From f1c2bf0971dd1c996da149faf3dd669d566074c7 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Wed, 13 Jun 2018 14:05:54 +0100 Subject: COMPMID-1201 - Implementing Winograd Convolution Layer 1x3 and 3x1 kernels on OpenCL Change-Id: I39667bab49daa4da009694163274a59fd3574c73 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137595 Tested-by: Jenkins Reviewed-by: Giorgio Arena Reviewed-by: Georgios Pinitas --- tests/validation/CL/Winograd.cpp | 353 +++++++++++++++++++++++++++++++-------- 1 file changed, 286 insertions(+), 67 deletions(-) (limited to 'tests/validation/CL') diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index b869f4c314..f68ec8c286 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -23,6 +23,7 @@ */ #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" @@ -51,12 +52,66 @@ namespace validation { namespace { +// *INDENT-OFF* +// clang-format off constexpr AbsoluteTolerance tolerance_f32(0.001f); constexpr AbsoluteTolerance tolerance_convolution_layer_f32(0.1f); -const auto SmallWinogradInputTransformDataset = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x2_3x3(), - framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), datasets::SmallWinogradInputTransformDataset4x4_5x5())); -const auto LargeWinogradInputTransformDataset = framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(), - framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), datasets::LargeWinogradInputTransformDataset4x4_5x5())); + +// 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(), + datasets::SmallWinogradInputTransformDataset4x4_5x5())))))); + +const auto SmallWinogradInputTransformDatasetNHWC = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), + datasets::SmallWinogradInputTransformDataset4x4_5x5()); + +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(), + datasets::LargeWinogradInputTransformDataset4x4_5x5())))))); + +const auto LargeWinogradInputTransformDatasetNHWC = + framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), + datasets::LargeWinogradInputTransformDataset4x4_5x5()); + +// 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) })), + combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) }))))); + +const auto SmallWinogradFilterTransformDatasetNHWC = + framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), + combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) }))); + +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) })), + combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) }))))); + +const auto LargeWinogradFilterTransformDatasetNHWC = + framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })), + combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) }))); + +// Output transform +const auto SmallWinogradOutputTransformDatasetNCHW = datasets::SmallWinogradOutputTransformDatasetNCHW(); + +const auto SmallWinogradOutputTransformDatasetNHWC = datasets::SmallWinogradOutputTransformDatasetNHWC(); + +const auto LargeWinogradOutputTransformDatasetNCHW = datasets::LargeWinogradOutputTransformDatasetNCHW(); + +const auto LargeWinogradOutputTransformDatasetNHWC = datasets::LargeWinogradOutputTransformDatasetNHWC(); } // namespace using namespace arm_compute::misc::shape_calculator; @@ -65,9 +120,6 @@ TEST_SUITE(CL) TEST_SUITE(Winograd) TEST_SUITE(InputTransform) - -// *INDENT-OFF* -// clang-format off 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 @@ -101,17 +153,20 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( { 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); } -// clang-format on -// *INDENT-ON* using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDataset, LargeWinogradInputTransformDataset), +TEST_SUITE(NCHW) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNCHW, + LargeWinogradInputTransformDatasetNCHW), framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 })), + framework::dataset::make("DataType", { DataType::F32 })), shape_in, winograd_info, data_layout, data_type) { - TensorShape shape_out = compute_winograd_input_transform_shape(TensorInfo(shape_in, 1, data_type), winograd_info); + TensorInfo tensor_info_in(shape_in, 1, data_type); + tensor_info_in.set_data_layout(data_layout); + + TensorShape shape_out = compute_winograd_input_transform_shape(tensor_info_in, winograd_info); // Create tensors CLTensor in = create_tensor(shape_in, data_type, 1, 0, QuantizationInfo(), data_layout); @@ -127,28 +182,70 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame winograd_input_transform.configure(&in, &out, winograd_info); } -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(framework::dataset::concat(combine(SmallWinogradInputTransformDataset, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - combine(framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), datasets::SmallWinogradInputTransformDataset4x4_5x5()), - framework::dataset::make("DataLayout", { DataLayout::NHWC }))), - framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, 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, CLWinogradInputTransformFixture, 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() // NCHW + +TEST_SUITE(NHWC) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNHWC, + LargeWinogradInputTransformDatasetNHWC), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 })), + shape_in, winograd_info, data_layout, data_type) +{ + TensorShape shape_in_nhwc(shape_in); + + // Convert the shape to NHWC + permute(shape_in_nhwc, PermutationVector(2U, 0U, 1U)); + + // TensorInfo + TensorInfo tensor_info_in(shape_in_nhwc, 1, data_type); + tensor_info_in.set_data_layout(data_layout); + + TensorShape shape_out = compute_winograd_input_transform_shape(tensor_info_in, winograd_info); + + // Create tensors + CLTensor in = create_tensor(shape_in_nhwc, data_type, 1, 0, QuantizationInfo(), data_layout); + CLTensor out = create_tensor(shape_out, data_type); + + ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLWinogradInputTransform winograd_input_transform; + + // Configure the function + winograd_input_transform.configure(&in, &out, winograd_info); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC, + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference, tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(framework::dataset::concat(combine(LargeWinogradInputTransformDataset, - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - combine(framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), datasets::LargeWinogradInputTransformDataset4x4_5x5()), - framework::dataset::make("DataLayout", { DataLayout::NHWC }))), - framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC, + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference, tolerance_f32); } +TEST_SUITE_END() // NHWC TEST_SUITE_END() // InputTransform TEST_SUITE(FilterTransform) -// *INDENT-OFF* -// clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported @@ -182,19 +279,19 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( { 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); } -// clang-format on -// *INDENT-ON* using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(framework::dataset::concat(datasets::Small3x3Shapes(), datasets::Large3x3Shapes()), - framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - framework::dataset::make("DataType", { DataType::F32 })), +TEST_SUITE(NCHW) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, + combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNCHW, + LargeWinogradFilterTransformDatasetNCHW), + framework::dataset::make("DataLayout", { DataLayout::NCHW })), + framework::dataset::make("DataType", { DataType::F32 })), shape_a, output_tile, data_layout, data_type) { - WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */); + WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, data_layout /* Not needed */); TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); @@ -210,37 +307,79 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combi winograd_filter_transform.configure(&a, &b, winograd_info); } -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, - combine(framework::dataset::concat(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))), - combine(datasets::Small3x3Shapes(), - framework::dataset::make("OutputTile", Size2D(4U, 4U)))), - combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - combine(combine(framework::dataset::concat(datasets::Small3x3Shapes(), datasets::Small5x5Shapes()), framework::dataset::make("OutputTile", Size2D(4U, 4U))), framework::dataset::make("DataLayout", { DataLayout::NHWC }))), - framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, 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, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, - combine(framework::dataset::concat(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))), - combine(datasets::Large3x3Shapes(), - framework::dataset::make("OutputTile", Size2D(4U, 4U)))), - combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))), - framework::dataset::make("DataLayout", { DataLayout::NCHW })), - combine(combine(framework::dataset::concat(datasets::Large3x3Shapes(), datasets::Large5x5Shapes()), framework::dataset::make("OutputTile", Size2D(4U, 4U))), framework::dataset::make("DataLayout", { DataLayout::NHWC }))), - framework::dataset::make("DataType", { DataType::F32 }))) + 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() // NCHW + +TEST_SUITE(NHWC) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, + combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNHWC, + LargeWinogradFilterTransformDatasetNHWC), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 })), + shape_in, output_tile, data_layout, data_type) +{ + TensorShape shape_in_nhwc(shape_in); + + // Convert the shape to NHWC + permute(shape_in_nhwc, PermutationVector(2U, 0U, 1U)); + + // TensorInfo + TensorInfo tensor_info_in(shape_in_nhwc, 1, data_type); + tensor_info_in.set_data_layout(data_layout); + + WinogradInfo winograd_info(output_tile, Size2D(shape_in[0], shape_in[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, data_layout /* Not needed */); + + TensorShape shape_b = compute_winograd_filter_transform_shape(tensor_info_in, winograd_info); + + // Create tensors + CLTensor a = create_tensor(shape_in_nhwc, data_type, 1, 0, QuantizationInfo(), data_layout); + CLTensor b = create_tensor(shape_b, data_type, 1, 0, QuantizationInfo(), data_layout); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + // Create and configure function + CLWinogradFilterTransform winograd_filter_transform; + winograd_filter_transform.configure(&a, &b, winograd_info); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::PRECOMMIT, + combine(combine(SmallWinogradFilterTransformDatasetNHWC, + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, + combine(combine(LargeWinogradFilterTransformDatasetNHWC, + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // NHWC TEST_SUITE_END() // FilterTransform TEST_SUITE(OutputTransform) -// *INDENT-OFF* -// clang-format off 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 not supported @@ -291,14 +430,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( { 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); } -// clang-format on -// *INDENT-ON* using CLWinogradOutputTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradOutputTransformFixture = WinogradOutputTransformValidationFixture; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradOutputTransformDataset(), datasets::LargeWinogradOutputTransformDataset()), - framework::dataset::make("DataType", { DataType::F32 })), +TEST_SUITE(NCHW) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNCHW, + LargeWinogradOutputTransformDatasetNCHW), + framework::dataset::make("DataType", { DataType::F32 })), shape_a, winograd_info, data_type) { TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); @@ -315,23 +454,62 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da winograd_output_transform.configure(&a, nullptr, &b, winograd_info); } -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, + combine(SmallWinogradOutputTransformDatasetNCHW, + framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, + combine(LargeWinogradOutputTransformDatasetNCHW, + framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } +TEST_SUITE_END() // NCHW +TEST_SUITE(NHWC) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNHWC, + LargeWinogradOutputTransformDatasetNHWC), + framework::dataset::make("DataType", { DataType::F32 })), + shape_a, winograd_info, data_type) +{ + TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); + + // Create tensors + CLTensor a = create_tensor(shape_a, data_type); + CLTensor b = create_tensor(shape_b, data_type, 1, 0, QuantizationInfo(), winograd_info.output_data_layout); + + ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLWinogradOutputTransform winograd_output_transform; + winograd_output_transform.configure(&a, nullptr, &b, winograd_info); +} + +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, + combine(SmallWinogradOutputTransformDatasetNHWC, + framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, + combine(LargeWinogradOutputTransformDatasetNHWC, + framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // NHWC TEST_SUITE_END() // OutputTransform TEST_SUITE(ConvolutionLayer) -// *INDENT-OFF* -// clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16), // FP16 not supported @@ -373,16 +551,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( { 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); } -// clang-format on -// *INDENT-ON* using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture; TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), - framework::dataset::make("DataLayout", { DataLayout::NCHW }))) + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); @@ -391,20 +567,64 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), - framework::dataset::make("DataLayout", { DataLayout::NCHW }))) + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } 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 })), + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + // 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 })), + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} +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 })), + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + // 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 })), + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); +} +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 })), - framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), - framework::dataset::make("DataLayout", { DataLayout::NCHW }))) + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) { // Validate output @@ -414,8 +634,8 @@ 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 })), - framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), - framework::dataset::make("DataLayout", { DataLayout::NCHW }))) + framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) { // Validate output @@ -424,7 +644,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, fram TEST_SUITE_END() // Conv5x5 TEST_SUITE_END() // ConvolutionLayer - TEST_SUITE_END() // Winograd TEST_SUITE_END() // CL } // namespace validation -- cgit v1.2.1