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/*
* Copyright (c) 2018-2020 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 "ChannelShuffle.h"
#include "arm_compute/core/Types.h"
#include "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
// Refence implementation for channel shuffle taken from https://github.com/pytorch/pytorch/blob/master/caffe2/operators/channel_shuffle_op.h
template <typename T>
SimpleTensor<T> channel_shuffle(const SimpleTensor<T> &src, int num_groups)
{
// Create reference
SimpleTensor<T> dst{ src.shape(), src.data_type(), src.num_channels(), src.quantization_info() };
const int M = src.shape()[0];
const int N = src.shape()[1];
const int num_channels = src.shape()[2];
const int batches = src.shape()[3];
const int MxN = M * N;
const int channels_in_group = num_channels / num_groups;
const T *src_ref = src.data();
T *dst_ref = dst.data();
#if defined(_OPENMP)
#pragma omp parallel for collapse(2)
#endif /* _OPENMP */
for(int n = 0; n < batches; ++n)
{
for(int g = 0; g < num_groups; ++g)
{
// Gather the group g block (of size channels_in_group * MxN) from output channels
// g + 0 * G, g + 1 * G, g + 2 * G, g + G * (K - 1) etc.
const T *src_ptr = src_ref + g * channels_in_group * MxN + n * num_channels * MxN;
T *dst_ptr = dst_ref + g * MxN + n * num_channels * MxN;
for(int i = 0; i < channels_in_group; ++i)
{
std::copy(src_ptr + i * MxN,
src_ptr + (i + 1) * MxN,
dst_ptr + i * num_groups * MxN);
}
}
}
return dst;
}
template SimpleTensor<uint8_t> channel_shuffle(const SimpleTensor<uint8_t> &src, int num_groups);
template SimpleTensor<uint16_t> channel_shuffle(const SimpleTensor<uint16_t> &src, int num_groups);
template SimpleTensor<uint32_t> channel_shuffle(const SimpleTensor<uint32_t> &src, int num_groups);
template SimpleTensor<half> channel_shuffle(const SimpleTensor<half> &src, int num_groups);
template SimpleTensor<float> channel_shuffle(const SimpleTensor<float> &src, int num_groups);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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