/*
All modification made by Intel Corporation: © 2016 Intel Corporation

All contributions by the University of California:
Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:
Copyright (c) 2014, 2015, the respective contributors
All rights reserved.
For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of Intel Corporation nor the names of its contributors
      may be used to endorse or promote products derived from this software
      without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#include <utility>
#include <vector>

#include "gtest/gtest.h"

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

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

template <typename Dtype>
class PriorBoxLayerTest : public CPUDeviceTest<Dtype> {
 protected:
  PriorBoxLayerTest()
      : blob_bottom_(new Blob<Dtype>(10, 10, 10, 10)),
        blob_data_(new Blob<Dtype>(10, 3, 100, 100)),
        blob_top_(new Blob<Dtype>()),
        min_size_(4),
        max_size_(9) {
    Caffe::set_random_seed(1701);
    // fill the values
    FillerParameter filler_param;
    GaussianFiller<Dtype> filler(filler_param);
    filler.Fill(this->blob_bottom_);
    filler.Fill(this->blob_data_);
    blob_bottom_vec_.push_back(blob_bottom_);
    blob_bottom_vec_.push_back(blob_data_);
    blob_top_vec_.push_back(blob_top_);
  }
  virtual ~PriorBoxLayerTest()
  {
    delete blob_top_;
    delete blob_data_;
    delete blob_bottom_;
  }
  Blob<Dtype>* const blob_bottom_;
  Blob<Dtype>* const blob_data_;
  Blob<Dtype>* const blob_top_;
  vector<Blob<Dtype>*> blob_bottom_vec_;
  vector<Blob<Dtype>*> blob_top_vec_;
  size_t min_size_;
  size_t max_size_;
};

TYPED_TEST_CASE(PriorBoxLayerTest, TestDtypes);

TYPED_TEST(PriorBoxLayerTest, TestSetup) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 2 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupMultiSize) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_min_size(this->min_size_ + 10);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_max_size(this->max_size_ + 10);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 4 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupNoMaxSize) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 1 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupMultiSizeNoMaxSize) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_min_size(this->min_size_ + 10);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 2 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupAspectRatio1) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(1.);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->set_flip(false);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 3 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupAspectRatioNoFlip) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->add_aspect_ratio(3.);
  prior_box_param->set_flip(false);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 4 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupAspectRatio) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->add_aspect_ratio(3.);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 6 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestSetupAspectRatioMultiSize) {
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_min_size(this->min_size_ + 10);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_max_size(this->max_size_ + 10);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->add_aspect_ratio(3.);
  PriorBoxLayer<TypeParam> layer(layer_param);
  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
  EXPECT_EQ(this->blob_top_->num(), 1);
  EXPECT_EQ(this->blob_top_->channels(), 2);
  EXPECT_EQ(this->blob_top_->height(), 100 * 12 * 4);
  EXPECT_EQ(this->blob_top_->width(), 1);
}

TYPED_TEST(PriorBoxLayerTest, TestCPU) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+3], 0.47, eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUNoMaxSize) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*1*4+4*1*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*1*4+4*1*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*1*4+4*1*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*1*4+4*1*4+3], 0.47, eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUVariance1) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_variance(1.);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+3], 0.47, eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 1., eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUVarianceMulti) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_variance(0.1);
  prior_box_param->add_variance(0.2);
  prior_box_param->add_variance(0.3);
  prior_box_param->add_variance(0.4);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*2*4+4*2*4+3], 0.47, eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1 * (d % 4 + 1), eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUAspectRatioNoFlip) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->set_flip(false);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // third prior
  EXPECT_NEAR(top_data[8], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[9], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[10], 0.05 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[11], 0.05 + 0.01*sqrt(2.), eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+3], 0.47, eps);
  // prior with ratio 1:2 in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+8], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+9], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+10], 0.45 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*3*4+4*3*4+11], 0.45 + 0.01*sqrt(2.), eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUAspectRatio) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(2.);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // third prior
  EXPECT_NEAR(top_data[8], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[9], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[10], 0.05 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[11], 0.05 + 0.01*sqrt(2.), eps);
  // forth prior
  EXPECT_NEAR(top_data[12], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[13], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14], 0.05 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[15], 0.05 + 0.02*sqrt(2.), eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+3], 0.47, eps);
  // prior with ratio 1:2 in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+8], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+9], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+10], 0.45 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+11], 0.45 + 0.01*sqrt(2.), eps);
  // prior with ratio 2:1 in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+12], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+13], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+14], 0.45 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[4*10*4*4+4*4*4+15], 0.45 + 0.02*sqrt(2.), eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUAspectRatioMultiSize) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_min_size(this->min_size_ + 4);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_max_size(this->max_size_ + 9);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->set_clip(true);
  PriorBoxLayer<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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // third prior
  EXPECT_NEAR(top_data[8], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[9], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[10], 0.05 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[11], 0.05 + 0.01*sqrt(2.), eps);
  // forth prior
  EXPECT_NEAR(top_data[12], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[13], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14], 0.05 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[15], 0.05 + 0.02*sqrt(2.), eps);
  // fifth prior
  EXPECT_NEAR(top_data[16], 0.01, eps);
  EXPECT_NEAR(top_data[17], 0.01, eps);
  EXPECT_NEAR(top_data[18], 0.09, eps);
  EXPECT_NEAR(top_data[19], 0.09, eps);
  // sixth prior
  EXPECT_NEAR(top_data[20], 0.00, eps);
  EXPECT_NEAR(top_data[21], 0.00, eps);
  EXPECT_NEAR(top_data[22], 0.11, eps);
  EXPECT_NEAR(top_data[23], 0.11, eps);
  // seventh prior
  EXPECT_NEAR(top_data[24], 0.00, eps);
  EXPECT_NEAR(top_data[25], 0.05 - 0.04/sqrt(2.), eps);
  EXPECT_NEAR(top_data[26], 0.05 + 0.04*sqrt(2.), eps);
  EXPECT_NEAR(top_data[27], 0.05 + 0.04/sqrt(2.), eps);
  // forth prior
  EXPECT_NEAR(top_data[28], 0.05 - 0.04/sqrt(2.), eps);
  EXPECT_NEAR(top_data[29], 0.00, eps);
  EXPECT_NEAR(top_data[30], 0.05 + 0.04/sqrt(2.), eps);
  EXPECT_NEAR(top_data[31], 0.05 + 0.04*sqrt(2.), eps);
  // prior in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4], 0.43, eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+1], 0.43, eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+3], 0.47, eps);
  // prior with ratio 1:2 in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+8], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+9], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+10], 0.45 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+11], 0.45 + 0.01*sqrt(2.), eps);
  // prior with ratio 2:1 in the 5-th row and 5-th col
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+12], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+13], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+14], 0.45 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[8*10*4*4+8*4*4+15], 0.45 + 0.02*sqrt(2.), eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

TYPED_TEST(PriorBoxLayerTest, TestCPUFixStep) {
  const TypeParam eps = 1e-6;
  LayerParameter layer_param;
  PriorBoxParameter* prior_box_param = layer_param.mutable_prior_box_param();
  prior_box_param->add_min_size(this->min_size_);
  prior_box_param->add_max_size(this->max_size_);
  prior_box_param->add_aspect_ratio(2.);
  prior_box_param->set_img_size(100);
  prior_box_param->set_step(10);
  PriorBoxLayer<TypeParam> layer(layer_param);
  vector<int> shape(4, 10);
  shape[2] = 20;
  this->blob_bottom_->Reshape(shape);
  shape[1] = 3;
  shape[2] = 200;
  shape[3] = 100;
  this->blob_data_->Reshape(shape);
  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* top_data = this->blob_top_->cpu_data();
  int dim = this->blob_top_->height();
  // pick a few generated priors and compare against the expected number.
  // first prior
  EXPECT_NEAR(top_data[0], 0.03, eps);
  EXPECT_NEAR(top_data[1], 0.03, eps);
  EXPECT_NEAR(top_data[2], 0.07, eps);
  EXPECT_NEAR(top_data[3], 0.07, eps);
  // second prior
  EXPECT_NEAR(top_data[4], 0.02, eps);
  EXPECT_NEAR(top_data[5], 0.02, eps);
  EXPECT_NEAR(top_data[6], 0.08, eps);
  EXPECT_NEAR(top_data[7], 0.08, eps);
  // third prior
  EXPECT_NEAR(top_data[8], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[9], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[10], 0.05 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[11], 0.05 + 0.01*sqrt(2.), eps);
  // forth prior
  EXPECT_NEAR(top_data[12], 0.05 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[13], 0.05 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14], 0.05 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[15], 0.05 + 0.02*sqrt(2.), eps);
  // prior in the 15-th row and 5-th col
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4], 0.43, eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+1], 1.43, eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+2], 0.47, eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+3], 1.47, eps);
  // prior with ratio 1:2 in the 15-th row and 5-th col
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+8], 0.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+9], 1.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+10], 0.45 + 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+11], 1.45 + 0.01*sqrt(2.), eps);
  // prior with ratio 2:1 in the 15-th row and 5-th col
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+12], 0.45 - 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+13], 1.45 - 0.02*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+14], 0.45 + 0.01*sqrt(2.), eps);
  EXPECT_NEAR(top_data[14*10*4*4+4*4*4+15], 1.45 + 0.02*sqrt(2.), eps);

  // check variance
  top_data += dim;
  for (int d = 0; d < dim; ++d) {
    EXPECT_NEAR(top_data[d], 0.1, eps);
  }
}

}  // namespace caffe
