/* * Copyright (c) 2019-2020 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 "src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.h" #include "tests/NEON/Accessor.h" #include "tests/NEON/Helper.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h" namespace arm_compute { namespace test { namespace validation { using namespace arm_compute::misc::shape_calculator; // Create function for NEDepthwiseConvolutionLayerKernel using NEDepthwiseConvolutionLayerNative = NESynthetizeFunctionWithZeroConstantKernelBorder; // Fixture for NEDepthwiseConvolutionLayerKernel template using NEDepthwiseConvolutionLayerNativeFixture = DepthwiseConvolutionLayerNativeValidationFixture; namespace { // *INDENT-OFF* // clang-format off RelativeTolerance rel_tolerance_f32(0.001f); constexpr float abs_tolerance_f32(0.0001f); /** Width values to test - Precommit */ const auto width_values_precommit = framework::dataset::make("width", { 17U } ); /** Width values to test - Nightly */ const auto width_values_nightly = framework::dataset::make("width", { 53U, 47U } ); /** Height values to test - Precommit */ const auto height_values_precommit = framework::dataset::make("height", { 19U } ); /** Height values to test - Nightly */ const auto height_values_nightly = framework::dataset::make("height", { 39U, 43U } ); /** Channel values to test - Precommit */ const auto channel_values_precommit = framework::dataset::make("channels", { 15U }); /** Channel values to test - Nightly */ const auto channel_values_nightly = framework::dataset::make("channels", { 33U, 19U }); /** Batch values to test - Precommit */ const auto batch_values_precommit = framework::dataset::make("batch", { 1U, 2U }); /** Batch values to test - Nightly */ const auto batch_values_nightly = framework::dataset::make("batch", { 1U, 3U }); /** Kernel size values to test - Precommit */ const auto kernel_sz_values_precommit = framework::dataset::make("kernel_size", { Size2D(1U, 1U), Size2D(1U, 3U) }); /** Kernel size values to test - Nightly */ const auto kernel_sz_values_nightly = framework::dataset::make("kernel_size", { Size2D(3U, 5U), Size2D(5U, 1U), Size2D(1U, 7U), Size2D(9U, 7U) }); /** Depth multiplier values to test - All */ const auto depth_multiplier_values = framework::dataset::make("depth_multiplier", { 1U, 3U }); /** Dilation values to test - All */ const auto dilation_values = framework::dataset::make("dilation", { Size2D(1U, 1U), Size2D(3U, 3U) }); /** Stride values to test - All */ const auto stride_values = framework::dataset::make("stride", { Size2D(1U, 1U), Size2D(3U, 2U) }); /** Padding values to test - All */ const auto padding_valid_values = framework::dataset::make("padding_valid", { true, false }); /** Data type values to test - All */ const auto data_type_values = framework::dataset::make("data_type", { DataType::F32 }); /** Data layout values to test - All */ const auto data_layout_values = framework::dataset::make("data_layout", { DataLayout::NHWC }); } // namespace TEST_SUITE(NEON) TEST_SUITE(DepthwiseConvolutionLayerNative) TEST_CASE(ValidateNoPadding, framework::DatasetMode::ALL) { // this test case will ensure that the kernel is not adding implicit padding constexpr uint32_t vector_size = 8; // Asummed vector size of the current native kernel constexpr auto depth = vector_size * 2 + 1; // mis-aligned depth to force padding if exists. constexpr auto data_layout = DataLayout::NHWC; constexpr auto data_type = DataType::F32; const auto input_size = Size2D{ 100, 100 }; // random plane size of the input const auto kernel_size = Size2D{ 4, 4 }; // random plane size of the kernel const auto pad_stride_info = PadStrideInfo(3, 3); // random convolution information to TensorShape src_shape{ depth, input_size.x(), input_size.y() }; TensorShape weights_shape{ depth, kernel_size.x(), kernel_size.y() }; TensorShape bias_shape{ depth }; auto src = create_tensor(src_shape, data_type, 1, QuantizationInfo(), data_layout); auto weights = create_tensor(weights_shape, data_type, 1, QuantizationInfo(), data_layout); auto biases = create_tensor(bias_shape, data_type, 1, QuantizationInfo(), data_layout); auto dst = create_tensor(TensorShape(), data_type, 1, QuantizationInfo(), data_layout); NEDepthwiseConvolutionLayerNativeKernel dwc; dwc.configure(&src, &weights, &biases, &dst, pad_stride_info); ARM_COMPUTE_EXPECT(src.info()->padding().empty(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(weights.info()->padding().empty(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(biases.info()->padding().empty(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->padding().empty(), framework::LogLevel::ERRORS); } TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerNativeFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(width_values_precommit, height_values_precommit), channel_values_precommit), batch_values_precommit), kernel_sz_values_precommit), depth_multiplier_values), dilation_values), stride_values), padding_valid_values), data_type_values), data_layout_values)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerNativeFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(width_values_nightly, height_values_nightly), channel_values_nightly), batch_values_nightly), kernel_sz_values_nightly), depth_multiplier_values), dilation_values), stride_values), padding_valid_values), data_type_values), data_layout_values)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float TEST_SUITE_END() // DepthwiseConvolutionLayerNative TEST_SUITE_END() // NEON } // namespace validation } // namespace test } // namespace arm_compute