text: #include <cmath>
  #include <vector>
  
  #include "boost/scoped_ptr.hpp"
  #include "gtest/gtest.h"
  
  #include "caffe/blob.hpp"
  #include "caffe/common.hpp"
  #include "caffe/filler.hpp"
  #include "caffe/layers/softmax_loss_layer.hpp"
  
  #include "caffe/test/test_caffe_main.hpp"
  #include "caffe/test/test_gradient_check_util.hpp"
  
  using boost::scoped_ptr;
  
  namespace caffe {
  
  template <typename TypeParam>
  class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
    typedef typename TypeParam::Dtype Dtype;
  
   protected:
    SoftmaxWithLossLayerTest()
        : blob_bottom_data_(new Blob<Dtype>(10, 5, 2, 3)),
          blob_bottom_label_(new Blob<Dtype>(10, 1, 2, 3)),
          blob_bottom_weight_(new Blob<Dtype>(10, 1, 2, 3)),
          blob_top_loss_(new Blob<Dtype>()) {
      // fill the values
      FillerParameter filler_param;
      filler_param.set_std(10);
      GaussianFiller<Dtype> filler(filler_param);
      filler.Fill(blob_bottom_data_);
      blob_bottom_vec_.push_back(blob_bottom_data_);
      large_bottom_vec_.push_back(blob_bottom_data_);
      for (int i = 0; i < blob_bottom_label_->count(); ++i) {
        blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
      }
      blob_bottom_vec_.push_back(blob_bottom_label_);
      large_bottom_vec_.push_back(blob_bottom_label_);
      filler.Fill(blob_bottom_weight_);
      large_bottom_vec_.push_back(blob_bottom_weight_);
      blob_top_vec_.push_back(blob_top_loss_);
    }
    virtual ~SoftmaxWithLossLayerTest() {
      delete blob_bottom_data_;
      delete blob_bottom_label_;
      delete blob_bottom_weight_;
      delete blob_top_loss_;
    }
    Blob<Dtype>* const blob_bottom_data_;
    Blob<Dtype>* const blob_bottom_label_;
    Blob<Dtype>* const blob_bottom_weight_;
    Blob<Dtype>* const blob_top_loss_;
    vector<Blob<Dtype>*> blob_bottom_vec_;
    vector<Blob<Dtype>*> large_bottom_vec_;
    vector<Blob<Dtype>*> blob_top_vec_;
  };
  
  TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    layer_param.add_loss_weight(3);
    SoftmaxWithLossLayer<Dtype> layer(layer_param);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
        this->blob_top_vec_, 0);
  }
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientWithWeights) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    //layer_param.add_loss_weight(3);
    SoftmaxWithLossLayer<Dtype> layer(layer_param);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->large_bottom_vec_,
        this->blob_top_vec_, 0);
  }
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestForwardIgnoreLabel) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    layer_param.mutable_loss_param()->set_normalize(false);
    // First, compute the loss with all labels
    scoped_ptr<SoftmaxWithLossLayer<Dtype> > layer(
        new SoftmaxWithLossLayer<Dtype>(layer_param));
    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
    Dtype full_loss = this->blob_top_loss_->cpu_data()[0];
    // Now, accumulate the loss, ignoring each label in {0, ..., 4} in turn.
    Dtype accum_loss = 0;
    for (int label = 0; label < 5; ++label) {
      layer_param.mutable_loss_param()->set_ignore_label(label);
      layer.reset(new SoftmaxWithLossLayer<Dtype>(layer_param));
      layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
      layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
      accum_loss += this->blob_top_loss_->cpu_data()[0];
    }
    // Check that each label was included all but once.
    EXPECT_NEAR(4 * full_loss, accum_loss, 1e-4);
  }
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientIgnoreLabel) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    // labels are in {0, ..., 4}, so we'll ignore about a fifth of them
    layer_param.mutable_loss_param()->set_ignore_label(0);
    SoftmaxWithLossLayer<Dtype> layer(layer_param);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
        this->blob_top_vec_, 0);
  }
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientIgnoreLabelWithWeights) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    // labels are in {0, ..., 4}, so we'll ignore about a fifth of them
    layer_param.mutable_loss_param()->set_ignore_label(0);
    SoftmaxWithLossLayer<Dtype> layer(layer_param);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->large_bottom_vec_,
        this->blob_top_vec_, 0);
  }
  
  TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientUnnormalized) {
    typedef typename TypeParam::Dtype Dtype;
    LayerParameter layer_param;
    layer_param.mutable_loss_param()->set_normalize(false);
    SoftmaxWithLossLayer<Dtype> layer(layer_param);
    GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
        this->blob_top_vec_, 0);
  }
  
  }  // namespace caffe