CaffeOnACL/unit_tests/test_neuron_layer.cpp at master · dlyshare/CaffeOnACL · GitHub

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#include <algorithm>

#include <vector>

#include <cmath>

#include "google/protobuf/text_format.h"

#include "gtest/gtest.h"

#include "caffe/blob.hpp"

#include "caffe/common.hpp"

#include "caffe/filler.hpp"

#include "caffe/layers/absval_layer.hpp"

#include "caffe/layers/bnll_layer.hpp"

#include "caffe/layers/dropout_layer.hpp"

#include "caffe/layers/elu_layer.hpp"

#include "caffe/layers/exp_layer.hpp"

#include "caffe/layers/inner_product_layer.hpp"

#include "caffe/layers/log_layer.hpp"

#include "caffe/layers/power_layer.hpp"

#include "caffe/layers/prelu_layer.hpp"

#include "caffe/layers/relu_layer.hpp"

#include "caffe/layers/sigmoid_layer.hpp"

#include "caffe/layers/tanh_layer.hpp"

#include "caffe/layers/threshold_layer.hpp"

#ifdef USE_CUDNN

#include "caffe/layers/cudnn_relu_layer.hpp"

#include "caffe/layers/cudnn_sigmoid_layer.hpp"

#include "caffe/layers/cudnn_tanh_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 NeuronLayerTest : public MultiDeviceTest<TypeParam> {

typedef typename TypeParam::Dtype Dtype;

protected:

NeuronLayerTest()

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

blob_top_(new Blob<Dtype>()) {

Caffe::set_random_seed(1701);

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

Blob<Dtype>* const blob_bottom_;

Blob<Dtype>* const blob_top_;

vector<Blob<Dtype>*> blob_bottom_vec_;

vector<Blob<Dtype>*> blob_top_vec_;

void TestPReLU(PReLULayer<Dtype> *layer) {

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

// Now, check values

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

int hw = this->blob_bottom_->height() * this->blob_bottom_->width();

int channels = this->blob_bottom_->channels();

bool channel_shared = layer->layer_param().prelu_param().channel_shared();

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

int c = channel_shared ? 0 : (i / hw) % channels;

EXPECT_EQ(top_data[i],

std::max(bottom_data[i], (Dtype)(0))

+ slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));

}

};

TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);

TYPED_TEST(NeuronLayerTest, TestAbsVal) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

SET_LAYER(AbsVal);

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

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

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

EXPECT_EQ(top_data[i], fabs(bottom_data[i]));

}

TYPED_TEST(NeuronLayerTest, TestReLU) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

SET_LAYER(ReLU);

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

// Now, check values

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

EXPECT_GE(top_data[i], 0.);

EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);

}

#if 1

TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

CHECK(google::protobuf::TextFormat::ParseFromString(

"relu_param { negative_slope: 0.01 }", &layer_param));

SET_LAYER(ReLU);

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

// Now, check values

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

if (top_data[i] >= 0) {

EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);

} else {

EXPECT_FLOAT_EQ(top_data[i], bottom_data[i] * 0.01);

}

TYPED_TEST(NeuronLayerTest, TestSigmoid) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

SET_LAYER(Sigmoid);

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

// Now, check values

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));

// check that we squashed the value between 0 and 1

EXPECT_GE(top_data[i], 0.);

EXPECT_LE(top_data[i], 1.);

}

TYPED_TEST(NeuronLayerTest, TestTanH) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

int number=10;

this->blob_bottom_->Reshape(1,2,number,2);

for(int i=0;i<number;i++)

this->blob_bottom_->mutable_cpu_data()[i]=i*10;

SET_LAYER(TanH);

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

// Test exact values

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

for (int j = 0; j < this->blob_bottom_->channels(); ++j) {

for (int k = 0; k < this->blob_bottom_->height(); ++k) {

for (int l = 0; l < this->blob_bottom_->width(); ++l) {

EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,

(exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /

(exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));

EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,

(exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /

(exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));

}

TYPED_TEST(NeuronLayerTest, TestBNLL) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

SET_LAYER(BNLL);

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

// Now, check values

const Dtype* bottom_data = this->blob_bottom_->cpu_data();

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

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

Dtype target=log(1+exp(bottom_data[i]));

EXPECT_NEAR(top_data[i], target,1e-4);

}

#endif

#if 0 /* Not try PReLU now */

TYPED_TEST(NeuronLayerTest, TestPReLUParam) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

PReLULayer<Dtype> layer(layer_param);

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

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

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

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

EXPECT_EQ(*slopes, 0.25);

}

TYPED_TEST(NeuronLayerTest, TestPReLUForward) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

PReLULayer<Dtype> layer(layer_param);

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

FillerParameter filler_param;

GaussianFiller<Dtype> filler(filler_param);

filler.Fill(layer.blobs()[0].get());

this->TestPReLU(&layer);

}

TYPED_TEST(NeuronLayerTest, TestPReLUForwardChannelShared) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter layer_param;

layer_param.mutable_prelu_param()->set_channel_shared(true);

PReLULayer<Dtype> layer(layer_param);

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

this->TestPReLU(&layer);

}

TYPED_TEST(NeuronLayerTest, TestPReLUConsistencyReLU) {

typedef typename TypeParam::Dtype Dtype;

LayerParameter prelu_layer_param;

LayerParameter relu_layer_param;

relu_layer_param.mutable_relu_param()->set_negative_slope(0.25);

PReLULayer<Dtype> prelu(prelu_layer_param);

ReLULayer<Dtype> relu(relu_layer_param);

// Set up blobs

vector<Blob<Dtype>*> blob_bottom_vec_2;

vector<Blob<Dtype>*> blob_top_vec_2;

shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());

shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());

blob_bottom_vec_2.push_back(blob_bottom_2.get());

blob_top_vec_2.push_back(blob_top_2.get());

blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);

// SetUp layers

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

relu.SetUp(blob_bottom_vec_2, blob_top_vec_2);

// Check forward

prelu.Forward(this->blob_bottom_vec_, this->blob_top_vec_);

relu.Forward(this->blob_bottom_vec_, blob_top_vec_2);

for (int s = 0; s < blob_top_2->count(); ++s) {

EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);

}

// Check backward

}

TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {

typedef typename TypeParam::Dtype Dtype;

// Set layer parameters

LayerParameter ip_layer_param;

LayerParameter prelu_layer_param;

InnerProductParameter *ip_param =

ip_layer_param.mutable_inner_product_param();

ip_param->mutable_weight_filler()->set_type("gaussian");

ip_param->set_num_output(3);

InnerProductLayer<Dtype> ip(ip_layer_param);

PReLULayer<Dtype> prelu(prelu_layer_param);

InnerProductLayer<Dtype> ip2(ip_layer_param);

PReLULayer<Dtype> prelu2(prelu_layer_param);

// Set up blobs

vector<Blob<Dtype>*> blob_bottom_vec_2;

vector<Blob<Dtype>*> blob_middle_vec_2;

vector<Blob<Dtype>*> blob_top_vec_2;

shared_ptr<Blob<Dtype> > blob_bottom_2(new Blob<Dtype>());

shared_ptr<Blob<Dtype> > blob_middle_2(new Blob<Dtype>());

shared_ptr<Blob<Dtype> > blob_top_2(new Blob<Dtype>());

blob_bottom_vec_2.push_back(blob_bottom_2.get());

blob_middle_vec_2.push_back(blob_middle_2.get());

blob_top_vec_2.push_back(blob_top_2.get());

blob_bottom_2->CopyFrom(*this->blob_bottom_, false, true);

// SetUp layers

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

prelu.SetUp(this->blob_top_vec_, this->blob_top_vec_);

ip2.SetUp(blob_bottom_vec_2, blob_middle_vec_2);

prelu2.SetUp(blob_middle_vec_2, blob_top_vec_2);

caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),

ip2.blobs()[0]->mutable_cpu_data());

// Forward in-place

ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);

prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);

// Forward non-in-place

ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);

prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);

// Check numbers

for (int s = 0; s < blob_top_2->count(); ++s) {

EXPECT_EQ(this->blob_top_->cpu_data()[s], blob_top_2->cpu_data()[s]);

}

// Fill top diff with random numbers

shared_ptr<Blob<Dtype> > tmp_blob(new Blob<Dtype>());

tmp_blob->ReshapeLike(*blob_top_2.get());

FillerParameter filler_param;

GaussianFiller<Dtype> filler(filler_param);

filler.Fill(tmp_blob.get());

caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),

this->blob_top_->mutable_cpu_diff());

caffe_copy(blob_top_2->count(), tmp_blob->cpu_data(),

blob_top_2->mutable_cpu_diff());

// Backward in-place

vector<bool> propagate_down;

propagate_down.push_back(true);

prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_top_vec_);

ip.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);

// Backward non-in-place

prelu2.Backward(blob_top_vec_2, propagate_down, blob_middle_vec_2);

ip2.Backward(blob_middle_vec_2, propagate_down, blob_bottom_vec_2);

// Check numbers

for (int s = 0; s < blob_bottom_2->count(); ++s) {

EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);

}

for (int s = 0; s < ip.blobs()[0]->count(); ++s) {

EXPECT_EQ(ip.blobs()[0]->cpu_diff()[s], ip2.blobs()[0]->cpu_diff()[s]);

}

for (int s = 0; s < ip.blobs()[1]->count(); ++s) {

EXPECT_EQ(ip.blobs()[1]->cpu_diff()[s], ip2.blobs()[1]->cpu_diff()[s]);

}

for (int s = 0; s < prelu.blobs()[0]->count(); ++s) {

EXPECT_EQ(prelu.blobs()[0]->cpu_diff()[s],

prelu2.blobs()[0]->cpu_diff()[s]);

}

#endif

} // namespace caffe

CaffeOnACL/unit_tests/test_neuron_layer.cpp at master · dlyshare/CaffeOnACL · GitHub

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