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457 lines
28 KiB
457 lines
28 KiB
/*
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* Copyright (c) 2018-2021 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/core/Helpers.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/core/utils/misc/ShapeCalculator.h"
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#include "arm_compute/runtime/CL/CLTensor.h"
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#include "arm_compute/runtime/CL/CLTensorAllocator.h"
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#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/CL/Helper.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/LargeConvolutionLayerDataset.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/datasets/SmallConvolutionLayerDataset.h"
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#include "tests/datasets/WinogradInputTransformDataset.h"
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#include "tests/datasets/WinogradOutputTransformDataset.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/WinogradConvolutionLayerFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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// *INDENT-OFF*
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// clang-format off
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const AbsoluteTolerance<half> tolerance_f16(half(1.f));
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constexpr AbsoluteTolerance<float> tolerance_convolution_layer_f32(0.1f);
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const AbsoluteTolerance<half> tolerance_convolution_layer_f16(half(0.4f));
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RelativeTolerance<half_float::half> rel_tolerance_f16(half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for FP16 data types */
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constexpr float tolerance_num = 0.05f; /**< Tolerance number */
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constexpr float abs_tolerance_convolution_layer_f16 = 2.5f; /**< Tolerance number */
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constexpr float tolerance_num_f16 = 0.15f; /**< Tolerance number */
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//Activation Functions
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const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU)
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});
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const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU)
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});
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} // namespace
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using namespace arm_compute::misc::shape_calculator;
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TEST_SUITE(CL)
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TEST_SUITE(Winograd)
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TEST_SUITE(ConvolutionLayer)
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", {
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TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // Insufficient padding
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TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Datatype mismatch
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TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported
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TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32), // Padding needed
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TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) // Kernel size not supported
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}),
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framework::dataset::make("WeightsInfo", {
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TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F16),
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TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8),
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TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32),
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TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
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})),
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framework::dataset::make("BiasesInfo", {
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TensorInfo(TensorShape(19U), 1, DataType::F16),
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TensorInfo(TensorShape(19U), 1, DataType::F32),
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TensorInfo(TensorShape(21U), 1, DataType::F32),
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TensorInfo(TensorShape(16U), 1, DataType::F32),
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TensorInfo(TensorShape(16U), 1, DataType::F32)
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})),
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framework::dataset::make("OutputInfo", {
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TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F16),
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TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
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TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32)
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})),
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framework::dataset::make("ConvInfo", {
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PadStrideInfo(1, 1, 1, 1),
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PadStrideInfo(1, 1, 1, 1),
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PadStrideInfo(1, 2, 0, 0),
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PadStrideInfo(1, 1, 1, 1),
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PadStrideInfo(1, 1, 1, 0)
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})),
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framework::dataset::make("Expected", { false, false, false, false, false })),
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input_info, weights_info, bias_info, output_info, conv_info, expected)
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{
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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);
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}
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TEST_SUITE(FP32)
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using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>;
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using CLWinogradConvolutionLayerFastMathMixedDataLayoutFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float, float, true, true>;
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TEST_SUITE(Conv3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLWinogradConvolutionLayerFastMathMixedDataLayoutFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(combine(combine(combine(combine(combine(
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framework::dataset::make("Input", TensorShape(8U, 8U, 32U)),
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framework::dataset::make("Weight", TensorShape(1U, 3U, 32U, 1U))),
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framework::dataset::make("Bias", TensorShape(1U))),
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framework::dataset::make("Output", TensorShape(8U, 6U, 1U))),
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framework::dataset::make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0))),
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framework::dataset::make("Dilation", Size2D(1U, 1U))),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv3x3
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TEST_SUITE(Conv3x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv3x1
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TEST_SUITE(Conv1x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv1x3
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TEST_SUITE(Conv5x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset ),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset ),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv5x5
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TEST_SUITE(Conv5x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv5x1
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TEST_SUITE(Conv1x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
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framework::dataset::make("DataType", { DataType::F32 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
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}
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TEST_SUITE_END() // Conv1x5
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TEST_SUITE_END() // FP32
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TEST_SUITE(FP16)
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using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, half, float>;
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TEST_SUITE(Conv3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv3x3
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TEST_SUITE(Conv3x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv3x1
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TEST_SUITE(Conv1x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv1x3
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TEST_SUITE(Conv5x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv5x5
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TEST_SUITE(Conv5x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsSmallDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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|
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
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framework::dataset::make("DataType", { DataType::F16 })),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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|
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{
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// Validate output
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validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv5x1
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TEST_SUITE(Conv1x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
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|
framework::dataset::make("DataType", { DataType::F16 })),
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|
ActivationFunctionsSmallDataset),
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|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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|
|
|
{
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// Validate output
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|
validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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|
}
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|
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FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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|
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
|
|
framework::dataset::make("DataType", { DataType::F16 })),
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|
ActivationFunctionsDataset),
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|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
|
|
|
|
{
|
|
// Validate output
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|
validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
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}
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TEST_SUITE_END() // Conv1x5
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|
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TEST_SUITE(Conv1x7)
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FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
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|
combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(),
|
|
framework::dataset::make("DataType", { DataType::F16 })),
|
|
ActivationFunctionsSmallDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })))
|
|
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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|
}
|
|
|
|
FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
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|
combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x7Dataset(),
|
|
framework::dataset::make("DataType", { DataType::F16 })),
|
|
ActivationFunctionsDataset),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })))
|
|
|
|
{
|
|
// Validate output
|
|
validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16);
|
|
}
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TEST_SUITE_END() // Conv1x7
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TEST_SUITE_END() // FP16
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TEST_SUITE_END() // ConvolutionLayer
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TEST_SUITE_END() // Winograd
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TEST_SUITE_END() // CL
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} // namespace validation
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|
} // namespace test
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} // namespace arm_compute
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