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test_neuron_layer.cpp
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842 lines (773 loc) · 30.4 KB
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#include <algorithm>
#include <cstring>
#include <vector>
#include "google/protobuf/text_format.h"
#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 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 TestDropoutForward(const float dropout_ratio) {
LayerParameter layer_param;
// Fill in the given dropout_ratio, unless it's 0.5, in which case we don't
// set it explicitly to test that 0.5 is the default.
if (dropout_ratio != 0.5) {
layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
}
DropoutLayer<Dtype> layer(layer_param);
layer_param.set_phase(TRAIN);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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();
float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
const int count = this->blob_bottom_->count();
// Initialize num_kept to count the number of inputs NOT dropped out.
int num_kept = 0;
for (int i = 0; i < count; ++i) {
if (top_data[i] != 0) {
++num_kept;
EXPECT_EQ(top_data[i], bottom_data[i] * scale);
}
}
const Dtype std_error = sqrt(dropout_ratio * (1 - dropout_ratio) / count);
// Fail if the number dropped was more than 1.96 * std_error away from the
// expected number -- requires 95% confidence that the dropout layer is not
// obeying the given dropout_ratio for test failure.
const Dtype empirical_dropout_ratio = 1 - num_kept / Dtype(count);
EXPECT_NEAR(empirical_dropout_ratio, dropout_ratio, 1.96 * std_error);
}
void TestExpForward(const float base, const float scale, const float shift) {
LayerParameter layer_param;
layer_param.mutable_exp_param()->set_base(base);
layer_param.mutable_exp_param()->set_scale(scale);
layer_param.mutable_exp_param()->set_shift(shift);
ExpLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
layer.Forward(blob_bottom_vec_, blob_top_vec_);
const Dtype kDelta = 2e-4;
const Dtype* bottom_data = blob_bottom_->cpu_data();
const Dtype* top_data = blob_top_->cpu_data();
for (int i = 0; i < blob_bottom_->count(); ++i) {
const Dtype bottom_val = bottom_data[i];
const Dtype top_val = top_data[i];
if (base == -1) {
EXPECT_NEAR(top_val, exp(shift + scale * bottom_val), kDelta);
} else {
EXPECT_NEAR(top_val, pow(base, shift + scale * bottom_val), kDelta);
}
}
}
void TestExpGradient(const float base, const float scale, const float shift) {
LayerParameter layer_param;
layer_param.mutable_exp_param()->set_base(base);
layer_param.mutable_exp_param()->set_scale(scale);
layer_param.mutable_exp_param()->set_shift(shift);
ExpLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, blob_bottom_vec_, 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)));
}
}
void LogBottomInit() {
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
filler.Fill(this->blob_bottom_);
Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data);
}
void TestLogForward(const float base, const float scale, const float shift) {
LogBottomInit();
LayerParameter layer_param;
layer_param.mutable_log_param()->set_base(base);
layer_param.mutable_log_param()->set_scale(scale);
layer_param.mutable_log_param()->set_shift(shift);
LogLayer<Dtype> layer(layer_param);
layer.SetUp(blob_bottom_vec_, blob_top_vec_);
layer.Forward(blob_bottom_vec_, blob_top_vec_);
const Dtype kDelta = 2e-4;
const Dtype* bottom_data = blob_bottom_->cpu_data();
const Dtype* top_data = blob_top_->cpu_data();
for (int i = 0; i < blob_bottom_->count(); ++i) {
const Dtype bottom_val = bottom_data[i];
const Dtype top_val = top_data[i];
if (base == -1) {
EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta);
} else {
EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base),
kDelta);
}
}
}
void TestLogGradient(const float base, const float scale, const float shift) {
LogBottomInit();
LayerParameter layer_param;
layer_param.mutable_log_param()->set_base(base);
layer_param.mutable_log_param()->set_scale(scale);
layer_param.mutable_log_param()->set_shift(shift);
LogLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-2);
checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
}
};
TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
TYPED_TEST(NeuronLayerTest, TestAbsVal) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
AbsValLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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, TestAbsGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
AbsValLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestReLU) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
ReLULayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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]);
}
}
TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
ReLULayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
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));
ReLULayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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, TestReLUGradientWithNegativeSlope) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
"relu_param { negative_slope: 0.01 }", &layer_param));
ReLULayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestSigmoid) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
SigmoidLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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, TestSigmoidGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
SigmoidLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestTanH) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
TanHLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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, TestTanHGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
TanHLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestExpLayer) {
typedef typename TypeParam::Dtype Dtype;
// Test default base of "-1" -- should actually set base := e.
const Dtype kBase = -1;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestExpForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpGradient) {
typedef typename TypeParam::Dtype Dtype;
// Test default base of "-1" -- should actually set base := e.
const Dtype kBase = -1;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestExpGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpLayerBase2) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestExpForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpGradientBase2) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestExpGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 1;
this->TestExpForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 1;
this->TestExpGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 0;
this->TestExpForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 0;
this->TestExpGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpLayerBase2Shift1Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 1;
this->TestExpForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 1;
this->TestExpGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogLayer) {
typedef typename TypeParam::Dtype Dtype;
// Test default base of "-1" -- should actually set base := e.
const Dtype kBase = -1;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestLogForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogGradient) {
typedef typename TypeParam::Dtype Dtype;
// Test default base of "-1" -- should actually set base := e.
const Dtype kBase = -1;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestLogGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestLogForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 0;
this->TestLogGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 1;
this->TestLogForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 1;
const Dtype kShift = 1;
this->TestLogGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 0;
this->TestLogForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 0;
this->TestLogGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 1;
this->TestLogForward(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) {
typedef typename TypeParam::Dtype Dtype;
const Dtype kBase = 2;
const Dtype kScale = 3;
const Dtype kShift = 1;
this->TestLogGradient(kBase, kScale, kShift);
}
TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
const float kDropoutRatio = 0.5;
this->TestDropoutForward(kDropoutRatio);
}
TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
const float kDropoutRatio = 0.75;
this->TestDropoutForward(kDropoutRatio);
}
TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.set_phase(TEST);
DropoutLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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_EQ(top_data[i], bottom_data[i]);
}
}
}
TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.set_phase(TRAIN);
DropoutLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
layer_param.set_phase(TEST);
DropoutLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestBNLL) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
BNLLLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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_GE(top_data[i], bottom_data[i]);
}
}
TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
BNLLLayer<Dtype> layer(layer_param);
GradientChecker<Dtype> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
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, TestPReLUGradient) {
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());
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(NeuronLayerTest, TestPReLUGradientChannelShared) {
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_);
GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
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
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());
vector<bool> propagate_down;
propagate_down.push_back(true);
prelu.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
relu.Backward(blob_top_vec_2, propagate_down, blob_bottom_vec_2);
for (int s = 0; s < blob_bottom_2->count(); ++s) {
EXPECT_EQ(this->blob_bottom_->cpu_diff()[s], blob_bottom_2->cpu_diff()[s]);
}
}
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]);
}
}
#ifdef USE_CUDNN
template <typename Dtype>
class CuDNNNeuronLayerTest : public GPUDeviceTest<Dtype> {
protected:
CuDNNNeuronLayerTest()
: 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 ~CuDNNNeuronLayerTest() { 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_;
};
TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
LayerParameter layer_param;
CuDNNReLULayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
const TypeParam* 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]);
}
}
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
LayerParameter layer_param;
CuDNNReLULayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
LayerParameter layer_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
"relu_param { negative_slope: 0.01 }", &layer_param));
CuDNNReLULayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
const TypeParam* 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(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
LayerParameter layer_param;
CHECK(google::protobuf::TextFormat::ParseFromString(
"relu_param { negative_slope: 0.01 }", &layer_param));
CuDNNReLULayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
LayerParameter layer_param;
CuDNNSigmoidLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Now, check values
const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
const TypeParam* 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(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
LayerParameter layer_param;
CuDNNSigmoidLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
LayerParameter layer_param;
CuDNNTanHLayer<TypeParam> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
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(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
LayerParameter layer_param;
CuDNNTanHLayer<TypeParam> layer(layer_param);
GradientChecker<TypeParam> checker(1e-2, 1e-3);
checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
this->blob_top_vec_);
}
#endif
} // namespace caffe