diff options
Diffstat (limited to 'tests/validation/CL/Winograd.cpp')
| -rw-r--r-- | tests/validation/CL/Winograd.cpp | 1059 |
1 files changed, 433 insertions, 626 deletions
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index 511aa4b773..196e7edb8c 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,18 +21,16 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#include "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h" -#include "arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/Types.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/CLWinogradConvolutionLayer.h" -#include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h" #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" @@ -50,629 +48,379 @@ namespace test { namespace validation { +using framework::dataset::make; namespace { // *INDENT-OFF* // clang-format off -constexpr AbsoluteTolerance<float> tolerance_f32(0.002f); -const AbsoluteTolerance<half> tolerance_f16(half(0.5f)); +const AbsoluteTolerance<half> tolerance_f16(half(1.f)); constexpr AbsoluteTolerance<float> tolerance_convolution_layer_f32(0.1f); 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 */ - -// Input transform -const auto SmallWinogradInputTransformDatasetNCHW = - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x2_3x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x1_3x1(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x2_1x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_3x1(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x4_1x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_5x5(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_5x1(), - datasets::SmallWinogradInputTransformDataset1x4_1x5())))))))); - -const auto SmallWinogradInputTransformDatasetNHWC = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_3x1(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x4_1x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_5x5(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_5x1(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x4_1x5(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x1_7x1(), - datasets::SmallWinogradInputTransformDataset1x2_1x7()))))))); - -const auto SmallWinogradInputTransformDatasetNHWC_FP32 = framework::dataset::concat(SmallWinogradInputTransformDatasetNHWC, - datasets::SmallWinogradInputTransformDataset2x2_7x7()); - -const auto LargeWinogradInputTransformDatasetNCHW = - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x1_3x1(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset1x2_1x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_3x1(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset1x4_1x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_5x5(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_5x1(), - datasets::LargeWinogradInputTransformDataset1x4_1x5())))))))); - -const auto LargeWinogradInputTransformDatasetNHWC = - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_5x5(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_5x1(), - datasets::LargeWinogradInputTransformDataset1x4_1x5()))); - -const auto LargeWinogradInputTransformDatasetNHWC_FP32 = - framework::dataset::concat(LargeWinogradInputTransformDatasetNHWC, - (datasets::LargeWinogradInputTransformDataset2x2_7x7())); - -// Filter transform -const auto SmallWinogradFilterTransformDatasetNCHW = - framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Small3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Small1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Small5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - combine(datasets::Small1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) }))))))); - -const auto SmallWinogradFilterTransformDatasetNHWC_F16 = - framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Small3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Small1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Small5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Small1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Small1x7Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U) })), - combine(datasets::Small7x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U) }))))))))); - -const auto SmallWinogradFilterTransformDatasetNHWC_F32 = - framework::dataset::concat(SmallWinogradFilterTransformDatasetNHWC_F16, - combine(datasets::Small7x7Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U) }))); - -const auto LargeWinogradFilterTransformDatasetNCHW = - framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Large3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Large1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Large5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - combine(datasets::Large1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) }))))))); - -const auto LargeWinogradFilterTransformDatasetNHWC_F16 = - framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Large3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Large1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), - framework::dataset::concat(combine(datasets::Large5x1Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 1U) })), - framework::dataset::concat(combine(datasets::Large1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 4U) })), - framework::dataset::concat(combine(datasets::Large7x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U) })), - combine(datasets::Large1x7Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U) }))))))))); - -const auto LargeWinogradFilterTransformDatasetNHWC_F32 = - framework::dataset::concat(LargeWinogradFilterTransformDatasetNHWC_F16, - combine(datasets::Large7x7Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U) }))); - -// Output transform -const auto SmallWinogradOutputTransformDatasetNCHW = datasets::SmallWinogradOutputTransformDatasetNCHW(); - -const auto SmallWinogradOutputTransformDatasetNHWC_F16 = datasets::SmallWinogradOutputTransformDatasetNHWC_F16(); - -const auto SmallWinogradOutputTransformDatasetNHWC_F32 = datasets::SmallWinogradOutputTransformDatasetNHWC_F32(); - -const auto LargeWinogradOutputTransformDatasetNCHW = datasets::LargeWinogradOutputTransformDatasetNCHW(); - -const auto LargeWinogradOutputTransformDatasetNHWC_F16 = datasets::LargeWinogradOutputTransformDatasetNHWC_F16(); - -const auto LargeWinogradOutputTransformDatasetNHWC_F32 = datasets::LargeWinogradOutputTransformDatasetNHWC_F32(); - -//Activation Functions -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +constexpr float tolerance_num_f16 = 0.15f; /**< Tolerance number */ + +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(InputTransform) -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( - framework::dataset::make("InputInfo",{ - TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F16), // F16 not supported - TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported - TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32), // Kernel size not supported - TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32), // Strides not supported - TensorInfo(TensorShape(53U, 33U, 4U), 1, DataType::F32), // Padding needed - TensorInfo(TensorShape(34U, 42U, 7U, 3U), 1, DataType::F32), // Padding needed - TensorInfo(TensorShape(31U, 37U, 37U), 1, DataType::F32) // Padding needed - }), - framework::dataset::make("OutputInfo", { - TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::F16), - TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::QASYMM8), - TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::F32), - TensorInfo(TensorShape(5U, 1U, 16U, 3U), 1, DataType::F32), - TensorInfo(TensorShape(4U, 442U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(7U, 320U, 16U, 3U), 1, DataType::F32), - TensorInfo(TensorShape(37U, 304U, 16U), 1, DataType::F32) - })), - framework::dataset::make("WinogradInfo", { - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 21U), PadStrideInfo(2, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(34U, 42U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), - WinogradInfo(Size2D(2, 2), Size2D(3, 3), Size2D(31U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW) - })), - framework::dataset::make("Expected", { false, false, false, false, false, false, false })), - input_info, output_info, winograd_info, expected) -{ - ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); -} - -using CLWinogradInputTransformFixtureFP32 = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, float>; -using CLWinogradInputTransformFixtureFP16 = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, half>; - -TEST_SUITE(NCHW) -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f32); -} - -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f32); -} -TEST_SUITE_END() // FP32 - -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f16); -} - -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f16); -} -TEST_SUITE_END() // FP16 -TEST_SUITE_END() // NCHW - -TEST_SUITE(NHWC) -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F16 }))) +TEST_SUITE(ConvolutionLayer) +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) { - validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num_f16); + 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); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num_f16); -} -TEST_SUITE_END() // FP16 -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC_FP32, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f32); +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); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC_FP32, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - validate(CLAccessor(_target), _reference, tolerance_f32); -} -TEST_SUITE_END() // FP32 -TEST_SUITE_END() // NHWC -TEST_SUITE_END() // InputTransform - -TEST_SUITE(FilterTransform) -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( - framework::dataset::make("InputInfo",{ - TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 supported - TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported - TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported - TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // Output tile not supported - TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), // valid - TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), // valid - TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32) // valid - }), - framework::dataset::make("OutputInfo", { - TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F16), - TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::QASYMM8), - TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(22U, 37U, 36U), 1, DataType::F32) - })), - framework::dataset::make("WinogradInfo", { - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(3U, 3U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ), - WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */ ) - })), - framework::dataset::make("Expected", { true, false, false, false, true, true, true })), - input_info, output_info, winograd_info, expected) -{ - ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); -} - -using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradFilterTransformKernel, 0>; -using CLWinogradFilterTransformFixtureFP32 = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>; -using CLWinogradFilterTransformFixtureFP16 = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, half>; - -TEST_SUITE(NCHW) TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, - combine(combine(SmallWinogradFilterTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); -} - -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradFilterTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); -} -TEST_SUITE_END() // FP32 -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, - combine(combine(SmallWinogradFilterTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16); -} - -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradFilterTransformDatasetNCHW, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16); -} -TEST_SUITE_END() // FP16 -TEST_SUITE_END() // NCHW - -TEST_SUITE(NHWC) -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, - combine(combine(SmallWinogradFilterTransformDatasetNHWC_F16, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F16 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num_f16); -} - -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradFilterTransformDatasetNHWC_F16, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F16 }))) +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(datasets::SmallWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num_f16); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -TEST_SUITE_END() // FP16 -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::PRECOMMIT, - combine(combine(SmallWinogradFilterTransformDatasetNHWC_F32, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F32 }))) +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_f32); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixtureFP32, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradFilterTransformDatasetNHWC_F32, - framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("DataType", { DataType::F32 }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); -} -TEST_SUITE_END() // FP32 -TEST_SUITE_END() // NHWC -TEST_SUITE_END() // FilterTransform - -TEST_SUITE(OutputTransform) -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( - framework::dataset::make("InputInfo",{ - TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F16), // F16 supported - TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::QASYMM8), // QASYMM8 not supported - TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Kernel size not supported - TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F32), // Valid - TensorInfo(TensorShape(13U, 108U, 16U, 4U), 1, DataType::F32), // Padding needed - TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Valid - TensorInfo(TensorShape(7U, 20U, 16U, 7U), 1, DataType::F32), // Wrong WinogradInfo - TensorInfo(TensorShape(7U, 256U, 36U, 3U), 1, DataType::F32), // Valid - TensorInfo(TensorShape(7U, 256U, 16U, 3U), 1, DataType::F32) // Wrong number of batches - }), - framework::dataset::make("BiasInfo", { - TensorInfo(TensorShape(512U), 1, DataType::F16), - TensorInfo(TensorShape(512U), 1, DataType::QASYMM8), - TensorInfo(TensorShape(512U), 1, DataType::F32), - TensorInfo(TensorShape(512U), 1, DataType::F32), - TensorInfo(TensorShape(13U), 1, DataType::F32), - TensorInfo(TensorShape(7U), 1, DataType::F32), - TensorInfo(TensorShape(7U), 1, DataType::F32), - TensorInfo(TensorShape(7U), 1, DataType::F32), - TensorInfo(TensorShape(7U), 1, DataType::F32) - })), - framework::dataset::make("OutputInfo", { - TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F16), - TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::QASYMM8), - TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F32), - TensorInfo(TensorShape(14U, 14U, 512U, 5U), 1, DataType::F32), - TensorInfo(TensorShape(17U, 23U, 13U, 4U), 1, DataType::F32), - TensorInfo(TensorShape(8U, 10U, 7U, 7U), 1, DataType::F32), - TensorInfo(TensorShape(7U, 9U, 7U, 7U), 1, DataType::F32), - TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32), - TensorInfo(TensorShape(64U, 64U, 7U, 3U), 1, DataType::F32) - })), - framework::dataset::make("WinogradInfo", { - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 2U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(17U, 23U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(2U, 3U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW), - WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW), - WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(64U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW) - })), - framework::dataset::make("Expected", { true, false, false, true, false, true, false, true, false })), - input_info, bias_info, output_info, winograd_info, expected) -{ - ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS); -} - -using CLWinogradOutputTransform = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradOutputTransformKernel, 0>; -using CLWinogradOutputTransformFixtureFP32 = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, float>; -using CLWinogradOutputTransformFixtureFP16 = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, half>; - -TEST_SUITE(NCHW) -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL, - combine(combine(SmallWinogradOutputTransformDatasetNCHW, - framework::dataset::make("DataType", { DataType::F16 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +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_f16); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } +TEST_SUITE_END() // Conv3x3 -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradOutputTransformDatasetNCHW, - framework::dataset::make("DataType", { DataType::F16 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16); -} -TEST_SUITE_END() // FP16 -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL, - combine(combine(SmallWinogradOutputTransformDatasetNCHW, - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +TEST_SUITE(Conv3x1) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradOutputTransformDatasetNCHW, - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -TEST_SUITE_END() // FP32 -TEST_SUITE_END() // NCHW +TEST_SUITE_END() // Conv3x1 -TEST_SUITE(NHWC) -TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL, - combine(combine(SmallWinogradOutputTransformDatasetNHWC_F16, - framework::dataset::make("DataType", { DataType::F16 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +TEST_SUITE(Conv1x3) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, + combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num_f16); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradOutputTransformDatasetNHWC_F16, - framework::dataset::make("DataType", { DataType::F16 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +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_f16, tolerance_num_f16); -} -TEST_SUITE_END() // FP16 -TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL, - combine(combine(SmallWinogradOutputTransformDatasetNHWC_F32, - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, - combine(combine(LargeWinogradOutputTransformDatasetNHWC_F32, - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) )) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, + combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_f32); -} -TEST_SUITE_END() // FP32 -TEST_SUITE_END() // NHWC -TEST_SUITE_END() // OutputTransform - -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) -{ - 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); + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } +TEST_SUITE_END() // Conv1x3 -TEST_SUITE(FP32) -using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>; -TEST_SUITE(Conv3x3) +TEST_SUITE(Conv5x5) 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::SmallWinogradConvolutionLayer5x5Dataset(), + 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::LargeWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); -} -TEST_SUITE_END() // Conv3x3 + combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) -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 }))) { // 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 }))) +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); } -TEST_SUITE_END() // Conv3x1 +TEST_SUITE_END() // Conv5x5 -TEST_SUITE(Conv1x3) +TEST_SUITE(Conv5x1) 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::SmallWinogradConvolutionLayer5x1Dataset(), + 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::LargeWinogradConvolutionLayer5x1Dataset(), + 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() // Conv1x3 +TEST_SUITE_END() // Conv5x1 -TEST_SUITE(Conv5x5) +TEST_SUITE(Conv1x5) 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::SmallWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -680,64 +428,63 @@ 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::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() // Conv5x5 +TEST_SUITE_END() // Conv1x5 -TEST_SUITE(Conv5x1) +TEST_SUITE(Conv1x7) 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::SmallWinogradConvolutionLayer1x7Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { 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::LargeWinogradConvolutionLayer5x1Dataset(), - 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(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() // Conv5x1 +TEST_SUITE_END() // Conv1x7 -TEST_SUITE(Conv1x5) +TEST_SUITE(Conv7x1) 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::SmallWinogradConvolutionLayer7x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsSmallDataset, + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } +TEST_SUITE_END() // Conv7x1 -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 }))) +/** @note: Although 7x7 is in the kernels, reference implementation + * does not support it. So, it remains as a "test gap". + */ -{ - // Validate output - validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); -} -TEST_SUITE_END() // Conv1x5 TEST_SUITE_END() // FP32 @@ -746,20 +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); @@ -768,20 +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); @@ -790,20 +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); @@ -812,10 +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 @@ -823,11 +586,27 @@ 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); @@ -836,10 +615,10 @@ 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 @@ -847,10 +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 @@ -860,10 +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 @@ -871,10 +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 @@ -884,10 +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 @@ -895,19 +674,47 @@ 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_END() // FP16 +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 TEST_SUITE_END() // CL |