CaffeOnACL/unit_tests/test_lrn_layer.cpp at master · rootkit/CaffeOnACL · GitHub

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#include <algorithm>

#include <vector>

#include <iostream>

#include "gtest/gtest.h"

#include "caffe/blob.hpp"

#include "caffe/common.hpp"

#include "caffe/filler.hpp"

#include "caffe/layers/lrn_layer.hpp"

#ifdef USE_CUDNN

#include "caffe/layers/cudnn_lcn_layer.hpp"

#include "caffe/layers/cudnn_lrn_layer.hpp"

#endif

#include "caffe/test/test_caffe_main.hpp"

#include "caffe/test/test_gradient_check_util.hpp"

using std::min;

using std::max;

int test_h=5;

int test_w=5;

namespace caffe {

template <typename Dtype>

static void dump_blob(const Blob<Dtype> * blob, const char * outfile)

{

std::ofstream os;

os.open(outfile);

for(int i=0;i<blob->shape(0);i++)

for(int j=0;j<blob->shape(1);j++)

for(int k=0;k<blob->shape(2);k++)

for(int l=0;l<blob->shape(3);l++)

{

Dtype data=blob->data_at(i,j,k,l);

os<<data<<std::endl;

}

os.close();

}

template <typename Dtype>

static void fill_blob_data(Blob<Dtype >* bottom, int fixed, float val)

{

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();

if(fixed)

ptr[offset]=val;

else

ptr[offset]=offset;

}

template <typename TypeParam>

class LRNLayerTest : public MultiDeviceTest<TypeParam> {

typedef typename TypeParam::Dtype Dtype;

protected:

LRNLayerTest()

: epsilon_(Dtype(1e-5)),

blob_bottom_(new Blob<Dtype>()),

blob_top_(new Blob<Dtype>()) {}

virtual void SetUp() {

Caffe::set_random_seed(1701);

blob_bottom_->Reshape(2, 7, test_h,test_w);

// 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 ~LRNLayerTest() { delete blob_bottom_; delete blob_top_; }

void ReferenceLRNForward(const Blob<Dtype>& blob_bottom,

const LayerParameter& layer_param, Blob<Dtype>* blob_top);

Dtype epsilon_;

Blob<Dtype>* const blob_bottom_;

Blob<Dtype>* const blob_top_;

vector<Blob<Dtype>*> blob_bottom_vec_;

vector<Blob<Dtype>*> blob_top_vec_;

};

template <typename TypeParam>

void LRNLayerTest<TypeParam>::ReferenceLRNForward(

const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,

Blob<Dtype>* blob_top) {

typedef typename TypeParam::Dtype Dtype;

blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),

blob_bottom.height(), blob_bottom.width());

Dtype* top_data = blob_top->mutable_cpu_data();

LRNParameter lrn_param = layer_param.lrn_param();

Dtype alpha = lrn_param.alpha();

Dtype beta = lrn_param.beta();

int size = lrn_param.local_size();

switch (lrn_param.norm_region()) {

case LRNParameter_NormRegion_ACROSS_CHANNELS:

for (int n = 0; n < blob_bottom.num(); ++n) {

for (int c = 0; c < blob_bottom.channels(); ++c) {

for (int h = 0; h < blob_bottom.height(); ++h) {

for (int w = 0; w < blob_bottom.width(); ++w) {

int c_start = c - (size - 1) / 2;

int c_end = min(c_start + size, blob_bottom.channels());

c_start = max(c_start, 0);

Dtype scale = 1.;

for (int i = c_start; i < c_end; ++i) {

Dtype value = blob_bottom.data_at(n, i, h, w);

scale += value * value * alpha / size;

}

*(top_data + blob_top->offset(n, c, h, w)) =

blob_bottom.data_at(n, c, h, w) / pow(scale, beta);

}

break;

case LRNParameter_NormRegion_WITHIN_CHANNEL:

for (int n = 0; n < blob_bottom.num(); ++n) {

for (int c = 0; c < blob_bottom.channels(); ++c) {

for (int h = 0; h < blob_bottom.height(); ++h) {

int h_start = h - (size - 1) / 2;

int h_end = min(h_start + size, blob_bottom.height());

h_start = max(h_start, 0);

for (int w = 0; w < blob_bottom.width(); ++w) {

Dtype scale = 1.;

int w_start = w - (size - 1) / 2;

int w_end = min(w_start + size, blob_bottom.width());

w_start = max(w_start, 0);

// std::cout<<"h,w ("<<h<<","<<w<<"): ";

// std::cout<<"box: ( h "<<h_start<<","<<h_end<<")";

// std::cout<<" (w "<<w_start<<","<<w_end<<")"<<std::endl;

for (int nh = h_start; nh < h_end; ++nh) {

for (int nw = w_start; nw < w_end; ++nw) {

Dtype value = blob_bottom.data_at(n, c, nh, nw);

scale += value * value * alpha / (size * size);

}

*(top_data + blob_top->offset(n, c, h, w)) =

blob_bottom.data_at(n, c, h, w) / pow(scale, beta);

}

break;

default:

LOG(FATAL) << "Unknown normalization region.";

}

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

#define TestDtypesAndDevices float_only

TYPED_TEST_CASE(LRNLayerTest, TestDtypesAndDevices);

#if 1

TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

LRNLayer<Dtype> layer(layer_param);

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

EXPECT_EQ(this->blob_top_->num(), 2);

EXPECT_EQ(this->blob_top_->channels(), 7);

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

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

}

TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

// LRNLayer<Dtype> layer(layer_param);

layer_param.mutable_lrn_param()->set_local_size(3);

layer_param.set_type("LRN");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<LRNLayer<Dtype> > layer=

boost::static_pointer_cast<LRNLayer<Dtype> > (new_layer);

vector<int> bottom_shape;

bottom_shape.push_back(1);

bottom_shape.push_back(5);

this->blob_bottom_vec_[0]->Reshape(bottom_shape);

fill_blob_data(this->blob_bottom_,1,1);

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

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

Blob<Dtype> top_reference;

this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,

&top_reference);

for (int i = 0; i < this->blob_bottom_->count(); ++i) {

EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],

this->epsilon_);

}

TYPED_TEST(LRNLayerTest, TestForwardAcrossChannelsLargeRegion) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

layer_param.mutable_lrn_param()->set_local_size(15);

layer_param.set_type("LRN");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<LRNLayer<Dtype> > layer=

boost::static_pointer_cast<LRNLayer<Dtype> > (new_layer);

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

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

Blob<Dtype> top_reference;

this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,

&top_reference);

for (int i = 0; i < this->blob_bottom_->count(); ++i) {

EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],

this->epsilon_);

}

TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

layer_param.mutable_lrn_param()->set_norm_region(

LRNParameter_NormRegion_WITHIN_CHANNEL);

layer_param.mutable_lrn_param()->set_local_size(3);

layer_param.set_type("LRN");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<LRNLayer<Dtype> > layer=

boost::static_pointer_cast<LRNLayer<Dtype> > (new_layer);

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

EXPECT_EQ(this->blob_top_->num(), 2);

EXPECT_EQ(this->blob_top_->channels(), 7);

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

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

}

#endif

#if 1

TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

layer_param.mutable_lrn_param()->set_norm_region(

LRNParameter_NormRegion_WITHIN_CHANNEL);

layer_param.mutable_lrn_param()->set_local_size(3);

// layer_param.mutable_lrn_param()->set_beta(1);

layer_param.set_type("LRN");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<LRNLayer<Dtype> > layer=

boost::static_pointer_cast<LRNLayer<Dtype> > (new_layer);

/* presetting bottom_vec and data */

vector<int> bottom_shape;

bottom_shape.push_back(1);

bottom_shape.push_back(5);

this->blob_bottom_vec_[0]->Reshape(bottom_shape);

fill_blob_data(this->blob_bottom_,1,1);

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

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

Blob<Dtype> top_reference;

this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,

&top_reference);

// for (int i = 0; i < this->blob_bottom_->count(); ++i) {

// EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],

// this->epsilon_);

// }

dump_blob(this->blob_bottom_,"lrn.bottom.data");

dump_blob(this->blob_top_,"lrn.top.data");

dump_blob(&top_reference,"lrn.reftop.data");

}

#endif

} // namespace caffe

CaffeOnACL/unit_tests/test_lrn_layer.cpp at master · rootkit/CaffeOnACL · GitHub

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