/* * 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/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/CLWinogradInputTransform.h" #include "tests/CL/CLAccessor.h" #include "tests/CL/Helper.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/ShapeDatasets.h" #include "tests/datasets/WinogradFilterTransformDataset.h" #include "tests/datasets/WinogradInputTransformDataset.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/WinogradLayerFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { constexpr AbsoluteTolerance tolerance_f32(0.0001f); } // namespace using namespace arm_compute::misc::shape_calculator; TEST_SUITE(CL) TEST_SUITE(Winograd) TEST_SUITE(InputTransform) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(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), // valid TensorInfo(TensorShape(34U, 42U, 7U, 3U), 1, DataType::F32), // valid TensorInfo(TensorShape(31U, 37U, 37U), 1, DataType::F32) // valid }), 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("PadStrideInfo", { PadStrideInfo(1, 1, 1, 0), PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 1, 1), PadStrideInfo(2, 1, 1, 1), PadStrideInfo(1, 1, 0, 1), PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 1, 1) })), framework::dataset::make("KernelDims", { Size2D(3U, 3U), Size2D(3U, 3U), Size2D(5U, 5U), Size2D(3U, 3U), Size2D(3U, 3U), Size2D(3U, 3U), Size2D(3U, 3U) })), framework::dataset::make("Expected", { false, false, false, false, true, true, true })), input_info, output_info, conv_info, kernel_dims, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, kernel_dims)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradInputTransformDataset(), datasets::LargeWinogradInputTransformDataset()), framework::dataset::make("DataType", { DataType::F32 })), shape_in, conv_info, kernel_dims, is_nchw_format, data_type) { ARM_COMPUTE_UNUSED(is_nchw_format); TensorShape shape_out = compute_winograd_input_transform_shape(TensorInfo(shape_in, 1, data_type), conv_info, kernel_dims); // Create tensors CLTensor in = create_tensor(shape_in, data_type); 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, conv_info, kernel_dims); } FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallWinogradInputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradInputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { validate(CLAccessor(_target), _reference); } TEST_SUITE_END() // InputTransform TEST_SUITE(FilterTransform) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not 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), // valid 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, 16U), 1, DataType::F32) })), framework::dataset::make("Expected", { false, false, false, true, true, true, true })), input_info, output_info, expected) { ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture; DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradFilterTransformDataset(), datasets::LargeWinogradFilterTransformDataset()), framework::dataset::make("DataType", { DataType::F32 })), shape_a, is_nchw_format, data_type) { ARM_COMPUTE_UNUSED(is_nchw_format); TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type)); // Create tensors CLTensor a = create_tensor(shape_a, data_type); CLTensor b = create_tensor(shape_b, data_type); 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); } FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // FilterTransform TEST_SUITE_END() // Winograd TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute