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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 "ReferenceCPP.h"
#include "TensorFactory.h"
#include "TensorOperations.h"
#include "TensorVisitors.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/runtime/Tensor.h"
#include "utils/TypePrinter.h"
#include "tests/validation_old/boost_wrapper.h"
#include <algorithm>
#include <functional>
#include <memory>
#include <numeric>
#include <vector>
using namespace arm_compute::test::validation::tensor_visitors;
namespace arm_compute
{
namespace test
{
namespace validation
{
// Harris corners
void ReferenceCPP::harris_corners(RawTensor &src, RawTensor &Gx, RawTensor &Gy, const RawTensor &candidates, const RawTensor &non_maxima, float threshold, float min_dist, float sensitivity,
int32_t gradient_size, int32_t block_size, KeyPointArray &corners, BorderMode border_mode, uint8_t constant_border_value)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || (Gx.data_type() != DataType::S16 && Gx.data_type() != DataType::S32) || (Gy.data_type() != DataType::S16 && Gy.data_type() != DataType::S32)
|| candidates.data_type() != DataType::F32 || non_maxima.data_type() != DataType::F32);
Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<float> c(candidates.shape(), candidates.data_type(), candidates.fixed_point_position(), const_cast<float *>(reinterpret_cast<const float *>(candidates.data()))); // NOLINT
Tensor<float> nm(non_maxima.shape(), non_maxima.data_type(), non_maxima.fixed_point_position(), const_cast<float *>(reinterpret_cast<const float *>(non_maxima.data()))); // NOLINT
if(gradient_size == 7)
{
Tensor<int32_t> gx(Gx.shape(), Gx.data_type(), Gx.fixed_point_position(), reinterpret_cast<int32_t *>(Gx.data()));
Tensor<int32_t> gy(Gy.shape(), Gy.data_type(), Gy.fixed_point_position(), reinterpret_cast<int32_t *>(Gy.data()));
tensor_operations::harris_corners(s, gx, gy, c, nm, threshold, min_dist, sensitivity, gradient_size, block_size, corners, border_mode, constant_border_value);
}
else
{
Tensor<int16_t> gx(Gx.shape(), Gx.data_type(), Gx.fixed_point_position(), reinterpret_cast<int16_t *>(Gx.data()));
Tensor<int16_t> gy(Gy.shape(), Gy.data_type(), Gy.fixed_point_position(), reinterpret_cast<int16_t *>(Gy.data()));
tensor_operations::harris_corners(s, gx, gy, c, nm, threshold, min_dist, sensitivity, gradient_size, block_size, corners, border_mode, constant_border_value);
}
}
// Absolute difference
void ReferenceCPP::absolute_difference(const RawTensor &src1, const RawTensor &src2, RawTensor &dst)
{
const TensorVariant s1 = TensorFactory::get_tensor(src1);
const TensorVariant s2 = TensorFactory::get_tensor(src2);
TensorVariant d = TensorFactory::get_tensor(dst);
boost::apply_visitor(absolute_difference_visitor(), s1, s2, d);
}
// Integral image
void ReferenceCPP::integral_image(const RawTensor &src, RawTensor &dst)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::U32);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<uint32_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<uint32_t *>(dst.data()));
tensor_operations::integral_image(s, d);
}
// Accumulate
void ReferenceCPP::accumulate(const RawTensor &src, RawTensor &dst)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::S16);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<int16_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<int16_t *>(dst.data()));
tensor_operations::accumulate(s, d);
}
// Accumulate squared
void ReferenceCPP::accumulate_squared(const RawTensor &src, RawTensor &dst, uint32_t shift)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::S16);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<int16_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<int16_t *>(dst.data()));
tensor_operations::accumulate_squared(s, d, shift);
}
// Accumulate weighted
void ReferenceCPP::accumulate_weighted(const RawTensor &src, RawTensor &dst, float alpha)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::U8);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<uint8_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<uint8_t *>(dst.data()));
tensor_operations::accumulate_weighted(s, d, alpha);
}
// Non linear filter
void ReferenceCPP::non_linear_filter(const RawTensor &src, RawTensor &dst, NonLinearFilterFunction function, unsigned int mask_size,
MatrixPattern pattern, const uint8_t *mask, BorderMode border_mode, uint8_t constant_border_value)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::U8);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<uint8_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<uint8_t *>(dst.data()));
tensor_operations::non_linear_filter(s, d, function, mask_size, pattern, mask, border_mode, constant_border_value);
}
// Threshold
void ReferenceCPP::threshold(const RawTensor &src, RawTensor &dst, uint8_t threshold, uint8_t false_value, uint8_t true_value, ThresholdType type, uint8_t upper)
{
ARM_COMPUTE_ERROR_ON(src.data_type() != DataType::U8 || dst.data_type() != DataType::U8);
const Tensor<uint8_t> s(src.shape(), src.data_type(), src.fixed_point_position(), reinterpret_cast<const uint8_t *>(src.data()));
Tensor<uint8_t> d(dst.shape(), dst.data_type(), dst.fixed_point_position(), reinterpret_cast<uint8_t *>(dst.data()));
tensor_operations::threshold(s, d, threshold, false_value, true_value, type, upper);
}
// ROI Pooling Layer
void ReferenceCPP::roi_pooling_layer(const RawTensor &src, RawTensor &dst, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
{
const TensorVariant s = TensorFactory::get_tensor(src);
TensorVariant d = TensorFactory::get_tensor(dst);
boost::apply_visitor(tensor_visitors::roi_pooling_layer_visitor(s, rois, pool_info), d);
}
} // namespace validation
} // namespace test
} // namespace arm_compute
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