CaffeOnACL/unit_tests/test_pooling_layer.cpp at master · mirsys/CaffeOnACL · GitHub

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#include <vector>

#include "gtest/gtest.h"

#include "caffe/blob.hpp"

#include "caffe/common.hpp"

#include "caffe/filler.hpp"

#include "caffe/layers/pooling_layer.hpp"

#ifdef USE_CUDNN

#include "caffe/layers/cudnn_pooling_layer.hpp"

#endif

#include "caffe/test/test_caffe_main.hpp"

#include "caffe/test/test_gradient_check_util.hpp"

namespace caffe {

typedef ::testing::Types<CPUDevice<float> > float_only;

#define TestDtypesAndDevices float_only

#define SET_LAYER(name) \

layer_param.set_type(#name);\

shared_ptr<Layer<Dtype> > new_layer=\

LayerRegistry<Dtype>::CreateLayer(layer_param);\

shared_ptr< name ## Layer <Dtype> > layer= \

boost::static_pointer_cast< name ## Layer <Dtype> > (new_layer);\

if(0) layer=shared_ptr<name ## Layer<Dtype> >(new name ## Layer<Dtype>(layer_param));\

layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);

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;

}

SET_LAYER(Pooling);

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]

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;

}

SET_LAYER(Pooling);

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;

}

SET_LAYER(Pooling);

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);

SET_LAYER(Pooling);

//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_);

SET_LAYER(Pooling);

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_);

SET_LAYER(Pooling);

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, 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, 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;

SET_LAYER(Pooling);

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);

}

template <typename Dtype>

void fill_bottom_data(Blob<Dtype >* bottom)

{

for(int i=0;i<bottom->num();i++)

for(int j=0;j<bottom->channels();j++)

for(int l=0;l<bottom->height();l++)

for(int k=0;k<bottom->width();k++)

{

int offset;

Dtype * ptr;

offset=i*bottom->channels()*bottom->height()*bottom->width()+

j*bottom->height()*bottom->width()+

l*bottom->width()+k;

ptr=bottom->mutable_cpu_data();

ptr[offset]=offset;

}

template <typename Dtype>

void check_top_data(Blob<Dtype>* bottom, Blob<Dtype>* top, int kernel_size, int stride)

{

Dtype epsilon = 1e-5;

int error_count=0;

for(int i=0;i<top->num();i++)

for(int j=0;j<top->channels();j++)

for(int l=0;l<top->height();l++)

for(int k=0;k<top->width();k++)

{

Dtype pool_data=top->data_at(i,j,l,k);

Dtype max_bottom_data=-100000000;

int top_h=stride*l;

int top_w=stride*k;

/* calculate max **/

for(int x=0;x<kernel_size;x++)

for(int y=0;y<kernel_size;y++)

{

Dtype d=bottom->data_at(i,j,top_h+x,top_w+y);

if(d>max_bottom_data)

max_bottom_data=d;

}

Dtype offset=pool_data-max_bottom_data;

if(offset>epsilon || offset<-epsilon)

{

std::cout<<error_count<<": ";

std::cout<<i<<" "<<j<<" "<<l<<" "<<k<<" bottom: ";

std::cout<<top_h<<" "<<top_w<<std::endl;

std::cout<<"pooled: "<<pool_data<<" bottom:"<<max_bottom_data<<std::endl;

error_count++;

}

if(error_count==10)

return ;

}

TYPED_TEST(PoolingLayerTest, TestMax) {

typedef typename TypeParam::Dtype Dtype;

int kernel_size=3;

int stride=2;

int channel_number=96;

int h=55;

int w=55;

LayerParameter layer_param;

PoolingParameter* pooling_param = layer_param.mutable_pooling_param();

pooling_param->set_kernel_size(kernel_size);

pooling_param->set_stride(stride);

pooling_param->set_pad(0);

pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);

this->blob_bottom_->Reshape(1, channel_number, h, w);

FillerParameter filler_param;

filler_param.set_value(Dtype(2));

GaussianFiller<Dtype> filler(filler_param);

filler.Fill(this->blob_bottom_);

SET_LAYER(Pooling);

//fill_bottom_data(this->blob_bottom_);

#if 0

EXPECT_EQ(this->blob_top_->num(), 1);

EXPECT_EQ(this->blob_top_->channels(), 96);

EXPECT_EQ(this->blob_top_->height(), 27);

EXPECT_EQ(this->blob_top_->width(), 27);

#endif

layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);

check_top_data(this->blob_bottom_,this->blob_top_,kernel_size,stride);

}

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);

}

} // namespace caffe

CaffeOnACL/unit_tests/test_pooling_layer.cpp at master · mirsys/CaffeOnACL · GitHub

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