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

 /*
  * Copyright (c) 2017-2020 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_SCALE_FIXTURE
 #define ARM_COMPUTE_TEST_SCALE_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/reference/Permute.h"
 #include "tests/validation/reference/Scale.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ScaleValidationGenericFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
                bool align_corners)
     {
         _shape             = shape;
         _policy            = policy;
         _border_mode       = border_mode;
         _sampling_policy   = sampling_policy;
         _data_type         = data_type;
         _quantization_info = quantization_info;
         _align_corners     = align_corners;

         generate_scale(shape);

         std::mt19937                           generator(library->seed());
         std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
         _constant_border_value = static_cast<T>(distribution_u8(generator));

         _target    = compute_target(shape, data_layout);
         _reference = compute_reference(shape);
     }

 protected:
     void generate_scale(const TensorShape &shape)
     {
         static constexpr float _min_scale{ 0.25f };
         static constexpr float _max_scale{ 3.f };

         constexpr float max_width{ 8192.0f };
         constexpr float max_height{ 6384.0f };

         const bool is_align_corners_used = _align_corners && arm_compute::is_align_corners_allowed(_sampling_policy);

         const float min_width  = is_align_corners_used ? 2.f : 1.f;
         const float min_height = is_align_corners_used ? 2.f : 1.f;

         std::mt19937                          generator(library->seed());
         std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);

         auto generate = [&](size_t input_size, float min_output, float max_output) -> float
         {
             const float generated_scale = distribution_float(generator);
             const float output_size     = utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output);
             return output_size / input_size;
         };

         // Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
         const int idx_width  = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
         const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

         _scale_x = generate(shape[idx_width], min_width, max_width);
         _scale_y = generate(shape[idx_height], min_height, max_height);
     }

     template <typename U>
     void fill(U &&tensor)
     {
         if(is_data_type_float(_data_type))
         {
             library->fill_tensor_uniform(tensor, 0);
         }
         else if(is_data_type_quantized(tensor.data_type()))
         {
             std::uniform_int_distribution<> distribution(0, 100);
             library->fill(tensor, distribution, 0);
         }
         else
         {
             // Restrict range for float to avoid any floating point issues
             std::uniform_real_distribution<> distribution(-5.0f, 5.0f);
             library->fill(tensor, distribution, 0);
         }
     }

     TensorType compute_target(TensorShape shape, DataLayout data_layout)
     {
         // Change shape in case of NHWC.
         if(data_layout == DataLayout::NHWC)
         {
             permute(shape, PermutationVector(2U, 0U, 1U));
         }

         // Create tensors
         TensorType src = create_tensor<TensorType>(shape, _data_type, 1, _quantization_info, data_layout);

         const int idx_width  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
         const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

         TensorShape shape_scaled(shape);
         shape_scaled.set(idx_width, shape[idx_width] * _scale_x);
         shape_scaled.set(idx_height, shape[idx_height] * _scale_y);
         TensorType dst = create_tensor<TensorType>(shape_scaled, _data_type, 1, _quantization_info, data_layout);

         // Create and configure function
         FunctionType scale;

         scale.configure(&src, &dst, ScaleKernelInfo{ _policy, _border_mode, _constant_border_value, _sampling_policy, /* use_padding */ true, _align_corners });

         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));

         // Compute function
         scale.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &shape)
     {
         // Create reference
         SimpleTensor<T> src{ shape, _data_type, 1, _quantization_info };

         // Fill reference
         fill(src);

         return reference::scale<T>(src, _scale_x, _scale_y, _policy, _border_mode, _constant_border_value, _sampling_policy, /* ceil_policy_scale */ false, _align_corners);
     }

     TensorType          _target{};
     SimpleTensor<T>     _reference{};
     TensorShape         _shape{};
     InterpolationPolicy _policy{};
     BorderMode          _border_mode{};
     T                   _constant_border_value{};
     SamplingPolicy      _sampling_policy{};
     DataType            _data_type{};
     QuantizationInfo    _quantization_info{};
     bool                _align_corners{ false };
     float               _scale_x{ 1.f };
     float               _scale_y{ 1.f };
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ScaleValidationQuantizedFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
                bool align_corners)
     {
         ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
                                                                                         data_type,
                                                                                         quantization_info,
                                                                                         data_layout,
                                                                                         policy,
                                                                                         border_mode,
                                                                                         sampling_policy,
                                                                                         align_corners);
     }
 };
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ScaleValidationFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy, bool align_corners)
     {
         ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
                                                                                         data_type,
                                                                                         QuantizationInfo(),
                                                                                         data_layout,
                                                                                         policy,
                                                                                         border_mode,
                                                                                         sampling_policy,
                                                                                         align_corners);
     }
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */
	/*
	* Copyright (c) 2017-2020 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_SCALE_FIXTURE
	#define ARM_COMPUTE_TEST_SCALE_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/reference/Permute.h"
	#include "tests/validation/reference/Scale.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ScaleValidationGenericFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
	bool align_corners)
	{
	_shape = shape;
	_policy = policy;
	_border_mode = border_mode;
	_sampling_policy = sampling_policy;
	_data_type = data_type;
	_quantization_info = quantization_info;
	_align_corners = align_corners;

	generate_scale(shape);

	std::mt19937 generator(library->seed());
	std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
	_constant_border_value = static_cast<T>(distribution_u8(generator));

	_target = compute_target(shape, data_layout);
	_reference = compute_reference(shape);
	}

	protected:
	void generate_scale(const TensorShape &shape)
	{
	static constexpr float _min_scale{ 0.25f };
	static constexpr float _max_scale{ 3.f };

	constexpr float max_width{ 8192.0f };
	constexpr float max_height{ 6384.0f };

	const bool is_align_corners_used = _align_corners && arm_compute::is_align_corners_allowed(_sampling_policy);

	const float min_width = is_align_corners_used ? 2.f : 1.f;
	const float min_height = is_align_corners_used ? 2.f : 1.f;

	std::mt19937 generator(library->seed());
	std::uniform_real_distribution<float> distribution_float(_min_scale, _max_scale);

	auto generate = [&](size_t input_size, float min_output, float max_output) -> float
	{
	const float generated_scale = distribution_float(generator);
	const float output_size = utility::clamp(static_cast<float>(input_size) * generated_scale, min_output, max_output);
	return output_size / input_size;
	};

	// Input shape is always given in NCHW layout. NHWC is dealt by permute in compute_target()
	const int idx_width = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(DataLayout::NCHW, DataLayoutDimension::HEIGHT);

	_scale_x = generate(shape[idx_width], min_width, max_width);
	_scale_y = generate(shape[idx_height], min_height, max_height);
	}

	template <typename U>
	void fill(U &&tensor)
	{
	if(is_data_type_float(_data_type))
	{
	library->fill_tensor_uniform(tensor, 0);
	}
	else if(is_data_type_quantized(tensor.data_type()))
	{
	std::uniform_int_distribution<> distribution(0, 100);
	library->fill(tensor, distribution, 0);
	}
	else
	{
	// Restrict range for float to avoid any floating point issues
	std::uniform_real_distribution<> distribution(-5.0f, 5.0f);
	library->fill(tensor, distribution, 0);
	}
	}

	TensorType compute_target(TensorShape shape, DataLayout data_layout)
	{
	// Change shape in case of NHWC.
	if(data_layout == DataLayout::NHWC)
	{
	permute(shape, PermutationVector(2U, 0U, 1U));
	}

	// Create tensors
	TensorType src = create_tensor<TensorType>(shape, _data_type, 1, _quantization_info, data_layout);

	const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
	const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

	TensorShape shape_scaled(shape);
	shape_scaled.set(idx_width, shape[idx_width] * _scale_x);
	shape_scaled.set(idx_height, shape[idx_height] * _scale_y);
	TensorType dst = create_tensor<TensorType>(shape_scaled, _data_type, 1, _quantization_info, data_layout);

	// Create and configure function
	FunctionType scale;

	scale.configure(&src, &dst, ScaleKernelInfo{ _policy, _border_mode, _constant_border_value, _sampling_policy, /* use_padding */ true, _align_corners });

	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));

	// Compute function
	scale.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &shape)
	{
	// Create reference
	SimpleTensor<T> src{ shape, _data_type, 1, _quantization_info };

	// Fill reference
	fill(src);

	return reference::scale<T>(src, _scale_x, _scale_y, _policy, _border_mode, _constant_border_value, _sampling_policy, /* ceil_policy_scale */ false, _align_corners);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	TensorShape _shape{};
	InterpolationPolicy _policy{};
	BorderMode _border_mode{};
	T _constant_border_value{};
	SamplingPolicy _sampling_policy{};
	DataType _data_type{};
	QuantizationInfo _quantization_info{};
	bool _align_corners{ false };
	float _scale_x{ 1.f };
	float _scale_y{ 1.f };
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ScaleValidationQuantizedFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape shape, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy,
	bool align_corners)
	{
	ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
	data_type,
	quantization_info,
	data_layout,
	policy,
	border_mode,
	sampling_policy,
	align_corners);
	}
	};
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ScaleValidationFixture : public ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy, bool align_corners)
	{
	ScaleValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape,
	data_type,
	QuantizationInfo(),
	data_layout,
	policy,
	border_mode,
	sampling_policy,
	align_corners);
	}
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */