| /* |
| * Copyright (c) 2017-2020 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| #include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h" |
| #include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/LargeConvolutionLayerDataset.h" |
| #include "tests/datasets/SmallConvolutionLayerDataset.h" |
| #include "tests/datasets/TinyConvolutionLayerDataset.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/ConvolutionLayerFixture.h" |
| #include "tests/validation/fixtures/WinogradConvolutionLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| const RelativeTolerance<float> rel_tolerance_f32(0.01f); /**< Relative tolerance for FP32 types */ |
| const RelativeTolerance<float> rel_tolerance_winograd_3x3_f32(0.05f); /**< Relative tolerance for FP32 types */ |
| const AbsoluteTolerance<float> abs_tolerance_f32(0.002f); /**< Absolute tolerance for FP32 types */ |
| const AbsoluteTolerance<float> abs_tolerance_1xN_f32(0.0041f); /**< Absolute tolerance for FP32 types */ |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const AbsoluteTolerance<half> tolerance_convolution_layer_f16(half(0.4f)); |
| constexpr float tolerance_num_f16 = 0.15f; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */ |
| const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */ |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| constexpr AbsoluteTolerance<float> tolerance_qasymm8(0.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ |
| |
| /** CNN data types */ |
| const auto CNNDataTypes = framework::dataset::make("DataType", |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| DataType::F16, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| DataType::F32, |
| DataType::QASYMM8, |
| }); |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f) |
| }); |
| |
| const auto QuantizationData = framework::dataset::make("QuantizationInfo", |
| { |
| QuantizationInfo(0.5f, 10), |
| QuantizationInfo(0.3f, 3), |
| QuantizationInfo(1.f, 10), |
| QuantizationInfo(1.1f, 10), |
| }); |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(ConvolutionLayer) |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32), |
| TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32), |
| TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) |
| }), |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 32U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16) |
| })), |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(19U, 23U, 21U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32) |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(2, 1, 0, 0), |
| PadStrideInfo(3, 2, 1, 0) |
| })), |
| framework::dataset::make("FastMath", { true, |
| true, |
| false, |
| false |
| })), |
| framework::dataset::make("Expected", { ConvolutionMethod::WINOGRAD, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), |
| input_info, weights_info, output_info, conv_info, fast_math, expected) |
| { |
| ConvolutionMethod is_valid = NEConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true), |
| &weights_info.clone()->set_is_resizable(true), |
| &output_info.clone()->set_is_resizable(true), conv_info, WeightsInfo(), Size2D(1U, 1U), ActivationLayerInfo(), fast_math); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| TEST_SUITE_END() // ConvolutionLayer |
| |
| TEST_SUITE(WinogradLayer) |
| template <typename T> |
| using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T>; |
| |
| template <typename T> |
| using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T, T, false>; |
| |
| TEST_SUITE(FP32) |
| |
| TEST_SUITE(Conv1x3) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| |
| TEST_SUITE_END() // Conv1x3 |
| |
| TEST_SUITE(Conv3x1) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| |
| TEST_SUITE_END() // Conv3x1 |
| |
| TEST_SUITE(Conv1x5) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| |
| TEST_SUITE_END() // Conv1x5 |
| |
| TEST_SUITE(Conv5x1) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| |
| TEST_SUITE_END() // Conv5x1 |
| |
| TEST_SUITE(Conv7x1) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| TEST_SUITE_END() // Conv7x1 |
| |
| TEST_SUITE(Conv1x7) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); |
| } |
| TEST_SUITE_END() // Conv1x7 |
| |
| TEST_SUITE(Conv3x3) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| // floating point arithmetic the Winograd results will not be exactly the same as direct convolution, especially for big shapes |
| validate(Accessor(_target), _reference, rel_tolerance_winograd_3x3_f32, 0.f, float(abs_tolerance_f32)); |
| } |
| TEST_SUITE_END() // Conv3x3 |
| |
| TEST_SUITE(Conv5x5) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| |
| TEST_SUITE_END() // Conv5x5 |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallNoBias, NEWinogradConvolutionLayerNoBiasFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(), |
| datasets::SmallWinogradConvolutionLayer5x5Dataset()), |
| framework::dataset::make("DataType", { DataType::F32 })), |
| ActivationFunctionsDataset), |
| |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, abs_tolerance_f32); |
| } |
| |
| TEST_SUITE_END() // FP32 |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, half, float>; |
| |
| TEST_SUITE(Conv3x3) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F16 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), |
| framework::dataset::make("DataType", { DataType::F16 })), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16); |
| } |
| TEST_SUITE_END() // Conv3x3 |
| TEST_SUITE_END() // FP16 |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| TEST_SUITE_END() // WinogradLayer |
| |
| TEST_SUITE(GEMMConvolutionLayer) |
| template <typename T> |
| using NEGEMMConvolutionLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEGEMMConvolutionLayer, T>; |
| |
| TEST_SUITE(Float) |
| #if defined(__ARM_FEATURE_BF16_VECTOR_ARITHMETIC) || defined(ARM_COMPUTE_FORCE_BF16) |
| TEST_SUITE(BFLOAT16) |
| 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, NEGEMMConvolutionLayer, T>; |
| |
| template <typename T> |
| using NEGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<Tensor, Accessor, NEGEMMConvolutionLayer, 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_END() // NEON |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |