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/*
* Copyright (c) 2017-2020 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 "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
const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */
const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */
constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */
/** Direct convolution data set.for FP32 */
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))));
/** Direct convolution data set.for FP16 */
const auto data_pad_f16 = 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 }),
combine(framework::dataset::make("PadY", { 0 }),
framework::dataset::make("KernelSize", 1))));
const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(),
combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
data_pad_f32)));
const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(),
combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
data_pad_f16)));
const auto data = combine(datasets::SmallDirectConvolutionShapes(),
combine(framework::dataset::make("StrideX", { 1 }),
combine(framework::dataset::make("StrideY", { 1 }),
combine(framework::dataset::make("PadX", { 1 }),
combine(framework::dataset::make("PadY", { 1 }),
framework::dataset::make("KernelSize", 3))))));
const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(),
combine(framework::dataset::make("StrideX", { 1 }),
combine(framework::dataset::make("StrideY", { 1 }),
combine(framework::dataset::make("PadX", { 0, 2 }),
combine(framework::dataset::make("PadY", { 0, 3 }),
framework::dataset::make("KernelSize", 9))))));
const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4 }));
const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4 }));
const auto data_precommit = combine(data, framework::dataset::make("NumKernels", { 1 }));
const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 }));
/** Activation function Dataset*/
const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
{
ActivationLayerInfo(),
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), // Mismatching data type input/weights
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
}),
framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
})),
framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
TensorInfo(TensorShape(3U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(4U), 1, DataType::F32),
})),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
})),
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(RunSmall, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
DataType::F16)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
// Validate output
validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f16_nightly, framework::dataset::make("DataType", DataType::F16)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", DataLayout::NCHW)))
{
// Validate output
validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
}
TEST_SUITE_END() // FP16
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
DataType::F32)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_fp32);
}
FIXTURE_DATA_TEST_CASE(RunSmall9x9, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit9x9, framework::dataset::make("DataType",
DataType::F32)),
ActivationFunctionsDataset),
framework::dataset::make("DataLayout", { DataLayout::NHWC })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_fp32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f32_nightly, 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() // FP32
TEST_SUITE_END() // Float
TEST_SUITE_END() // DirectConvolutionLayer
TEST_SUITE_END() // NEON
} // namespace validation
} // namespace test
} // namespace arm_compute
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