diff options
Diffstat (limited to 'tests/validation/CL/Winograd.cpp')
| -rw-r--r-- | tests/validation/CL/Winograd.cpp | 599 |
1 files changed, 433 insertions, 166 deletions
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index 6ac37d1475..196e7edb8c 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2021 Arm Limited. + * Copyright (c) 2018-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -30,6 +30,7 @@ #include "tests/CL/CLAccessor.h" #include "tests/CL/Helper.h" #include "tests/PaddingCalculator.h" +#include "tests/datasets/ActivationFunctionsDataset.h" #include "tests/datasets/LargeConvolutionLayerDataset.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/datasets/SmallConvolutionLayerDataset.h" @@ -47,6 +48,7 @@ namespace test { namespace validation { +using framework::dataset::make; namespace { // *INDENT-OFF* @@ -57,108 +59,232 @@ const AbsoluteTolerance<half> tolerance_convolution_layer_f16(half(0.4f)); RelativeTolerance<half_float::half> rel_tolerance_f16(half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for FP16 data types */ constexpr float tolerance_num = 0.05f; /**< Tolerance number */ constexpr float abs_tolerance_convolution_layer_f16 = 2.5f; /**< Tolerance number */ -constexpr float tolerance_num_f16 = 0.15f; /**< Tolerance number */ +constexpr float tolerance_num_f16 = 0.15f; /**< Tolerance number */ -//Activation Functions -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +const auto ActivationFunctionsDataset = make("ActivationInfo", { - ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.8f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SQUARE), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::HARD_SWISH), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 2.f, 1.f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::GELU) }); -const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", + +const auto ActivationFunctionsSmallDataset = make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.8f, -0.5f) }); } // namespace using namespace arm_compute::misc::shape_calculator; +/* + Testing Strategy of CL Winograd: + - For nchw and nhwc and for each kernel size, we have a dedicated OpenCL kernel. + (except 1xN and Nx1 uses NxN under the hood). Therefore, test cases should be + stressed for each of these configurations. + - Fp32 and Fp16 kernels are the same. Only the DATA_TYPE build option changes + between these two. Because the same kernel is stressed thoroughly for both + small and large shapes for Fp32 data type, Fp16 kernels are run on a subset + of the shapes, because we get diminishing returns by exhaustively testing the + same kernel. + - Activations only affect the output stage and it's calculated on the output tile. + Exhaustively testing all activations with all the shapes does not provide much + value but increases the testing time quite significantly. Therefore, all activations + are tested in a subset of the shapes, and for all MxM kernels and data layouts as + they represent different OpenCL kernels. (1xM and Mx1 kernels use MxM under the hood). +*/ TEST_SUITE(CL) TEST_SUITE(Winograd) TEST_SUITE(ConvolutionLayer) -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { - TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // Insufficient padding - TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Datatype mismatch - TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported - TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32), // Padding needed - TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) // Kernel size not supported - }), - framework::dataset::make("WeightsInfo", { - TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F16), - TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8), - TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), - TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16) - })), - framework::dataset::make("BiasesInfo", { - TensorInfo(TensorShape(19U), 1, DataType::F16), - TensorInfo(TensorShape(19U), 1, DataType::F32), - TensorInfo(TensorShape(21U), 1, DataType::F32), - TensorInfo(TensorShape(16U), 1, DataType::F32), - TensorInfo(TensorShape(16U), 1, DataType::F32) - })), - framework::dataset::make("OutputInfo", { - TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F16), - TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), - TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), - TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32) - })), - framework::dataset::make("ConvInfo", { - PadStrideInfo(1, 1, 1, 1), - PadStrideInfo(1, 1, 1, 1), - PadStrideInfo(1, 2, 0, 0), - PadStrideInfo(1, 1, 1, 1), - PadStrideInfo(1, 1, 1, 0) - })), - framework::dataset::make("Expected", { false, false, false, false, false })), - input_info, weights_info, bias_info, output_info, conv_info, expected) +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip( + make("InputInfo", { + TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // Insufficient padding + TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32), // Datatype mismatch + TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported + TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32), // Padding needed + TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) // Kernel size not supported + }), + make("WeightsInfo", { + TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F16), + TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8), + TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16) + }), + make("BiasesInfo", { + TensorInfo(TensorShape(19U), 1, DataType::F16), + TensorInfo(TensorShape(19U), 1, DataType::F32), + TensorInfo(TensorShape(21U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32) + }), + make("OutputInfo", { + TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F16), + TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32), + TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32), + TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32) + }), + make("ConvInfo", { + PadStrideInfo(1, 1, 1, 1), + PadStrideInfo(1, 1, 1, 1), + PadStrideInfo(1, 2, 0, 0), + PadStrideInfo(1, 1, 1, 1), + PadStrideInfo(1, 1, 1, 0) + }), + make("Expected", { false, false, false, false, false })), + input_info, weights_info, bias_info, output_info, conv_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info)) == expected, framework::LogLevel::ERRORS); } +DATA_TEST_CASE(SupportedKernels, framework::DatasetMode::ALL, zip( + make("WeightsInfo", { + // Shapes are always in NCHW format. When layout is NHWC, the shape is permuted + + // Fp32/16, NCHW + // 3x1, 1x3, 3x3 --> all TRUE + TensorInfo(TensorShape(3U, 3U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 3U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(3U, 1U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + + // 5x1, 1x5, 5x5 --> all TRUE + TensorInfo(TensorShape(5U, 5U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 5U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + TensorInfo(TensorShape(5U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // 7x1, 1x7, 7x7 + // nchw does not support kernels with size 7 --> all FALSE + TensorInfo(TensorShape(7U, 7U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 7U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(7U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // unsupported kernel sizes + TensorInfo(TensorShape(2U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(5U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(3U, 6U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // Fp32/16, NHWC + // 7x1, 1x7, 7x7 --> all TRUE + TensorInfo(TensorShape(7U, 7U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(1U, 7U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(7U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + + // 3x1, 1x3, 3x3 --> all TRUE + TensorInfo(TensorShape(3U, 3U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(1U, 3U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + + // 5x1, 1x5, 5x5 --> all TRUE + TensorInfo(TensorShape(5U, 5U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(1U, 5U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 1U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + + // unsupported kernel sizes + TensorInfo(TensorShape(2U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 6U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + + }), + make("Expected", { + true, true, true, // nchw, 3x3, 1x3, 3x1 + true, true, true, // nchw, 5x5, 1x5, 5x1 + false, false, false, // nchw, 7x7, 1x7, 7x1 + false, false, false, // nchw, random unsupported kernels + true, true, true, // nhwc, 7x7, 1x7, 7x1 + true, true, true, // nhwc, 3x3, 1x3, 3x1 + true, true, true, // nhwc, 5x5, 1x5, 5x1 + false, false, false, // nchw, random unsupported kernels + })), + weights_info_const, expected) +{ + DataType data_type = weights_info_const.data_type(); + DataLayout data_layout = weights_info_const.data_layout(); + + TensorInfo input_info = TensorInfo(TensorShape(17U, 31U, 2U), 1, data_type); + TensorInfo bias_info = TensorInfo(TensorShape(8U), 1, data_type); + TensorInfo weights_info = weights_info_const; + + if(data_layout == DataLayout::NHWC) + { + // Convert to NHWC + PermutationVector perm = PermutationVector(2U, 0U, 1U); + + TensorShape input_shape = input_info.tensor_shape(); + TensorShape weights_shape = weights_info.tensor_shape(); + permute(input_shape, perm); + permute(weights_shape, perm); + + input_info.set_tensor_shape(input_shape); + weights_info.set_tensor_shape(weights_shape); + + input_info.set_data_layout(data_layout); + weights_info.set_data_layout(data_layout); + bias_info.set_data_layout(data_layout); + } + + PadStrideInfo conv_info(1, 1, 0, 0); + + TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, conv_info); + TensorInfo output_info = TensorInfo(output_shape, 1, data_type, data_layout); + + Status status = CLWinogradConvolutionLayer::validate( + &input_info, + &weights_info, + &bias_info, + &output_info, + conv_info, + ActivationLayerInfo(), + true /* fast math */); + + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); +} + TEST_SUITE(FP32) using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>; using CLWinogradConvolutionLayerFastMathMixedDataLayoutFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float, float, true, true>; TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLWinogradConvolutionLayerFastMathMixedDataLayoutFixture, 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 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(8U, 8U, 32U)), + make("Weight", TensorShape(3U, 3U, 32U, 4U)), + make("Bias", TensorShape(4U)), + make("Output", TensorShape(6U, 6U, 4U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); @@ -167,20 +293,20 @@ TEST_SUITE_END() // Conv3x3 TEST_SUITE(Conv3x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); @@ -189,20 +315,36 @@ TEST_SUITE_END() // Conv3x1 TEST_SUITE(Conv1x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} + +FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, CLWinogradConvolutionLayerFastMathMixedDataLayoutFixture, framework::DatasetMode::PRECOMMIT, + combine( + make("Input", TensorShape(8U, 8U, 32U)), + make("Weight", TensorShape(1U, 3U, 32U, 1U)), + make("Bias", TensorShape(1U)), + make("Output", TensorShape(8U, 6U, 1U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); @@ -211,10 +353,10 @@ TEST_SUITE_END() // Conv1x3 TEST_SUITE(Conv5x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset ), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -222,11 +364,27 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset ), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(13U, 13U, 32U)), + make("Weight", TensorShape(5U, 5U, 32U, 4U)), + make("Bias", TensorShape(4U)), + make("Output", TensorShape(9U, 9U, 4U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); @@ -235,10 +393,10 @@ TEST_SUITE_END() // Conv5x5 TEST_SUITE(Conv5x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -246,10 +404,10 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -259,10 +417,10 @@ TEST_SUITE_END() // Conv5x1 TEST_SUITE(Conv1x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -270,16 +428,63 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv1x5 + +TEST_SUITE(Conv1x7) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NHWC }))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} + +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(13U, 13U, 32U)), + make("Weight", TensorShape(1U, 7U, 32U, 4U)), + make("Bias", TensorShape(4U)), + make("Output", TensorShape(13U, 11U, 4U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 2)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NHWC }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} +TEST_SUITE_END() // Conv1x7 + +TEST_SUITE(Conv7x1) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NHWC }))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} +TEST_SUITE_END() // Conv7x1 + +/** @note: Although 7x7 is in the kernels, reference implementation + * does not support it. So, it remains as a "test gap". + */ + TEST_SUITE_END() // FP32 @@ -288,20 +493,36 @@ TEST_SUITE(FP16) using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, 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 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_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 }))) + combine(datasets::LargeWinogradConvolutionLayer3x3DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); +} + +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(8U, 8U, 32U)), + make("Weight", TensorShape(3U, 3U, 32U, 6U)), + make("Bias", TensorShape(6U)), + make("Output", TensorShape(6U, 6U, 6U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F16 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); @@ -310,20 +531,20 @@ TEST_SUITE_END() // Conv3x3 TEST_SUITE(Conv3x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer3x1DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); @@ -332,20 +553,20 @@ TEST_SUITE_END() // Conv3x1 TEST_SUITE(Conv1x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x3DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); @@ -354,10 +575,10 @@ TEST_SUITE_END() // Conv1x3 TEST_SUITE(Conv5x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -365,23 +586,39 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x5DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); } + +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(13U, 13U, 32U)), + make("Weight", TensorShape(5U, 5U, 32U, 6U)), + make("Bias", TensorShape(6U)), + make("Output", TensorShape(9U, 9U, 6U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F16 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); +} TEST_SUITE_END() // Conv5x5 TEST_SUITE(Conv5x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -389,10 +626,10 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x1DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -402,10 +639,10 @@ TEST_SUITE_END() // Conv5x1 TEST_SUITE(Conv1x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -413,10 +650,10 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x5DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -426,10 +663,10 @@ TEST_SUITE_END() // Conv1x5 TEST_SUITE(Conv1x7) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsSmallDataset), - framework::dataset::make("DataLayout", { DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NHWC }))) { // Validate output @@ -437,16 +674,46 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x7Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x7DatasetFp16Subset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); +} +FIXTURE_DATA_TEST_CASE(RunActivations, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, + combine( + make("Input", TensorShape(13U, 13U, 32U)), + make("Weight", TensorShape(1U, 7U, 32U, 6U)), + make("Bias", TensorShape(6U)), + make("Output", TensorShape(13U, 7U, 6U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F16 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_convolution_layer_f16); } TEST_SUITE_END() // Conv1x7 + +TEST_SUITE(Conv7x1) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NHWC }))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16, tolerance_num_f16); +} +TEST_SUITE_END() // Conv7x1 + TEST_SUITE_END() // FP16 TEST_SUITE_END() // ConvolutionLayer TEST_SUITE_END() // Winograd |