input: 2x 2 channels of:
// [35 1 6 26 19 24]
// [ 3 32 7 21 23 25]
// [31 9 2 22 27 20]
// [ 8 28 33 17 10 15]
// [30 5 34 12 14 16]
// [ 4 36 29 13 18 11]
// (this is generated by magic(6) in MATLAB)
for (int i = 0; i < 36 * num * channels; i += 36) {
blob_bottom_->mutable_cpu_data()[i + 0] = 35;
blob_bottom_->mutable_cpu_data()[i + 1] = 1;
blob_bottom_->mutable_cpu_data()[i + 2] = 6;
blob_bottom_->mutable_cpu_data()[i + 3] = 26;
blob_bottom_->mutable_cpu_data()[i + 4] = 19;
blob_bottom_->mutable_cpu_data()[i + 5] = 24;
blob_bottom_->mutable_cpu_data()[i + 6] = 3;
blob_bottom_->mutable_cpu_data()[i + 7] = 32;
blob_bottom_->mutable_cpu_data()[i + 8] = 7;
blob_bottom_->mutable_cpu_data()[i + 9] = 21;
blob_bottom_->mutable_cpu_data()[i + 10] = 23;
blob_bottom_->mutable_cpu_data()[i + 11] = 25;
blob_bottom_->mutable_cpu_data()[i + 12] = 31;
blob_bottom_->mutable_cpu_data()[i + 13] = 9;
blob_bottom_->mutable_cpu_data()[i + 14] = 2;
blob_bottom_->mutable_cpu_data()[i + 15] = 22;
blob_bottom_->mutable_cpu_data()[i + 16] = 27;
blob_bottom_->mutable_cpu_data()[i + 17] = 20;
blob_bottom_->mutable_cpu_data()[i + 18] = 8;
blob_bottom_->mutable_cpu_data()[i + 19] = 28;
blob_bottom_->mutable_cpu_data()[i + 20] = 33;
blob_bottom_->mutable_cpu_data()[i + 21] = 17;
blob_bottom_->mutable_cpu_data()[i + 22] = 10;
blob_bottom_->mutable_cpu_data()[i + 23] = 15;
blob_bottom_->mutable_cpu_data()[i + 24] = 30;
blob_bottom_->mutable_cpu_data()[i + 25] = 5;
blob_bottom_->mutable_cpu_data()[i + 26] = 34;
blob_bottom_->mutable_cpu_data()[i + 27] = 12;
blob_bottom_->mutable_cpu_data()[i + 28] = 14;
blob_bottom_->mutable_cpu_data()[i + 29] = 16;
blob_bottom_->mutable_cpu_data()[i + 30] = 4;
blob_bottom_->mutable_cpu_data()[i + 31] = 36;
blob_bottom_->mutable_cpu_data()[i + 32] = 29;
blob_bottom_->mutable_cpu_data()[i + 33] = 13;
blob_bottom_->mutable_cpu_data()[i + 34] = 18;
blob_bottom_->mutable_cpu_data()[i + 35] = 11;
}
CuDNNPoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 4);
EXPECT_EQ(blob_top_->width(), 5);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 4);
EXPECT_EQ(blob_top_mask_->width(), 5);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [35 32 26 27 27]
// [32 33 33 27 27]
// [31 34 34 27 27]
// [36 36 34 18 18]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 35);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 8], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 9], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 10], 31);
EXPECT_EQ(blob_top_->cpu_data()[i + 11], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 13], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 14], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 15], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 17], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 18], 18);
EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
}
if (blob_top_vec_.size() > 1) {
// [ 1 8 4 17 17]
// [ 8 21 21 17 17]
// [13 27 27 17 17]
// [32 32 27 35 35]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 0);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 8], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 9], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 12);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 34);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
}
}
}
// Test for rectangular pooling layer with kernel_w > kernel_h
void TestForwardRectWide() {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_h(2);
pooling_param->set_kernel_w(3);
pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
const int num = 2;
const int channels = 2;
blob_bottom_->Reshape(num, channels, 6, 6);
// Input: 2x 2 channels of:
// [35 1 6 26 19 24]
// [ 3 32 7 21 23 25]
// [31 9 2 22 27 20]
// [ 8 28 33 17 10 15]
// [30 5 34 12 14 16]
// [ 4 36 29 13 18 11]
// (this is generated by magic(6) in MATLAB)
for (int i = 0; i < 36 * num * channels; i += 36) {
blob_bottom_->mutable_cpu_data()[i + 0] = 35;
blob_bottom_->mutable_cpu_data()[i + 1] = 1;
blob_bottom_->mutable_cpu_data()[i + 2] = 6;
blob_bottom_->mutable_cpu_data()[i + 3] = 26;
blob_bottom_->mutable_cpu_data()[i + 4] = 19;
blob_bottom_->mutable_cpu_data()[i + 5] = 24;
blob_bottom_->mutable_cpu_data()[i + 6] = 3;
blob_bottom_->mutable_cpu_data()[i + 7] = 32;
blob_bottom_->mutable_cpu_data()[i + 8] = 7;
blob_bottom_->mutable_cpu_data()[i + 9] = 21;
blob_bottom_->mutable_cpu_data()[i + 10] = 23;
blob_bottom_->mutable_cpu_data()[i + 11] = 25;
blob_bottom_->mutable_cpu_data()[i + 12] = 31;
blob_bottom_->mutable_cpu_data()[i + 13] = 9;
blob_bottom_->mutable_cpu_data()[i + 14] = 2;
blob_bottom_->mutable_cpu_data()[i + 15] = 22;
blob_bottom_->mutable_cpu_data()[i + 16] = 27;
blob_bottom_->mutable_cpu_data()[i + 17] = 20;
blob_bottom_->mutable_cpu_data()[i + 18] = 8;
blob_bottom_->mutable_cpu_data()[i + 19] = 28;
blob_bottom_->mutable_cpu_data()[i + 20] = 33;
blob_bottom_->mutable_cpu_data()[i + 21] = 17;
blob_bottom_->mutable_cpu_data()[i + 22] = 10;
blob_bottom_->mutable_cpu_data()[i + 23] = 15;
blob_bottom_->mutable_cpu_data()[i + 24] = 30;
blob_bottom_->mutable_cpu_data()[i + 25] = 5;
blob_bottom_->mutable_cpu_data()[i + 26] = 34;
blob_bottom_->mutable_cpu_data()[i + 27] = 12;
blob_bottom_->mutable_cpu_data()[i + 28] = 14;
blob_bottom_->mutable_cpu_data()[i + 29] = 16;
blob_bottom_->mutable_cpu_data()[i + 30] = 4;
blob_bottom_->mutable_cpu_data()[i + 31] = 36;
blob_bottom_->mutable_cpu_data()[i + 32] = 29;
blob_bottom_->mutable_cpu_data()[i + 33] = 13;
blob_bottom_->mutable_cpu_data()[i + 34] = 18;
blob_bottom_->mutable_cpu_data()[i + 35] = 11;
}
CuDNNPoolingLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
EXPECT_EQ(blob_top_->num(), num);
EXPECT_EQ(blob_top_->channels(), channels);
EXPECT_EQ(blob_top_->height(), 5);
EXPECT_EQ(blob_top_->width(), 4);
if (blob_top_vec_.size() > 1) {
EXPECT_EQ(blob_top_mask_->num(), num);
EXPECT_EQ(blob_top_mask_->channels(), channels);
EXPECT_EQ(blob_top_mask_->height(), 5);
EXPECT_EQ(blob_top_mask_->width(), 4);
}
layer.Forward(blob_bottom_vec_, blob_top_vec_);
// Expected output: 2x 2 channels of:
// [35 32 26 26]
// [32 32 27 27]
// [33 33 33 27]
// [34 34 34 17]
// [36 36 34 18]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_->cpu_data()[i + 0], 35);
EXPECT_EQ(blob_top_->cpu_data()[i + 1], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 2], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 3], 26);
EXPECT_EQ(blob_top_->cpu_data()[i + 4], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 5], 32);
EXPECT_EQ(blob_top_->cpu_data()[i + 6], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 7], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 8], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 9], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 10], 33);
EXPECT_EQ(blob_top_->cpu_data()[i + 11], 27);
EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 13], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 14], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 15], 17);
EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 17], 36);
EXPECT_EQ(blob_top_->cpu_data()[i + 18], 34);
EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
}
if (blob_top_vec_.size() > 1) {
// [ 1 8 4 4]
// [ 8 8 17 17]
// [21 21 21 17]
// [27 27 27 22]
// [32 32 27 35]
for (int i = 0; i < 20 * num * channels; i += 20) {
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0], 0);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3], 3);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 7);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 8], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 9], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 20);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 16);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 21);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 31);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 26);
EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
}
}
}
};
TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
pooling_param->set_stride(2);
CuDNNPoolingLayer<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(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
LayerParameter layer_param;
PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
pooling_param->set_kernel_size(3);
pooling_param->set_stride(2);
pooling_param->set_pad(1);
pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
CuDNNPoolingLayer<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(), 4);
EXPECT_EQ(this->blob_top_->width(), 3);
}
/*
TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
LayerParameter layer_param;
layer_param.set_kernelsize(3);
layer_param.set_stride(2);
layer_param.set_pool(LayerParameter_PoolMethod_MAX);
CuDNNPoolingLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);