| /* |
| * Copyright (c) 2017-2018 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/Types.h" |
| #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/ShapeDatasets.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/DirectConvolutionLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| constexpr AbsoluteTolerance<float> tolerance_fp16(0.01f); /**< Tolerance for half precision floating point tests */ |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */ |
| |
| /** Direct convolution data set. */ |
| const auto data_pad_f32 = concat(concat(combine(framework::dataset::make("PadX", { 0, 1 }), |
| combine(framework::dataset::make("PadY", { 0, 1 }), |
| framework::dataset::make("KernelSize", 3))), |
| combine(framework::dataset::make("PadX", { 0, 2 }), |
| combine(framework::dataset::make("PadY", { 0, 2 }), |
| framework::dataset::make("KernelSize", 3)))), |
| combine(framework::dataset::make("PadX", { 0, 3 }), |
| combine(framework::dataset::make("PadY", { 0, 3 }), |
| framework::dataset::make("KernelSize", 5)))); |
| |
| const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1, 3 }), |
| combine(framework::dataset::make("StrideY", { 1, 3 }), |
| combine(data_pad_f32, |
| framework::dataset::make("NumKernels", { 1, 4, 8, 16 }))))); |
| |
| /** Activation function Dataset*/ |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(DirectConvolutionLayer) |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Non-rectangular weights dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid stride |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases size |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid biases dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid output size |
| }), |
| framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16, 0), |
| TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0), |
| })), |
| framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(3U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(4U), 1, DataType::F32, 0), |
| })), |
| framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32, 0), |
| TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32, 0), |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(3, 3, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| })), |
| framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) |
| })), |
| framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })), |
| input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) |
| { |
| bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info)); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| |
| //TODO(COMPMID-415): Configuration tests? |
| |
| template <typename T> |
| using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>; |
| |
| TEST_SUITE(Float) |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(data_f32, framework::dataset::make("DataType", DataType::F16)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", DataLayout::NCHW))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp16); |
| } |
| TEST_SUITE_END() |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(data_f32, framework::dataset::make("DataType", DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| TEST_SUITE_END() |
| TEST_SUITE_END() |
| |
| const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) |
| }); |
| |
| TEST_SUITE_END() |
| TEST_SUITE_END() |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |