300 lines
20 KiB
C++
300 lines
20 KiB
C++
/*
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* Copyright (c) 2017-2020 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 "arm_compute/core/Helpers.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
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#include "arm_compute/runtime/Tensor.h"
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#include "arm_compute/runtime/TensorAllocator.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/framework/Asserts.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/DirectConvolutionLayerFixture.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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namespace
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{
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */
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const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */
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constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */
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/** Direct convolution data set.for FP32 */
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const auto data_pad_f32 = concat(concat(combine(framework::dataset::make("PadX", { 0, 1 }),
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combine(framework::dataset::make("PadY", { 0, 1 }),
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framework::dataset::make("KernelSize", 3))),
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combine(framework::dataset::make("PadX", { 0, 2 }),
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combine(framework::dataset::make("PadY", { 0, 2 }),
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framework::dataset::make("KernelSize", 3)))),
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combine(framework::dataset::make("PadX", { 0, 3 }),
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combine(framework::dataset::make("PadY", { 0, 3 }),
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framework::dataset::make("KernelSize", 5))));
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/** Direct convolution data set.for FP16 */
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const auto data_pad_f16 = concat(combine(framework::dataset::make("PadX", { 0, 1 }),
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combine(framework::dataset::make("PadY", { 0, 1 }),
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framework::dataset::make("KernelSize", 3))),
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combine(framework::dataset::make("PadX", { 0 }),
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combine(framework::dataset::make("PadY", { 0 }),
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framework::dataset::make("KernelSize", 1))));
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const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(),
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combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
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combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
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data_pad_f32)));
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const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(),
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combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
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combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
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data_pad_f16)));
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const auto data_prec = combine(datasets::SmallDirectConvolutionShapes(),
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combine(framework::dataset::make("StrideX", { 1 }),
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combine(framework::dataset::make("StrideY", { 1 }),
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combine(framework::dataset::make("PadX", { 1 }),
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combine(framework::dataset::make("PadY", { 1 }),
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framework::dataset::make("KernelSize", 3))))));
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const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(),
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combine(framework::dataset::make("StrideX", { 1 }),
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combine(framework::dataset::make("StrideY", { 1 }),
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combine(framework::dataset::make("PadX", { 0, 2 }),
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combine(framework::dataset::make("PadY", { 0, 3 }),
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framework::dataset::make("KernelSize", 9))))));
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const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4 }));
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const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4 }));
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const auto data_precommit = combine(data_prec, framework::dataset::make("NumKernels", { 1 }));
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const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 }));
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/* The following tests is from real use-case that made DirectConvolution
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* overflows in terms of its tensor indexing. This test case is using
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* a separate tolerance due to the following reason.
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* - It has shown that it requires generally larger absolute tolerance
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* for large numbers or larger relative tolerance for small numbers.
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* - With the first reason, since it is mainly testing index overflow,
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* a value with a margin is used to avoid uninteded test failures
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* during nightly.
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*/
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constexpr AbsoluteTolerance<float> usecase_tolerance_fp32(0.05f);
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const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }),
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combine(framework::dataset::make("StrideX", { 1 }),
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combine(framework::dataset::make("StrideY", { 1 }),
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combine(framework::dataset::make("PadX", { 4 }),
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combine(framework::dataset::make("PadY", { 4 }),
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combine(framework::dataset::make("KernelSize", 9),
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framework::dataset::make("NumKernels", { 16 })))))));
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/** Activation function Dataset*/
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const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
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});
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} // namespace
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TEST_SUITE(NEON)
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TEST_SUITE(DirectConvolutionLayer)
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
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TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
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}),
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framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
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TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
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})),
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framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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TensorInfo(TensorShape(3U), 1, DataType::F32),
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TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(4U), 1, DataType::F32),
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})),
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framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
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TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
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})),
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framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(3, 3, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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PadStrideInfo(1, 1, 0, 0),
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})),
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framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
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})),
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framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
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input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
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{
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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));
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ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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DATA_TEST_CASE(NoPaddingNHWCKernel, framework::DatasetMode::ALL, combine(combine(combine(data_precommit,
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framework::dataset::make("DataType", DataType::F32)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })),
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shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, act_info, data_layout)
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{
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TensorShape input_shape = TensorShape(shape);
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TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
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const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
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TensorInfo input_info = TensorInfo(input_shape, 1, data_type);
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TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);
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TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info);
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if(data_layout == DataLayout::NHWC)
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{
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permute(input_shape, PermutationVector(2U, 0U, 1U));
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permute(weights_shape, PermutationVector(2U, 0U, 1U));
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permute(output_shape, PermutationVector(2U, 0U, 1U));
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}
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// Create tensors
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Tensor src = create_tensor<Tensor>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
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Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1, QuantizationInfo(), data_layout);
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Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1, QuantizationInfo(), data_layout);
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// Create and configure function
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NEDirectConvolutionLayer conv;
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conv.configure(&src, &weights, nullptr, &dst, info, act_info);
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validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
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validate(weights.info()->padding(), PaddingSize(0, 0, 0, 0));
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validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0));
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}
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template <typename T>
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using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>;
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TEST_SUITE(Float)
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
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DataType::F16)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", DataLayout::NCHW)))
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{
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// Validate output
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validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f16_nightly, framework::dataset::make("DataType", DataType::F16)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", DataLayout::NCHW)))
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{
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// Validate output
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validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
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}
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TEST_SUITE_END() // FP16
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
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DataType::F32)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, tolerance_fp32);
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}
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FIXTURE_DATA_TEST_CASE(RunSmall9x9, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit9x9, framework::dataset::make("DataType",
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DataType::F32)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, tolerance_fp32);
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}
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FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f32_nightly, framework::dataset::make("DataType",
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DataType::F32)),
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ActivationFunctionsDataset),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, tolerance_fp32);
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}
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FIXTURE_DATA_TEST_CASE(RunLargeUsecase, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_usecase, framework::dataset::make("DataType",
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DataType::F32)),
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framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })),
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framework::dataset::make("DataLayout", { DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, usecase_tolerance_fp32);
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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() // DirectConvolutionLayer
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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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