/* * Copyright (c) 2018 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/core/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/LargeConvolutionLayerDataset.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/datasets/SmallConvolutionLayerDataset.h" #include "tests/datasets/WinogradInputTransformDataset.h" #include "tests/datasets/WinogradOutputTransformDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/WinogradConvolutionLayerFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { // *INDENT-OFF* // clang-format off constexpr AbsoluteTolerance tolerance_f32(0.001f); const AbsoluteTolerance tolerance_f16(half(0.5f)); constexpr AbsoluteTolerance tolerance_convolution_layer_f32(0.1f); const AbsoluteTolerance tolerance_convolution_layer_f16(half(0.4f)); // 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(), datasets::SmallWinogradInputTransformDataset1x4_1x5()))))); 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()))); // 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 = 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) })), combine(datasets::Small1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 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) })), 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 = 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) })), combine(datasets::Large1x5Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 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; 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; using CLWinogradInputTransformFixtureFP16 = WinogradInputTransformValidationFixture; 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, DataType::F16 })), shape_in, winograd_info, data_layout, data_type) { 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, 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); } 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) 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, DataType::F16 })), 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, 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); } 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 }))) { validate(CLAccessor(_target), _reference, tolerance_f16); } 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); } TEST_SUITE_END() // FP16 TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixtureFP32, 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, CLWinogradInputTransformFixtureFP32, 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() // 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; using CLWinogradFilterTransformFixtureFP32 = WinogradFilterTransformValidationFixture; using CLWinogradFilterTransformFixtureFP16 = WinogradFilterTransformValidationFixture; 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, DataType::F16 })), 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 */, data_layout /* Not needed */); TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info); // Create tensors CLTensor a = create_tensor(shape_a, data_type, 1, QuantizationInfo(), data_layout); CLTensor b = create_tensor(shape_b, data_type, 1, 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); } 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) 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, DataType::F16 })), 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, QuantizationInfo(), data_layout); CLTensor b = create_tensor(shape_b, data_type, 1, 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); } TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP16, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradFilterTransformDatasetNHWC, framework::dataset::make("DataLayout", { DataLayout::NHWC })), 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(LargeWinogradFilterTransformDatasetNHWC, framework::dataset::make("DataLayout", { DataLayout::NHWC })), framework::dataset::make("DataType", { DataType::F16 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16); } TEST_SUITE_END() // FP16 TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixtureFP32, 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, CLWinogradFilterTransformFixtureFP32, 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() // 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; using CLWinogradOutputTransformFixtureFP32 = WinogradOutputTransformValidationFixture; using CLWinogradOutputTransformFixtureFP16 = WinogradOutputTransformValidationFixture; TEST_SUITE(NCHW) DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNCHW, LargeWinogradOutputTransformDatasetNCHW), framework::dataset::make("DataType", { DataType::F32, DataType::F16 })), 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, 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); } TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL, combine(SmallWinogradOutputTransformDatasetNCHW, framework::dataset::make("DataType", { DataType::F16 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(LargeWinogradOutputTransformDatasetNCHW, framework::dataset::make("DataType", { DataType::F16 }))) { // 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(SmallWinogradOutputTransformDatasetNCHW, framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(LargeWinogradOutputTransformDatasetNCHW, framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // FP32 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, DataType::F16 })), 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, 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); } TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL, combine(SmallWinogradOutputTransformDatasetNHWC, framework::dataset::make("DataType", { DataType::F16 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY, combine(LargeWinogradOutputTransformDatasetNHWC, framework::dataset::make("DataType", { DataType::F16 }))) { // 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(SmallWinogradOutputTransformDatasetNHWC, framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY, combine(LargeWinogradOutputTransformDatasetNHWC, 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() // 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); } TEST_SUITE(FP32) 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, 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // 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, 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // 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, 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // 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, 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::LargeWinogradConvolutionLayer5x5Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv5x5 TEST_SUITE(Conv5x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), 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::LargeWinogradConvolutionLayer5x1Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv5x1 TEST_SUITE(Conv1x5) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), 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::LargeWinogradConvolutionLayer1x5Dataset(), framework::dataset::make("DataType", { DataType::F32 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32); } TEST_SUITE_END() // Conv1x5 TEST_SUITE_END() // FP32 #ifdef WINOGRAD_F16_SUPPORT //to be reintroduced after COMPMID-1266 is resolved TEST_SUITE(FP16) using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture; TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } TEST_SUITE_END() // Conv5x5 TEST_SUITE(Conv5x1) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } 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 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), framework::dataset::make("DataType", { DataType::F16 })), framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f16); } TEST_SUITE_END() // Conv1x5 TEST_SUITE_END() // FP16 #endif /*#ifdef WINOGRAD_F16_SUPPORT*/ TEST_SUITE_END() // ConvolutionLayer TEST_SUITE_END() // Winograd TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute