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

 /*
  * Copyright (c) 2017-2023 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_DIRECTCONVOLUTIONLAYERFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H

 #include "arm_compute/core/Helpers.h"
 #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/fixtures/ConvolutionLayerFixture.h"
 #include "tests/validation/reference/ConvolutionLayer.h"
 #include "tests/validation/reference/Permute.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>
 class DirectConvolutionValidationGenericFixture : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type;

     void setup_quantization(const TensorShape &input_shape, const TensorShape &weights_shape, QuantizationInfo &input_q_info,
         QuantizationInfo &weights_q_info, DataType data_type)
     {
         const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
         const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());

         std::mt19937                           generator(library->seed() + _hash);
         std::uniform_real_distribution<float>  distribution_float(-5.0f, 3.0f);
         std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);

         const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
         const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]

         const int32_t offset_lhs = distribution_t(generator);
         const int32_t offset_rhs = distribution_t(generator);

         input_q_info = QuantizationInfo(scale_lhs, offset_lhs);
         weights_q_info = QuantizationInfo(scale_rhs, offset_rhs);

         QuantizationHint q_hint = suggest_conv_dst_q_info_and_bias(input_q_info, weights_q_info,
             weights_shape.y() /* heights */, weights_shape.x() /* width */, input_shape.z() /* channels */,
             data_type, 0.5f /* bias_fraction */);

         _dst_q_info = q_hint.q_info;
         _min_bias = q_hint.bias_min;
         _max_bias = q_hint.bias_max;

         // Do not change here as these limits are the natural limits of the associated data types and
         // are embeded in the computation of the dst quantization info.
         _min_u8 = 0;
         _max_u8 = 255;
         _min_s8 = -128;
         _max_s8 = 127;
     }

     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
                DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout, bool mixed_layout = false)
     {
         // This hash is used by random generators. There may be hash collisions but
         // this is intentional as it's a very easy way to make the the current
         // random generation process almost different for many test configurations,
         // which were using the same set of values before.
         _hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] +
                 stride_x + stride_y + pad_x + pad_y + kernel_size + num_kernels + mixed_layout
                 + (data_layout == DataLayout::NHWC);

         _data_type         = data_type;
         _mixed_layout      = mixed_layout;

         TensorShape         weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
         const DataType      bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

         TensorInfo input_info   = TensorInfo(input_shape, 1, data_type);
         TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);

         const TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info);

         QuantizationInfo input_q_info = quantization_info;
         QuantizationInfo weights_q_info = quantization_info;
         _dst_q_info = quantization_info;

         if(is_data_type_quantized(data_type) && (!act_info.enabled() || act_info.activation() == ActivationFunction::IDENTITY))
         {
             setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type);
         }

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info);
     }

     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
                DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout)
     {
         ARM_COMPUTE_ERROR_ON(data_layout == DataLayout::UNKNOWN);
         ARM_COMPUTE_UNUSED(dilation);

         // This hash is used by random generators. There may be hash collisions but
         // this is intentional as it's a very easy way to make the the current
         // random generation process almost different for many test configurations,
         // which were using the same set of values before.
         _hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] +
             weights_shape[0] + weights_shape[1] + weights_shape[2] + weights_shape[3] + dilation.x() +
             dilation.y() + info.pad_bottom() + info.pad_left() + info.pad_right() + info.pad_top();

         _data_type         = data_type;

         const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

         QuantizationInfo input_q_info = quantization_info;
         QuantizationInfo weights_q_info = quantization_info;
         _dst_q_info = quantization_info;

         if(is_data_type_quantized(data_type) && (!act_info.enabled() || act_info.activation() == ActivationFunction::IDENTITY))
         {
             setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type);
         }

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info);
     }

 protected:
     void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
     {
         DataLayout data_layout = src.info()->data_layout();
         // Test Multi DataLayout graph cases, when the data layout changes after configure
         src.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW);
         dst.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW);

         // Compute Convolution function
         layer.run();

         // Reinstating original data layout for the test suite to properly check the values
         src.info()->set_data_layout(data_layout);
         dst.info()->set_data_layout(data_layout);
     }

     template <typename U>
     void fill(U &&tensor, int i)
     {
         switch(tensor.data_type())
         {
             case DataType::QASYMM8:
             {
                 std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8);
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::QASYMM8_SIGNED:
             {
                 // Use small input range to avoid all the test results being saturated at the end.
                 std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8);
                 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;
             }
             case DataType::S32:
             {
                 std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info,
                               DataType data_type, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info, const DataLayout &data_layout)
     {
         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_q_info, data_layout);
         TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_q_info, data_layout);
         TensorType bias    = create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo());
         TensorType dst     = create_tensor<TensorType>(output_shape, data_type, 1, _dst_q_info, data_layout);

         add_padding_x({ &src, &bias, &dst }, data_layout);
         add_padding_x({ &weights }, data_layout, input_shape[0] % 4 == 0); // Don't add left padding if cl image will be used

         // Create and configure function
         FunctionType conv;
         conv.configure(&src, &weights, &bias, &dst, info, act_info);

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

         // Allocate tensors
         src.allocator()->allocate();
         weights.allocator()->allocate();
         bias.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!weights.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

         // Fill tensors
         fill(AccessorType(src), 0 + _hash);
         fill(AccessorType(weights), 1 + _hash);
         fill(AccessorType(bias), 2 + _hash);

         if(_mixed_layout)
         {
             mix_layout(conv, src, dst);
         }
         else
         {
             // Compute Convolution 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,
                                       DataType data_type, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, data_type, 1, input_q_info };
         SimpleTensor<T>     weights{ weights_shape, data_type, 1, weights_q_info };
         SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, QuantizationInfo() };

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

         SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info,
             Size2D(1U, 1U) /* dilation */, 1 /* num_groups */, _dst_q_info);
         SimpleTensor<T> dst2 = (act_info.enabled()) ? reference::activation_layer<T>(dst, act_info) : dst;
         return dst2;
     }
     TensorType       _target{};
     SimpleTensor<T>  _reference{};
     QuantizationInfo _dst_q_info{};
     DataType         _data_type{};
     bool             _mixed_layout{ false };
     int32_t _hash{0};

     // Random initialization limits
     // Default values are previously handcrafted limits
     // that sould be used when we don't use dynamic quantization
     int32_t _min_bias{-5};
     int32_t _max_bias{5};
     int32_t _min_u8{0};
     int32_t _max_u8{50};
     int32_t _min_s8{-25};
     int32_t _max_s8{25};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
 class DirectConvolutionValidationFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, ActivationLayerInfo act_info,
                DataLayout data_layout)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, QuantizationInfo(),
                                                                                                     act_info, data_layout, mixed_layout);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
 class DirectConvolutionValidationQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info,
                ActivationLayerInfo act_info, DataLayout data_layout)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, quantization_info,
                                                                                                     act_info, data_layout, mixed_layout);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DirectConvolutionValidationWithTensorShapesQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
                DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, quantization_info,
                                                                                                     act_info, data_layout);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DirectConvolutionValidationWithTensorShapesFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
                DataType data_type, ActivationLayerInfo act_info)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, QuantizationInfo(),
                                                                                                     act_info, DataLayout::NCHW);
     }
 };

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

 #endif // ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H
	/*
	* Copyright (c) 2017-2023 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_DIRECTCONVOLUTIONLAYERFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H

	#include "arm_compute/core/Helpers.h"
	#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/fixtures/ConvolutionLayerFixture.h"
	#include "tests/validation/reference/ConvolutionLayer.h"
	#include "tests/validation/reference/Permute.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>
	class DirectConvolutionValidationGenericFixture : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional < std::is_same<T, uint8_t>::value \|\| std::is_same<T, int8_t>::value, int32_t, T >::type;

	void setup_quantization(const TensorShape &input_shape, const TensorShape &weights_shape, QuantizationInfo &input_q_info,
	QuantizationInfo &weights_q_info, DataType data_type)
	{
	const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
	const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());

	std::mt19937 generator(library->seed() + _hash);
	std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f);
	std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);

	const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
	const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]

	const int32_t offset_lhs = distribution_t(generator);
	const int32_t offset_rhs = distribution_t(generator);

	input_q_info = QuantizationInfo(scale_lhs, offset_lhs);
	weights_q_info = QuantizationInfo(scale_rhs, offset_rhs);

	QuantizationHint q_hint = suggest_conv_dst_q_info_and_bias(input_q_info, weights_q_info,
	weights_shape.y() /* heights /, weights_shape.x() / width /, input_shape.z() / channels */,
	data_type, 0.5f /* bias_fraction */);

	_dst_q_info = q_hint.q_info;
	_min_bias = q_hint.bias_min;
	_max_bias = q_hint.bias_max;

	// Do not change here as these limits are the natural limits of the associated data types and
	// are embeded in the computation of the dst quantization info.
	_min_u8 = 0;
	_max_u8 = 255;
	_min_s8 = -128;
	_max_s8 = 127;
	}

	void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
	DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout, bool mixed_layout = false)
	{
	// This hash is used by random generators. There may be hash collisions but
	// this is intentional as it's a very easy way to make the the current
	// random generation process almost different for many test configurations,
	// which were using the same set of values before.
	_hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] +
	stride_x + stride_y + pad_x + pad_y + kernel_size + num_kernels + mixed_layout
	+ (data_layout == DataLayout::NHWC);

	_data_type = data_type;
	_mixed_layout = mixed_layout;

	TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
	const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

	TensorInfo input_info = TensorInfo(input_shape, 1, data_type);
	TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);

	const TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info);

	QuantizationInfo input_q_info = quantization_info;
	QuantizationInfo weights_q_info = quantization_info;
	_dst_q_info = quantization_info;

	if(is_data_type_quantized(data_type) && (!act_info.enabled() \|\| act_info.activation() == ActivationFunction::IDENTITY))
	{
	setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type);
	}

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info);
	}

	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
	DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout)
	{
	ARM_COMPUTE_ERROR_ON(data_layout == DataLayout::UNKNOWN);
	ARM_COMPUTE_UNUSED(dilation);

	// This hash is used by random generators. There may be hash collisions but
	// this is intentional as it's a very easy way to make the the current
	// random generation process almost different for many test configurations,
	// which were using the same set of values before.
	_hash = input_shape[0] + input_shape[1] + input_shape[2] + input_shape[3] +
	weights_shape[0] + weights_shape[1] + weights_shape[2] + weights_shape[3] + dilation.x() +
	dilation.y() + info.pad_bottom() + info.pad_left() + info.pad_right() + info.pad_top();

	_data_type = data_type;

	const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

	QuantizationInfo input_q_info = quantization_info;
	QuantizationInfo weights_q_info = quantization_info;
	_dst_q_info = quantization_info;

	if(is_data_type_quantized(data_type) && (!act_info.enabled() \|\| act_info.activation() == ActivationFunction::IDENTITY))
	{
	setup_quantization(input_shape, weights_shape, input_q_info, weights_q_info, data_type);
	}

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info, data_layout);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, input_q_info, weights_q_info, act_info);
	}

	protected:
	void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst)
	{
	DataLayout data_layout = src.info()->data_layout();
	// Test Multi DataLayout graph cases, when the data layout changes after configure
	src.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW);
	dst.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW);

	// Compute Convolution function
	layer.run();

	// Reinstating original data layout for the test suite to properly check the values
	src.info()->set_data_layout(data_layout);
	dst.info()->set_data_layout(data_layout);
	}

	template <typename U>
	void fill(U &&tensor, int i)
	{
	switch(tensor.data_type())
	{
	case DataType::QASYMM8:
	{
	std::uniform_int_distribution<uint32_t> distribution(_min_u8, _max_u8);
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::QASYMM8_SIGNED:
	{
	// Use small input range to avoid all the test results being saturated at the end.
	std::uniform_int_distribution<int32_t> distribution(_min_s8, _max_s8);
	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;
	}
	case DataType::S32:
	{
	std::uniform_int_distribution<int32_t> distribution(_min_bias, _max_bias);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info,
	DataType data_type, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info, const DataLayout &data_layout)
	{
	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_q_info, data_layout);
	TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, weights_q_info, data_layout);
	TensorType bias = create_tensor<TensorType>(bias_shape, bias_data_type, 1, QuantizationInfo());
	TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, _dst_q_info, data_layout);

	add_padding_x({ &src, &bias, &dst }, data_layout);
	add_padding_x({ &weights }, data_layout, input_shape[0] % 4 == 0); // Don't add left padding if cl image will be used

	// Create and configure function
	FunctionType conv;
	conv.configure(&src, &weights, &bias, &dst, info, act_info);

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

	// Allocate tensors
	src.allocator()->allocate();
	weights.allocator()->allocate();
	bias.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!weights.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!bias.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

	// Fill tensors
	fill(AccessorType(src), 0 + _hash);
	fill(AccessorType(weights), 1 + _hash);
	fill(AccessorType(bias), 2 + _hash);

	if(_mixed_layout)
	{
	mix_layout(conv, src, dst);
	}
	else
	{
	// Compute Convolution 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,
	DataType data_type, DataType bias_data_type, QuantizationInfo input_q_info, QuantizationInfo weights_q_info, ActivationLayerInfo act_info)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1, input_q_info };
	SimpleTensor<T> weights{ weights_shape, data_type, 1, weights_q_info };
	SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, QuantizationInfo() };

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

	SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info,
	Size2D(1U, 1U) /* dilation /, 1 / num_groups */, _dst_q_info);
	SimpleTensor<T> dst2 = (act_info.enabled()) ? reference::activation_layer<T>(dst, act_info) : dst;
	return dst2;
	}
	TensorType _target{};
	SimpleTensor<T> _reference{};
	QuantizationInfo _dst_q_info{};
	DataType _data_type{};
	bool _mixed_layout{ false };
	int32_t _hash{0};

	// Random initialization limits
	// Default values are previously handcrafted limits
	// that sould be used when we don't use dynamic quantization
	int32_t _min_bias{-5};
	int32_t _max_bias{5};
	int32_t _min_u8{0};
	int32_t _max_u8{50};
	int32_t _min_s8{-25};
	int32_t _max_s8{25};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
	class DirectConvolutionValidationFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, ActivationLayerInfo act_info,
	DataLayout data_layout)
	{
	DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, QuantizationInfo(),
	act_info, data_layout, mixed_layout);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false>
	class DirectConvolutionValidationQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info,
	ActivationLayerInfo act_info, DataLayout data_layout)
	{
	DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, quantization_info,
	act_info, data_layout, mixed_layout);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DirectConvolutionValidationWithTensorShapesQuantizedFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
	DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout)
	{
	DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, quantization_info,
	act_info, data_layout);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DirectConvolutionValidationWithTensorShapesFixture : public DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
	DataType data_type, ActivationLayerInfo act_info)
	{
	DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, QuantizationInfo(),
	act_info, DataLayout::NCHW);
	}
	};

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

	#endif // ACL_TESTS_VALIDATION_FIXTURES_DIRECTCONVOLUTIONLAYERFIXTURE_H