tests/validation/CL/IndirectConvolutionLayer.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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