text: /*
   * Copyright (c) 2017-2021 Arm Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #include "arm_compute/core/TensorShape.h"
  #include "arm_compute/core/Types.h"
  #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
  #include "tests/AssetsLibrary.h"
  #include "tests/Globals.h"
  #include "tests/IAccessor.h"
  #include "tests/framework/Asserts.h"
  #include "tests/framework/Fixture.h"
  #include "tests/validation/Helpers.h"
  #include "tests/validation/fixtures/ConvolutionLayerFixture.h"
  #include "tests/validation/reference/ConvolutionLayer.h"
  
  #include <random>
  
  namespace arm_compute
  {
  namespace test
  {
  namespace validation
  {
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  class DirectConvolutionValidationGenericTensorShiftFixture : public framework::Fixture
  {
  public:
      using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;
  
  public:
      template <typename...>
      void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
                 DataType data_type, QuantizationInfo quantization_info)
      {
          _quantization_info = quantization_info;
          _data_type         = data_type;
  
          const TensorShape   weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
          const TensorShape   bias_shape(num_kernels);
          const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
          const TensorShape   output_shape   = get_output_shape(input_shape, weights_shape, info);
          const DataType      bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
  
          _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
          _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
      }
  
      template <typename...>
      void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y,
                 DataType data_type, QuantizationInfo quantization_info)
      {
          ARM_COMPUTE_UNUSED(dilation_x, dilation_y);
  
          _quantization_info = quantization_info;
          _data_type         = data_type;
  
          const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
  
          _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
          _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
      }
  
  protected:
      template <typename U>
      void fill(U &&tensor, int i)
      {
          switch(tensor.data_type())
          {
              case DataType::QASYMM8:
              {
                  std::uniform_int_distribution<uint8_t> distribution(0, 50);
                  library->fill(tensor, distribution, i);
                  break;
              }
              case DataType::F16:
              {
                  arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
                  library->fill(tensor, distribution, i);
                  break;
              }
              case DataType::F32:
              {
                  std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
                  library->fill(tensor, distribution, i);
                  break;
              }
              case DataType::S32:
              {
                  std::uniform_int_distribution<int32_t> distribution(-5, 5);
                  library->fill(tensor, distribution, i);
                  break;
              }
              default:
                  library->fill_tensor_uniform(tensor, i);
          }
      }
  
      TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
                                DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
      {
          // Create tensors
          TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1, quantization_info);
          TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, quantization_info);
          TensorType bias    = create_tensor<TensorType>(bias_shape, bias_data_type, 1, quantization_info);
          TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, quantization_info);
  
          TensorShape output_shape1 = get_output_shape(output_shape, weights_shape, info);
          TensorType  dst1          = create_tensor<TensorType>(output_shape1, data_type, 1, quantization_info);
  
          // Create and configure function
          FunctionType conv;
          conv.configure(&src, &weights, &bias, &dst, info);
          FunctionType conv1;
          conv1.configure(&dst, &weights, &bias, &dst1, info);
  
          ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(dst1.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Allocate tensors
          src.allocator()->allocate();
          weights.allocator()->allocate();
          bias.allocator()->allocate();
          dst.allocator()->allocate();
          dst1.allocator()->allocate();
  
          ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
          ARM_COMPUTE_EXPECT(!dst1.info()->is_resizable(), framework::LogLevel::ERRORS);
  
          // Fill tensors
          fill(AccessorType(src), 0);
          fill(AccessorType(weights), 1);
          fill(AccessorType(bias), 2);
  
          // Compute NEConvolutionLayer function
          GCScheduler::get().memory_barrier();
          conv.run();
          GCScheduler::get().memory_barrier();
          conv1.run();
  
          return dst1;
      }
  
      SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
                                        DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
      {
          // Create reference
          SimpleTensor<T>     src{ input_shape, data_type, 1, quantization_info };
          SimpleTensor<T>     weights{ weights_shape, data_type, 1, quantization_info };
          SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info };
  
          SimpleTensor<T> dst{ output_shape, data_type, 1, quantization_info };
          TensorShape     output_shape1 = get_output_shape(output_shape, weights_shape, info);
  
          // Fill reference
          fill(src, 0);
          fill(weights, 1);
          fill(bias, 2);
  
          dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
          return reference::convolution_layer<T>(dst, weights, bias, output_shape1, info);
      }
  
      TensorType       _target{};
      SimpleTensor<T>  _reference{};
      QuantizationInfo _quantization_info{};
      DataType         _data_type{};
  
  private:
      TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &info)
      {
          TensorShape out_shape(in_shape);
          const std::pair<unsigned int, unsigned int> scaled_dims = scaled_dimensions(in_shape.x(),
                                                                                      in_shape.y(),
                                                                                      kernel_shape.x(),
                                                                                      kernel_shape.y(),
                                                                                      info);
          out_shape.set(0, scaled_dims.first);
          out_shape.set(1, scaled_dims.second);
          out_shape.set(2, kernel_shape[3]);
          return out_shape;
      }
  };
  
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  class DirectConvolutionValidationTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>
  {
  public:
      template <typename...>
      void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type)
      {
          DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
                                                                                                                 QuantizationInfo());
      }
  };
  
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  class DirectConvolutionValidationQuantizedTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>
  {
  public:
      template <typename...>
      void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info)
      {
          DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
                                                                                                                 quantization_info);
      }
  };
  
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  class DirectConvolutionValidationWithTensorShapesQuantizedTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>
  {
  public:
      template <typename...>
      void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y,
                 DataType data_type, QuantizationInfo quantization_info)
      {
          DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation_x, dilation_y, data_type,
                                                                                                                 quantization_info);
      }
  };
  
  template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
  class DirectConvolutionValidationWithTensorShapesTensorShiftFixture : public DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>
  {
  public:
      template <typename...>
      void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, unsigned int dilation_x, unsigned int dilation_y,
                 DataType data_type)
      {
          DirectConvolutionValidationGenericTensorShiftFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation_x, dilation_y, data_type,
                                                                                                                 QuantizationInfo());
      }
  };
  
  } // namespace validation
  } // namespace test
  } // namespace arm_compute
  