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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 "ConvertFullyConnectedWeights.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
template <typename T>
SimpleTensor<T> convert_fully_connected_weights(const SimpleTensor<T> &src, const TensorShape &original_input_shape, const DataLayout training_data_layout)
{
SimpleTensor<T> dst(src.shape(), src.data_type());
const DataLayout original_input_data_layout = (training_data_layout == DataLayout::NCHW) ? DataLayout::NHWC : DataLayout::NCHW;
const int width_idx = get_data_layout_dimension_index(original_input_data_layout, DataLayoutDimension::WIDTH);
const int height_idx = get_data_layout_dimension_index(original_input_data_layout, DataLayoutDimension::HEIGHT);
const int channel_idx = get_data_layout_dimension_index(original_input_data_layout, DataLayoutDimension::CHANNEL);
const bool is_nchw_to_nhwc = training_data_layout == DataLayout::NCHW;
const unsigned int num_elems_per_input_plane = original_input_shape[width_idx] * original_input_shape[height_idx];
const unsigned int num_channels = original_input_shape[channel_idx];
const unsigned int factor_1 = is_nchw_to_nhwc ? num_elems_per_input_plane : num_channels;
const unsigned int factor_2 = is_nchw_to_nhwc ? num_channels : num_elems_per_input_plane;
const uint32_t num_elements = src.num_elements();
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(uint32_t i = 0; i < num_elements; ++i)
{
const Coordinates coords_in = index2coords(src.shape(), i);
const Coordinates coords_out(coords_in.x(), coords_in.y() % factor_1 * factor_2 + coords_in.y() / factor_1);
dst[coords2index(dst.shape(), coords_out)] = src[i];
}
return dst;
}
template SimpleTensor<uint8_t> convert_fully_connected_weights(const SimpleTensor<uint8_t> &src, const TensorShape &original_input_shape,
const DataLayout training_data_layout);
template SimpleTensor<half> convert_fully_connected_weights(const SimpleTensor<half> &src, const TensorShape &original_input_shape,
const DataLayout training_data_layout);
template SimpleTensor<float> convert_fully_connected_weights(const SimpleTensor<float> &src, const TensorShape &original_input_shape,
const DataLayout training_data_layout);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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