178 lines
9.3 KiB
C++
178 lines
9.3 KiB
C++
/*
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* Copyright (c) 2019-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/NEON/Helper.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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using namespace arm_compute::misc::shape_calculator;
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// Create function for NEDepthwiseConvolutionLayerKernel
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using NEDepthwiseConvolutionLayerNative = NESynthetizeFunctionWithZeroConstantKernelBorder<NEDepthwiseConvolutionLayerNativeKernel>;
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// Fixture for NEDepthwiseConvolutionLayerKernel
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template <typename T>
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using NEDepthwiseConvolutionLayerNativeFixture = DepthwiseConvolutionLayerNativeValidationFixture<Tensor, Accessor, NEDepthwiseConvolutionLayerNative, T>;
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namespace
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{
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// *INDENT-OFF*
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// clang-format off
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RelativeTolerance<float> rel_tolerance_f32(0.001f);
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constexpr float abs_tolerance_f32(0.0001f);
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/** Width values to test - Precommit */
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const auto width_values_precommit = framework::dataset::make("width", { 17U } );
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/** Width values to test - Nightly */
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const auto width_values_nightly = framework::dataset::make("width", { 53U, 47U } );
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/** Height values to test - Precommit */
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const auto height_values_precommit = framework::dataset::make("height", { 19U } );
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/** Height values to test - Nightly */
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const auto height_values_nightly = framework::dataset::make("height", { 39U, 43U } );
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/** Channel values to test - Precommit */
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const auto channel_values_precommit = framework::dataset::make("channels", { 15U });
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/** Channel values to test - Nightly */
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const auto channel_values_nightly = framework::dataset::make("channels", { 33U, 19U });
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/** Batch values to test - Precommit */
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const auto batch_values_precommit = framework::dataset::make("batch", { 1U, 2U });
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/** Batch values to test - Nightly */
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const auto batch_values_nightly = framework::dataset::make("batch", { 1U, 3U });
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/** Kernel size values to test - Precommit */
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const auto kernel_sz_values_precommit = framework::dataset::make("kernel_size", { Size2D(1U, 1U), Size2D(1U, 3U) });
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/** Kernel size values to test - Nightly */
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const auto kernel_sz_values_nightly = framework::dataset::make("kernel_size", { Size2D(3U, 5U), Size2D(5U, 1U), Size2D(1U, 7U), Size2D(9U, 7U) });
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/** Depth multiplier values to test - All */
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const auto depth_multiplier_values = framework::dataset::make("depth_multiplier", { 1U, 3U });
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/** Dilation values to test - All */
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const auto dilation_values = framework::dataset::make("dilation", { Size2D(1U, 1U), Size2D(3U, 3U) });
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/** Stride values to test - All */
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const auto stride_values = framework::dataset::make("stride", { Size2D(1U, 1U), Size2D(3U, 2U) });
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/** Padding values to test - All */
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const auto padding_valid_values = framework::dataset::make("padding_valid", { true, false });
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/** Data type values to test - All */
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const auto data_type_values = framework::dataset::make("data_type", { DataType::F32 });
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/** Data layout values to test - All */
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const auto data_layout_values = framework::dataset::make("data_layout", { DataLayout::NHWC });
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} // namespace
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TEST_SUITE(NEON)
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TEST_SUITE(DepthwiseConvolutionLayerNative)
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TEST_CASE(ValidateNoPadding, framework::DatasetMode::ALL)
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{
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// this test case will ensure that the kernel is not adding implicit padding
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constexpr uint32_t vector_size = 8; // Asummed vector size of the current native kernel
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constexpr auto depth = vector_size * 2 + 1; // mis-aligned depth to force padding if exists.
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constexpr auto data_layout = DataLayout::NHWC;
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constexpr auto data_type = DataType::F32;
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const auto input_size = Size2D{ 100, 100 }; // random plane size of the input
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const auto kernel_size = Size2D{ 4, 4 }; // random plane size of the kernel
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const auto pad_stride_info = PadStrideInfo(3, 3); // random convolution information to
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TensorShape src_shape{ depth, input_size.x(), input_size.y() };
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TensorShape weights_shape{ depth, kernel_size.x(), kernel_size.y() };
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TensorShape bias_shape{ depth };
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auto src = create_tensor<Tensor>(src_shape, data_type, 1, QuantizationInfo(), data_layout);
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auto weights = create_tensor<Tensor>(weights_shape, data_type, 1, QuantizationInfo(), data_layout);
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auto biases = create_tensor<Tensor>(bias_shape, data_type, 1, QuantizationInfo(), data_layout);
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auto dst = create_tensor<Tensor>(TensorShape(), data_type, 1, QuantizationInfo(), data_layout);
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NEDepthwiseConvolutionLayerNativeKernel dwc;
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dwc.configure(&src, &weights, &biases, &dst, pad_stride_info);
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ARM_COMPUTE_EXPECT(src.info()->padding().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(weights.info()->padding().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(biases.info()->padding().empty(), framework::LogLevel::ERRORS);
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ARM_COMPUTE_EXPECT(dst.info()->padding().empty(), framework::LogLevel::ERRORS);
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}
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TEST_SUITE(Float)
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::ALL,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(width_values_precommit,
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height_values_precommit),
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channel_values_precommit),
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batch_values_precommit),
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kernel_sz_values_precommit),
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depth_multiplier_values),
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dilation_values),
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stride_values),
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padding_valid_values),
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data_type_values),
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data_layout_values))
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{
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// Validate output
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validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEDepthwiseConvolutionLayerNativeFixture<float>, framework::DatasetMode::NIGHTLY,
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combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(width_values_nightly,
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height_values_nightly),
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channel_values_nightly),
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batch_values_nightly),
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kernel_sz_values_nightly),
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depth_multiplier_values),
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dilation_values),
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stride_values),
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padding_valid_values),
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data_type_values),
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data_layout_values))
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{
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// Validate output
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validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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TEST_SUITE_END() // Float
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TEST_SUITE_END() // DepthwiseConvolutionLayerNative
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TEST_SUITE_END() // NEON
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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