/* * Copyright (c) 2017-2021, 2023 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" /** Synced with tests/validation/dynamic_fusion/gpu/cl/DirectConv2d.cpp * Please check there for any differences in the coverage */ 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 tolerance_f32(0.1f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ RelativeTolerance tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ constexpr AbsoluteTolerance 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 &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 post_ops{}; post_ops.push_back_op>(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 using CLGEMMConvolutionLayerFixture = ConvolutionValidationFixture; template using CLGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixture; template using CLConvolutionValidationWithPaddingFixture = ConvolutionValidationWithPaddingFixture; 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 post_ops{}; post_ops.push_back_op>( &post_op_arg_info, 1, ConvertPolicy::SATURATE); post_ops.push_back_op>( 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 post_ops{}; post_ops.push_back_op>( &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 post_ops{}; post_ops.push_back_op>( &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 post_ops{}; post_ops.push_back_op>( &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 post_ops{}; post_ops.push_back_op>( &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 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 post_ops{}; post_ops.push_back_op>( &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, 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, 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, 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, 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 using CLGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture; template using CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidationQuantizedFixture; template using CLGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture; 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, 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, 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, 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, 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, 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, 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, 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 using CLGEMMGroupedConvolutionLayerFixture = ConvolutionValidationFixture; TEST_SUITE(GroupedGEMMConvolutionLayer) TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture, 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, 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, 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, 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