Caffe-HRT/unit_tests/test_inner_product_layer.cpp at master · OAID/Caffe-HRT · 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/inner_product_layer.hpp"

#include "caffe/test/test_caffe_main.hpp"

#include "caffe/test/test_gradient_check_util.hpp"

namespace caffe {

#ifndef CPU_ONLY

extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;

#endif

template <typename TypeParam>

class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {

typedef typename TypeParam::Dtype Dtype;

protected:

InnerProductLayerTest()

: blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),

blob_bottom_nobatch_(new Blob<Dtype>(1, 2, 3, 4)),

blob_top_(new Blob<Dtype>()) {

// fill the values

FillerParameter filler_param;

UniformFiller<Dtype> filler(filler_param);

filler.Fill(this->blob_bottom_);

blob_top_vec_.push_back(blob_top_);

}

virtual ~InnerProductLayerTest() {

delete blob_bottom_;

delete blob_bottom_nobatch_;

delete blob_top_;

}

Blob<Dtype>* const blob_bottom_;

Blob<Dtype>* const blob_bottom_nobatch_;

Blob<Dtype>* const blob_top_;

vector<Blob<Dtype>*> blob_bottom_vec_;

vector<Blob<Dtype>*> blob_top_vec_;

};

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

#define TestDtypesAndDevices float_only

TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices);

TYPED_TEST(InnerProductLayerTest, TestSetUp) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_);

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

boost::static_pointer_cast<InnerProductLayer<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_->height(), 1);

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

EXPECT_EQ(this->blob_top_->channels(), 10);

}

/** @brief TestSetUp while toggling transpose flag

TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeFalse) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_);

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

inner_product_param->set_transpose(false);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

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

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

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

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

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

EXPECT_EQ(10, this->blob_top_->channels());

EXPECT_EQ(2, layer->blobs()[0]->num_axes());

EXPECT_EQ(10, layer->blobs()[0]->shape(0));

EXPECT_EQ(60, layer->blobs()[0]->shape(1));

}

/** @brief TestSetUp while toggling transpose flag

TYPED_TEST(InnerProductLayerTest, TestSetUpTransposeTrue) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_);

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

inner_product_param->set_transpose(true);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

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

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

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

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

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

EXPECT_EQ(10, this->blob_top_->channels());

EXPECT_EQ(2, layer->blobs()[0]->num_axes());

EXPECT_EQ(60, layer->blobs()[0]->shape(0));

EXPECT_EQ(10, layer->blobs()[0]->shape(1));

}

TYPED_TEST(InnerProductLayerTest, TestForward) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_);

bool IS_VALID_CUDA = false;

#ifndef CPU_ONLY

IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;

#endif

if (Caffe::mode() == Caffe::CPU ||

sizeof(Dtype) == 4 || IS_VALID_CUDA) {

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

inner_product_param->mutable_weight_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_min(1);

inner_product_param->mutable_bias_filler()->set_max(2);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

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

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

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

const Dtype* data = this->blob_top_->cpu_data();

const int count = this->blob_top_->count();

for (int i = 0; i < count; ++i) {

EXPECT_GE(data[i], 1.);

}

} else {

LOG(ERROR) << "Skipping test due to old architecture.";

}

/**

* @brief Init. an IP layer without transpose + random weights,

* run Forward, save the result.

* Init. another IP layer with transpose.

* manually copy and transpose the weights from the first IP layer,

* then run Forward on the same input and check that the result is the same

TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_);

bool IS_VALID_CUDA = false;

#ifndef CPU_ONLY

IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;

#endif

if (Caffe::mode() == Caffe::CPU ||

sizeof(Dtype) == 4 || IS_VALID_CUDA) {

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

inner_product_param->mutable_weight_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_min(1);

inner_product_param->mutable_bias_filler()->set_max(2);

inner_product_param->set_transpose(false);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

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

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

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

const int count = this->blob_top_->count();

Blob<Dtype>* const top = new Blob<Dtype>();

top->ReshapeLike(*this->blob_top_);

caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data());

this->blob_top_vec_.clear();

this->blob_top_vec_.push_back(new Blob<Dtype>());

inner_product_param->set_transpose(true);

shared_ptr<InnerProductLayer<Dtype> > ip_t(

new InnerProductLayer<Dtype>(layer_param));

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

const int count_w = layer->blobs()[0]->count();

EXPECT_EQ(count_w, ip_t->blobs()[0]->count());

// manually copy and transpose the weights from 1st IP layer into 2nd

const Dtype* w = layer->blobs()[0]->cpu_data();

Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();

const int width = layer->blobs()[0]->shape(1);

const int width_t = ip_t->blobs()[0]->shape(1);

for (int i = 0; i < count_w; ++i) {

int r = i / width;

int c = i % width;

w_t[c*width_t+r] = w[r*width+c]; // copy while transposing

}

// copy bias from 1st IP layer to 2nd IP layer

ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());

caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),

ip_t->blobs()[1]->mutable_cpu_data());

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

EXPECT_EQ(count, this->blob_top_->count())

<< "Invalid count for top blob for IP with transpose.";

Blob<Dtype>* const top_t = new Blob<Dtype>();\

top_t->ReshapeLike(*this->blob_top_vec_[0]);

caffe_copy(count,

this->blob_top_vec_[0]->cpu_data(),

top_t->mutable_cpu_data());

const Dtype* data = top->cpu_data();

const Dtype* data_t = top_t->cpu_data();

for (int i = 0; i < count; ++i) {

EXPECT_FLOAT_EQ(data[i], data_t[i]);

}

} else {

LOG(ERROR) << "Skipping test due to old architecture.";

}

TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {

typedef typename TypeParam::Dtype Dtype;

this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);

bool IS_VALID_CUDA = false;

#ifndef CPU_ONLY

IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;

#endif

if (Caffe::mode() == Caffe::CPU ||

sizeof(Dtype) == 4 || IS_VALID_CUDA) {

LayerParameter layer_param;

InnerProductParameter* inner_product_param =

layer_param.mutable_inner_product_param();

inner_product_param->set_num_output(10);

inner_product_param->mutable_weight_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_type("uniform");

inner_product_param->mutable_bias_filler()->set_min(1);

inner_product_param->mutable_bias_filler()->set_max(2);

layer_param.set_type("InnerProduct");

shared_ptr<Layer<Dtype> > new_layer=

LayerRegistry<Dtype>::CreateLayer(layer_param);

shared_ptr<InnerProductLayer<Dtype> > layer=

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

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

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

const Dtype* data = this->blob_top_->cpu_data();

const int count = this->blob_top_->count();

for (int i = 0; i < count; ++i) {

EXPECT_GE(data[i], 1.);

}

} else {

LOG(ERROR) << "Skipping test due to old architecture.";

}

} // namespace caffe

Caffe-HRT/unit_tests/test_inner_product_layer.cpp at master · OAID/Caffe-HRT · GitHub

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