/* * Copyright (c) 2018 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/CL/kernels/CLWinogradFilterTransformKernel.h" #include "arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" #include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h" #include "tests/CL/CLAccessor.h" #include "tests/CL/Helper.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/LargeConvolutionLayerDataset.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/datasets/SmallConvolutionLayerDataset.h" #include "tests/datasets/WinogradInputTransformDataset.h" #include "tests/datasets/WinogradOutputTransformDataset.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/WinogradConvolutionLayerFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { constexpr AbsoluteTolerance tolerance_f32(0.001f); constexpr AbsoluteTolerance tolerance_convolution_layer_f32(0.1f); } // namespace using namespace arm_compute::misc::shape_calculator; TEST_SUITE(CL) TEST_SUITE(Winograd) TEST_SUITE(InputTransform) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F16), // F16 not supported TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32), // Kernel size not supported TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32), // Strides not supported TensorInfo(TensorShape(53U, 33U, 4U), 1, DataType::F32), // Padding needed TensorInfo(TensorShape(34U, 42U, 7U, 3U), 1, DataType::F32), // Padding needed TensorInfo(TensorShape(31U, 37U, 37U), 1, DataType::F32) // Padding needed }), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::F16), TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::QASYMM8), TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::F32), TensorInfo(TensorShape(5U, 1U, 16U, 3U), 1, DataType::F32), TensorInfo(TensorShape(4U, 442U, 16U), 1, DataType::F32), TensorInfo(TensorShape(7U, 320U, 16U, 3U), 1, DataType::F32), TensorInfo(TensorShape(37U, 304U, 16U), 1, DataType::F32) })), framework::dataset::make("WinogradInfo", { WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(2, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(34U, 42U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(31U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW) })), framework::dataset::make("Expected", { false, false, false, false, false, false, false })), input_info, output_info, winograd_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallWinogradInputTransformDataset(), datasets::LargeWinogradInputTransformDataset()), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 })), shape_in, winograd_info, data_layout, data_type) { TensorShape shape_out = compute_winograd_input_transform_shape(TensorInfo(shape_in, 1, data_type), winograd_info); // Create tensors CLTensor in = create_tensor(shape_in, data_type, 1, 0, QuantizationInfo(), data_layout); CLTensor out = create_tensor(shape_out, data_type); ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLWinogradInputTransform winograd_input_transform; // Configure the function winograd_input_transform.configure(&in, &out, winograd_info); } FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallWinogradInputTransformDataset(), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradInputTransformDataset(), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // InputTransform TEST_SUITE(FilterTransform) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // Output tile not supported TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), // valid TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), // valid TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32) // valid }), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F16), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::QASYMM8), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32), TensorInfo(TensorShape(22U, 37U, 36U), 1, DataType::F32) })), framework::dataset::make("WinogradInfo", { WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(3U, 3U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ) })), framework::dataset::make("Expected", { false, false, false, false, true, true, true })), input_info, output_info, winograd_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(framework::dataset::concat(datasets::Small3x3Shapes(), datasets::Large3x3Shapes()), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 })), shape_a, output_tile, data_layout, data_type) { WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */); TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); // Create tensors CLTensor a = create_tensor(shape_a, data_type, 1, 0, QuantizationInfo(), data_layout); CLTensor b = create_tensor(shape_b, data_type, 1, 0, QuantizationInfo(), data_layout); ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLWinogradFilterTransform winograd_filter_transform; winograd_filter_transform.configure(&a, &b, winograd_info); } FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))), combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))), combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), framework::dataset::make("DataLayout", { DataLayout::NCHW })), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // FilterTransform TEST_SUITE(OutputTransform) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F16), // F16 not supported TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::QASYMM8), // QASYMM8 not supported TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Kernel size not supported TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Valid TensorInfo(TensorShape(13U, 108U, 16U, 4U), 1, DataType::F32), // Padding needed TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Valid TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Wrong WinogradInfo TensorInfo(TensorShape(7U, 256U, 36U, 3U), 1, DataType::F32), // Valid TensorInfo(TensorShape(7U, 256U, 16U, 3U), 1, DataType::F32) // Wrong number of batches }), framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(512U), 1, DataType::F16), TensorInfo(TensorShape(512U), 1, DataType::QASYMM8), TensorInfo(TensorShape(512U), 1, DataType::F32), TensorInfo(TensorShape(512U), 1, DataType::F32), TensorInfo(TensorShape(13U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32), TensorInfo(TensorShape(7U), 1, DataType::F32) })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F16), TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::QASYMM8), TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F32), TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F32), TensorInfo(TensorShape(17U, 23U, 13U, 4U), 1, DataType::F32), TensorInfo(TensorShape(8U, 10U, 7U, 7U), 1, DataType::F32), TensorInfo(TensorShape(7U, 9U, 7U, 7U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32), TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32) })), framework::dataset::make("WinogradInfo", { WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(17U, 23U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(2U, 3U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW) })), framework::dataset::make("Expected", { false, false, false, true, false, true, false, true, false })), input_info, bias_info, output_info, winograd_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradOutputTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradOutputTransformFixture = WinogradOutputTransformValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradOutputTransformDataset(), datasets::LargeWinogradOutputTransformDataset()), framework::dataset::make("DataType", { DataType::F32 })), shape_a, winograd_info, data_type) { TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); // Create tensors CLTensor a = create_tensor(shape_a, data_type); CLTensor b = create_tensor(shape_b, data_type); ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); // Create and configure function CLWinogradOutputTransform winograd_output_transform; winograd_output_transform.configure(&a, nullptr, &b, winograd_info); } FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // OutputTransform TEST_SUITE(ConvolutionLayer) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // FP16 not supported TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Datatype mismatch TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32), // Padding needed TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) // Kernel size not supported }), framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8), TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32), TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16) })), framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(19U), 1, DataType::F32), TensorInfo(TensorShape(19U), 1, DataType::F32), TensorInfo(TensorShape(21U), 1, DataType::F32), TensorInfo(TensorShape(16U), 1, DataType::F32), TensorInfo(TensorShape(16U), 1, DataType::F32) })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32), TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32), TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32) })), framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 2, 0, 0), PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 1, 0) })), framework::dataset::make("Expected", { false, false, false, false, false })), input_info, weights_info, bias_info, output_info, conv_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture; TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv3x3 TEST_SUITE(Conv5x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv5x5 TEST_SUITE_END() // ConvolutionLayer TEST_SUITE_END() // Winograd TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute