#include <cstring>

#include <vector>

#include "gtest/gtest.h"

#include "caffe/blob.hpp"

#include "caffe/common.hpp"

#include "caffe/filler.hpp"

#include "caffe/layers/rnn_layer.hpp"

#include "caffe/test/test_caffe_main.hpp"

#include "caffe/test/test_gradient_check_util.hpp"

namespace caffe {

template <typename TypeParam>

class RNNLayerTest : public MultiDeviceTest<TypeParam> {

typedef typename TypeParam::Dtype Dtype;

protected:

RNNLayerTest() : num_output_(7) {

blob_bottom_vec_.push_back(&blob_bottom_);

blob_bottom_vec_.push_back(&blob_bottom_cont_);

blob_top_vec_.push_back(&blob_top_);

ReshapeBlobs(1, 3);

layer_param_.mutable_recurrent_param()->set_num_output(num_output_);

FillerParameter* weight_filler =

layer_param_.mutable_recurrent_param()->mutable_weight_filler();

weight_filler->set_type("gaussian");

weight_filler->set_std(0.2);

FillerParameter* bias_filler =

layer_param_.mutable_recurrent_param()->mutable_bias_filler();

bias_filler->set_type("gaussian");

bias_filler->set_std(0.1);

layer_param_.set_phase(TEST);

}

void ReshapeBlobs(int num_timesteps, int num_instances) {

blob_bottom_.Reshape(num_timesteps, num_instances, 3, 2);

blob_bottom_static_.Reshape(num_instances, 2, 3, 4);

vector<int> shape(2);

shape[0] = num_timesteps;

shape[1] = num_instances;

blob_bottom_cont_.Reshape(shape);

FillerParameter filler_param;

filler_param.set_min(-1);

filler_param.set_max(1);

UniformFiller<Dtype> filler(filler_param);

filler.Fill(&blob_bottom_);

}

int num_output_;

LayerParameter layer_param_;

Blob<Dtype> blob_bottom_;

Blob<Dtype> blob_bottom_cont_;

Blob<Dtype> blob_bottom_static_;

Blob<Dtype> blob_top_;

vector<Blob<Dtype>*> blob_bottom_vec_;

vector<Blob<Dtype>*> blob_top_vec_;

};

TYPED_TEST_CASE(RNNLayerTest, TestDtypesAndDevices);

TYPED_TEST(RNNLayerTest, TestSetUp) {

typedef typename TypeParam::Dtype Dtype;

RNNLayer<Dtype> layer(this->layer_param_);

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

vector<int> expected_top_shape = this->blob_bottom_.shape();

expected_top_shape.resize(3);

expected_top_shape[2] = this->num_output_;

EXPECT_TRUE(this->blob_top_.shape() == expected_top_shape);

}

TYPED_TEST(RNNLayerTest, TestForward) {

typedef typename TypeParam::Dtype Dtype;

const int kNumTimesteps = 3;

const int num = this->blob_bottom_.shape(1);

this->ReshapeBlobs(kNumTimesteps, num);

// Fill the cont blob with <0, 1, 1, ..., 1>,

// indicating a sequence that begins at the first timestep

// then continues for the rest of the sequence.

for (int t = 0; t < kNumTimesteps; ++t) {

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

this->blob_bottom_cont_.mutable_cpu_data()[t * num + n] = t > 0;

}

}

// Process the full sequence in a single batch.

FillerParameter filler_param;

filler_param.set_mean(0);

filler_param.set_std(1);

GaussianFiller<Dtype> sequence_filler(filler_param);

sequence_filler.Fill(&this->blob_bottom_);

shared_ptr<RNNLayer<Dtype> > layer(new RNNLayer<Dtype>(this->layer_param_));

Caffe::set_random_seed(1701);

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

LOG(INFO) << "Calling forward for full sequence RNN";

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

// Copy the inputs and outputs to reuse/check them later.

Blob<Dtype> bottom_copy(this->blob_bottom_.shape());

bottom_copy.CopyFrom(this->blob_bottom_);

Blob<Dtype> top_copy(this->blob_top_.shape());

top_copy.CopyFrom(this->blob_top_);

// Process the batch one timestep at a time;

// check that we get the same result.

this->ReshapeBlobs(1, num);

layer.reset(new RNNLayer<Dtype>(this->layer_param_));

Caffe::set_random_seed(1701);

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

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

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

const Dtype kEpsilon = 1e-5;

for (int t = 0; t < kNumTimesteps; ++t) {

caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,

this->blob_bottom_.mutable_cpu_data());

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

this->blob_bottom_cont_.mutable_cpu_data()[n] = t > 0;

}

LOG(INFO) << "Calling forward for RNN timestep " << t;

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

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

ASSERT_LT(t * top_count + i, top_copy.count());

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

top_copy.cpu_data()[t * top_count + i], kEpsilon)

<< "t = " << t << "; i = " << i;

}

}

// Process the batch one timestep at a time with all cont blobs set to 0.

// Check that we get a different result, except in the first timestep.

Caffe::set_random_seed(1701);

layer.reset(new RNNLayer<Dtype>(this->layer_param_));

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

for (int t = 0; t < kNumTimesteps; ++t) {

caffe_copy(bottom_count, bottom_copy.cpu_data() + t * bottom_count,

this->blob_bottom_.mutable_cpu_data());

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

this->blob_bottom_cont_.mutable_cpu_data()[n] = 0;

}

LOG(INFO) << "Calling forward for RNN timestep " << t;

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

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

if (t == 0) {

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

top_copy.cpu_data()[t * top_count + i], kEpsilon)

<< "t = " << t << "; i = " << i;

} else {

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

top_copy.cpu_data()[t * top_count + i])

<< "t = " << t << "; i = " << i;

}

}

}

}

TYPED_TEST(RNNLayerTest, TestGradient) {

typedef typename TypeParam::Dtype Dtype;

RNNLayer<Dtype> layer(this->layer_param_);

GradientChecker<Dtype> checker(1e-2, 1e-3);

checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

this->blob_top_vec_, 0);

}

TYPED_TEST(RNNLayerTest, TestGradientNonZeroCont) {

typedef typename TypeParam::Dtype Dtype;

RNNLayer<Dtype> layer(this->layer_param_);

GradientChecker<Dtype> checker(1e-2, 1e-3);

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

this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;

}

checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

this->blob_top_vec_, 0);

}

TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2) {

typedef typename TypeParam::Dtype Dtype;

this->ReshapeBlobs(2, 2);

// fill the values

FillerParameter filler_param;

UniformFiller<Dtype> filler(filler_param);

filler.Fill(&this->blob_bottom_);

RNNLayer<Dtype> layer(this->layer_param_);

GradientChecker<Dtype> checker(1e-2, 1e-3);

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

this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;

}

checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

this->blob_top_vec_, 0);

}

TYPED_TEST(RNNLayerTest, TestGradientNonZeroContBufferSize2WithStaticInput) {

typedef typename TypeParam::Dtype Dtype;

this->ReshapeBlobs(2, 2);

FillerParameter filler_param;

UniformFiller<Dtype> filler(filler_param);

filler.Fill(&this->blob_bottom_);

filler.Fill(&this->blob_bottom_static_);

this->blob_bottom_vec_.push_back(&this->blob_bottom_static_);

RNNLayer<Dtype> layer(this->layer_param_);

GradientChecker<Dtype> checker(1e-2, 1e-3);

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

this->blob_bottom_cont_.mutable_cpu_data()[i] = i > 2;

}

checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

this->blob_top_vec_, 0);

checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,

this->blob_top_vec_, 2);

}

} // namespace caffe