tests/validation/fixtures/DeconvolutionLayerFixture.h - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2017-2021 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.
  */
 #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/DeconvolutionLayer.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, typename TW>
 class DeconvolutionLayerFixtureBase : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value || std::is_same<typename std::decay<T>::type, int8_t>::value, int32_t, T >::type;

 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
                DataType data_type, DataType weights_data_type, DataLayout data_layout,
                QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, QuantizationInfo weights_quantization_info, bool add_bias)
     {
         _data_type                 = data_type;
         _weights_data_type         = weights_data_type;
         _bias_data_type            = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
         _data_layout               = data_layout;
         _input_quantization_info   = input_quantization_info;
         _output_quantization_info  = output_quantization_info;
         _weights_quantization_info = weights_quantization_info;

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

 protected:
     template <typename U>
     void fill(U &&tensor, int i)
     {
         switch(tensor.data_type())
         {
             case DataType::QASYMM8:
             {
                 std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
                 std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::QASYMM8_SIGNED:
             {
                 std::pair<int, int> bounds = get_quantized_qasymm8_signed_bounds(tensor.quantization_info(), -1.0f, 1.0f);
                 std::uniform_int_distribution<int8_t> distribution(bounds.first, bounds.second);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::QSYMM8_PER_CHANNEL:
             {
                 int min_bound = 128;
                 int max_bound = -127;
                 for(size_t i = 0; i < _input_quantization_info.scale().size(); i++)
                 {
                     std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f);
                     if(bounds.first < min_bound)
                     {
                         min_bound = bounds.first;
                     }
                     if(bounds.second > max_bound)
                     {
                         max_bound = bounds.second;
                     }
                 }
                 std::uniform_int_distribution<int8_t> distribution(min_bound, max_bound);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::S32:
             {
                 std::uniform_int_distribution<int32_t> distribution(-100, 100);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F16:
             {
                 arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F32:
             {
                 std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     template <typename U>
     void fill_zeros(U &&tensor)
     {
         switch(tensor.data_type())
         {
             case DataType::S32:
             {
                 library->fill_tensor_value(tensor, 0);
                 break;
             }
             case DataType::F16:
                 library->fill_tensor_value(tensor, static_cast<half>(0.0f));
                 break;
             case DataType::F32:
                 library->fill_tensor_value(tensor, static_cast<float>(0.0f));
                 break;
             default:
                 ARM_COMPUTE_ERROR("Not supported");
         }
     }

     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
                               const PadStrideInfo &info, bool add_bias)
     {
         if(_data_layout == DataLayout::NHWC)
         {
             permute(input_shape, PermutationVector(2U, 0U, 1U));
             permute(weights_shape, PermutationVector(2U, 0U, 1U));
             permute(output_shape, PermutationVector(2U, 0U, 1U));
         }

         // Create tensors
         TensorType src     = create_tensor<TensorType>(input_shape, _data_type, 1, _input_quantization_info, _data_layout);
         TensorType weights = create_tensor<TensorType>(weights_shape, _weights_data_type, 1, _weights_quantization_info, _data_layout);
         TensorType bias    = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _input_quantization_info, _data_layout);
         TensorType dst     = create_tensor<TensorType>(output_shape, _data_type, 1, _output_quantization_info, _data_layout);

         // Create and configure function
         FunctionType conv;
         conv.configure(&src, &weights, add_bias ? &bias : nullptr, &dst, info);

         ARM_COMPUTE_ASSERT(src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(weights.info()->is_resizable());
         if(add_bias)
         {
             ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
         }
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

         // Allocate tensors
         src.allocator()->allocate();
         weights.allocator()->allocate();
         if(add_bias)
         {
             bias.allocator()->allocate();
         }
         dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!weights.info()->is_resizable());
         if(add_bias)
         {
             ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
         }
         ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

         // Fill tensors
         fill(AccessorType(src), 0);
         fill(AccessorType(weights), 1);
         if(add_bias)
         {
             fill(AccessorType(bias), 2);
         }

         // Compute DeconvolutionLayer 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, bool add_bias)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, _data_type, 1, _input_quantization_info };
         SimpleTensor<TW>    weights{ weights_shape, _weights_data_type, 1, _weights_quantization_info };
         SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _input_quantization_info };

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

         if(add_bias)
         {
             fill(bias, 2);
         }
         else
         {
             fill_zeros(bias);
         }
         return reference::deconvolution_layer<T, TW>(src, weights, bias, output_shape, info, _output_quantization_info);
     }

     TensorType       _target{};
     SimpleTensor<T>  _reference{};
     DataType         _data_type{};
     DataType         _weights_data_type{};
     DataType         _bias_data_type{};
     DataLayout       _data_layout{};
     QuantizationInfo _input_quantization_info{};
     QuantizationInfo _output_quantization_info{};
     QuantizationInfo _weights_quantization_info{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
                unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
         TensorInfo          input_info(input_shape, 1, data_type);
         TensorInfo          weights_info(weights_shape, 1, data_type);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
                                                                                            QuantizationInfo(), QuantizationInfo(), add_bias);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationAsymmFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int pad_left, unsigned int pad_right, unsigned int pad_top,
                unsigned int pad_bottom, unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, pad_left, pad_right, pad_top, pad_bottom, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
         TensorInfo          input_info(input_shape, 1, data_type);
         TensorInfo          weights_info(weights_shape, 1, data_type);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
                                                                                            QuantizationInfo(), QuantizationInfo(), add_bias);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
                unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
         TensorInfo          input_info(input_shape, 1, data_type, input_quantization_info);
         TensorInfo          weights_info(weights_shape, 1, data_type, input_quantization_info);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout,
                                                                                            input_quantization_info,
                                                                                            output_quantization_info, input_quantization_info, add_bias);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW, unsigned int kernel_size_x, unsigned int kernel_size_y>
 class DeconvolutionValidationQuantizedPerChannelFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
                unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias,
                DataType weights_data_type)
     {
         ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
         const TensorShape   weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
         auto                out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
         TensorInfo          input_info(input_shape, 1, data_type, input_quantization_info);
         TensorInfo          weights_info(weights_shape, 1, weights_data_type, input_quantization_info);
         TensorShape         output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);

         std::vector<float>                    weights_scales{};
         std::mt19937                          gen(library->seed());
         std::uniform_real_distribution<float> dis(0.01f, 1.f);
         for(size_t i = 0; i < output_shape[2]; ++i)
         {
             weights_scales.push_back(dis(gen));
         }
         DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, weights_data_type, data_layout,
                                                                                             input_quantization_info,
                                                                                             output_quantization_info, QuantizationInfo(weights_scales), add_bias);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2021 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.
	*/
	#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/DeconvolutionLayer.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, typename TW>
	class DeconvolutionLayerFixtureBase : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value \|\| std::is_same<typename std::decay<T>::type, int8_t>::value, int32_t, T >::type;

	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
	DataType data_type, DataType weights_data_type, DataLayout data_layout,
	QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, QuantizationInfo weights_quantization_info, bool add_bias)
	{
	_data_type = data_type;
	_weights_data_type = weights_data_type;
	_bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
	_data_layout = data_layout;
	_input_quantization_info = input_quantization_info;
	_output_quantization_info = output_quantization_info;
	_weights_quantization_info = weights_quantization_info;

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

	protected:
	template <typename U>
	void fill(U &&tensor, int i)
	{
	switch(tensor.data_type())
	{
	case DataType::QASYMM8:
	{
	std::pair<int, int> bounds = get_quantized_bounds(tensor.quantization_info(), -1.0f, 1.0f);
	std::uniform_int_distribution<uint8_t> distribution(bounds.first, bounds.second);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::QASYMM8_SIGNED:
	{
	std::pair<int, int> bounds = get_quantized_qasymm8_signed_bounds(tensor.quantization_info(), -1.0f, 1.0f);
	std::uniform_int_distribution<int8_t> distribution(bounds.first, bounds.second);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::QSYMM8_PER_CHANNEL:
	{
	int min_bound = 128;
	int max_bound = -127;
	for(size_t i = 0; i < _input_quantization_info.scale().size(); i++)
	{
	std::pair<int, int> bounds = get_symm_quantized_per_channel_bounds(tensor.quantization_info(), -1.0f, 1.0f);
	if(bounds.first < min_bound)
	{
	min_bound = bounds.first;
	}
	if(bounds.second > max_bound)
	{
	max_bound = bounds.second;
	}
	}
	std::uniform_int_distribution<int8_t> distribution(min_bound, max_bound);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::S32:
	{
	std::uniform_int_distribution<int32_t> distribution(-100, 100);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F16:
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F32:
	{
	std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	template <typename U>
	void fill_zeros(U &&tensor)
	{
	switch(tensor.data_type())
	{
	case DataType::S32:
	{
	library->fill_tensor_value(tensor, 0);
	break;
	}
	case DataType::F16:
	library->fill_tensor_value(tensor, static_cast<half>(0.0f));
	break;
	case DataType::F32:
	library->fill_tensor_value(tensor, static_cast<float>(0.0f));
	break;
	default:
	ARM_COMPUTE_ERROR("Not supported");
	}
	}

	TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape bias_shape, TensorShape output_shape,
	const PadStrideInfo &info, bool add_bias)
	{
	if(_data_layout == DataLayout::NHWC)
	{
	permute(input_shape, PermutationVector(2U, 0U, 1U));
	permute(weights_shape, PermutationVector(2U, 0U, 1U));
	permute(output_shape, PermutationVector(2U, 0U, 1U));
	}

	// Create tensors
	TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _input_quantization_info, _data_layout);
	TensorType weights = create_tensor<TensorType>(weights_shape, _weights_data_type, 1, _weights_quantization_info, _data_layout);
	TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _input_quantization_info, _data_layout);
	TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _output_quantization_info, _data_layout);

	// Create and configure function
	FunctionType conv;
	conv.configure(&src, &weights, add_bias ? &bias : nullptr, &dst, info);

	ARM_COMPUTE_ASSERT(src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(weights.info()->is_resizable());
	if(add_bias)
	{
	ARM_COMPUTE_ASSERT(bias.info()->is_resizable());
	}
	ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

	// Allocate tensors
	src.allocator()->allocate();
	weights.allocator()->allocate();
	if(add_bias)
	{
	bias.allocator()->allocate();
	}
	dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!weights.info()->is_resizable());
	if(add_bias)
	{
	ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
	}
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

	// Fill tensors
	fill(AccessorType(src), 0);
	fill(AccessorType(weights), 1);
	if(add_bias)
	{
	fill(AccessorType(bias), 2);
	}

	// Compute DeconvolutionLayer 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, bool add_bias)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, _data_type, 1, _input_quantization_info };
	SimpleTensor<TW> weights{ weights_shape, _weights_data_type, 1, _weights_quantization_info };
	SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _input_quantization_info };

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

	if(add_bias)
	{
	fill(bias, 2);
	}
	else
	{
	fill_zeros(bias);
	}
	return reference::deconvolution_layer<T, TW>(src, weights, bias, output_shape, info, _output_quantization_info);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	DataType _data_type{};
	DataType _weights_data_type{};
	DataType _bias_data_type{};
	DataLayout _data_layout{};
	QuantizationInfo _input_quantization_info{};
	QuantizationInfo _output_quantization_info{};
	QuantizationInfo _weights_quantization_info{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
	unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
	TensorInfo input_info(input_shape, 1, data_type);
	TensorInfo weights_info(weights_shape, 1, data_type);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
	QuantizationInfo(), QuantizationInfo(), add_bias);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationAsymmFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int pad_left, unsigned int pad_right, unsigned int pad_top,
	unsigned int pad_bottom, unsigned int num_kernels, DataType data_type, DataLayout data_layout, bool add_bias)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, pad_left, pad_right, pad_top, pad_bottom, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
	TensorInfo input_info(input_shape, 1, data_type);
	TensorInfo weights_info(weights_shape, 1, data_type);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout, QuantizationInfo(),
	QuantizationInfo(), QuantizationInfo(), add_bias);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationQuantizedFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
	unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
	TensorInfo input_info(input_shape, 1, data_type, input_quantization_info);
	TensorInfo weights_info(weights_shape, 1, data_type, input_quantization_info);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, data_type, data_layout,
	input_quantization_info,
	output_quantization_info, input_quantization_info, add_bias);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW, unsigned int kernel_size_x, unsigned int kernel_size_y>
	class DeconvolutionValidationQuantizedPerChannelFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
	unsigned int num_kernels, DataType data_type, DataLayout data_layout, QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, bool add_bias,
	DataType weights_data_type)
	{
	ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
	const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
	auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, info);
	TensorInfo input_info(input_shape, 1, data_type, input_quantization_info);
	TensorInfo weights_info(weights_shape, 1, weights_data_type, input_quantization_info);
	TensorShape output_shape = compute_deconvolution_output_shape(out_dim, input_info, weights_info);

	std::vector<float> weights_scales{};
	std::mt19937 gen(library->seed());
	std::uniform_real_distribution<float> dis(0.01f, 1.f);
	for(size_t i = 0; i < output_shape[2]; ++i)
	{
	weights_scales.push_back(dis(gen));
	}
	DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T, TW>::setup(input_shape, weights_shape, bias_shape, output_shape, info, data_type, weights_data_type, data_layout,
	input_quantization_info,
	output_quantization_info, QuantizationInfo(weights_scales), add_bias);
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute