#include <vector>

#include "gtest/gtest.h"

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/layers/conv_layer.hpp"

#ifdef USE_CUDNN
#include "caffe/layers/cudnn_conv_layer.hpp"
#endif

#include "caffe/test/test_caffe_main.hpp"
#include "caffe/test/test_gradient_check_util.hpp"

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->LegacyShape(0);i++)
   {
     os<<"batch: "<<i<<std::endl;

     for(int j=0;j<blob->LegacyShape(1);j++)
     {
        os<<"channel: "<<j<<std::endl;

        for(int k=0;k<blob->LegacyShape(2);k++)
        {
            for(int l=0;l<blob->LegacyShape(3);l++)
            {
                Dtype data=blob->data_at(i,j,k,l);
                os<<" "<<data;
            }
            os<<std::endl;
        }
      os<<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;

        }


}


// Reference convolution for checking results:
// accumulate through explicit loops over input, output, and filters.
template <typename Dtype>
void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
    const vector<shared_ptr<Blob<Dtype> > >& weights,
    Blob<Dtype>* out) {
  const bool has_depth = (out->num_axes() == 5);
  if (!has_depth) { CHECK_EQ(4, out->num_axes()); }
  // Kernel size, stride, and pad
  int kernel_h, kernel_w;
  if (conv_param->has_kernel_h() || conv_param->has_kernel_w()) {
    kernel_h = conv_param->kernel_h();
    kernel_w = conv_param->kernel_w();
  } else {
    kernel_h = kernel_w = conv_param->kernel_size(0);
  }
  int pad_h, pad_w;
  if (conv_param->has_pad_h() || conv_param->has_pad_w()) {
    pad_h = conv_param->pad_h();
    pad_w = conv_param->pad_w();
  } else {
    pad_h = pad_w = conv_param->pad_size() ? conv_param->pad(0) : 0;
  }
  int stride_h, stride_w;
  if (conv_param->has_stride_h() || conv_param->has_stride_w()) {
    stride_h = conv_param->stride_h();
    stride_w = conv_param->stride_w();
  } else {
    stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1;
  }
  int dilation_h, dilation_w;
  dilation_h = dilation_w = conv_param->dilation_size() ?
                            conv_param->dilation(0) : 1;
  int kernel_d, pad_d, stride_d, dilation_d;
  if (has_depth) {
    kernel_d = kernel_h;
    stride_d = stride_h;
    pad_d = pad_h;
    dilation_d = dilation_h;
  } else {
    kernel_d = stride_d = dilation_d = 1;
    pad_d = 0;
  }
  // Groups
  int groups = conv_param->group();
  int o_g = out->shape(1) / groups;
  int k_g = in->shape(1) / groups;
  int o_head, k_head;
  // Convolution
  vector<int> weight_offset(4 + has_depth);
  vector<int> in_offset(4 + has_depth);
  vector<int> out_offset(4 + has_depth);
  Dtype* out_data = out->mutable_cpu_data();
  for (int n = 0; n < out->shape(0); n++) {
    for (int g = 0; g < groups; g++) {
      o_head = o_g * g;
      k_head = k_g * g;
      for (int o = 0; o < o_g; o++) {
        for (int k = 0; k < k_g; k++) {
          for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
            for (int y = 0; y < out->shape(2 + has_depth); y++) {
              for (int x = 0; x < out->shape(3 + has_depth); x++) {
                for (int r = 0; r < kernel_d; r++) {
                  for (int p = 0; p < kernel_h; p++) {
                    for (int q = 0; q < kernel_w; q++) {
                      int in_z = z * stride_d - pad_d + r * dilation_d;
                      int in_y = y * stride_h - pad_h + p * dilation_h;
                      int in_x = x * stride_w - pad_w + q * dilation_w;
                      if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1)
                          && in_y >= 0 && in_y < in->shape(2 + has_depth)
                          && in_x >= 0 && in_x < in->shape(3 + has_depth)) {
                        weight_offset[0] = o + o_head;
                        weight_offset[1] = k;
                        if (has_depth) { weight_offset[2] = r; }
                        weight_offset[2 + has_depth] = p;
                        weight_offset[3 + has_depth] = q;
                        in_offset[0] = n;
                        in_offset[1] = k + k_head;
                        if (has_depth) { in_offset[2] = in_z; }
                        in_offset[2 + has_depth] = in_y;
                        in_offset[3 + has_depth] = in_x;
                        out_offset[0] = n;
                        out_offset[1] = o + o_head;
                        if (has_depth) { out_offset[2] = z; }
                        out_offset[2 + has_depth] = y;
                        out_offset[3 + has_depth] = x;
                        out_data[out->offset(out_offset)] +=
                            in->data_at(in_offset)
                            * weights[0]->data_at(weight_offset);
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
  // Bias
  if (conv_param->bias_term()) {
    const Dtype* bias_data = weights[1]->cpu_data();
    for (int n = 0; n < out->shape(0); n++) {
      for (int o = 0; o < out->shape(1); o++) {
        for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
          for (int y = 0; y < out->shape(2 + has_depth); y++) {
            for (int x = 0; x < out->shape(3 + has_depth); x++) {
              out_offset[0] = n;
              out_offset[1] = o;
              if (has_depth) { out_offset[2] = z; }
              out_offset[2 + has_depth] = y;
              out_offset[3 + has_depth] = x;
              out_data[out->offset(out_offset)] += bias_data[o];
            }
          }
        }
      }
    }
  }
}

template void caffe_conv(const Blob<float>* in,
    ConvolutionParameter* conv_param,
    const vector<shared_ptr<Blob<float> > >& weights,
    Blob<float>* out);
template void caffe_conv(const Blob<double>* in,
    ConvolutionParameter* conv_param,
    const vector<shared_ptr<Blob<double> > >& weights,
    Blob<double>* out);

template <typename TypeParam>
class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
  typedef typename TypeParam::Dtype Dtype;

 protected:
  ConvolutionLayerTest()
      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
        blob_top_(new Blob<Dtype>()),
        blob_top_2_(new Blob<Dtype>()) {}
  virtual void SetUp() {
    // fill the values
    FillerParameter filler_param;
    filler_param.set_value(1.);
    GaussianFiller<Dtype> filler(filler_param);
    filler.Fill(this->blob_bottom_);
    filler.Fill(this->blob_bottom_2_);
    blob_bottom_vec_.push_back(blob_bottom_);
    blob_top_vec_.push_back(blob_top_);
  }

  virtual ~ConvolutionLayerTest() {
    delete blob_bottom_;
    delete blob_bottom_2_;
    delete blob_top_;
    delete blob_top_2_;
  }

  virtual Blob<Dtype>* MakeReferenceTop(Blob<Dtype>* top) {
    this->ref_blob_top_.reset(new Blob<Dtype>());
    this->ref_blob_top_->ReshapeLike(*top);
    return this->ref_blob_top_.get();
  }

  Blob<Dtype>* const blob_bottom_;
  Blob<Dtype>* const blob_bottom_2_;
  Blob<Dtype>* const blob_top_;
  Blob<Dtype>* const blob_top_2_;
  shared_ptr<Blob<Dtype> > ref_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(ConvolutionLayerTest, TestDtypesAndDevices);

TYPED_TEST(ConvolutionLayerTest, TestSetup) {
  typedef typename TypeParam::Dtype Dtype;
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_stride(2);
  convolution_param->set_num_output(4);
  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
  this->blob_top_vec_.push_back(this->blob_top_2_);

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(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(), 4);
  EXPECT_EQ(this->blob_top_->height(), 2);
  EXPECT_EQ(this->blob_top_->width(), 1);
  EXPECT_EQ(this->blob_top_2_->num(), 2);
  EXPECT_EQ(this->blob_top_2_->channels(), 4);
  EXPECT_EQ(this->blob_top_2_->height(), 2);
  EXPECT_EQ(this->blob_top_2_->width(), 1);
  // setting group should not change the shape
  convolution_param->set_num_output(3);
  convolution_param->set_group(3);
  layer.reset(new ConvolutionLayer<Dtype>(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(), 3);
  EXPECT_EQ(this->blob_top_->height(), 2);
  EXPECT_EQ(this->blob_top_->width(), 1);
  EXPECT_EQ(this->blob_top_2_->num(), 2);
  EXPECT_EQ(this->blob_top_2_->channels(), 3);
  EXPECT_EQ(this->blob_top_2_->height(), 2);
  EXPECT_EQ(this->blob_top_2_->width(), 1);
}


TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
  typedef typename TypeParam::Dtype Dtype;


  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_stride(2);
  convolution_param->set_num_output(3);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("constant");
  convolution_param->mutable_bias_filler()->set_value(0.1);

  vector<int> bottom_shape;
  bottom_shape.push_back(2);
  bottom_shape.push_back(3);
  bottom_shape.push_back(5);
  bottom_shape.push_back(5);

  this->blob_bottom_->Reshape(bottom_shape);
  this->blob_bottom_2_->Reshape(bottom_shape);

   fill_blob_data(this->blob_bottom_,0,1);
   fill_blob_data(this->blob_bottom_2_,1,1);

  layer_param.set_type("Convolution");

  shared_ptr<Layer<Dtype> > layer=
   LayerRegistry<Dtype>::CreateLayer(layer_param);

  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
  this->blob_top_vec_.push_back(this->blob_top_2_);

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

   //fill_blob_data(layer->blobs()[0].get(),1,1);

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

#ifdef LAYER_PERF_STAT
  perf_stat * p_time_stat;
  p_time_stat=layer->get_time_stat();
  std::cout<<"start: "<<p_time_stat->start;
  std::cout<<" end: "<<p_time_stat->end;
  std::cout<<" used: "<<p_time_stat->used;
  std::cout<<" total: "<<p_time_stat->total;
  std::cout<<" count: "<<p_time_stat->count<<std::endl;
#endif

  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }


//   dump_blob(this->blob_bottom_,"bottom.data");
//   dump_blob(this->blob_top_,"top.data");
//   dump_blob(this->ref_blob_top_.get(),"reftop.data");
//   dump_blob(layer->blobs()[0].get(),"weight.data");
//   dump_blob(layer->blobs()[1].get(),"bias.data");
#if 1
  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_2_));
  top_data = this->blob_top_2_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
#endif
}

#if 0

TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) {
  typedef typename TypeParam::Dtype Dtype;
  vector<int> bottom_shape;
  bottom_shape.push_back(2);
  bottom_shape.push_back(3);
  bottom_shape.push_back(8);
  bottom_shape.push_back(7);
  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
  this->blob_top_vec_.push_back(this->blob_top_2_);
  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
  }
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_dilation(2);
  convolution_param->set_num_output(4);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("constant");
  convolution_param->mutable_bias_filler()->set_value(0.1);

 layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);


  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
             this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
             this->MakeReferenceTop(this->blob_top_2_));
  top_data = this->blob_top_2_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
}

TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
  typedef typename TypeParam::Dtype Dtype;
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  const int kNumOutput = 3;
  convolution_param->set_num_output(kNumOutput);
  convolution_param->set_axis(3);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("gaussian");


 layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);

  vector<int> top_shape = this->blob_bottom_->shape();
  top_shape[3] = kNumOutput;
  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(top_shape, this->blob_top_->shape());
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  vector<int> weight_offset(2);
  const Blob<Dtype>* weight = layer->blobs()[0].get();
  const Blob<Dtype>* bias = layer->blobs()[1].get();
  const int num = this->blob_top_->count(3);
  const int dim = this->blob_top_->shape(3);
  const int bottom_dim = this->blob_bottom_->shape(3);
  for (int n = 0; n < num; ++n) {
    for (int d = 0; d < dim; ++d) {
      weight_offset[0] = d;
      Dtype value = bias->cpu_data()[d];
      for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) {
        weight_offset[1] = bottom_d;
        value += weight->data_at(weight_offset) *
                 this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d];
      }
      EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4);
    }
  }
}

TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
  typedef typename TypeParam::Dtype Dtype;
  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
  this->blob_top_vec_.push_back(this->blob_top_2_);
  vector<int> bottom_shape(5);
  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
  bottom_shape[2] = 5;
  bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
  bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
  FillerParameter filler_param;
  GaussianFiller<Dtype> filler(filler_param);
  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
    filler.Fill(this->blob_bottom_vec_[i]);
  }
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_stride(2);
  convolution_param->set_num_output(4);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("gaussian");

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);
    
  
  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_2_));
  top_data = this->blob_top_2_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
}

TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) {
  typedef typename TypeParam::Dtype Dtype;
  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
  this->blob_top_vec_.push_back(this->blob_top_2_);
  vector<int> bottom_shape(5);
  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
  bottom_shape[2] = 6;
  bottom_shape[3] = 7;
  bottom_shape[4] = 8;
  FillerParameter filler_param;
  GaussianFiller<Dtype> filler(filler_param);
  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
    filler.Fill(this->blob_bottom_vec_[i]);
  }
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_dilation(2);
  convolution_param->set_num_output(4);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("gaussian");

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);
    

  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
             this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
             this->MakeReferenceTop(this->blob_top_2_));
  top_data = this->blob_top_2_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
}

#endif

TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
  typedef typename TypeParam::Dtype Dtype;
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
#if 0    
  convolution_param->add_kernel_size(1);
  convolution_param->set_num_output(2);

  vector<int> bottom_shape;
  bottom_shape.push_back(1);
  bottom_shape.push_back(32);
  bottom_shape.push_back(133);
  bottom_shape.push_back(98);

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

#else 
  convolution_param->add_kernel_size(1);
  convolution_param->add_stride(1);
  convolution_param->set_num_output(4);
#endif
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("constant");
  convolution_param->mutable_bias_filler()->set_value(1);

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);

  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  
  fill_blob_data(this->blob_bottom_,1,1);
  fill_blob_data(layer->blobs()[0].get(),1,1);
 

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

  fill_blob_data(this->blob_bottom_,1,3);

  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  //   std::cout<<i<<": "<< top_data[i]<<" "<<ref_top_data[i]<<std::endl;
  }
}


#if 0
TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
  typedef typename TypeParam::Dtype Dtype;
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_stride(2);
  convolution_param->set_num_output(3);
  convolution_param->set_group(3);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  convolution_param->mutable_bias_filler()->set_type("constant");
  convolution_param->mutable_bias_filler()->set_value(0.1);

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);
    

  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Check against reference convolution.
  const Dtype* top_data;
  const Dtype* ref_top_data;
  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
      this->MakeReferenceTop(this->blob_top_));
  top_data = this->blob_top_->cpu_data();
  ref_top_data = this->ref_blob_top_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
  }
}

TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
  // Test separable convolution by computing the Sobel operator
  // as a single filter then comparing the result
  // as the convolution of two rectangular filters.
  typedef typename TypeParam::Dtype Dtype;
  // Fill bottoms with identical Gaussian noise.
  shared_ptr<GaussianFiller<Dtype> > filler;
  FillerParameter filler_param;
  filler_param.set_value(1.);
  filler.reset(new GaussianFiller<Dtype>(filler_param));
  filler->Fill(this->blob_bottom_);
  this->blob_bottom_2_->CopyFrom(*this->blob_bottom_);
  // Compute Sobel G_x operator as 3 x 3 convolution.
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->add_kernel_size(3);
  convolution_param->add_stride(2);
  convolution_param->set_num_output(1);
  convolution_param->set_bias_term(false);

  layer_param.set_type("Convolution");
  shared_ptr<Layer<Dtype> > layer=
    LayerRegistry<Dtype>::CreateLayer(layer_param);
    

  layer->blobs().resize(1);
  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
  Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
  for (int c = 0; c < 3; ++c) {
    int i = c * 9;  // 3 x 3 filter
    weights[i +  0] = -1;
    weights[i +  1] =  0;
    weights[i +  2] =  1;
    weights[i +  3] = -2;
    weights[i +  4] =  0;
    weights[i +  5] =  2;
    weights[i +  6] = -1;
    weights[i +  7] =  0;
    weights[i +  8] =  1;
  }
  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
  // (1) the [1 2 1] column filter
  vector<Blob<Dtype>*> sep_blob_bottom_vec;
  vector<Blob<Dtype>*> sep_blob_top_vec;
  shared_ptr<Blob<Dtype> > blob_sep(new Blob<Dtype>());
  sep_blob_bottom_vec.push_back(this->blob_bottom_2_);
  sep_blob_top_vec.push_back(this->blob_top_2_);
  convolution_param->clear_kernel_size();
  convolution_param->clear_stride();
  convolution_param->set_kernel_h(3);
  convolution_param->set_kernel_w(1);
  convolution_param->set_stride_h(2);
  convolution_param->set_stride_w(1);
  convolution_param->set_num_output(1);
  convolution_param->set_bias_term(false);
  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
  layer->blobs().resize(1);
  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
  Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
  for (int c = 0; c < 3; ++c) {
    int i = c * 3;  // 3 x 1 filter
    weights_1[i +  0] = 1;
    weights_1[i +  1] = 2;
    weights_1[i +  2] = 1;
  }
  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
  // (2) the [-1 0 1] row filter
  blob_sep->CopyFrom(*this->blob_top_2_, false, true);
  sep_blob_bottom_vec.clear();
  sep_blob_bottom_vec.push_back(blob_sep.get());
  convolution_param->set_kernel_h(1);
  convolution_param->set_kernel_w(3);
  convolution_param->set_stride_h(1);
  convolution_param->set_stride_w(2);
  convolution_param->set_num_output(1);
  convolution_param->set_bias_term(false);
  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
  layer->blobs().resize(1);
  layer->blobs()[0].reset(new Blob<Dtype>(1, 1, 1, 3));
  Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
  weights_2[0] = -1;
  weights_2[1] =  0;
  weights_2[2] =  1;
  layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
  layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
  // Test equivalence of full and separable filters.
  const Dtype* top_data = this->blob_top_->cpu_data();
  const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
  for (int i = 0; i < this->blob_top_->count(); ++i) {
    EXPECT_NEAR(top_data[i], sep_top_data[i], 1e-4);
  }
}

TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
  typedef typename TypeParam::Dtype Dtype;
  const int kernel_h = 11;
  const int kernel_w = 13;
  vector<int> bottom_shape(4);
  bottom_shape[0] = 15;
  bottom_shape[1] = 18;
  bottom_shape[2] = kernel_h * 2;
  bottom_shape[3] = kernel_w * 2;
  FillerParameter filler_param;
  GaussianFiller<Dtype> filler(filler_param);
  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
    filler.Fill(this->blob_bottom_vec_[i]);
  }
  LayerParameter layer_param;
  ConvolutionParameter* convolution_param =
      layer_param.mutable_convolution_param();
  convolution_param->set_num_output(12);
  convolution_param->set_bias_term(false);
  convolution_param->set_group(6);
  convolution_param->set_kernel_h(kernel_h);
  convolution_param->set_kernel_w(kernel_w);
  convolution_param->mutable_weight_filler()->set_type("gaussian");
  Blob<Dtype> weights;
  Blob<Dtype> top_diff;
  // Shape and fill weights and top_diff.
  bool copy_diff;
  bool reshape;
  {
    ConvolutionLayer<Dtype> layer(layer_param);
    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    top_diff.ReshapeLike(*this->blob_top_);
    filler.Fill(&top_diff);
    ASSERT_EQ(1, layer.blobs().size());
    copy_diff = false; reshape = true;
    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
  }
  vector<bool> propagate_down(1, true);
  Blob<Dtype> result_2d;
  Blob<Dtype> backward_result_2d;
  Blob<Dtype> backward_weight_result_2d;
  // Test with 2D im2col
  {
    caffe_set(this->blob_top_->count(), Dtype(0),
              this->blob_top_->mutable_cpu_data());
    caffe_set(this->blob_bottom_->count(), Dtype(0),
              this->blob_bottom_->mutable_cpu_diff());
    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
    // Do SetUp and Forward; save Forward result in result_2d.
    convolution_param->set_force_nd_im2col(false);

    layer_param.set_type("Convolution");

    Layer<Dtype> & layer_2d=*LayerRegistry<Dtype>::CreateLayer(layer_param);


    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    ASSERT_EQ(1, layer_2d.blobs().size());
    copy_diff = false; reshape = false;
    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
    copy_diff = false; reshape = true;
    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
    // Copy pre-generated top diff into actual top diff;
    // do Backward and save result in backward_result_2d.
    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
    caffe_copy(top_diff.count(), top_diff.cpu_data(),
               this->blob_top_->mutable_cpu_diff());
    layer_2d.Backward(this->blob_top_vec_, propagate_down,
                      this->blob_bottom_vec_);
    copy_diff = true; reshape = true;
    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
  }
  Blob<Dtype> result_nd;
  Blob<Dtype> backward_result_nd;
  Blob<Dtype> backward_weight_result_nd;
  // Test with ND im2col
  {
    caffe_set(this->blob_top_->count(), Dtype(0),
              this->blob_top_->mutable_cpu_data());
    caffe_set(this->blob_bottom_->count(), Dtype(0),
              this->blob_bottom_->mutable_cpu_diff());
    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
    // Do SetUp and Forward; save Forward result in result_nd.
    convolution_param->set_force_nd_im2col(true);

   layer_param.set_type("Convolution");

    Layer<Dtype>& layer_nd=*LayerRegistry<Dtype>::CreateLayer(layer_param);

    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    ASSERT_EQ(1, layer_nd.blobs().size());
    copy_diff = false; reshape = false;
    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
    copy_diff = false; reshape = true;
    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
    // Copy pre-generated top diff into actual top diff;
    // do Backward and save result in backward_result_nd.
    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
    caffe_copy(top_diff.count(), top_diff.cpu_data(),
               this->blob_top_->mutable_cpu_diff());
    layer_nd.Backward(this->blob_top_vec_, propagate_down,
                      this->blob_bottom_vec_);
    copy_diff = true; reshape = true;
    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
  }
  ASSERT_EQ(result_nd.count(), result_2d.count());
  for (int i = 0; i < result_2d.count(); ++i)  {
    EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
  }
  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
  for (int i = 0; i < backward_result_2d.count(); ++i) {
    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
              backward_result_nd.cpu_diff()[i]);
  }
  ASSERT_EQ(backward_weight_result_nd.count(),
            backward_weight_result_2d.count());
  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
              backward_weight_result_nd.cpu_diff()[i]);
  }
}
#endif


}  // namespace caffe