tests/validation/fixtures/ChannelCombineFixture.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_CHANNEL_COMBINE_FIXTURE
 #define ARM_COMPUTE_TEST_CHANNEL_COMBINE_FIXTURE

 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.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/ChannelCombine.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace
 {
 template <typename TensorType>
 inline std::vector<TensorType> create_tensor_planes(const TensorShape &shape, Format format)
 {
     TensorShape image_shape = adjust_odd_shape(shape, format);
     TensorInfo  info(image_shape, Format::U8);

     std::vector<TensorType> tensor_planes;

     switch(format)
     {
         case Format::RGB888:
         case Format::RGBA8888:
         case Format::YUV444:
         {
             tensor_planes.resize(3);

             if(format == Format::RGBA8888)
             {
                 tensor_planes.resize(4);
             }

             for(unsigned int plane_idx = 0; plane_idx < tensor_planes.size(); ++plane_idx)
             {
                 tensor_planes[plane_idx].allocator()->init(info);
             }

             break;
         }
         case Format::YUYV422:
         case Format::UYVY422:
         {
             const TensorShape uv_shape = calculate_subsampled_shape(image_shape, format);
             const TensorInfo  info_hor2(uv_shape, Format::U8);

             tensor_planes.resize(3);

             tensor_planes[0].allocator()->init(info);
             tensor_planes[1].allocator()->init(info_hor2);
             tensor_planes[2].allocator()->init(info_hor2);
             break;
         }
         case Format::NV12:
         case Format::NV21:
         case Format::IYUV:
         {
             const TensorShape sub2_shape = calculate_subsampled_shape(image_shape, format);
             const TensorInfo  info_sub2(sub2_shape, Format::U8);

             tensor_planes.resize(3);

             tensor_planes[0].allocator()->init(info);
             tensor_planes[1].allocator()->init(info_sub2);
             tensor_planes[2].allocator()->init(info_sub2);
             break;
         }
         default:
             ARM_COMPUTE_ERROR("Not supported");
             break;
     }

     return tensor_planes;
 }
 } // namespace

 template <typename MultiImageType, typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ChannelCombineValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape, Format format)
     {
         _num_planes = num_planes_from_format(format);
         _target     = compute_target(shape, format);
         _reference  = compute_reference(shape, format);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         library->fill_tensor_uniform(tensor, i);
     }

     template <typename U>
     std::vector<SimpleTensor<U>> create_tensor_planes_reference(const TensorShape &shape, Format format)
     {
         std::vector<SimpleTensor<U>> tensor_planes;

         TensorShape image_shape = adjust_odd_shape(shape, format);

         switch(format)
         {
             case Format::RGB888:
             case Format::RGBA8888:
             case Format::YUV444:
             {
                 if(format == Format::RGBA8888)
                 {
                     tensor_planes.emplace_back(image_shape, Format::U8);
                 }

                 tensor_planes.emplace_back(image_shape, Format::U8);
                 tensor_planes.emplace_back(image_shape, Format::U8);
                 tensor_planes.emplace_back(image_shape, Format::U8);
                 break;
             }
             case Format::YUYV422:
             case Format::UYVY422:
             {
                 const TensorShape hor2_shape = calculate_subsampled_shape(image_shape, format);

                 tensor_planes.emplace_back(image_shape, Format::U8);
                 tensor_planes.emplace_back(hor2_shape, Format::U8);
                 tensor_planes.emplace_back(hor2_shape, Format::U8);
                 break;
             }
             case Format::NV12:
             case Format::NV21:
             case Format::IYUV:
             {
                 const TensorShape shape_sub2 = calculate_subsampled_shape(image_shape, format);

                 tensor_planes.emplace_back(image_shape, Format::U8);
                 tensor_planes.emplace_back(shape_sub2, Format::U8);
                 tensor_planes.emplace_back(shape_sub2, Format::U8);
                 break;
             }
             default:
                 ARM_COMPUTE_ERROR("Not supported");
                 break;
         }

         return tensor_planes;
     }

     MultiImageType compute_target(const TensorShape &shape, Format format)
     {
         // Create tensors
         std::vector<TensorType> ref_src = create_tensor_planes<TensorType>(shape, format);
         MultiImageType          dst     = create_multi_image<MultiImageType>(shape, format);

         // Create and configure function
         FunctionType channel_combine;

         if(1 == _num_planes)
         {
             const TensorType *tensor_extra = ((Format::RGBA8888 == format) ? &ref_src[3] : nullptr);
             TensorType       *tensor_dst   = dynamic_cast<TensorType *>(dst.plane(0));

             channel_combine.configure(&ref_src[0], &ref_src[1], &ref_src[2], tensor_extra, tensor_dst);
         }
         else
         {
             channel_combine.configure(&ref_src[0], &ref_src[1], &ref_src[2], &dst);
         }

         for(unsigned int plane_idx = 0; plane_idx < _num_planes; ++plane_idx)
         {
             const TensorType *dst_plane = static_cast<const TensorType *>(dst.plane(plane_idx));

             ARM_COMPUTE_EXPECT(dst_plane->info()->is_resizable(), framework::LogLevel::ERRORS);
         }

         for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
         {
             ARM_COMPUTE_EXPECT(ref_src[plane_idx].info()->is_resizable(), framework::LogLevel::ERRORS);
         }

         // Allocate tensors
         dst.allocate();

         for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
         {
             ref_src[plane_idx].allocator()->allocate();
         }

         for(unsigned int plane_idx = 0; plane_idx < _num_planes; ++plane_idx)
         {
             const TensorType *dst_plane = static_cast<const TensorType *>(dst.plane(plane_idx));

             ARM_COMPUTE_EXPECT(!dst_plane->info()->is_resizable(), framework::LogLevel::ERRORS);
         }

         for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
         {
             ARM_COMPUTE_EXPECT(!ref_src[plane_idx].info()->is_resizable(), framework::LogLevel::ERRORS);
         }

         // Fill tensor planes
         for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
         {
             fill(AccessorType(ref_src[plane_idx]), plane_idx);
         }

         // Compute function
         channel_combine.run();

         return dst;
     }

     std::vector<SimpleTensor<T>> compute_reference(const TensorShape &shape, Format format)
     {
         // Create reference
         std::vector<SimpleTensor<T>> ref_src = create_tensor_planes_reference<T>(shape, format);

         // Fill references
         for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
         {
             fill(ref_src[plane_idx], plane_idx);
         }

         return reference::channel_combine<T>(shape, ref_src, format);
     }

     unsigned int                 _num_planes{};
     MultiImageType               _target{};
     std::vector<SimpleTensor<T>> _reference{};
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_CHANNEL_COMBINE_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_CHANNEL_COMBINE_FIXTURE
	#define ARM_COMPUTE_TEST_CHANNEL_COMBINE_FIXTURE

	#include "arm_compute/core/TensorShape.h"
	#include "arm_compute/core/Types.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/ChannelCombine.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace
	{
	template <typename TensorType>
	inline std::vector<TensorType> create_tensor_planes(const TensorShape &shape, Format format)
	{
	TensorShape image_shape = adjust_odd_shape(shape, format);
	TensorInfo info(image_shape, Format::U8);

	std::vector<TensorType> tensor_planes;

	switch(format)
	{
	case Format::RGB888:
	case Format::RGBA8888:
	case Format::YUV444:
	{
	tensor_planes.resize(3);

	if(format == Format::RGBA8888)
	{
	tensor_planes.resize(4);
	}

	for(unsigned int plane_idx = 0; plane_idx < tensor_planes.size(); ++plane_idx)
	{
	tensor_planes[plane_idx].allocator()->init(info);
	}

	break;
	}
	case Format::YUYV422:
	case Format::UYVY422:
	{
	const TensorShape uv_shape = calculate_subsampled_shape(image_shape, format);
	const TensorInfo info_hor2(uv_shape, Format::U8);

	tensor_planes.resize(3);

	tensor_planes[0].allocator()->init(info);
	tensor_planes[1].allocator()->init(info_hor2);
	tensor_planes[2].allocator()->init(info_hor2);
	break;
	}
	case Format::NV12:
	case Format::NV21:
	case Format::IYUV:
	{
	const TensorShape sub2_shape = calculate_subsampled_shape(image_shape, format);
	const TensorInfo info_sub2(sub2_shape, Format::U8);

	tensor_planes.resize(3);

	tensor_planes[0].allocator()->init(info);
	tensor_planes[1].allocator()->init(info_sub2);
	tensor_planes[2].allocator()->init(info_sub2);
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not supported");
	break;
	}

	return tensor_planes;
	}
	} // namespace

	template <typename MultiImageType, typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ChannelCombineValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape, Format format)
	{
	_num_planes = num_planes_from_format(format);
	_target = compute_target(shape, format);
	_reference = compute_reference(shape, format);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	library->fill_tensor_uniform(tensor, i);
	}

	template <typename U>
	std::vector<SimpleTensor<U>> create_tensor_planes_reference(const TensorShape &shape, Format format)
	{
	std::vector<SimpleTensor<U>> tensor_planes;

	TensorShape image_shape = adjust_odd_shape(shape, format);

	switch(format)
	{
	case Format::RGB888:
	case Format::RGBA8888:
	case Format::YUV444:
	{
	if(format == Format::RGBA8888)
	{
	tensor_planes.emplace_back(image_shape, Format::U8);
	}

	tensor_planes.emplace_back(image_shape, Format::U8);
	tensor_planes.emplace_back(image_shape, Format::U8);
	tensor_planes.emplace_back(image_shape, Format::U8);
	break;
	}
	case Format::YUYV422:
	case Format::UYVY422:
	{
	const TensorShape hor2_shape = calculate_subsampled_shape(image_shape, format);

	tensor_planes.emplace_back(image_shape, Format::U8);
	tensor_planes.emplace_back(hor2_shape, Format::U8);
	tensor_planes.emplace_back(hor2_shape, Format::U8);
	break;
	}
	case Format::NV12:
	case Format::NV21:
	case Format::IYUV:
	{
	const TensorShape shape_sub2 = calculate_subsampled_shape(image_shape, format);

	tensor_planes.emplace_back(image_shape, Format::U8);
	tensor_planes.emplace_back(shape_sub2, Format::U8);
	tensor_planes.emplace_back(shape_sub2, Format::U8);
	break;
	}
	default:
	ARM_COMPUTE_ERROR("Not supported");
	break;
	}

	return tensor_planes;
	}

	MultiImageType compute_target(const TensorShape &shape, Format format)
	{
	// Create tensors
	std::vector<TensorType> ref_src = create_tensor_planes<TensorType>(shape, format);
	MultiImageType dst = create_multi_image<MultiImageType>(shape, format);

	// Create and configure function
	FunctionType channel_combine;

	if(1 == _num_planes)
	{
	const TensorType *tensor_extra = ((Format::RGBA8888 == format) ? &ref_src[3] : nullptr);
	TensorType tensor_dst = dynamic_cast<TensorType >(dst.plane(0));

	channel_combine.configure(&ref_src[0], &ref_src[1], &ref_src[2], tensor_extra, tensor_dst);
	}
	else
	{
	channel_combine.configure(&ref_src[0], &ref_src[1], &ref_src[2], &dst);
	}

	for(unsigned int plane_idx = 0; plane_idx < _num_planes; ++plane_idx)
	{
	const TensorType dst_plane = static_cast<const TensorType >(dst.plane(plane_idx));

	ARM_COMPUTE_EXPECT(dst_plane->info()->is_resizable(), framework::LogLevel::ERRORS);
	}

	for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
	{
	ARM_COMPUTE_EXPECT(ref_src[plane_idx].info()->is_resizable(), framework::LogLevel::ERRORS);
	}

	// Allocate tensors
	dst.allocate();

	for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
	{
	ref_src[plane_idx].allocator()->allocate();
	}

	for(unsigned int plane_idx = 0; plane_idx < _num_planes; ++plane_idx)
	{
	const TensorType dst_plane = static_cast<const TensorType >(dst.plane(plane_idx));

	ARM_COMPUTE_EXPECT(!dst_plane->info()->is_resizable(), framework::LogLevel::ERRORS);
	}

	for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
	{
	ARM_COMPUTE_EXPECT(!ref_src[plane_idx].info()->is_resizable(), framework::LogLevel::ERRORS);
	}

	// Fill tensor planes
	for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
	{
	fill(AccessorType(ref_src[plane_idx]), plane_idx);
	}

	// Compute function
	channel_combine.run();

	return dst;
	}

	std::vector<SimpleTensor<T>> compute_reference(const TensorShape &shape, Format format)
	{
	// Create reference
	std::vector<SimpleTensor<T>> ref_src = create_tensor_planes_reference<T>(shape, format);

	// Fill references
	for(unsigned int plane_idx = 0; plane_idx < ref_src.size(); ++plane_idx)
	{
	fill(ref_src[plane_idx], plane_idx);
	}

	return reference::channel_combine<T>(shape, ref_src, format);
	}

	unsigned int _num_planes{};
	MultiImageType _target{};
	std::vector<SimpleTensor<T>> _reference{};
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_CHANNEL_COMBINE_FIXTURE */