| /* |
| * 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 CONCLCTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #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/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 |
| { |
| 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<CLTensor, CLAccessor, CLWinogradInputTransform, float>; |
| |
| 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<CLTensor>(shape_in, data_type); |
| CLTensor out = create_tensor<CLTensor>(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() |
| |
| TEST_SUITE_END() |
| TEST_SUITE_END() |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |