tests/validation/NEON/Convolution.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017 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/Types.h"
 #include "arm_compute/runtime/NEON/functions/NEConvolution.h"
 #include "arm_compute/runtime/Tensor.h"
 #include "arm_compute/runtime/TensorAllocator.h"
 #include "tests/NEON/Accessor.h"
 #include "tests/PaddingCalculator.h"
 #include "tests/datasets/BorderModeDataset.h"
 #include "tests/datasets/ShapeDatasets.h"
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Macros.h"
 #include "tests/framework/datasets/Datasets.h"
 #include "tests/validation/Validation.h"
 #include "tests/validation/fixtures/ConvolutionFixture.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace
 {
 /** Tolerance value for comparing reference's output against implementation
  *
  * This is due to the fact that NEON target performs multiplication with reciprocal of scale,
  * while reference performs direct division with scale.
  */
 constexpr AbsoluteTolerance<uint8_t> tolerance_u8(1);

 /* Convolution3x3 */
 constexpr unsigned int filter_size_3x3 = 3;                  /* Size of the kernel/filter in number of elements. */
 constexpr BorderSize   border_size_3x3(filter_size_3x3 / 2); /* Border size of the kernel/filter around its central element. */

 /* Convolution5x5 */
 constexpr unsigned int filter_size_5x5 = 5;                  /* Size of the kernel/filter in number of elements. */
 constexpr BorderSize   border_size_5x5(filter_size_5x5 / 2); /* Border size of the kernel/filter around its central element. */

 /* Convolution7x7 */
 constexpr unsigned int filter_size_7x7 = 7;                  /* Size of the kernel/filter in number of elements. */
 constexpr BorderSize   border_size_7x7(filter_size_7x7 / 2); /* Border size of the kernel/filter around its central element. */

 /* Convolutionx */
 constexpr unsigned int filter_size_9x9 = 9;                  /* Size of the kernel/filter in number of elements. */
 constexpr BorderSize   border_size_9x9(filter_size_9x9 / 2); /* Border size of the kernel/filter around its central element. */

 /** Create conv matrix with filter size, and fill them with random value
  *
  * @param[in/out] conv        Convolution matrix to be filled with random int16_t
  * @param[in]     filter_size Filter Size.
  */
 void create_conv(int16_t *conv, const unsigned int filter_size)
 {
     std::mt19937                           gen(library->seed());
     std::uniform_int_distribution<int16_t> distribution_int16(-32768, 32767);

     for(unsigned int i = 0; i < filter_size * filter_size; ++i)
     {
         conv[i] = distribution_int16(gen);
     }
 }
 } // namespace

 TEST_SUITE(NEON)
 TEST_SUITE(CustomConvolution)
 TEST_SUITE(CustomConvolution3x3)

 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
                                                                    datasets::BorderModes()),
                shape, data_type, border_mode)
 {
     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, data_type);
     Tensor dst = create_tensor<Tensor>(shape, data_type);

     // Create conv matrix
     int16_t conv[9];
     create_conv(conv, filter_size_3x3);

     // Generate random scale value between 0 and 255.
     std::mt19937                           gen(library->seed());
     std::uniform_int_distribution<uint8_t> distribution(0, 255);
     uint32_t                               scale = distribution(gen);

     ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

     // Create and configure function
     NEConvolution3x3 convolution;
     convolution.configure(&src, &dst, conv, scale, border_mode);

     // Validate valid region
     const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_3x3);
     validate(dst.info()->valid_region(), dst_valid_region);

     // Validate padding
     PaddingCalculator calculator(shape.x(), 8);
     calculator.set_border_size(1);
     calculator.set_border_mode(border_mode);

     const PaddingSize dst_padding = calculator.required_padding();

     calculator.set_accessed_elements(16);
     calculator.set_access_offset(-1);

     const PaddingSize src_padding = calculator.required_padding();

     validate(src.info()->padding(), src_padding);
     validate(dst.info()->padding(), dst_padding);
 }

 template <typename T>
 using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution3x3, T, filter_size_3x3>;

 FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
                                                                                                                    DataType::U8)),
                                                                                                            datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_3x3), tolerance_u8);
 }

 FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
                                                                                                                  DataType::U8)),
                                                                                                          datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_3x3), tolerance_u8);
 }
 TEST_SUITE_END() /* Custom Convolution3x3 */

 TEST_SUITE(CustomConvolution5x5)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
                                                                    datasets::BorderModes()),
                shape, data_type, border_mode)
 {
     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, data_type);
     Tensor dst = create_tensor<Tensor>(shape, data_type);

     // Create conv matrix
     int16_t conv[25];
     create_conv(conv, filter_size_5x5);

     // Generate random scale value between 0 and 255.
     std::mt19937                           gen(library->seed());
     std::uniform_int_distribution<uint8_t> distribution(0, 255);
     uint32_t                               scale = distribution(gen);

     ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

     // Create and configure function
     NEConvolution5x5 convolution;
     convolution.configure(&src, &dst, conv, scale, border_mode);

     // Validate valid region
     const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_5x5);
     validate(dst.info()->valid_region(), dst_valid_region);

     // Validate padding
     PaddingCalculator calculator(shape.x(), 8);
     calculator.set_border_size(2);
     calculator.set_border_mode(border_mode);

     const PaddingSize dst_padding = calculator.required_padding();

     calculator.set_accessed_elements(16);
     calculator.set_access_offset(-2);

     const PaddingSize src_padding = calculator.required_padding();

     validate(src.info()->padding(), src_padding);
     validate(dst.info()->padding(), dst_padding);
 }

 template <typename T>
 using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution5x5, T, filter_size_5x5>;

 FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
                                                                                                                    DataType::U8)),
                                                                                                            datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_5x5), tolerance_u8);
 }

 FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
                                                                                                                  DataType::U8)),
                                                                                                          datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_5x5), tolerance_u8);
 }
 TEST_SUITE_END() /* Custom Convolution 5x5 */

 TEST_SUITE(CustomConvolution7x7)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
                                                                    datasets::BorderModes()),
                shape, data_type, border_mode)
 {
     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, data_type);
     Tensor dst = create_tensor<Tensor>(shape, data_type);

     // Create conv matrix
     int16_t conv[49];
     create_conv(conv, filter_size_7x7);

     // Generate random scale value between 0 and 255.
     std::mt19937                           gen(library->seed());
     std::uniform_int_distribution<uint8_t> distribution(0, 255);
     uint32_t                               scale = distribution(gen);

     ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

     // Create and configure function
     NEConvolution7x7 convolution;
     convolution.configure(&src, &dst, conv, scale, border_mode);

     // Validate valid region
     const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_7x7);
     validate(dst.info()->valid_region(), dst_valid_region);

     // Validate padding
     PaddingCalculator calculator(shape.x(), 8);
     calculator.set_border_size(3);
     calculator.set_border_mode(border_mode);

     const PaddingSize dst_padding = calculator.required_padding();

     calculator.set_accessed_elements(16);
     calculator.set_access_offset(-3);

     const PaddingSize src_padding = calculator.required_padding();

     validate(src.info()->padding(), src_padding);
     validate(dst.info()->padding(), dst_padding);
 }

 template <typename T>
 using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution7x7, T, filter_size_7x7>;

 FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
                                                                                                                    DataType::U8)),
                                                                                                            datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_7x7), tolerance_u8);
 }

 FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
                                                                                                                  DataType::U8)),
                                                                                                          datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_7x7), tolerance_u8);
 }
 TEST_SUITE_END() /* Custom Convolution 7x7 */

 TEST_SUITE(CustomConvolution9x9)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
                                                                    datasets::BorderModes()),
                shape, data_type, border_mode)
 {
     // Create tensors
     Tensor src = create_tensor<Tensor>(shape, data_type);
     Tensor dst = create_tensor<Tensor>(shape, data_type);

     // Create conv matrix
     int16_t conv[81];
     create_conv(conv, filter_size_9x9);

     // Generate random scale value between 0 and 255.
     std::mt19937                           gen(library->seed());
     std::uniform_int_distribution<uint8_t> distribution(0, 255);
     uint32_t                               scale = distribution(gen);

     ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
     ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

     // Create and configure function
     NEConvolution9x9 convolution;
     convolution.configure(&src, &dst, conv, scale, border_mode);

     // Validate valid region
     const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_9x9);
     validate(dst.info()->valid_region(), dst_valid_region);

     // Validate padding
     PaddingCalculator calculator(shape.x(), 8);
     calculator.set_border_size(4);
     calculator.set_border_mode(border_mode);

     const PaddingSize dst_padding = calculator.required_padding();

     calculator.set_accessed_elements(16);
     calculator.set_access_offset(-4);

     const PaddingSize src_padding = calculator.required_padding();

     validate(src.info()->padding(), src_padding);
     validate(dst.info()->padding(), dst_padding);
 }

 template <typename T>
 using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution9x9, T, filter_size_9x9>;

 FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
                                                                                                                    DataType::U8)),
                                                                                                            datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_9x9), tolerance_u8);
 }

 FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
                                                                                                                  DataType::U8)),
                                                                                                          datasets::BorderModes()))
 {
     // Validate output
     validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_9x9), tolerance_u8);
 }
 TEST_SUITE_END() /* Custom Convolution 9x9 */
 TEST_SUITE_END()
 TEST_SUITE_END()
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017 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/Types.h"
	#include "arm_compute/runtime/NEON/functions/NEConvolution.h"
	#include "arm_compute/runtime/Tensor.h"
	#include "arm_compute/runtime/TensorAllocator.h"
	#include "tests/NEON/Accessor.h"
	#include "tests/PaddingCalculator.h"
	#include "tests/datasets/BorderModeDataset.h"
	#include "tests/datasets/ShapeDatasets.h"
	#include "tests/framework/Asserts.h"
	#include "tests/framework/Macros.h"
	#include "tests/framework/datasets/Datasets.h"
	#include "tests/validation/Validation.h"
	#include "tests/validation/fixtures/ConvolutionFixture.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace
	{
	/** Tolerance value for comparing reference's output against implementation
	*
	* This is due to the fact that NEON target performs multiplication with reciprocal of scale,
	* while reference performs direct division with scale.
	*/
	constexpr AbsoluteTolerance<uint8_t> tolerance_u8(1);

	/* Convolution3x3 */
	constexpr unsigned int filter_size_3x3 = 3; /* Size of the kernel/filter in number of elements. */
	constexpr BorderSize border_size_3x3(filter_size_3x3 / 2); /* Border size of the kernel/filter around its central element. */

	/* Convolution5x5 */
	constexpr unsigned int filter_size_5x5 = 5; /* Size of the kernel/filter in number of elements. */
	constexpr BorderSize border_size_5x5(filter_size_5x5 / 2); /* Border size of the kernel/filter around its central element. */

	/* Convolution7x7 */
	constexpr unsigned int filter_size_7x7 = 7; /* Size of the kernel/filter in number of elements. */
	constexpr BorderSize border_size_7x7(filter_size_7x7 / 2); /* Border size of the kernel/filter around its central element. */

	/* Convolutionx */
	constexpr unsigned int filter_size_9x9 = 9; /* Size of the kernel/filter in number of elements. */
	constexpr BorderSize border_size_9x9(filter_size_9x9 / 2); /* Border size of the kernel/filter around its central element. */

	/** Create conv matrix with filter size, and fill them with random value
	*
	* @param[in/out] conv Convolution matrix to be filled with random int16_t
	* @param[in] filter_size Filter Size.
	*/
	void create_conv(int16_t *conv, const unsigned int filter_size)
	{
	std::mt19937 gen(library->seed());
	std::uniform_int_distribution<int16_t> distribution_int16(-32768, 32767);

	for(unsigned int i = 0; i < filter_size * filter_size; ++i)
	{
	conv[i] = distribution_int16(gen);
	}
	}
	} // namespace

	TEST_SUITE(NEON)
	TEST_SUITE(CustomConvolution)
	TEST_SUITE(CustomConvolution3x3)

	DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
	datasets::BorderModes()),
	shape, data_type, border_mode)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, data_type);
	Tensor dst = create_tensor<Tensor>(shape, data_type);

	// Create conv matrix
	int16_t conv[9];
	create_conv(conv, filter_size_3x3);

	// Generate random scale value between 0 and 255.
	std::mt19937 gen(library->seed());
	std::uniform_int_distribution<uint8_t> distribution(0, 255);
	uint32_t scale = distribution(gen);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Create and configure function
	NEConvolution3x3 convolution;
	convolution.configure(&src, &dst, conv, scale, border_mode);

	// Validate valid region
	const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_3x3);
	validate(dst.info()->valid_region(), dst_valid_region);

	// Validate padding
	PaddingCalculator calculator(shape.x(), 8);
	calculator.set_border_size(1);
	calculator.set_border_mode(border_mode);

	const PaddingSize dst_padding = calculator.required_padding();

	calculator.set_accessed_elements(16);
	calculator.set_access_offset(-1);

	const PaddingSize src_padding = calculator.required_padding();

	validate(src.info()->padding(), src_padding);
	validate(dst.info()->padding(), dst_padding);
	}

	template <typename T>
	using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution3x3, T, filter_size_3x3>;

	FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_3x3), tolerance_u8);
	}

	FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_3x3), tolerance_u8);
	}
	TEST_SUITE_END() /* Custom Convolution3x3 */

	TEST_SUITE(CustomConvolution5x5)
	DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
	datasets::BorderModes()),
	shape, data_type, border_mode)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, data_type);
	Tensor dst = create_tensor<Tensor>(shape, data_type);

	// Create conv matrix
	int16_t conv[25];
	create_conv(conv, filter_size_5x5);

	// Generate random scale value between 0 and 255.
	std::mt19937 gen(library->seed());
	std::uniform_int_distribution<uint8_t> distribution(0, 255);
	uint32_t scale = distribution(gen);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Create and configure function
	NEConvolution5x5 convolution;
	convolution.configure(&src, &dst, conv, scale, border_mode);

	// Validate valid region
	const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_5x5);
	validate(dst.info()->valid_region(), dst_valid_region);

	// Validate padding
	PaddingCalculator calculator(shape.x(), 8);
	calculator.set_border_size(2);
	calculator.set_border_mode(border_mode);

	const PaddingSize dst_padding = calculator.required_padding();

	calculator.set_accessed_elements(16);
	calculator.set_access_offset(-2);

	const PaddingSize src_padding = calculator.required_padding();

	validate(src.info()->padding(), src_padding);
	validate(dst.info()->padding(), dst_padding);
	}

	template <typename T>
	using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution5x5, T, filter_size_5x5>;

	FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_5x5), tolerance_u8);
	}

	FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_5x5), tolerance_u8);
	}
	TEST_SUITE_END() /* Custom Convolution 5x5 */

	TEST_SUITE(CustomConvolution7x7)
	DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
	datasets::BorderModes()),
	shape, data_type, border_mode)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, data_type);
	Tensor dst = create_tensor<Tensor>(shape, data_type);

	// Create conv matrix
	int16_t conv[49];
	create_conv(conv, filter_size_7x7);

	// Generate random scale value between 0 and 255.
	std::mt19937 gen(library->seed());
	std::uniform_int_distribution<uint8_t> distribution(0, 255);
	uint32_t scale = distribution(gen);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Create and configure function
	NEConvolution7x7 convolution;
	convolution.configure(&src, &dst, conv, scale, border_mode);

	// Validate valid region
	const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_7x7);
	validate(dst.info()->valid_region(), dst_valid_region);

	// Validate padding
	PaddingCalculator calculator(shape.x(), 8);
	calculator.set_border_size(3);
	calculator.set_border_mode(border_mode);

	const PaddingSize dst_padding = calculator.required_padding();

	calculator.set_accessed_elements(16);
	calculator.set_access_offset(-3);

	const PaddingSize src_padding = calculator.required_padding();

	validate(src.info()->padding(), src_padding);
	validate(dst.info()->padding(), dst_padding);
	}

	template <typename T>
	using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution7x7, T, filter_size_7x7>;

	FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_7x7), tolerance_u8);
	}

	FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_7x7), tolerance_u8);
	}
	TEST_SUITE_END() /* Custom Convolution 7x7 */

	TEST_SUITE(CustomConvolution9x9)
	DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType", DataType::U8)),
	datasets::BorderModes()),
	shape, data_type, border_mode)
	{
	// Create tensors
	Tensor src = create_tensor<Tensor>(shape, data_type);
	Tensor dst = create_tensor<Tensor>(shape, data_type);

	// Create conv matrix
	int16_t conv[81];
	create_conv(conv, filter_size_9x9);

	// Generate random scale value between 0 and 255.
	std::mt19937 gen(library->seed());
	std::uniform_int_distribution<uint8_t> distribution(0, 255);
	uint32_t scale = distribution(gen);

	ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
	ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

	// Create and configure function
	NEConvolution9x9 convolution;
	convolution.configure(&src, &dst, conv, scale, border_mode);

	// Validate valid region
	const ValidRegion dst_valid_region = shape_to_valid_region(shape, (border_mode == BorderMode::UNDEFINED), border_size_9x9);
	validate(dst.info()->valid_region(), dst_valid_region);

	// Validate padding
	PaddingCalculator calculator(shape.x(), 8);
	calculator.set_border_size(4);
	calculator.set_border_mode(border_mode);

	const PaddingSize dst_padding = calculator.required_padding();

	calculator.set_accessed_elements(16);
	calculator.set_access_offset(-4);

	const PaddingSize src_padding = calculator.required_padding();

	validate(src.info()->padding(), src_padding);
	validate(dst.info()->padding(), dst_padding);
	}

	template <typename T>
	using NEConvolutionFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolution9x9, T, filter_size_9x9>;

	FIXTURE_DATA_TEST_CASE(RunSmall, NEConvolutionFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_9x9), tolerance_u8);
	}

	FIXTURE_DATA_TEST_CASE(RunLarge, NEConvolutionFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), framework::dataset::make("DataType",
	DataType::U8)),
	datasets::BorderModes()))
	{
	// Validate output
	validate(Accessor(_target), _reference, shape_to_valid_region(_reference.shape(), (_border_mode == BorderMode::UNDEFINED), border_size_9x9), tolerance_u8);
	}
	TEST_SUITE_END() /* Custom Convolution 9x9 */
	TEST_SUITE_END()
	TEST_SUITE_END()
	} // namespace validation
	} // namespace test
	} // namespace arm_compute