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  #include "test_precomp.hpp"
  #include "npy_blob.hpp"
  #include <opencv2/dnn/shape_utils.hpp>
  #include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
  
  namespace opencv_test
  {
  
  using namespace std;
  using namespace testing;
  using namespace cv;
  using namespace cv::dnn;
  
  template<typename TStr>
  static std::string _tf(TStr filename, bool inTorchDir = true, bool required = true)
  {
      String path = "dnn/";
      if (inTorchDir)
          path += "torch/";
      path += filename;
      return findDataFile(path, required);
  }
  
  TEST(Torch_Importer, simple_read)
  {
      Net net;
      ASSERT_NO_THROW(net = readNetFromTorch(_tf("net_simple_net.txt"), false));
      ASSERT_FALSE(net.empty());
  }
  
  class Test_Torch_layers : public DNNTestLayer
  {
  public:
      void runTorchNet(const String& prefix, String outLayerName = "",
                       bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
                       double l1 = 0.0, double lInf = 0.0)
      {
          String suffix = (isBinary) ? ".dat" : ".txt";
  
          Mat inp, outRef;
          ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
          ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );
  
          checkBackend(backend, target, &inp, &outRef);
  
          Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
          ASSERT_FALSE(net.empty());
  
          net.setPreferableBackend(backend);
          net.setPreferableTarget(target);
  
          if (outLayerName.empty())
              outLayerName = net.getLayerNames().back();
  
          net.setInput(inp);
          std::vector<Mat> outBlobs;
          net.forward(outBlobs, outLayerName);
          l1 = l1 ? l1 : default_l1;
          lInf = lInf ? lInf : default_lInf;
          normAssert(outRef, outBlobs[0], "", l1, lInf);
  
          if (check2ndBlob && backend == DNN_BACKEND_OPENCV)
          {
              Mat out2 = outBlobs[1];
              Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
              normAssert(out2, ref2, "", l1, lInf);
          }
      }
  };
  
  TEST_P(Test_Torch_layers, run_convolution)
  {
      // Output reference values are in range [23.4018, 72.0181]
      double l1 = default_l1, lInf = default_lInf;
      if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
      {
          l1 = 0.08;
          lInf = 0.43;
      }
      else if (target == DNN_TARGET_CUDA_FP16)
      {
          l1 = 0.08;
          lInf = 0.5;
      }
      runTorchNet("net_conv", "", false, true, true, l1, lInf);
  }
  
  TEST_P(Test_Torch_layers, run_pool_max)
  {
      if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
      if (target == DNN_TARGET_CUDA_FP16)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
      double l1 = 0.0, lInf = 0.0;
      runTorchNet("net_pool_max", "", true, false, true, l1, lInf);
  }
  
  TEST_P(Test_Torch_layers, run_pool_ave)
  {
      runTorchNet("net_pool_ave");
  }
  
  TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
  {
      runTorchNet("net_reshape");
  }
  
  TEST_P(Test_Torch_layers, run_reshape)
  {
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
      runTorchNet("net_reshape_batch");
      runTorchNet("net_reshape_channels", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, run_reshape_single_sample)
  {
      // Reference output values in range [14.4586, 18.4492].
      double l1 = default_l1, lInf = default_lInf;
      if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
      {
          l1 = 0.033;
          lInf = 0.05;
      }
      else if (target == DNN_TARGET_CUDA_FP16)
      {
          l1 = 0.01;
      }
      runTorchNet("net_reshape_single_sample", "", false, false, true, l1, lInf);
  }
  
  TEST_P(Test_Torch_layers, run_linear)
  {
      if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
      runTorchNet("net_linear_2d");
  }
  
  TEST_P(Test_Torch_layers, run_concat)
  {
      runTorchNet("net_concat", "l5_torchMerge");
  }
  
  TEST_P(Test_Torch_layers, run_depth_concat)
  {
      double lInf = 0.0;
      if (target == DNN_TARGET_OPENCL_FP16)
      {
          lInf = 0.032;
      }
      else if (target == DNN_TARGET_CUDA_FP16)
      {
          lInf = 0.03;
      }
      runTorchNet("net_depth_concat", "", false, true, true, 0.0, lInf);
  }
  
  TEST_P(Test_Torch_layers, run_deconv)
  {
      runTorchNet("net_deconv");
  }
  
  TEST_P(Test_Torch_layers, run_batch_norm)
  {
      runTorchNet("net_batch_norm", "", false, true);
      runTorchNet("net_batch_norm_train", "", false, true, false);
  }
  
  TEST_P(Test_Torch_layers, net_prelu)
  {
      runTorchNet("net_prelu");
  }
  
  TEST_P(Test_Torch_layers, net_cadd_table)
  {
      runTorchNet("net_cadd_table");
  }
  
  TEST_P(Test_Torch_layers, net_softmax)
  {
      runTorchNet("net_softmax");
      runTorchNet("net_softmax_spatial");
  }
  
  TEST_P(Test_Torch_layers, net_logsoftmax)
  {
      runTorchNet("net_logsoftmax");
      runTorchNet("net_logsoftmax_spatial");
  }
  
  TEST_P(Test_Torch_layers, net_lp_pooling_square)
  {
      runTorchNet("net_lp_pooling_square", "", false, true);
  }
  TEST_P(Test_Torch_layers, net_lp_pooling_power)
  {
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
      runTorchNet("net_lp_pooling_power", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
  {
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
      double l1 = 0.0, lInf = 0.0;
      if (target == DNN_TARGET_OPENCL_FP16)
      {
          l1 = 0.046;
          lInf = 0.023;
      }
      else if (target == DNN_TARGET_CUDA_FP16)
      {
          l1 = 0.0042;
          lInf = 0.021;
      }
      runTorchNet("net_conv_gemm_lrn", "", false, true, true, l1, lInf);
  }
  
  TEST_P(Test_Torch_layers, net_inception_block)
  {
      runTorchNet("net_inception_block", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, net_normalize)
  {
      if(backend == DNN_BACKEND_CUDA)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* only L1 and L2 norms are supported */
      runTorchNet("net_normalize", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, net_padding)
  {
      runTorchNet("net_padding", "", false, true);
      runTorchNet("net_spatial_zero_padding", "", false, true);
      runTorchNet("net_spatial_reflection_padding", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, net_non_spatial)
  {
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
          (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
          applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
                       CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
      runTorchNet("net_non_spatial", "", false, true);
  }
  
  TEST_P(Test_Torch_layers, run_paralel)
  {
      if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
          throw SkipTestException("");  // TODO: Check this
      runTorchNet("net_parallel", "l5_torchMerge");
  }
  
  TEST_P(Test_Torch_layers, net_residual)
  {
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_OPENCL ||
                                                      target == DNN_TARGET_OPENCL_FP16))
          applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
                       CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
  #endif
      runTorchNet("net_residual", "", false, true);
  }
  
  class Test_Torch_nets : public DNNTestLayer {};
  
  TEST_P(Test_Torch_nets, OpenFace_accuracy)
  {
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
  #endif
      checkBackend();
  
      const string model = findDataFile("dnn/openface_nn4.small2.v1.t7", false);
      Net net = readNetFromTorch(model);
  
      net.setPreferableBackend(backend);
      net.setPreferableTarget(target);
  
      Mat sample = imread(findDataFile("cv/shared/lena.png"));
      Mat sampleF32(sample.size(), CV_32FC3);
      sample.convertTo(sampleF32, sampleF32.type());
      sampleF32 /= 255;
      resize(sampleF32, sampleF32, Size(96, 96), 0, 0, INTER_NEAREST);
  
      Mat inputBlob = blobFromImage(sampleF32, 1.0, Size(), Scalar(), /*swapRB*/true);
  
      net.setInput(inputBlob);
      Mat out = net.forward();
  
      // Reference output values are in range [-0.17212, 0.263492]
      // on Myriad problem layer: l4_Pooling - does not use pads_begin
      float l1 = 1e-5, lInf = 1e-3;
      if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
      {
          l1 = 2e-3;
          lInf = 5e-3;
      }
      else if (target == DNN_TARGET_CUDA_FP16)
      {
          l1 = 0.0004;
          lInf = 0.0012;
      }
      Mat outRef = readTorchBlob(_tf("net_openface_output.dat"), true);
      normAssert(out, outRef, "", l1, lInf);
  }
  
  static Mat getSegmMask(const Mat& scores)
  {
      const int rows = scores.size[2];
      const int cols = scores.size[3];
      const int numClasses = scores.size[1];
  
      Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
      Mat maxVal(rows, cols, CV_32FC1, Scalar(0));
      for (int ch = 0; ch < numClasses; ch++)
      {
          for (int row = 0; row < rows; row++)
          {
              const float *ptrScore = scores.ptr<float>(0, ch, row);
              uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
              float *ptrMaxVal = maxVal.ptr<float>(row);
              for (int col = 0; col < cols; col++)
              {
                  if (ptrScore[col] > ptrMaxVal[col])
                  {
                      ptrMaxVal[col] = ptrScore[col];
                      ptrMaxCl[col] = (uchar)ch;
                  }
              }
          }
      }
      return maxCl;
  }
  
  // Computer per-class intersection over union metric.
  static void normAssertSegmentation(const Mat& ref, const Mat& test)
  {
      CV_Assert_N(ref.dims == 4, test.dims == 4);
      const int numClasses = ref.size[1];
      CV_Assert(numClasses == test.size[1]);
  
      Mat refMask = getSegmMask(ref);
      Mat testMask = getSegmMask(test);
      EXPECT_EQ(countNonZero(refMask != testMask), 0);
  }
  
  TEST_P(Test_Torch_nets, ENet_accuracy)
  {
      applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
      checkBackend();
      if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
          throw SkipTestException("");
      if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020010000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
  #else
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
      {
          if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
          if (target == DNN_TARGET_OPENCL)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
          if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
          throw SkipTestException("");
      }
  #endif
  #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
  #endif
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
      {
          if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
          if (target == DNN_TARGET_OPENCL)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
          if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
          throw SkipTestException("");
      }
  
      Net net;
      {
          const string model = findDataFile("dnn/Enet-model-best.net", false);
          net = readNetFromTorch(model, true);
          ASSERT_TRUE(!net.empty());
      }
  
      net.setPreferableBackend(backend);
      net.setPreferableTarget(target);
  
      Mat sample = imread(_tf("street.png", false));
      Mat inputBlob = blobFromImage(sample, 1./255, Size(), Scalar(), /*swapRB*/true);
  
      net.setInput(inputBlob, "");
      Mat out = net.forward();
      Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
      // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
      // thresholds for ENet must be changed. Accuracy of results was checked on
      // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
      normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
      normAssertSegmentation(ref, out);
  
      const int N = 3;
      for (int i = 0; i < N; i++)
      {
          net.setInput(inputBlob, "");
          Mat out = net.forward();
          normAssert(ref, out, "", 0.00044, /*target == DNN_TARGET_CPU ? 0.453 : */0.552);
          normAssertSegmentation(ref, out);
      }
  }
  
  // Check accuracy of style transfer models from https://github.com/jcjohnson/fast-neural-style
  // th fast_neural_style.lua \
  //   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
  //   -output_image lena.png \
  //   -median_filter 0 \
  //   -image_size 0 \
  //   -model models/eccv16/starry_night.t7
  // th fast_neural_style.lua \
  //   -input_image ~/opencv_extra/testdata/dnn/googlenet_1.png \
  //   -output_image lena.png \
  //   -median_filter 0 \
  //   -image_size 0 \
  //   -model models/instance_norm/feathers.t7
  TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
  {
  #if defined INF_ENGINE_RELEASE
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
              && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD
              && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
  #endif
  
      checkBackend();
  
  #if defined(INF_ENGINE_RELEASE)
  #if INF_ENGINE_RELEASE <= 2018050000
      if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
          applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
  #endif
  #endif
  
      std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                              "dnn/fast_neural_style_instance_norm_feathers.t7"};
      std::string targets[] = {"dnn/lena_starry_night.png", "dnn/lena_feathers.png"};
  
      for (int i = 0; i < 2; ++i)
      {
          const string model = findDataFile(models[i], false);
          Net net = readNetFromTorch(model);
  
          net.setPreferableBackend(backend);
          net.setPreferableTarget(target);
  
          Mat img = imread(findDataFile("dnn/googlenet_1.png"));
          Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
  
          net.setInput(inputBlob);
          Mat out = net.forward();
  
          // Deprocessing.
          getPlane(out, 0, 0) += 103.939;
          getPlane(out, 0, 1) += 116.779;
          getPlane(out, 0, 2) += 123.68;
          out = cv::min(cv::max(0, out), 255);
  
          Mat ref = imread(findDataFile(targets[i]));
          Mat refBlob = blobFromImage(ref, 1.0, Size(), Scalar(), false);
  
          if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
          {
              double normL1 = cvtest::norm(refBlob, out, cv::NORM_L1) / refBlob.total();
              if (target == DNN_TARGET_MYRIAD)
                  EXPECT_LE(normL1, 4.0f);
              else
                  EXPECT_LE(normL1, 0.6f);
          }
          else if(target == DNN_TARGET_CUDA_FP16)
          {
              normAssert(out, refBlob, "", 0.6, 25);
          }
          else
              normAssert(out, refBlob, "", 0.5, 1.1);
      }
  }
  
  INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_nets, dnnBackendsAndTargets());
  
  // Test a custom layer
  // https://github.com/torch/nn/blob/master/doc/convolution.md#nn.SpatialUpSamplingNearest
  class SpatialUpSamplingNearestLayer CV_FINAL : public Layer
  {
  public:
      SpatialUpSamplingNearestLayer(const LayerParams &params) : Layer(params)
      {
          scale = params.get<int>("scale_factor");
      }
  
      static Ptr<Layer> create(LayerParams& params)
      {
          return Ptr<Layer>(new SpatialUpSamplingNearestLayer(params));
      }
  
      virtual bool getMemoryShapes(const std::vector<std::vector<int> > &inputs,
                                   const int requiredOutputs,
                                   std::vector<std::vector<int> > &outputs,
                                   std::vector<std::vector<int> > &internals) const CV_OVERRIDE
      {
          std::vector<int> outShape(4);
          outShape[0] = inputs[0][0];  // batch size
          outShape[1] = inputs[0][1];  // number of channels
          outShape[2] = scale * inputs[0][2];
          outShape[3] = scale * inputs[0][3];
          outputs.assign(1, outShape);
          return false;
      }
  
      void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays) CV_OVERRIDE
      {
          CV_TRACE_FUNCTION();
          CV_TRACE_ARG_VALUE(name, "name", name.c_str());
  
          std::vector<Mat> inputs, outputs;
          inputs_arr.getMatVector(inputs);
          outputs_arr.getMatVector(outputs);
  
          Mat& inp = inputs[0];
          Mat& out = outputs[0];
          const int outHeight = out.size[2];
          const int outWidth = out.size[3];
          for (size_t n = 0; n < inp.size[0]; ++n)
          {
              for (size_t ch = 0; ch < inp.size[1]; ++ch)
              {
                  resize(getPlane(inp, n, ch), getPlane(out, n, ch),
                         Size(outWidth, outHeight), 0, 0, INTER_NEAREST);
              }
          }
      }
  
  private:
      int scale;
  };
  
  TEST_P(Test_Torch_layers, upsampling_nearest)
  {
      // Test a custom layer.
      CV_DNN_REGISTER_LAYER_CLASS(SpatialUpSamplingNearest, SpatialUpSamplingNearestLayer);
      try
      {
          runTorchNet("net_spatial_upsampling_nearest", "", false, true);
      }
      catch (...)
      {
          LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
          throw;
      }
      LayerFactory::unregisterLayer("SpatialUpSamplingNearest");
  
      // Test an implemented layer.
      runTorchNet("net_spatial_upsampling_nearest", "", false, true);
  }
  
  INSTANTIATE_TEST_CASE_P(/**/, Test_Torch_layers, dnnBackendsAndTargets());
  
  }
  