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

 /*
  * Copyright (c) 2017-2021, 2023-2024 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 ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H

 #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/framework/ParametersLibrary.h"
 #include "tests/validation/Helpers.h"
 #include "tests/validation/reference/ActivationLayer.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ActivationValidationGenericFixture : public framework::Fixture
 {
 public:

     void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type, QuantizationInfo quantization_info)
     {
         ActivationLayerInfo info(function, alpha_beta, alpha_beta);

         _in_place                 = in_place;
         _data_type                = data_type;
         _output_quantization_info = calculate_output_quantization_info(_data_type, info, quantization_info);
         _input_quantization_info  = in_place ? _output_quantization_info : quantization_info;

         _function  = function;
         _target    = compute_target(shape, info);
         _reference = compute_reference(shape, info);
     }

 protected:
     std::vector<T> get_boundary_values(T min, T max)
     {
         // This function will return a vector filled with the following values that can
         // represent two partitions derived from equivalent partitioning.
         // * Lower parition: min, min + delta, lower quarter (nominal), center - delta
         // * Upper partition: center, center + delta, upper quarter (nominal), max - delta, max
         const auto delta         = is_data_type_float(_data_type) ? T(0.1f) : T(1);
         const auto center_value  = (min + max) / 2;
         const auto lower_quarter = (min + center_value) / 2;
         const auto upper_quarter = (center_value + max) / 2;

         std::vector<T> boundary_values{};

         // To ensure all the inserted values are within the given range after subtracing/adding delta
         auto insert_values = [&boundary_values, &min, &max](const std::initializer_list<T> &new_values)
         {
             for(auto &v : new_values)
             {
                 if(v >= min && v <= max)
                 {
                     boundary_values.emplace_back(v);
                 }
             }
         };

         insert_values({ min, static_cast<T>(min + delta), static_cast<T>(lower_quarter), static_cast<T>(center_value - delta) });                               // lower partition
         insert_values({ static_cast<T>(center_value), static_cast<T>(center_value + delta), static_cast<T>(upper_quarter), static_cast<T>(max - delta), max }); // upper partition

         return boundary_values;
     }

     template <typename U>
     void fill(U &&tensor)
     {
         if(is_data_type_float(_data_type))
         {
             float min_bound = 0;
             float max_bound = 0;
             std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<T>(_function, _data_type);
             library->fill_static_values(tensor, get_boundary_values(static_cast<T>(min_bound), static_cast<T>(max_bound)));
         }
         else
         {
             PixelValue min{};
             PixelValue max{};
             std::tie(min, max) = get_min_max(tensor.data_type());
             library->fill_static_values(tensor, get_boundary_values(min.get<T>(), max.get<T>()));
         }
     }

     TensorType compute_target(const TensorShape &shape, ActivationLayerInfo info)
     {
         // Create tensors
         TensorType src = create_tensor<TensorType>(shape, _data_type, 1, _input_quantization_info, DataLayout::NCHW);
         TensorType dst = create_tensor<TensorType>(shape, _data_type, 1, _output_quantization_info, DataLayout::NCHW);

         // Create and configure function
         FunctionType act_layer;

         TensorType *dst_ptr = _in_place ? nullptr : &dst;

         act_layer.configure(&src, dst_ptr, info);

         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

         // Allocate tensors
         src.allocator()->allocate();
         ARM_COMPUTE_ASSERT(!src.info()->is_resizable());

         if(!_in_place)
         {
             dst.allocator()->allocate();
             ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
         }

         // Fill tensors
         fill(AccessorType(src));

         // Compute function
         act_layer.run();

         if(_in_place)
         {
             return src;
         }
         else
         {
             return dst;
         }
     }

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

         // Fill reference
         fill(src);

         return reference::activation_layer<T>(src, info, _output_quantization_info);
     }

 private:
     QuantizationInfo calculate_output_quantization_info(DataType dt, const ActivationLayerInfo &act_info, const QuantizationInfo &default_qinfo)
     {
         auto qasymm8_max        = float(std::numeric_limits<uint8_t>::max()) + 1.f;
         auto qasymm8_signed_max = float(std::numeric_limits<int8_t>::max()) + 1.f;
         auto qsymm16_max        = float(std::numeric_limits<int16_t>::max()) + 1.f;

         switch(act_info.activation())
         {
             case ActivationLayerInfo::ActivationFunction::TANH:
                 if(dt == DataType::QSYMM16)
                 {
                     return QuantizationInfo(1.f / qsymm16_max, 0);
                 }
                 else if(dt == DataType::QASYMM8)
                 {
                     return QuantizationInfo(1.f / (0.5 * qasymm8_max), int(0.5 * qasymm8_max));
                 }
                 else if(dt == DataType::QASYMM8_SIGNED)
                 {
                     return QuantizationInfo(1.f / qasymm8_signed_max, 0);
                 }
                 else
                 {
                     return default_qinfo;
                 }
             case ActivationLayerInfo::ActivationFunction::LOGISTIC:
                 if(dt == DataType::QSYMM16)
                 {
                     return QuantizationInfo(1.f / qsymm16_max, 0);
                 }
                 else if(dt == DataType::QASYMM8)
                 {
                     return QuantizationInfo(1.f / qasymm8_max, 0);
                 }
                 else if(dt == DataType::QASYMM8_SIGNED)
                 {
                     return QuantizationInfo(1.f / (2.f * qasymm8_signed_max), -int(qasymm8_signed_max));
                 }
                 else
                 {
                     return default_qinfo;
                 }
             default:
                 return default_qinfo;
         }
     }

 protected:
     TensorType                              _target{};
     SimpleTensor<T>                         _reference{};
     bool                                    _in_place{};
     QuantizationInfo                        _input_quantization_info{};
     QuantizationInfo                        _output_quantization_info{};
     DataType                                _data_type{};
     ActivationLayerInfo::ActivationFunction _function{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ActivationValidationFixture : public ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type)
     {
         ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, in_place, function, alpha_beta, data_type, QuantizationInfo());
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class ActivationValidationQuantizedFixture : public ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type, QuantizationInfo quantization_info)
     {
         ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, in_place, function, alpha_beta, data_type, quantization_info);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif // ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H
	/*
	* Copyright (c) 2017-2021, 2023-2024 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 ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H

	#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/framework/ParametersLibrary.h"
	#include "tests/validation/Helpers.h"
	#include "tests/validation/reference/ActivationLayer.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ActivationValidationGenericFixture : public framework::Fixture
	{
	public:

	void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type, QuantizationInfo quantization_info)
	{
	ActivationLayerInfo info(function, alpha_beta, alpha_beta);

	_in_place = in_place;
	_data_type = data_type;
	_output_quantization_info = calculate_output_quantization_info(_data_type, info, quantization_info);
	_input_quantization_info = in_place ? _output_quantization_info : quantization_info;

	_function = function;
	_target = compute_target(shape, info);
	_reference = compute_reference(shape, info);
	}

	protected:
	std::vector<T> get_boundary_values(T min, T max)
	{
	// This function will return a vector filled with the following values that can
	// represent two partitions derived from equivalent partitioning.
	// * Lower parition: min, min + delta, lower quarter (nominal), center - delta
	// * Upper partition: center, center + delta, upper quarter (nominal), max - delta, max
	const auto delta = is_data_type_float(_data_type) ? T(0.1f) : T(1);
	const auto center_value = (min + max) / 2;
	const auto lower_quarter = (min + center_value) / 2;
	const auto upper_quarter = (center_value + max) / 2;

	std::vector<T> boundary_values{};

	// To ensure all the inserted values are within the given range after subtracing/adding delta
	auto insert_values = [&boundary_values, &min, &max](const std::initializer_list<T> &new_values)
	{
	for(auto &v : new_values)
	{
	if(v >= min && v <= max)
	{
	boundary_values.emplace_back(v);
	}
	}
	};

	insert_values({ min, static_cast<T>(min + delta), static_cast<T>(lower_quarter), static_cast<T>(center_value - delta) }); // lower partition
	insert_values({ static_cast<T>(center_value), static_cast<T>(center_value + delta), static_cast<T>(upper_quarter), static_cast<T>(max - delta), max }); // upper partition

	return boundary_values;
	}

	template <typename U>
	void fill(U &&tensor)
	{
	if(is_data_type_float(_data_type))
	{
	float min_bound = 0;
	float max_bound = 0;
	std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<T>(_function, _data_type);
	library->fill_static_values(tensor, get_boundary_values(static_cast<T>(min_bound), static_cast<T>(max_bound)));
	}
	else
	{
	PixelValue min{};
	PixelValue max{};
	std::tie(min, max) = get_min_max(tensor.data_type());
	library->fill_static_values(tensor, get_boundary_values(min.get<T>(), max.get<T>()));
	}
	}

	TensorType compute_target(const TensorShape &shape, ActivationLayerInfo info)
	{
	// Create tensors
	TensorType src = create_tensor<TensorType>(shape, _data_type, 1, _input_quantization_info, DataLayout::NCHW);
	TensorType dst = create_tensor<TensorType>(shape, _data_type, 1, _output_quantization_info, DataLayout::NCHW);

	// Create and configure function
	FunctionType act_layer;

	TensorType *dst_ptr = _in_place ? nullptr : &dst;

	act_layer.configure(&src, dst_ptr, info);

	ARM_COMPUTE_ASSERT(src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

	// Allocate tensors
	src.allocator()->allocate();
	ARM_COMPUTE_ASSERT(!src.info()->is_resizable());

	if(!_in_place)
	{
	dst.allocator()->allocate();
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
	}

	// Fill tensors
	fill(AccessorType(src));

	// Compute function
	act_layer.run();

	if(_in_place)
	{
	return src;
	}
	else
	{
	return dst;
	}
	}

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

	// Fill reference
	fill(src);

	return reference::activation_layer<T>(src, info, _output_quantization_info);
	}

	private:
	QuantizationInfo calculate_output_quantization_info(DataType dt, const ActivationLayerInfo &act_info, const QuantizationInfo &default_qinfo)
	{
	auto qasymm8_max = float(std::numeric_limits<uint8_t>::max()) + 1.f;
	auto qasymm8_signed_max = float(std::numeric_limits<int8_t>::max()) + 1.f;
	auto qsymm16_max = float(std::numeric_limits<int16_t>::max()) + 1.f;

	switch(act_info.activation())
	{
	case ActivationLayerInfo::ActivationFunction::TANH:
	if(dt == DataType::QSYMM16)
	{
	return QuantizationInfo(1.f / qsymm16_max, 0);
	}
	else if(dt == DataType::QASYMM8)
	{
	return QuantizationInfo(1.f / (0.5 * qasymm8_max), int(0.5 * qasymm8_max));
	}
	else if(dt == DataType::QASYMM8_SIGNED)
	{
	return QuantizationInfo(1.f / qasymm8_signed_max, 0);
	}
	else
	{
	return default_qinfo;
	}
	case ActivationLayerInfo::ActivationFunction::LOGISTIC:
	if(dt == DataType::QSYMM16)
	{
	return QuantizationInfo(1.f / qsymm16_max, 0);
	}
	else if(dt == DataType::QASYMM8)
	{
	return QuantizationInfo(1.f / qasymm8_max, 0);
	}
	else if(dt == DataType::QASYMM8_SIGNED)
	{
	return QuantizationInfo(1.f / (2.f * qasymm8_signed_max), -int(qasymm8_signed_max));
	}
	else
	{
	return default_qinfo;
	}
	default:
	return default_qinfo;
	}
	}

	protected:
	TensorType _target{};
	SimpleTensor<T> _reference{};
	bool _in_place{};
	QuantizationInfo _input_quantization_info{};
	QuantizationInfo _output_quantization_info{};
	DataType _data_type{};
	ActivationLayerInfo::ActivationFunction _function{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ActivationValidationFixture : public ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type)
	{
	ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, in_place, function, alpha_beta, data_type, QuantizationInfo());
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class ActivationValidationQuantizedFixture : public ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape shape, bool in_place, ActivationLayerInfo::ActivationFunction function, float alpha_beta, DataType data_type, QuantizationInfo quantization_info)
	{
	ActivationValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(shape, in_place, function, alpha_beta, data_type, quantization_info);
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif // ACL_TESTS_VALIDATION_FIXTURES_ACTIVATIONLAYERFIXTURE_H