text: #include <cmath>
  #include <vector>
  
  #include "gtest/gtest.h"
  
  #include "caffe/blob.hpp"
  #include "caffe/common.hpp"
  #include "caffe/filler.hpp"
  #include "caffe/layers/euclidean_loss_layer.hpp"
  
  #include "caffe/test/test_caffe_main.hpp"
  #include "caffe/test/test_gradient_check_util.hpp"
  
  namespace caffe {
  
  template <typename TypeParam>
  class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
    typedef typename TypeParam::Dtype Dtype;
  
   protected:
    EuclideanLossLayerTest()
        : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
          blob_bottom_label_(new Blob<Dtype>(10, 5, 1, 1)),
          blob_top_loss_(new Blob<Dtype>()) {
      // fill the values
      FillerParameter filler_param;
      GaussianFiller<Dtype> filler(filler_param);
      filler.Fill(this->blob_bottom_data_);
      blob_bottom_vec_.push_back(blob_bottom_data_);
      filler.Fill(this->blob_bottom_label_);
      blob_bottom_vec_.push_back(blob_bottom_label_);
      blob_top_vec_.push_back(blob_top_loss_);
    }
    virtual ~EuclideanLossLayerTest() {
      delete blob_bottom_data_;
      delete blob_bottom_label_;
      delete blob_top_loss_;
    }
  
    void TestForward() {
      // Get the loss without a specified objective weight -- should be
      // equivalent to explicitly specifying a weight of 1.
      LayerParameter layer_param;
      EuclideanLossLayer<Dtype> layer_weight_1(layer_param);
      layer_weight_1.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
      const Dtype loss_weight_1 =
          layer_weight_1.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
  
      // Get the loss again with a different objective weight; check that it is
      // scaled appropriately.
      const Dtype kLossWeight = 3.7;
      layer_param.add_loss_weight(kLossWeight);
      EuclideanLossLayer<Dtype> layer_weight_2(layer_param);
      layer_weight_2.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
      const Dtype loss_weight_2 =
          layer_weight_2.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
      const Dtype kErrorMargin = 1e-5;
      EXPECT_NEAR(loss_weight_1 * kLossWeight, loss_weight_2, kErrorMargin);
      // Make sure the loss is non-trivial.
      const Dtype kNonTrivialAbsThresh = 1e-1;
      EXPECT_GE(fabs(loss_weight_1), kNonTrivialAbsThresh);
    }
  
    Blob<Dtype>* const blob_bottom_data_;
    Blob<Dtype>* const blob_bottom_label_;
    Blob<Dtype>* const blob_top_loss_;
    vector<Blob<Dtype>*> blob_bottom_vec_;
    vector<Blob<Dtype>*> blob_top_vec_;
  };
  
  TYPED_TEST_CASE(EuclideanLossLayerTest, TestDtypesAndDevices);
  
  TYPED_TEST(EuclideanLossLayerTest, TestForward) {
    this->TestForward();
  }
  
  TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    const Dtype kLossWeight = 3.7;
    layer_param.add_loss_weight(kLossWeight);
    EuclideanLossLayer<Dtype> layer(layer_param);
    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
        this->blob_top_vec_);
  }
  
  }  // namespace caffe
  