| /* |
| * 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/core/experimental/PostOps.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" |
| #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" |
| #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" |
| #include "tests/CL/CLAccessor.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" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| class SmallConvolutionLayerDatasetCases final : public datasets::ConvolutionLayerDataset |
| { |
| public: |
| SmallConvolutionLayerDatasetCases() |
| { |
| // 1D Kernel |
| add_config(TensorShape(1U, 130U, 2000U), TensorShape(1U, 1U, 2000U, 2000U), TensorShape(2000U), TensorShape(1U, 130U, 2000U), PadStrideInfo(1, 1, 0, 0)); |
| } |
| }; |
| |
| RelativeTolerance<float> tolerance_f32(0.1f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ |
| constexpr AbsoluteTolerance<float> tolerance_qasymm8(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number */ |
| |
| /** CNN data types */ |
| const auto CNNDataTypes = framework::dataset::make("DataType", |
| { |
| DataType::F16, |
| DataType::F32, |
| DataType::QASYMM8, |
| DataType::QASYMM8_SIGNED, |
| }); |
| |
| /** Grouped CNN data types */ |
| const auto GroupedCNNDataTypes = framework::dataset::make("DataType", |
| { |
| DataType::F16, |
| DataType::F32 |
| }); |
| |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| |
| bool is_post_op_list_valid_in_gemmconv(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &output_shape, DataType data_type, DataLayout data_layout, |
| const PadStrideInfo &conv_info, const experimental::PostOpList<ITensorInfo *> &post_ops) |
| { |
| const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); |
| |
| const auto dilation = Size2D(1U, 1U); |
| const unsigned int num_groups = 1U; |
| |
| TensorInfo input_info(input_shape, 1, data_type, data_layout); |
| TensorInfo weights_info(weights_shape, 1, data_type, data_layout); |
| |
| TensorInfo output_info(output_shape, 1, data_type, data_layout); |
| |
| WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels)); |
| |
| const auto status = CLGEMMConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true), |
| &weights_info.clone()->set_is_resizable(true), nullptr, &output_info.clone()->set_is_resizable(true), |
| conv_info, w_info, dilation, ActivationLayerInfo(), num_groups, post_ops); |
| return bool(status); |
| } |
| } // namespace |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(ConvolutionLayer) |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(23U, 27U, 31U, 4U), 1, DataType::F32), // Select WINOGRAD |
| TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32), // Select WINOGRAD |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::QASYMM8_SIGNED), // Select GEMM |
| }), |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 31U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16), |
| TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 5U, 2U, 19U), 1, DataType::QASYMM8_SIGNED), |
| })), |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32), |
| TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::QASYMM8_SIGNED), |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 2, 1, 1), |
| PadStrideInfo(1, 2, 1, 1), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(2, 1, 0, 0), |
| PadStrideInfo(3, 2, 1, 0), |
| PadStrideInfo(1, 1, 2, 2), |
| PadStrideInfo(1, 1, 2, 2), |
| PadStrideInfo(1, 1, 2, 2), |
| })), |
| framework::dataset::make("GpuTarget", { GPUTarget::BIFROST, |
| GPUTarget::MIDGARD, |
| GPUTarget::G71, |
| GPUTarget::G71, |
| GPUTarget::MIDGARD, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| GPUTarget::BIFROST, |
| })), |
| framework::dataset::make("Dilation", { Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(1U, 1U), |
| Size2D(2U, 1U), |
| Size2D(2U, 1U), |
| })), |
| framework::dataset::make("EnableFastMath", { false, false, false, false, false, false, true, true, true })), |
| framework::dataset::make("Expected",{ ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::WINOGRAD, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::WINOGRAD, |
| ConvolutionMethod::GEMM, |
| ConvolutionMethod::GEMM, |
| })), |
| input_info, weights_info, output_info, conv_info, gpu_target, dilation, enable_fast_math, expected) |
| { |
| ConvolutionMethod is_valid = CLConvolutionLayer::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(), |
| ActivationLayerInfo(), |
| gpu_target, |
| dilation, |
| enable_fast_math); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| |
| DATA_TEST_CASE(ValidatePostOpSupportInConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC), // Select GEMM |
| TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32, DataLayout::NCHW), // Select WINOGRAD |
| TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW), // Select Direct |
| TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW), // Select FFT |
| }), |
| framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(2U, 1U, 1U, 19U), 1, DataType::F32, DataLayout::NHWC), |
| TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32, DataLayout::NCHW), |
| TensorInfo(TensorShape(5U, 5U, 48U, 128U), 1, DataType::F32, DataLayout::NCHW), |
| TensorInfo(TensorShape(11U, 11U, 48U, 24), 1, DataType::F32, DataLayout::NCHW), |
| })), |
| framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(19U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC), |
| TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, DataLayout::NCHW), |
| TensorInfo(TensorShape(27U, 27U, 128U), 1, DataType::F32, DataLayout::NCHW), |
| TensorInfo(TensorShape(27U, 27U, 24U), 1, DataType::F32, DataLayout::NCHW), |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1U, 1U, 0U, 0U), |
| PadStrideInfo(1U, 1U, 2U, 2U), |
| PadStrideInfo(1U, 1U, 2U, 2U), |
| PadStrideInfo(1U, 1U, 5U, 5U), |
| })), |
| framework::dataset::make("EnableFastMath", { false, true, false, false})), |
| framework::dataset::make("ExpectedMethod",{ ConvolutionMethod::GEMM, |
| ConvolutionMethod::WINOGRAD, |
| ConvolutionMethod::DIRECT, |
| ConvolutionMethod::FFT, |
| })), |
| framework::dataset::make("PostOpSupported",{ true, false, false, false |
| })), |
| input_info, weights_info, output_info, conv_info, enable_fast_math, expected_method, post_op_supported) |
| { |
| const int idx_width = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::WIDTH); |
| const int idx_height = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::HEIGHT); |
| const int idx_kernels = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::BATCHES); |
| |
| const auto dilation = Size2D(1U, 1U); |
| const unsigned int num_groups = 1U; |
| |
| WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels)); |
| |
| experimental::PostOpList<ITensorInfo*> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpAct<ITensorInfo*>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); |
| |
| ConvolutionMethod actual_method = CLConvolutionLayer::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(), |
| ActivationLayerInfo(), |
| GPUTarget::BIFROST, |
| dilation, |
| enable_fast_math); |
| ARM_COMPUTE_EXPECT(actual_method == expected_method, framework::LogLevel::ERRORS); |
| const auto is_valid = CLConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true), |
| &weights_info.clone()->set_is_resizable(true), |
| nullptr, |
| &output_info.clone()->set_is_resizable(true), |
| conv_info, |
| w_info, |
| dilation, |
| ActivationLayerInfo(), |
| enable_fast_math, |
| num_groups, |
| post_ops); |
| ARM_COMPUTE_EXPECT( bool(is_valid) == post_op_supported, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| TEST_SUITE_END() // ConvolutionLayer |
| |
| TEST_SUITE(GEMMConvolutionLayer) |
| template <typename T> |
| using CLGEMMConvolutionLayerFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| template <typename T> |
| using CLGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, true>; |
| template <typename T> |
| using CLConvolutionValidationWithPaddingFixture = ConvolutionValidationWithPaddingFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| |
| TEST_SUITE(ValidateFusedPostOpsConfigs) |
| TEST_SUITE(Invalid) |
| TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| const TensorShape post_op_arg0_shape(output_shape); |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| auto post_op_arg1_info = post_op_arg_info.clone(); |
| |
| // Unsupported sequence of post ops |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| post_op_arg1_info.get(), |
| 0, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); |
| } |
| TEST_CASE(OnlyNHWCIsSupported, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NCHW; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(14U, 12U, 16U, 2U); |
| const auto weights_shape = TensorShape(1U, 1U, 16U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| const TensorShape post_op_arg0_shape(output_shape); |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); |
| } |
| TEST_CASE(OnlyFloatingTypeIsSupported, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::QASYMM8; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| const TensorShape post_op_arg0_shape(output_shape); |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); |
| } |
| TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedKernelSize, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 3U, 3U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| const TensorShape post_op_arg0_shape(output_shape); |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); |
| } |
| TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedStride, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(3, 3, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| const TensorShape post_op_arg0_shape(output_shape); |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); |
| } |
| TEST_SUITE_END() // Invalid |
| TEST_SUITE(Valid) |
| TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS); |
| } |
| TEST_CASE(SupportedPostOps, framework::DatasetMode::ALL) |
| { |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| const auto input_shape = TensorShape(16U, 14U, 12U, 2U); |
| const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); |
| |
| const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); |
| |
| TensorShape post_op_arg0_shape(output_shape); |
| post_op_arg0_shape[1] = 1; // Broadcast in "Y" (second) dimension |
| TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); |
| |
| experimental::PostOpList<ITensorInfo *> post_ops{}; |
| post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( |
| &post_op_arg_info, |
| 1, |
| ConvertPolicy::SATURATE); |
| |
| ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS); |
| } |
| TEST_SUITE_END() // Valid |
| TEST_SUITE_END() // ValidateFusedPostOps |
| TEST_SUITE(Float) |
| TEST_SUITE(FP16) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<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, DataLayout::NHWC })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); |
| } |
| TEST_SUITE_END() // FP16 |
| |
| TEST_SUITE(FP32) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<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 })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLGEMMConvolutionLayerMixedDataLayoutFixture<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 })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallWithPadding, CLConvolutionValidationWithPaddingFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerPrePaddingDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), |
| framework::dataset::make("PrePadLayer", { PaddingList({ { 1, 1 }, { 1, 1 } }) }))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32); |
| } |
| |
| TEST_SUITE_END() // FP32 |
| TEST_SUITE_END() // Float |
| |
| template <typename T> |
| using CLGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| template <typename T> |
| using CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, true>; |
| template <typename T> |
| using CLGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, int8_t>; |
| |
| const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) |
| }); |
| const auto QuantizedActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) |
| }); |
| |
| TEST_SUITE(Quantized) |
| |
| const auto QuantizationData = framework::dataset::make("QuantizationInfo", |
| { |
| QuantizationInfo(0.5f, 10), |
| QuantizationInfo(0.3f, 3), |
| QuantizationInfo(1.1f, 10), |
| }); |
| TEST_SUITE(QASYMM8) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallCases, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(SmallConvolutionLayerDatasetCases(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::QASYMM8)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<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 })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture<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 })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QASYMM8 |
| TEST_SUITE(QASYMM8_SIGNED) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<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 })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture<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 })), |
| QuantizationData), |
| QuantizedActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QASYMM8_SIGNED |
| TEST_SUITE(QSYMM8_PER_CHANNEL) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallSigned, CLGEMMConvolutionLayerQuantizedPerChannelFixture<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), |
| QuantizedActivationFunctionsSmallDataset), |
| framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedPerChannelFixture<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), |
| QuantizedActivationFunctionsSmallDataset), |
| framework::dataset::make("WeightsDataType", { DataType::QSYMM8_PER_CHANNEL }))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_qasymm8); |
| } |
| TEST_SUITE_END() // QSYMM8_PER_CHANNEL |
| TEST_SUITE_END() // Quantized |
| |
| TEST_SUITE_END() // GEMMConvolutionLayer |
| |
| template <typename T> |
| using CLGEMMGroupedConvolutionLayerFixture = ConvolutionValidationFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; |
| |
| TEST_SUITE(GroupedGEMMConvolutionLayer) |
| |
| TEST_SUITE(Float) |
| TEST_SUITE(FP32) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(datasets::LargeGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| TEST_SUITE_END() // FP32 |
| |
| TEST_SUITE(FP16) |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsSmallDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(datasets::LargeGroupedConvolutionLayerDataset(), |
| framework::dataset::make("ReshapeWeights", { true })), |
| framework::dataset::make("DataType", DataType::F16)), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW })), |
| ActivationFunctionsDataset)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, tolerance_num); |
| } |
| TEST_SUITE_END() // FP16 |
| TEST_SUITE_END() // Float |
| |
| TEST_SUITE_END() // GroupedGEMMConvolutionLayer |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |