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/*
* Copyright (c) 2017 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 "NEON/Helper.h"
#include "NEON/NEAccessor.h"
#include "TypePrinter.h"
#include "validation/Datasets.h"
#include "validation/Reference.h"
#include "validation/Validation.h"
#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.h"
#include <random>
using namespace arm_compute;
using namespace arm_compute::test;
using namespace arm_compute::test::neon;
using namespace arm_compute::test::validation;
namespace
{
/** Define tolerance of the normalization layer depending on values data type.
*
* @param[in] dt Data type of the tensors' values.
*
* @return Tolerance depending on the data type.
*/
float normalization_layer_tolerance(DataType dt)
{
switch(dt)
{
case DataType::QS8:
return 2.0f;
case DataType::F32:
return 1e-05;
default:
return 0.f;
}
}
/** Compute Neon normalization layer function.
*
* @param[in] shape Shape of the input and output tensors.
* @param[in] dt Data type of input and output tensors.
* @param[in] norm_info Normalization Layer information.
* @param[in] fixed_point_position (Optional) Fixed point position that expresses the number of bits for the fractional part of the number when the tensor's data type is QS8 or QS16 (default = 0).
*
* @return Computed output tensor.
*/
Tensor compute_normalization_layer(const TensorShape &shape, DataType dt, NormalizationLayerInfo norm_info, int fixed_point_position = 0)
{
// Create tensors
Tensor src = create_tensor(shape, dt, 1, fixed_point_position);
Tensor dst = create_tensor(shape, dt, 1, fixed_point_position);
// Create and configure function
NENormalizationLayer norm;
norm.configure(&src, &dst, norm_info);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
BOOST_TEST(!src.info()->is_resizable());
BOOST_TEST(!dst.info()->is_resizable());
// Fill tensors
if(dt == DataType::QS8)
{
const int8_t one_fixed_point = 1 << fixed_point_position;
const int8_t minus_one_fixed_point = -one_fixed_point;
library->fill_tensor_uniform(NEAccessor(src), 0, minus_one_fixed_point, one_fixed_point);
}
else
{
library->fill_tensor_uniform(NEAccessor(src), 0);
}
// Compute function
norm.run();
return dst;
}
} // namespace
#ifndef DOXYGEN_SKIP_THIS
BOOST_AUTO_TEST_SUITE(NEON)
BOOST_AUTO_TEST_SUITE(NormalizationLayer)
BOOST_AUTO_TEST_SUITE(Float)
BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
BOOST_DATA_TEST_CASE(RunSmall,
SmallShapes() * DataType::F32 *NormalizationTypes() * boost::unit_test::data::xrange(3, 9, 2) * boost::unit_test::data::make({ 0.5f, 1.0f, 2.0f }),
shape, dt, norm_type, norm_size, beta)
{
// Provide normalization layer information
NormalizationLayerInfo norm_info(norm_type, norm_size, 5, beta);
// Compute function
Tensor dst = compute_normalization_layer(shape, dt, norm_info);
// Compute reference
RawTensor ref_dst = Reference::compute_reference_normalization_layer(shape, dt, norm_info);
// Validate output
validate(NEAccessor(dst), ref_dst, normalization_layer_tolerance(DataType::F32));
}
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE(Quantized)
BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
BOOST_DATA_TEST_CASE(RunSmall,
SmallShapes() * DataType::QS8 *NormalizationTypes() * boost::unit_test::data::xrange(3, 7, 2) * (boost::unit_test::data::xrange(1, 6) * boost::unit_test::data::make({ 0.5f, 1.0f, 2.0f })),
shape, dt, norm_type, norm_size, fixed_point_position, beta)
{
// Provide normalization layer information
NormalizationLayerInfo norm_info(norm_type, norm_size, 5, beta, 1.f);
// Compute function
Tensor dst = compute_normalization_layer(shape, dt, norm_info, fixed_point_position);
// Compute reference
RawTensor ref_dst = Reference::compute_reference_normalization_layer(shape, dt, norm_info, fixed_point_position);
// Validate output
validate(NEAccessor(dst), ref_dst, normalization_layer_tolerance(DataType::QS8));
}
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE_END()
BOOST_AUTO_TEST_SUITE_END()
#endif
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