tests/validation/fixtures/WinogradLayerFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2018 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.
  */
 #ifndef ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE
 #define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE

 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.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/reference/ConvolutionLayer.h"
 #include "tests/validation/reference/Utils.h"
 #include "tests/validation/reference/Winograd.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 using namespace arm_compute::misc::shape_calculator;

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class WinogradConvolutionLayerValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type)
     {
         ARM_COMPUTE_UNUSED(dilation);

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i, float min, float max)
     {
         switch(tensor.data_type())
         {
             case DataType::F32:
             {
                 std::uniform_real_distribution<> distribution(min, max);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Not supported");
                 library->fill_tensor_uniform(tensor, i);
                 break;
             }
         }
     }

     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)
     {
         // Create tensors
         TensorType src     = create_tensor<TensorType>(input_shape, data_type, 1);
         TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1);
         TensorType bias    = create_tensor<TensorType>(bias_shape, data_type, 1);
         TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1);

         // Create and configure function
         FunctionType conv;
         conv.configure(&src, &weights, &bias, &dst, 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);

         // Allocate tensors
         src.allocator()->allocate();
         weights.allocator()->allocate();
         dst.allocator()->allocate();
         bias.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);

         // Fill tensors
         fill(AccessorType(src), 0, -1.f, 1.f);
         fill(AccessorType(weights), 1, -1.f, 1.f);
         fill(AccessorType(bias), 2, -1.f, 1.f);

         // Compute Winograd Convolution function
         conv.run();

         return dst;
     }

     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)
     {
         // Create reference
         SimpleTensor<T> src{ input_shape, data_type, 1 };
         SimpleTensor<T> weights{ weights_shape, data_type, 1 };
         SimpleTensor<T> bias{ bias_shape, data_type, 1 };

         // Fill reference
         fill(src, 0, -1.f, 1.f);
         fill(weights, 1, -1.f, 1.f);
         fill(bias, 2, -1.f, 1.f);

         return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class WinogradInputTransformValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, PadStrideInfo conv_info, Size2D kernel_dims, bool is_nchw_format, DataType data_type)
     {
         TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), conv_info, kernel_dims);

         _target    = compute_target(input_shape, output_shape, conv_info, kernel_dims, is_nchw_format, data_type);
         _reference = compute_reference(input_shape, output_shape, conv_info, kernel_dims, is_nchw_format, data_type);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i, float min, float max)
     {
         switch(tensor.data_type())
         {
             case DataType::F32:
             {
                 std::uniform_real_distribution<> distribution(min, max);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Not supported");
                 library->fill_tensor_uniform(tensor, i);
                 break;
             }
         }
     }

     TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims, bool is_nchw_format, DataType data_type)
     {
         ARM_COMPUTE_UNUSED(is_nchw_format);

         TensorType src = create_tensor<TensorType>(input_shape, data_type);
         TensorType dst = create_tensor<TensorType>(output_shape, data_type);

         // Create and configure function
         FunctionType transf;
         transf.configure(&src, &dst, conv_info, kernel_dims);

         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(src), 0, -1.f, 1.f);

         // Compute CLWinogradInputTransform function
         transf.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims, bool is_nchw_format, DataType data_type)
     {
         ARM_COMPUTE_UNUSED(is_nchw_format);

         // Create reference
         SimpleTensor<T> src{ input_shape, data_type };

         // Fill reference
         fill(src, 0, -1.f, 1.f);

         return reference::winograd_input_transform<T>(src, output_shape, conv_info, kernel_dims);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class WinogradFilterTransformValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, bool is_nchw_format, Size2D output_tile, DataType data_type)
     {
         TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), output_tile);

         _target    = compute_target(input_shape, output_shape, is_nchw_format, output_tile, data_type);
         _reference = compute_reference(input_shape, output_shape, is_nchw_format, output_tile, data_type);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i, float min, float max)
     {
         switch(tensor.data_type())
         {
             case DataType::F32:
             {
                 std::uniform_real_distribution<> distribution(min, max);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Not supported");
                 library->fill_tensor_uniform(tensor, i);
                 break;
             }
         }
     }

     TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type)
     {
         ARM_COMPUTE_UNUSED(is_nchw_format);

         // Create tensors
         TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
         TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);

         // Create and configure function
         FunctionType filter_transform;
         filter_transform.configure(&src, &dst, output_tile);

         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(src), 0, -1.f, 1.f);

         filter_transform.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type)
     {
         ARM_COMPUTE_UNUSED(is_nchw_format);

         // Create reference
         SimpleTensor<T> src{ input_shape, data_type, 1 };

         // Fill reference
         fill(src, 0, -1.f, 1.f);

         return reference::winograd_filter_transform<T>(src, output_shape, output_tile);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class WinogradOutputTransformValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, Size2D kernel_dims, Size2D output_convolved_dims, Size2D num_tiles, DataLayout data_layout, DataType data_type)
     {
         TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), output_convolved_dims, data_layout);

         _target    = compute_target(input_shape, output_shape, kernel_dims, output_convolved_dims, num_tiles, data_layout, data_type);
         _reference = compute_reference(input_shape, output_shape, kernel_dims, output_convolved_dims, num_tiles, data_layout, data_type);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i, float min, float max)
     {
         switch(tensor.data_type())
         {
             case DataType::F32:
             {
                 std::uniform_real_distribution<> distribution(min, max);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
             {
                 ARM_COMPUTE_ERROR("Not supported");
                 library->fill_tensor_uniform(tensor, i);
                 break;
             }
         }
     }

     TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &output_convolved_dims, Size2D &num_tiles, DataLayout data_layout,
                               DataType data_type)
     {
         // Create tensors
         TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
         TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout);

         // Create and configure function
         FunctionType output_transform;
         output_transform.configure(&src, nullptr, &dst, kernel_dims, output_convolved_dims, num_tiles);

         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Allocate tensors
         src.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

         // Fill tensors
         fill(AccessorType(src), 0, -1.f, 1.f);

         output_transform.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &output_convolved_dims, Size2D &num_tiles,
                                       DataLayout data_layout,
                                       DataType   data_type)
     {
         // Create reference
         SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout };

         // Fill reference
         fill(src, 0, -1.f, 1.f);

         return reference::winograd_output_transform<T>(src, output_shape, kernel_dims, num_tiles);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */
	/*
	* Copyright (c) 2017-2018 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.
	*/
	#ifndef ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE
	#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE

	#include "arm_compute/core/TensorShape.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.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/reference/ConvolutionLayer.h"
	#include "tests/validation/reference/Utils.h"
	#include "tests/validation/reference/Winograd.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	using namespace arm_compute::misc::shape_calculator;

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class WinogradConvolutionLayerValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type)
	{
	ARM_COMPUTE_UNUSED(dilation);

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i, float min, float max)
	{
	switch(tensor.data_type())
	{
	case DataType::F32:
	{
	std::uniform_real_distribution<> distribution(min, max);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported");
	library->fill_tensor_uniform(tensor, i);
	break;
	}
	}
	}

	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)
	{
	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
	TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1);
	TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
	TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);

	// Create and configure function
	FunctionType conv;
	conv.configure(&src, &weights, &bias, &dst, 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);

	// Allocate tensors
	src.allocator()->allocate();
	weights.allocator()->allocate();
	dst.allocator()->allocate();
	bias.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);

	// Fill tensors
	fill(AccessorType(src), 0, -1.f, 1.f);
	fill(AccessorType(weights), 1, -1.f, 1.f);
	fill(AccessorType(bias), 2, -1.f, 1.f);

	// Compute Winograd Convolution function
	conv.run();

	return dst;
	}

	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)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1 };
	SimpleTensor<T> weights{ weights_shape, data_type, 1 };
	SimpleTensor<T> bias{ bias_shape, data_type, 1 };

	// Fill reference
	fill(src, 0, -1.f, 1.f);
	fill(weights, 1, -1.f, 1.f);
	fill(bias, 2, -1.f, 1.f);

	return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class WinogradInputTransformValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, PadStrideInfo conv_info, Size2D kernel_dims, bool is_nchw_format, DataType data_type)
	{
	TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), conv_info, kernel_dims);

	_target = compute_target(input_shape, output_shape, conv_info, kernel_dims, is_nchw_format, data_type);
	_reference = compute_reference(input_shape, output_shape, conv_info, kernel_dims, is_nchw_format, data_type);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i, float min, float max)
	{
	switch(tensor.data_type())
	{
	case DataType::F32:
	{
	std::uniform_real_distribution<> distribution(min, max);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported");
	library->fill_tensor_uniform(tensor, i);
	break;
	}
	}
	}

	TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims, bool is_nchw_format, DataType data_type)
	{
	ARM_COMPUTE_UNUSED(is_nchw_format);

	TensorType src = create_tensor<TensorType>(input_shape, data_type);
	TensorType dst = create_tensor<TensorType>(output_shape, data_type);

	// Create and configure function
	FunctionType transf;
	transf.configure(&src, &dst, conv_info, kernel_dims);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	src.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(src), 0, -1.f, 1.f);

	// Compute CLWinogradInputTransform function
	transf.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims, bool is_nchw_format, DataType data_type)
	{
	ARM_COMPUTE_UNUSED(is_nchw_format);

	// Create reference
	SimpleTensor<T> src{ input_shape, data_type };

	// Fill reference
	fill(src, 0, -1.f, 1.f);

	return reference::winograd_input_transform<T>(src, output_shape, conv_info, kernel_dims);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class WinogradFilterTransformValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, bool is_nchw_format, Size2D output_tile, DataType data_type)
	{
	TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), output_tile);

	_target = compute_target(input_shape, output_shape, is_nchw_format, output_tile, data_type);
	_reference = compute_reference(input_shape, output_shape, is_nchw_format, output_tile, data_type);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i, float min, float max)
	{
	switch(tensor.data_type())
	{
	case DataType::F32:
	{
	std::uniform_real_distribution<> distribution(min, max);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported");
	library->fill_tensor_uniform(tensor, i);
	break;
	}
	}
	}

	TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type)
	{
	ARM_COMPUTE_UNUSED(is_nchw_format);

	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
	TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1);

	// Create and configure function
	FunctionType filter_transform;
	filter_transform.configure(&src, &dst, output_tile);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	src.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(src), 0, -1.f, 1.f);

	filter_transform.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type)
	{
	ARM_COMPUTE_UNUSED(is_nchw_format);

	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1 };

	// Fill reference
	fill(src, 0, -1.f, 1.f);

	return reference::winograd_filter_transform<T>(src, output_shape, output_tile);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class WinogradOutputTransformValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, Size2D kernel_dims, Size2D output_convolved_dims, Size2D num_tiles, DataLayout data_layout, DataType data_type)
	{
	TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), output_convolved_dims, data_layout);

	_target = compute_target(input_shape, output_shape, kernel_dims, output_convolved_dims, num_tiles, data_layout, data_type);
	_reference = compute_reference(input_shape, output_shape, kernel_dims, output_convolved_dims, num_tiles, data_layout, data_type);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i, float min, float max)
	{
	switch(tensor.data_type())
	{
	case DataType::F32:
	{
	std::uniform_real_distribution<> distribution(min, max);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported");
	library->fill_tensor_uniform(tensor, i);
	break;
	}
	}
	}

	TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &output_convolved_dims, Size2D &num_tiles, DataLayout data_layout,
	DataType data_type)
	{
	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, 0, QuantizationInfo(), data_layout);
	TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, 0, QuantizationInfo(), data_layout);

	// Create and configure function
	FunctionType output_transform;
	output_transform.configure(&src, nullptr, &dst, kernel_dims, output_convolved_dims, num_tiles);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Allocate tensors
	src.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Fill tensors
	fill(AccessorType(src), 0, -1.f, 1.f);

	output_transform.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &output_convolved_dims, Size2D &num_tiles,
	DataLayout data_layout,
	DataType data_type)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1, 0, QuantizationInfo(), data_layout };

	// Fill reference
	fill(src, 0, -1.f, 1.f);

	return reference::winograd_output_transform<T>(src, output_shape, kernel_dims, num_tiles);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */