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/*
* Copyright (c) 2018-2019 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 "PadLayer.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> pad_layer(const SimpleTensor<T> &src, const PaddingList &paddings, const PixelValue const_value, const PaddingMode mode)
{
const DataType dst_data_type = src.data_type();
const TensorShape orig_shape = src.shape();
std::vector<PaddingInfo> paddings_extended = paddings;
for(size_t i = paddings.size(); i < TensorShape::num_max_dimensions; ++i)
{
paddings_extended.emplace_back(PaddingInfo{ 0, 0 });
}
const TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(orig_shape, paddings);
SimpleTensor<T> dst(padded_shape, dst_data_type);
// Reference algorithm: loop over the different dimension of the input.
const uint32_t num_elements = dst.num_elements();
for(uint32_t idx = 0; idx < num_elements; ++idx)
{
const Coordinates coord = index2coord(padded_shape, idx);
const size_t i = coord.x();
const size_t j = coord.y();
const size_t k = coord.z();
const size_t l = coord[3];
const size_t m = coord[4];
const size_t n = coord[5];
const std::array<size_t, TensorShape::num_max_dimensions> dims = { { 0, 1, 2, 3, 4, 5 } };
const std::array<size_t, TensorShape::num_max_dimensions> coords = { { i, j, k, l, m, n } };
auto is_padding_area = [&](size_t i)
{
return (coords[i] < paddings_extended[i].first || coords[i] > orig_shape[i] + paddings_extended[i].first - 1);
};
auto orig_coord_reflect = [&](size_t i)
{
if(is_padding_area(i))
{
if(coords[i] < paddings_extended[i].first)
{
return paddings_extended[i].first - coords[i];
}
else
{
return 2 * orig_shape[i] + paddings_extended[i].first - 2 - coords[i];
}
}
return coords[i] - paddings_extended[i].first;
};
auto orig_coord_symm = [&](size_t i)
{
if(is_padding_area(i))
{
if(coords[i] < paddings_extended[i].first)
{
return paddings_extended[i].first - coords[i] - 1;
}
else
{
return 2 * orig_shape[i] + paddings_extended[i].first - 1 - coords[i];
}
}
return coords[i] - paddings_extended[i].first;
};
// If the tuple [i,j,k,l,m] is in the padding area, then simply set the value
if(std::any_of(dims.begin(), dims.end(), is_padding_area))
{
switch(mode)
{
case PaddingMode::CONSTANT:
const_value.get(dst[idx]);
break;
case PaddingMode::REFLECT:
{
const Coordinates orig_coords{ orig_coord_reflect(0),
orig_coord_reflect(1),
orig_coord_reflect(2),
orig_coord_reflect(3),
orig_coord_reflect(4),
orig_coord_reflect(5) };
const size_t idx_src = coord2index(orig_shape, orig_coords);
dst[idx] = src[idx_src];
break;
}
case PaddingMode::SYMMETRIC:
{
const Coordinates orig_coords{ orig_coord_symm(0),
orig_coord_symm(1),
orig_coord_symm(2),
orig_coord_symm(3),
orig_coord_symm(4),
orig_coord_symm(5) };
const size_t idx_src = coord2index(orig_shape, orig_coords);
dst[idx] = src[idx_src];
break;
}
default:
ARM_COMPUTE_ERROR("Padding mode not supported.");
break;
}
}
else
{
// If the tuple[i,j,k,l,m] is not in the padding area, then copy the input into the output
const Coordinates orig_coords
{
i - paddings_extended[0].first,
j - paddings_extended[1].first,
k - paddings_extended[2].first,
l - paddings_extended[3].first,
m - paddings_extended[4].first,
n - paddings_extended[5].first
};
const size_t idx_src = coord2index(orig_shape, orig_coords);
dst[idx] = src[idx_src];
}
}
return dst;
}
template SimpleTensor<float> pad_layer(const SimpleTensor<float> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<half> pad_layer(const SimpleTensor<half> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<uint8_t> pad_layer(const SimpleTensor<uint8_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<int8_t> pad_layer(const SimpleTensor<int8_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<uint16_t> pad_layer(const SimpleTensor<uint16_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<int16_t> pad_layer(const SimpleTensor<int16_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<uint32_t> pad_layer(const SimpleTensor<uint32_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
template SimpleTensor<int32_t> pad_layer(const SimpleTensor<int32_t> &src, const PaddingList &paddings, const PixelValue const_value = PixelValue(), const PaddingMode mode);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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