| /* |
| * Copyright (c) 2017-2021 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/NEGEMMConv2d.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 "src/core/helpers/MemoryHelpers.h" |
| #include "src/runtime/cpu/operators/CpuConv2d.h" |
| #include "src/runtime/cpu/operators/CpuGemmConvolution.h" |
| #include "src/runtime/cpu/operators/CpuGemmDirectConv2d.h" |
| #include "src/runtime/cpu/operators/CpuWinogradConv2d.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 detail |
| { |
| template <> |
| void configure_conv_function<NEGEMMConv2d, Tensor>(NEGEMMConv2d &func, |
| Tensor *src, const Tensor *weights, const Tensor *bias, Tensor *dst, |
| const PadStrideInfo &info, const WeightsInfo &weights_info, |
| const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) |
| { |
| ARM_COMPUTE_UNUSED(weights_info); |
| |
| Conv2dInfo conv_info(info, dilation, act_info, false, num_groups); |
| func.configure(src, weights, bias, dst, conv_info); |
| } |
| } // namespace detail |
| 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 = cpu::CpuConv2d::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 NEWinogradConvolutionLayerMixedDataLayoutFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T, T, true, true>; |
| |
| template <typename T> |
| using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerFastMathValidationFixture<Tensor, Accessor, NEWinogradConvolutionLayer, T, T, false>; |
| |
| /** Test case for memory injection in @ref cpu::CpuWinogradConv2d. |
| * |
| * Configure the operator once and inject memory at run-time in multiple executions. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) |
| { |
| auto winograd = std::make_unique<cpu::CpuWinogradConv2d>(); |
| const auto src_info = TensorInfo(TensorShape(8U, 8U, 32U), 1, DataType::F32); |
| const auto w_info = TensorInfo(TensorShape(1U), 1, DataType::F32); |
| const auto b_info = TensorInfo(TensorShape(1U, 3U, 32U, 1U), 1, DataType::F32); |
| auto dst_info = TensorInfo(TensorShape(8U, 6U, 1U), 1, DataType::F32); |
| const PadStrideInfo pad_info{}; |
| |
| winograd->configure(&src_info, &b_info, &w_info, &dst_info, pad_info); |
| |
| // telhs are newly created every call of this lambda function |
| auto a = create_tensor<Tensor>(src_info); |
| auto b = create_tensor<Tensor>(b_info); |
| auto c = create_tensor<Tensor>(w_info); |
| a.allocator()->allocate(); |
| b.allocator()->allocate(); |
| c.allocator()->allocate(); |
| |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &a }, { TensorType::ACL_SRC_1, &b }, { TensorType::ACL_SRC_2, &c } }; |
| ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &b }, { TensorType::ACL_SRC_2, &c } }; |
| |
| auto mg = MemoryGroup{}; |
| auto ws = manage_workspace<Tensor>(winograd->workspace(), mg, run_pack, prep_pack); |
| auto run_conv = [&]() -> Tensor |
| { |
| auto dst = create_tensor<Tensor>(dst_info); |
| dst.allocator()->allocate(); |
| |
| run_pack.add_tensor(TensorType::ACL_DST, &dst); |
| library->fill_tensor_value(Accessor(a), 1.f); |
| library->fill_tensor_value(Accessor(b), 2.f); |
| library->fill_tensor_value(Accessor(c), 3.f); |
| |
| // This operator is configured once and captured by this lambda. |
| winograd->prepare(prep_pack); |
| winograd->run(run_pack); |
| return dst; |
| }; |
| |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| /** Test case for memory injection in @ref NEWinogradConvolutionLayer. |
| * |
| * Make sure @ref NEWinogradConvolutionLayer still works through injecting the memory at configure time using the old API. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) |
| { |
| auto gemm = std::make_unique<NEWinogradConvolutionLayer>(); |
| const auto src_info = TensorInfo(TensorShape(8U, 8U, 32U), 1, DataType::F32); |
| const auto w_info = TensorInfo(TensorShape(1U), 1, DataType::F32); |
| const auto b_info = TensorInfo(TensorShape(1U, 3U, 32U, 1U), 1, DataType::F32); |
| auto dst_info = TensorInfo(TensorShape(8U, 6U, 1U), 1, DataType::F32); |
| const PadStrideInfo pad_info{}; |
| |
| auto run_conv = [&]() |
| { |
| auto src = create_tensor<Tensor>(src_info); |
| auto w = create_tensor<Tensor>(w_info); |
| auto b = create_tensor<Tensor>(b_info); |
| auto dst = create_tensor<Tensor>(dst_info); |
| |
| gemm->configure(&src, &b, &w, &dst, pad_info); |
| |
| src.allocator()->allocate(); |
| b.allocator()->allocate(); |
| w.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(b), 2.f); |
| library->fill_tensor_value(Accessor(w), 3.f); |
| gemm->run(); |
| return dst; |
| }; |
| |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| 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(RunMixedDataLayout, NEWinogradConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, |
| combine(combine(combine(combine(combine(combine(combine(combine( |
| framework::dataset::make("Input", TensorShape(8U, 8U, 32U)), |
| framework::dataset::make("Weight", TensorShape(1U, 3U, 32U, 1U))), |
| framework::dataset::make("Bias", TensorShape(1U))), |
| framework::dataset::make("Output", TensorShape(8U, 6U, 1U))), |
| framework::dataset::make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0))), |
| framework::dataset::make("Dilation", Size2D(1U, 1U))), |
| 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, NEConvolutionLayer, T>; |
| template <typename T> |
| using NEGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T, true>; |
| |
| /** Test case for memory injection in @ref cpu::CpuGemmConvolution. |
| * |
| * Configure the operator once and inject memory at run-time in multiple executions. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) |
| { |
| auto conv = std::make_unique<cpu::CpuGemmConvolution>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 5U, 2U), 1, DataType::F32, DataLayout::NCHW); |
| const auto weight_info = TensorInfo(TensorShape(1U, 3U, 2U, 3U), 1, DataType::F32, DataLayout::NCHW); |
| const auto bias_info = TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NCHW); |
| auto dst_info = TensorInfo(TensorShape(1U, 7U, 3U), 1, DataType::F32, DataLayout::NCHW); |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0, 2, 2, DimensionRoundingType::FLOOR); |
| WeightsInfo weights_info(false, 3U, 3U, 1U); |
| conv->configure(&src_info, &weight_info, &bias_info, &dst_info, conv_info, weights_info); |
| |
| // tensors are newly created every call of this lambda function |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| |
| auto mg = MemoryGroup{}; |
| auto ws = manage_workspace<Tensor>(conv->workspace(), mg, run_pack, prep_pack); |
| |
| auto run_conv = [&]() -> Tensor |
| { |
| auto dst = create_tensor<Tensor>(dst_info); |
| dst.allocator()->allocate(); |
| run_pack.add_tensor(TensorType::ACL_DST, &dst); |
| |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| // This operator is configured once and captured by this lambda. |
| conv->prepare(prep_pack); |
| conv->run(run_pack); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| /** Test case for memory injection in @ref NEGEMMConvolutionLayer. |
| * |
| * Make sure @ref NEGEMMConvolutionLayer still works through injecting the memory at configure time using the old API. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) |
| { |
| auto conv = std::make_unique<NEGEMMConvolutionLayer>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 5U, 2U), 1, DataType::F32, DataLayout::NCHW); |
| const auto weight_info = TensorInfo(TensorShape(1U, 3U, 2U, 3U), 1, DataType::F32, DataLayout::NCHW); |
| const auto bias_info = TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NCHW); |
| auto dst_info = TensorInfo(TensorShape(1U, 7U, 3U), 1, DataType::F32, DataLayout::NCHW); |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0, 2, 2, DimensionRoundingType::FLOOR); |
| WeightsInfo weights_info(false, 3U, 3U, 1U); |
| auto run_conv = [&]() |
| { |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| auto dst = create_tensor<Tensor>(dst_info); |
| conv->configure(&src, &weight, &bias, &dst, conv_info, weights_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| conv->run(); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| 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)); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEGEMMConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| framework::dataset::make("Input", TensorShape(23U, 27U, 5U)), |
| framework::dataset::make("Weights", TensorShape(3U, 3U, 5U, 2U))), |
| framework::dataset::make("Bias", TensorShape(2U))), |
| framework::dataset::make("Output", TensorShape(11U, 25U, 2U))), |
| framework::dataset::make("PadStrideInfo", PadStrideInfo(2, 1, 0, 0))), |
| framework::dataset::make("Dilation", Size2D(1, 1))), |
| 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 NEGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEConvolutionLayer, T, true>; |
| |
| 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); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| framework::dataset::make("Input", TensorShape(23U, 27U, 5U)), |
| framework::dataset::make("Weights", TensorShape(3U, 3U, 5U, 2U))), |
| framework::dataset::make("Bias", TensorShape(2U))), |
| framework::dataset::make("Output", TensorShape(11U, 25U, 2U))), |
| framework::dataset::make("PadStrideInfo", PadStrideInfo(2, 1, 0, 0))), |
| framework::dataset::make("Dilation", Size2D(1, 1))), |
| 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); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEGEMMConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| framework::dataset::make("Input", TensorShape(23U, 27U, 5U)), |
| framework::dataset::make("Weights", TensorShape(3U, 3U, 5U, 2U))), |
| framework::dataset::make("Bias", TensorShape(2U))), |
| framework::dataset::make("Output", TensorShape(11U, 25U, 2U))), |
| framework::dataset::make("PadStrideInfo", PadStrideInfo(2, 1, 0, 0))), |
| framework::dataset::make("Dilation", Size2D(1, 1))), |
| 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(2.f / 255.f, 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 case for memory injection in @ref cpu::CpuGemmDirectConv2d. |
| * |
| * Configure the operator once and inject memory at run-time in multiple executions. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) |
| { |
| auto conv = std::make_unique<cpu::CpuGemmDirectConv2d>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 5U, 2U), 1, DataType::F32, DataLayout::NHWC); |
| const auto weight_info = TensorInfo(TensorShape(1U, 3U, 2U, 3U), 1, DataType::F32, DataLayout::NHWC); |
| const auto bias_info = TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC); |
| auto dst_info = TensorInfo(TensorShape(1U, 7U, 3U), 1, DataType::F32, DataLayout::NHWC); |
| const auto conv_info = Conv2dInfo{}; |
| conv->configure(&src_info, &weight_info, &bias_info, &dst_info, conv_info); |
| |
| // tensors are newly created every call of this lambda function |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| |
| ITensorPack run_pack{ { TensorType::ACL_SRC_0, &src }, { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &weight }, { TensorType::ACL_SRC_2, &bias } }; |
| |
| auto mg = MemoryGroup{}; |
| auto ws = manage_workspace<Tensor>(conv->workspace(), mg, run_pack, prep_pack); |
| |
| auto run_conv = [&]() -> Tensor |
| { |
| auto dst = create_tensor<Tensor>(dst_info); |
| dst.allocator()->allocate(); |
| run_pack.add_tensor(TensorType::ACL_DST, &dst); |
| |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| // This operator is configured once and captured by this lambda. |
| conv->prepare(prep_pack); |
| conv->run(run_pack); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| /** Test case for memory injection in @ref NEGEMMConv2d. |
| * |
| * Make sure @ref NEGEMMConv2d still works through injecting the memory at configure time using the old API. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) |
| { |
| auto conv = std::make_unique<NEGEMMConv2d>(); |
| const auto src_info = TensorInfo(TensorShape(1U, 5U, 2U), 1, DataType::F32, DataLayout::NHWC); |
| const auto weight_info = TensorInfo(TensorShape(1U, 3U, 2U, 3U), 1, DataType::F32, DataLayout::NHWC); |
| const auto bias_info = TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC); |
| auto dst_info = TensorInfo(TensorShape(1U, 7U, 3U), 1, DataType::F32, DataLayout::NHWC); |
| const auto conv_info = Conv2dInfo{}; |
| auto run_conv = [&]() |
| { |
| auto src = create_tensor<Tensor>(src_info); |
| auto weight = create_tensor<Tensor>(weight_info); |
| auto bias = create_tensor<Tensor>(bias_info); |
| auto dst = create_tensor<Tensor>(dst_info); |
| conv->configure(&src, &weight, &bias, &dst, conv_info); |
| src.allocator()->allocate(); |
| weight.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weight), 2.f); |
| library->fill_tensor_value(Accessor(bias), 3.f); |
| conv->run(); |
| return dst; |
| }; |
| auto result_0 = run_conv(); |
| auto result_1 = run_conv(); |
| for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) |
| { |
| ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); |
| } |
| } |
| |
| 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 |