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

 /*
  * Copyright (c) 2017-2019 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_FULLY_CONNECTED_LAYER_FIXTURE
 #define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE

 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "tests/AssetsLibrary.h"
 #include "tests/Globals.h"
 #include "tests/IAccessor.h"
 #include "tests/RawTensor.h"
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Fixture.h"
 #include "tests/validation/Helpers.h"
 #include "tests/validation/reference/FullyConnectedLayer.h"
 #include "tests/validation/reference/Utils.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class FullyConnectedLayerValidationGenericFixture : 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, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights,
                DataType data_type, QuantizationInfo quantization_info)
     {
         ARM_COMPUTE_UNUSED(weights_shape);
         ARM_COMPUTE_UNUSED(bias_shape);

         _data_type         = data_type;
         _bias_data_type    = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
         _quantization_info = quantization_info;

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         if(is_data_type_quantized_asymmetric(_data_type))
         {
             std::uniform_int_distribution<uint8_t> distribution(0, 30);
             library->fill(tensor, distribution, i);
         }
         else if(_data_type == DataType::S32)
         {
             std::uniform_int_distribution<int32_t> distribution(-50, 50);
             library->fill(tensor, distribution, i);
         }
         else if(is_data_type_float(_data_type))
         {
             std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
             library->fill(tensor, distribution, i);
         }
         else
         {
             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, bool transpose_weights,
                               bool reshape_weights)
     {
         TensorShape reshaped_weights_shape(weights_shape);

         // Test actions depending on the target settings
         //
         //            | reshape   | !reshape
         // -----------+-----------+---------------------------
         //  transpose |           | ***
         // -----------+-----------+---------------------------
         // !transpose | transpose | transpose
         //            |           |
         //
         // ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
         if(!reshape_weights || !transpose_weights)
         {
             const size_t shape_x = reshaped_weights_shape.x();
             reshaped_weights_shape.set(0, reshaped_weights_shape.y());
             reshaped_weights_shape.set(1, shape_x);
         }

         // Create tensors
         TensorType src     = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info);
         TensorType weights = create_tensor<TensorType>(reshaped_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);

         // Create Fully Connected layer info
         FullyConnectedLayerInfo fc_info;
         fc_info.transpose_weights    = transpose_weights;
         fc_info.are_weights_reshaped = !reshape_weights;

         // Create and configure function.
         FunctionType fc;
         fc.configure(&src, &weights, &bias, &dst, fc_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();
         bias.allocator()->allocate();
         dst.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);
         fill(AccessorType(bias), 2);

         if(!reshape_weights || !transpose_weights)
         {
             TensorShape tmp_shape(weights_shape);
             RawTensor   tmp(tmp_shape, _data_type, 1);

             // Fill with original shape
             fill(tmp, 1);

             // Transpose elementwise
             tmp = transpose(tmp);

             AccessorType weights_accessor(weights);

             for(int i = 0; i < tmp.num_elements(); ++i)
             {
                 Coordinates coord = index2coord(tmp.shape(), i);
                 std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
                             tmp.element_size(),
                             static_cast<RawTensor::value_type *>(weights_accessor(coord)));
             }
         }
         else
         {
             fill(AccessorType(weights), 1);
         }

         // Compute NEFullyConnectedLayer function
         fc.run();

         return dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape)
     {
         // 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 };

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

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

     TensorType       _target{};
     SimpleTensor<T>  _reference{};
     DataType         _data_type{};
     DataType         _bias_data_type{};
     QuantizationInfo _quantization_info{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
     {
         FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
                                                                                                       reshape_weights, data_type,
                                                                                                       QuantizationInfo());
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type,
                QuantizationInfo quantization_info)
     {
         FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
                                                                                                       reshape_weights, data_type,
                                                                                                       quantization_info);
     }
 };
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */
	/*
	* Copyright (c) 2017-2019 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_FULLY_CONNECTED_LAYER_FIXTURE
	#define ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE

	#include "arm_compute/core/TensorShape.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "tests/AssetsLibrary.h"
	#include "tests/Globals.h"
	#include "tests/IAccessor.h"
	#include "tests/RawTensor.h"
	#include "tests/framework/Asserts.h"
	#include "tests/framework/Fixture.h"
	#include "tests/validation/Helpers.h"
	#include "tests/validation/reference/FullyConnectedLayer.h"
	#include "tests/validation/reference/Utils.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class FullyConnectedLayerValidationGenericFixture : 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, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights,
	DataType data_type, QuantizationInfo quantization_info)
	{
	ARM_COMPUTE_UNUSED(weights_shape);
	ARM_COMPUTE_UNUSED(bias_shape);

	_data_type = data_type;
	_bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
	_quantization_info = quantization_info;

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	if(is_data_type_quantized_asymmetric(_data_type))
	{
	std::uniform_int_distribution<uint8_t> distribution(0, 30);
	library->fill(tensor, distribution, i);
	}
	else if(_data_type == DataType::S32)
	{
	std::uniform_int_distribution<int32_t> distribution(-50, 50);
	library->fill(tensor, distribution, i);
	}
	else if(is_data_type_float(_data_type))
	{
	std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
	library->fill(tensor, distribution, i);
	}
	else
	{
	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, bool transpose_weights,
	bool reshape_weights)
	{
	TensorShape reshaped_weights_shape(weights_shape);

	// Test actions depending on the target settings
	//
	// \| reshape \| !reshape
	// -----------+-----------+---------------------------
	// transpose \| \| ***
	// -----------+-----------+---------------------------
	// !transpose \| transpose \| transpose
	// \| \|
	//
	// ***: That combination is invalid. But we can ignore the transpose flag and handle all !reshape the same
	if(!reshape_weights \|\| !transpose_weights)
	{
	const size_t shape_x = reshaped_weights_shape.x();
	reshaped_weights_shape.set(0, reshaped_weights_shape.y());
	reshaped_weights_shape.set(1, shape_x);
	}

	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _quantization_info);
	TensorType weights = create_tensor<TensorType>(reshaped_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);

	// Create Fully Connected layer info
	FullyConnectedLayerInfo fc_info;
	fc_info.transpose_weights = transpose_weights;
	fc_info.are_weights_reshaped = !reshape_weights;

	// Create and configure function.
	FunctionType fc;
	fc.configure(&src, &weights, &bias, &dst, fc_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();
	bias.allocator()->allocate();
	dst.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);
	fill(AccessorType(bias), 2);

	if(!reshape_weights \|\| !transpose_weights)
	{
	TensorShape tmp_shape(weights_shape);
	RawTensor tmp(tmp_shape, _data_type, 1);

	// Fill with original shape
	fill(tmp, 1);

	// Transpose elementwise
	tmp = transpose(tmp);

	AccessorType weights_accessor(weights);

	for(int i = 0; i < tmp.num_elements(); ++i)
	{
	Coordinates coord = index2coord(tmp.shape(), i);
	std::copy_n(static_cast<const RawTensor::value_type *>(tmp(coord)),
	tmp.element_size(),
	static_cast<RawTensor::value_type *>(weights_accessor(coord)));
	}
	}
	else
	{
	fill(AccessorType(weights), 1);
	}

	// Compute NEFullyConnectedLayer function
	fc.run();

	return dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape)
	{
	// 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 };

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

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

	TensorType _target{};
	SimpleTensor<T> _reference{};
	DataType _data_type{};
	DataType _bias_data_type{};
	QuantizationInfo _quantization_info{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
	{
	FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
	reshape_weights, data_type,
	QuantizationInfo());
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type,
	QuantizationInfo quantization_info)
	{
	FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
	reshape_weights, data_type,
	quantization_info);
	}
	};
	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* ARM_COMPUTE_TEST_FULLY_CONNECTED_LAYER_FIXTURE */