580 lines
35 KiB
C++
580 lines
35 KiB
C++
/*
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* Copyright (c) 2017-2020 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/Types.h"
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#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMMConv2d.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h"
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#include "arm_compute/runtime/Tensor.h"
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#include "arm_compute/runtime/TensorAllocator.h"
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#include "tests/NEON/Accessor.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/SmallConvolutionLayerDataset.h"
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#include "tests/datasets/TinyConvolutionLayerDataset.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/ConvolutionLayerFixture.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 detail
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{
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template <>
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void configure_conv_function<NEGEMMConv2d, Tensor>(NEGEMMConv2d &func,
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Tensor *src, const Tensor *weights, const Tensor *bias, Tensor *dst,
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const PadStrideInfo &info, const WeightsInfo &weights_info,
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const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
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{
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ARM_COMPUTE_UNUSED(weights_info);
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Conv2dInfo conv_info(info, dilation, act_info, false, num_groups);
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func.configure(src, weights, bias, dst, conv_info);
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}
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} // namespace detail
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namespace
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{
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const RelativeTolerance<float> rel_tolerance_f32(0.01f); /**< Relative tolerance for FP32 types */
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const RelativeTolerance<float> rel_tolerance_winograd_3x3_f32(0.05f); /**< Relative tolerance for FP32 types */
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const AbsoluteTolerance<float> abs_tolerance_f32(0.002f); /**< Absolute tolerance for FP32 types */
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const AbsoluteTolerance<float> abs_tolerance_1xN_f32(0.0041f); /**< Absolute tolerance for FP32 types */
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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const AbsoluteTolerance<half> tolerance_convolution_layer_f16(half(0.4f));
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constexpr float tolerance_num_f16 = 0.15f;
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */
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const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */
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constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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constexpr AbsoluteTolerance<float> tolerance_qasymm8(0.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */
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/** CNN data types */
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const auto CNNDataTypes = framework::dataset::make("DataType",
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{
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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DataType::F16,
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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DataType::F32,
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DataType::QASYMM8,
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});
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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, 0.5f)
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});
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const auto QuantizationData = framework::dataset::make("QuantizationInfo",
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{
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QuantizationInfo(0.5f, 10),
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QuantizationInfo(0.3f, 3),
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QuantizationInfo(1.f, 10),
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QuantizationInfo(1.1f, 10),
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});
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} // namespace
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TEST_SUITE(NEON)
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TEST_SUITE(ConvolutionLayer)
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32),
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TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32),
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TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32)
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}),
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framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32),
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TensorInfo(TensorShape(5U, 5U, 32U, 21U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 5U, 21U), 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("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32),
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TensorInfo(TensorShape(19U, 23U, 21U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(11U, 25U, 21U), 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", { PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(2, 1, 0, 0),
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PadStrideInfo(3, 2, 1, 0)
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})),
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framework::dataset::make("FastMath", { true,
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true,
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false,
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false
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})),
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framework::dataset::make("Expected", { ConvolutionMethod::WINOGRAD, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })),
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input_info, weights_info, output_info, conv_info, fast_math, expected)
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{
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ConvolutionMethod is_valid = NEConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true),
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&weights_info.clone()->set_is_resizable(true),
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&output_info.clone()->set_is_resizable(true), conv_info, WeightsInfo(), Size2D(1U, 1U), ActivationLayerInfo(), fast_math);
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ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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TEST_SUITE_END() // ConvolutionLayer
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TEST_SUITE(WinogradLayer)
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template <typename T>
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using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T>;
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template <typename T>
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using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T, T, false>;
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TEST_SUITE(FP32)
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TEST_SUITE(Conv1x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv1x3
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TEST_SUITE(Conv3x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv3x1
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TEST_SUITE(Conv1x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv1x5
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TEST_SUITE(Conv5x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv5x1
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TEST_SUITE(Conv7x1)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(),
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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(),
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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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv7x1
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TEST_SUITE(Conv1x7)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(),
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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(),
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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(Accessor(_target), _reference, abs_tolerance_1xN_f32);
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}
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TEST_SUITE_END() // Conv1x7
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TEST_SUITE(Conv3x3)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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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// floating point arithmetic the Winograd results will not be exactly the same as direct convolution, especially for big shapes
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validate(Accessor(_target), _reference, rel_tolerance_winograd_3x3_f32, 0.f, float(abs_tolerance_f32));
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}
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TEST_SUITE_END() // Conv3x3
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TEST_SUITE(Conv5x5)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, 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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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, 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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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TEST_SUITE_END() // Conv5x5
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FIXTURE_DATA_TEST_CASE(RunSmallNoBias, NEWinogradConvolutionLayerNoBiasFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
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datasets::SmallWinogradConvolutionLayer5x5Dataset()),
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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(Accessor(_target), _reference, abs_tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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TEST_SUITE(FP16)
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using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, 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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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(Accessor(_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(Accessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16);
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}
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TEST_SUITE_END() // Conv3x3
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TEST_SUITE_END() // FP16
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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TEST_SUITE_END() // WinogradLayer
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TEST_SUITE(GEMMConvolutionLayer)
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template <typename T>
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using NEGEMMConvolutionLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T>;
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TEST_SUITE(Float)
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#if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16)
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TEST_SUITE(BFLOAT16)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::BFLOAT16)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
|
|
ActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32));
|
|
}
|
|
TEST_SUITE_END() // BFLOAT16
|
|
#endif /* defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) */
|
|
|
|
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
|
TEST_SUITE(FP16)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::F16)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW })),
|
|
ActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
|
|
}
|
|
TEST_SUITE_END() // FP16
|
|
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
|
|
|
|
TEST_SUITE(FP32)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
|
|
ActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32));
|
|
}
|
|
TEST_SUITE_END() // FP32
|
|
TEST_SUITE_END() // Float
|
|
|
|
template <typename T>
|
|
using NEGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEConvolutionLayer, T>;
|
|
|
|
template <typename T>
|
|
using NEGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEConvolutionLayer, T, int8_t>;
|
|
|
|
const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
|
|
{
|
|
ActivationLayerInfo(),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
|
|
});
|
|
TEST_SUITE(Quantized)
|
|
TEST_SUITE(QASYMM8)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
|
|
QuantizedActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8
|
|
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })),
|
|
QuantizedActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8_SIGNED
|
|
|
|
TEST_SUITE(QSYMM8_PER_CHANNEL)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedPerChannelFixture<uint8_t>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", { DataType::QASYMM8 })),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
|
|
QuantizationData),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
FIXTURE_DATA_TEST_CASE(RunSmallSigned, NEGEMMConvolutionLayerQuantizedPerChannelFixture<int8_t>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", { DataType::QASYMM8_SIGNED })),
|
|
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
|
|
QuantizationData),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QSYMM8_PER_CHANNEL
|
|
TEST_SUITE_END() // Quantized
|
|
|
|
TEST_SUITE_END() // GEMMConvolutionLayer
|
|
|
|
TEST_SUITE(DirectGEMMConv2d)
|
|
template <typename T>
|
|
using NEDirectGEMMConv2dLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEGEMMConv2d, T>;
|
|
|
|
TEST_SUITE(Float)
|
|
TEST_SUITE(FP32)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::F32)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
|
|
ActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32));
|
|
}
|
|
TEST_SUITE_END() // FP32
|
|
TEST_SUITE_END() // Float
|
|
|
|
#ifdef __aarch64__
|
|
template <typename T>
|
|
using NEDirectGEMMConv2dLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEGEMMConv2d, T>;
|
|
|
|
template <typename T>
|
|
using NEDirectGEMMConv2dLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEGEMMConv2d, T, int8_t>;
|
|
|
|
const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
|
|
{
|
|
ActivationLayerInfo(),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
|
|
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
|
|
});
|
|
TEST_SUITE(Quantized)
|
|
TEST_SUITE(QASYMM8)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
|
|
QuantizedActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8
|
|
|
|
TEST_SUITE(QASYMM8_SIGNED)
|
|
FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
|
|
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })),
|
|
QuantizedActivationFunctionsDataset))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QASYMM8_SIGNED
|
|
|
|
TEST_SUITE(QSYMM8_PER_CHANNEL)
|
|
FIXTURE_DATA_TEST_CASE(RunSmallSigned, NEDirectGEMMConv2dLayerQuantizedPerChannelFixture<int8_t>, framework::DatasetMode::ALL,
|
|
combine(combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
|
|
framework::dataset::make("ReshapeWeights", { true })),
|
|
framework::dataset::make("DataType", { DataType::QASYMM8_SIGNED })),
|
|
framework::dataset::make("DataLayout", { DataLayout::NHWC })),
|
|
QuantizationData),
|
|
QuantizedActivationFunctionsDataset),
|
|
framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL })))
|
|
{
|
|
// Validate output
|
|
validate(Accessor(_target), _reference, tolerance_qasymm8);
|
|
}
|
|
TEST_SUITE_END() // QSYMM8_PER_CHANNEL
|
|
TEST_SUITE_END() // Quantized
|
|
#endif // __aarch64__
|
|
|
|
TEST_SUITE_END() // DirectGEMMConv2d
|
|
|
|
TEST_SUITE_END() // NEON
|
|
} // namespace validation
|
|
} // namespace test
|
|
} // namespace arm_compute
|