tests/validation/CL/IndirectConvolutionLayer.cpp - platform/external/ComputeLibrary - Git at Google


            
            /*

            
             * Copyright (c) 2022 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/CL/CLTensor.h"

            
            #include "arm_compute/runtime/CL/CLTensorAllocator.h"

            
            #include "arm_compute/runtime/CL/functions/CLIndirectConvolutionLayer.h"

            
            #include "tests/CL/CLAccessor.h"

            
            #include "tests/datasets/ShapeDatasets.h"

            
            #include "tests/framework/Macros.h"

            
            #include "tests/validation/Validation.h"

            
            #include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"


            // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse

            
            // the direct convolution fixture


            namespace arm_compute

            
            {

            
            namespace test

            
            {

            
            namespace validation

            
            {

            
            namespace

            
            {

            
            RelativeTolerance<half>  tolerance_fp16(half(0.2));  /**< Tolerance for floating point tests */

            
            RelativeTolerance<float> tolerance_fp32(0.05f);      /**< Tolerance for floating point tests */

            
            constexpr float          abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/

            
            constexpr float          tolerance_num = 0.07f;      /**< Tolerance number */


            /** Activation function Dataset*/

            
            const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",

            
            { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) });

            
            } // namespace


            TEST_SUITE(CL)

            
            TEST_SUITE(IndirectConvolutionLayer)


            /** Check whether the configuration of a indirect convolution layer with no

            
             * bias leads to a successful run.

            
             */

            
            TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT)

            
            {

            
                const TensorShape    src_shape_nhwc = TensorShape(8U, 27U, 13U);

            
                const TensorShape    wei_shape_nhwc = TensorShape(8U, 3U, 3U, 4U);

            
                const TensorShape    bia_shape      = TensorShape(4U);

            
                const TensorShape    dst_shape_nhwc = TensorShape(4U, 25U, 11U);

            
                constexpr DataType   dt             = DataType::F32;

            
                constexpr DataLayout data_layout    = DataLayout::NHWC;


                auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

            
                auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

            
                auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);


                TensorShape src_shape_nchw = src_shape_nhwc;

            
                TensorShape wei_shape_nchw = wei_shape_nhwc;

            
                TensorShape dst_shape_nchw = dst_shape_nhwc;


                permute(src_shape_nchw, PermutationVector(1U, 2U, 0U));

            
                permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U));

            
                permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U));


                const PadStrideInfo conv_info = PadStrideInfo(1, 1, 0, 0);


                // Create indirect Convolution function

            
                CLIndirectConvolutionLayer conv{};

            
                conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info);


                src_nhwc.allocator()->allocate();

            
                wei_nhwc.allocator()->allocate();

            
                dst_nhwc.allocator()->allocate();


                library->fill_tensor_value(CLAccessor(src_nhwc), 1.f);

            
                library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f);


                conv.run();


                // Compute reference to compare

            
                SimpleTensor<float> ref_src{ src_shape_nchw, dt };

            
                SimpleTensor<float> ref_wei{ wei_shape_nchw, dt };

            
                SimpleTensor<float> ref_bia{ bia_shape, dt };

            
                library->fill_tensor_value(ref_src, 1.f);

            
                library->fill_tensor_value(ref_wei, 1.f);

            
                // No bias

            
                library->fill_tensor_value(ref_bia, 0.f);

            
                auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info);


                validate(CLAccessor(dst_nhwc), ref_dst);

            
            }


            /** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal

            
             *  would lead to successful run

            
             */

            
            TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT)

            
            {

            
                const TensorShape    src_shape_nhwc = TensorShape(3U, 33U, 27U);

            
                const TensorShape    wei_shape_nhwc = TensorShape(3U, 5U, 7U, 4U); // non-square kernel

            
                const TensorShape    bia_shape      = TensorShape(4U);

            
                const TensorShape    dst_shape_nhwc = TensorShape(4U, 11U, 12U);

            
                constexpr DataType   dt             = DataType::F32;

            
                constexpr DataLayout data_layout    = DataLayout::NHWC;


                auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

            
                auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

            
                auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);


                TensorShape src_shape_nchw = src_shape_nhwc;

            
                TensorShape wei_shape_nchw = wei_shape_nhwc;

            
                TensorShape dst_shape_nchw = dst_shape_nhwc;


                permute(src_shape_nchw, PermutationVector(1U, 2U, 0U));

            
                permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U));

            
                permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U));


                const PadStrideInfo conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR);


                // Create indirect convolution function

            
                CLIndirectConvolutionLayer conv{};

            
                conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info);


                src_nhwc.allocator()->allocate();

            
                wei_nhwc.allocator()->allocate();

            
                dst_nhwc.allocator()->allocate();


                library->fill_tensor_value(CLAccessor(src_nhwc), 1.f);

            
                library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f);


                conv.run();


                // Compute reference to compare

            
                SimpleTensor<float> ref_src{ src_shape_nchw, dt };

            
                SimpleTensor<float> ref_wei{ wei_shape_nchw, dt };

            
                SimpleTensor<float> ref_bia{ bia_shape, dt };

            
                library->fill_tensor_value(ref_src, 1.f);

            
                library->fill_tensor_value(ref_wei, 1.f);

            
                // No bias

            
                library->fill_tensor_value(ref_bia, 0.f);

            
                auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info);


                validate(CLAccessor(dst_nhwc), ref_dst);

            
            }

            
            // *INDENT-OFF*

            
            // clang-format off

            
            // Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse

            
            // the direct convolution fixture

            
            template <typename T>

            
            using CLIndirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T>;

            
            template <typename T>

            
            using CLIndirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T, true>;


            TEST_SUITE(NHWC)

            
            TEST_SUITE(FP16)

            
            FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT,

            
                           combine(combine(combine(zip(zip(zip(zip(zip(zip(

            
                           framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),

            
                                                                    TensorShape(19U, 5U, 16U, 4U),

            
                                                                    TensorShape(13U, 5U, 17U, 2U),

            
                                                                    TensorShape(32U, 37U, 13U) } ),

            
                           framework::dataset::make("StrideX", { 1, 3, 1, 1 })),

            
                           framework::dataset::make("StrideY", { 1, 3, 2, 1 })),

            
                           framework::dataset::make("PadX", { 1, 3, 0, 4 })),

            
                           framework::dataset::make("PadY", { 1, 3, 0, 4 })),

            
                           framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),

            
                           framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),

            
                           framework::dataset::make("DataType",  DataType::F16)),

            
                           framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),

            
                           framework::dataset::make("DataLayout", DataLayout::NHWC)))

            
            {

            
                validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);

            
            }


            FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY,

            
                           combine(combine(combine(zip(zip(zip(zip(zip(zip(

            
                           framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),

            
                           framework::dataset::make("StrideX", { 1 })),

            
                           framework::dataset::make("StrideY", { 1 })),

            
                           framework::dataset::make("PadX", { 1 })),

            
                           framework::dataset::make("PadY", { 1 })),

            
                           framework::dataset::make("KernelSize", { 9 })),

            
                           framework::dataset::make("NumKernels", { 3 })),

            
                           framework::dataset::make("DataType",  DataType::F16)),

            
                           framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),

            
                           framework::dataset::make("DataLayout", DataLayout::NHWC)))

            
            {

            
                validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);

            
            }


            TEST_SUITE_END() // FP16


            TEST_SUITE(FP32)

            
            FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,

            
                           combine(combine(combine(zip(zip(zip(zip(zip(zip(

            
                           framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),

            
                                                                    TensorShape(19U, 5U, 16U, 4U),

            
                                                                    TensorShape(13U, 5U, 17U, 2U),

            
                                                                    TensorShape(32U, 37U, 13U) } ),

            
                           framework::dataset::make("StrideX", { 1, 3, 1, 1 })),

            
                           framework::dataset::make("StrideY", { 1, 3, 2, 1 })),

            
                           framework::dataset::make("PadX", { 1, 3, 0, 4 })),

            
                           framework::dataset::make("PadY", { 1, 3, 0, 4 })),

            
                           framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),

            
                           framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),

            
                           framework::dataset::make("DataType",  DataType::F32)),

            
                           framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),

            
                           framework::dataset::make("DataLayout", DataLayout::NHWC)))

            
            {

            
                validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);

            
            }

            
            FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLIndirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT,

            
                           combine(combine(combine(zip(zip(zip(zip(zip(zip(

            
                           framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),

            
                                                                    TensorShape(19U, 5U, 16U, 4U),

            
                                                                    TensorShape(13U, 5U, 17U, 2U),

            
                                                                    TensorShape(32U, 37U, 13U) } ),

            
                           framework::dataset::make("StrideX", { 1 })),

            
                           framework::dataset::make("StrideY", { 2 })),

            
                           framework::dataset::make("PadX", { 1 })),

            
                           framework::dataset::make("PadY", { 3 })),

            
                           framework::dataset::make("KernelSize", { 3 })),

            
                           framework::dataset::make("NumKernels", { 3 })),

            
                           framework::dataset::make("DataType",  DataType::F32)),

            
                           framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),

            
                           framework::dataset::make("DataLayout", DataLayout::NHWC)))

            
            {

            
                validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);

            
            }

            
            FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,

            
                           combine(combine(combine(zip(zip(zip(zip(zip(zip(

            
                           framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),

            
                           framework::dataset::make("StrideX", { 1 })),

            
                           framework::dataset::make("StrideY", { 1 })),

            
                           framework::dataset::make("PadX", { 1 })),

            
                           framework::dataset::make("PadY", { 1 })),

            
                           framework::dataset::make("KernelSize", { 9 })),

            
                           framework::dataset::make("NumKernels", { 3 })),

            
                           framework::dataset::make("DataType",  DataType::F32)),

            
                           framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),

            
                           framework::dataset::make("DataLayout", DataLayout::NHWC)))

            
            {

            
                validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);

            
            }

            
            TEST_SUITE_END() // FP32

            
            TEST_SUITE_END() // NHWC

            
            TEST_SUITE_END() // IndirectConvolutionLayer

            
            TEST_SUITE_END() // CL


            } // namespace validation

            
            } // namespace test

            
            } // namespace arm_compute
	/*
	* Copyright (c) 2022 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/CL/CLTensor.h"
	#include "arm_compute/runtime/CL/CLTensorAllocator.h"
	#include "arm_compute/runtime/CL/functions/CLIndirectConvolutionLayer.h"
	#include "tests/CL/CLAccessor.h"
	#include "tests/datasets/ShapeDatasets.h"
	#include "tests/framework/Macros.h"
	#include "tests/validation/Validation.h"
	#include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"

	// Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse
	// the direct convolution fixture

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace
	{
	RelativeTolerance<half> tolerance_fp16(half(0.2)); /*< Tolerance for floating point tests /
	RelativeTolerance<float> tolerance_fp32(0.05f); /*< Tolerance for floating point tests /
	constexpr float abs_tolerance_f32(0.0001f); /*< Absolute tolerance for FP32 tests/
	constexpr float tolerance_num = 0.07f; /*< Tolerance number /

	/** Activation function Dataset*/
	const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
	{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) });
	} // namespace

	TEST_SUITE(CL)
	TEST_SUITE(IndirectConvolutionLayer)

	/** Check whether the configuration of a indirect convolution layer with no
	* bias leads to a successful run.
	*/
	TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT)
	{
	const TensorShape src_shape_nhwc = TensorShape(8U, 27U, 13U);
	const TensorShape wei_shape_nhwc = TensorShape(8U, 3U, 3U, 4U);
	const TensorShape bia_shape = TensorShape(4U);
	const TensorShape dst_shape_nhwc = TensorShape(4U, 25U, 11U);
	constexpr DataType dt = DataType::F32;
	constexpr DataLayout data_layout = DataLayout::NHWC;

	auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);
	auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);
	auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

	TensorShape src_shape_nchw = src_shape_nhwc;
	TensorShape wei_shape_nchw = wei_shape_nhwc;
	TensorShape dst_shape_nchw = dst_shape_nhwc;

	permute(src_shape_nchw, PermutationVector(1U, 2U, 0U));
	permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U));
	permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U));

	const PadStrideInfo conv_info = PadStrideInfo(1, 1, 0, 0);

	// Create indirect Convolution function
	CLIndirectConvolutionLayer conv{};
	conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info);

	src_nhwc.allocator()->allocate();
	wei_nhwc.allocator()->allocate();
	dst_nhwc.allocator()->allocate();

	library->fill_tensor_value(CLAccessor(src_nhwc), 1.f);
	library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f);

	conv.run();

	// Compute reference to compare
	SimpleTensor<float> ref_src{ src_shape_nchw, dt };
	SimpleTensor<float> ref_wei{ wei_shape_nchw, dt };
	SimpleTensor<float> ref_bia{ bia_shape, dt };
	library->fill_tensor_value(ref_src, 1.f);
	library->fill_tensor_value(ref_wei, 1.f);
	// No bias
	library->fill_tensor_value(ref_bia, 0.f);
	auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info);

	validate(CLAccessor(dst_nhwc), ref_dst);
	}

	/** Check whether the case of rectangle kernels i.e. when width and height of the weight_shape are not equal
	* would lead to successful run
	*/
	TEST_CASE(NonSquareKernel, framework::DatasetMode::PRECOMMIT)
	{
	const TensorShape src_shape_nhwc = TensorShape(3U, 33U, 27U);
	const TensorShape wei_shape_nhwc = TensorShape(3U, 5U, 7U, 4U); // non-square kernel
	const TensorShape bia_shape = TensorShape(4U);
	const TensorShape dst_shape_nhwc = TensorShape(4U, 11U, 12U);
	constexpr DataType dt = DataType::F32;
	constexpr DataLayout data_layout = DataLayout::NHWC;

	auto src_nhwc = create_tensor<CLTensor>(src_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);
	auto wei_nhwc = create_tensor<CLTensor>(wei_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);
	auto dst_nhwc = create_tensor<CLTensor>(dst_shape_nhwc, dt, 1, QuantizationInfo(), data_layout);

	TensorShape src_shape_nchw = src_shape_nhwc;
	TensorShape wei_shape_nchw = wei_shape_nhwc;
	TensorShape dst_shape_nchw = dst_shape_nhwc;

	permute(src_shape_nchw, PermutationVector(1U, 2U, 0U));
	permute(wei_shape_nchw, PermutationVector(1U, 2U, 0U, 3U));
	permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U));

	const PadStrideInfo conv_info = PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR);

	// Create indirect convolution function
	CLIndirectConvolutionLayer conv{};
	conv.configure(&src_nhwc, &wei_nhwc, nullptr, &dst_nhwc, conv_info);

	src_nhwc.allocator()->allocate();
	wei_nhwc.allocator()->allocate();
	dst_nhwc.allocator()->allocate();

	library->fill_tensor_value(CLAccessor(src_nhwc), 1.f);
	library->fill_tensor_value(CLAccessor(wei_nhwc), 1.f);

	conv.run();

	// Compute reference to compare
	SimpleTensor<float> ref_src{ src_shape_nchw, dt };
	SimpleTensor<float> ref_wei{ wei_shape_nchw, dt };
	SimpleTensor<float> ref_bia{ bia_shape, dt };
	library->fill_tensor_value(ref_src, 1.f);
	library->fill_tensor_value(ref_wei, 1.f);
	// No bias
	library->fill_tensor_value(ref_bia, 0.f);
	auto ref_dst = reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info);

	validate(CLAccessor(dst_nhwc), ref_dst);
	}
	// INDENT-OFF
	// clang-format off
	// Note: Since the interface of indirect convolution is the same of direct convolution, we can reuse
	// the direct convolution fixture
	template <typename T>
	using CLIndirectConvolutionLayerFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T>;
	template <typename T>
	using CLIndirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<CLTensor, CLAccessor, CLIndirectConvolutionLayer, T, true>;

	TEST_SUITE(NHWC)
	TEST_SUITE(FP16)
	FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT,
	combine(combine(combine(zip(zip(zip(zip(zip(zip(
	framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
	TensorShape(19U, 5U, 16U, 4U),
	TensorShape(13U, 5U, 17U, 2U),
	TensorShape(32U, 37U, 13U) } ),
	framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
	framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
	framework::dataset::make("PadX", { 1, 3, 0, 4 })),
	framework::dataset::make("PadY", { 1, 3, 0, 4 })),
	framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
	framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
	framework::dataset::make("DataType", DataType::F16)),
	framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
	framework::dataset::make("DataLayout", DataLayout::NHWC)))
	{
	validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
	}

	FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY,
	combine(combine(combine(zip(zip(zip(zip(zip(zip(
	framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
	framework::dataset::make("StrideX", { 1 })),
	framework::dataset::make("StrideY", { 1 })),
	framework::dataset::make("PadX", { 1 })),
	framework::dataset::make("PadY", { 1 })),
	framework::dataset::make("KernelSize", { 9 })),
	framework::dataset::make("NumKernels", { 3 })),
	framework::dataset::make("DataType", DataType::F16)),
	framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),
	framework::dataset::make("DataLayout", DataLayout::NHWC)))
	{
	validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
	}

	TEST_SUITE_END() // FP16

	TEST_SUITE(FP32)
	FIXTURE_DATA_TEST_CASE(RunSmall, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
	combine(combine(combine(zip(zip(zip(zip(zip(zip(
	framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
	TensorShape(19U, 5U, 16U, 4U),
	TensorShape(13U, 5U, 17U, 2U),
	TensorShape(32U, 37U, 13U) } ),
	framework::dataset::make("StrideX", { 1, 3, 1, 1 })),
	framework::dataset::make("StrideY", { 1, 3, 2, 1 })),
	framework::dataset::make("PadX", { 1, 3, 0, 4 })),
	framework::dataset::make("PadY", { 1, 3, 0, 4 })),
	framework::dataset::make("KernelSize", { 3, 8, 1, 9 })),
	framework::dataset::make("NumKernels", { 17, 3, 1, 19 })),
	framework::dataset::make("DataType", DataType::F32)),
	framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
	framework::dataset::make("DataLayout", DataLayout::NHWC)))
	{
	validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
	}
	FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLIndirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT,
	combine(combine(combine(zip(zip(zip(zip(zip(zip(
	framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U),
	TensorShape(19U, 5U, 16U, 4U),
	TensorShape(13U, 5U, 17U, 2U),
	TensorShape(32U, 37U, 13U) } ),
	framework::dataset::make("StrideX", { 1 })),
	framework::dataset::make("StrideY", { 2 })),
	framework::dataset::make("PadX", { 1 })),
	framework::dataset::make("PadY", { 3 })),
	framework::dataset::make("KernelSize", { 3 })),
	framework::dataset::make("NumKernels", { 3 })),
	framework::dataset::make("DataType", DataType::F32)),
	framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) )),
	framework::dataset::make("DataLayout", DataLayout::NHWC)))
	{
	validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
	}
	FIXTURE_DATA_TEST_CASE(RunLarge, CLIndirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
	combine(combine(combine(zip(zip(zip(zip(zip(zip(
	framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ),
	framework::dataset::make("StrideX", { 1 })),
	framework::dataset::make("StrideY", { 1 })),
	framework::dataset::make("PadX", { 1 })),
	framework::dataset::make("PadY", { 1 })),
	framework::dataset::make("KernelSize", { 9 })),
	framework::dataset::make("NumKernels", { 3 })),
	framework::dataset::make("DataType", DataType::F32)),
	framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::IDENTITY) )),
	framework::dataset::make("DataLayout", DataLayout::NHWC)))
	{
	validate(CLAccessor(_target), _reference, tolerance_fp32, 0.0, abs_tolerance_f32);
	}
	TEST_SUITE_END() // FP32
	TEST_SUITE_END() // NHWC
	TEST_SUITE_END() // IndirectConvolutionLayer
	TEST_SUITE_END() // CL

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