109 lines
4.1 KiB
C++
109 lines
4.1 KiB
C++
/*
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* Copyright (c) 2018 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "Unstack.h"
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#include "tests/validation/Helpers.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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inline Coordinates expand_coordinates(Coordinates in_coord, size_t axis, size_t slice, size_t num_dimensions)
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{
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/*
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Reconstruct input_coord to read the corresponding value from the correct slice. This is done by adding an extra dimension
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to the coordinates and shuffling around the values based on the info below.
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For example, if input tensor shape is (X, Y, Z, W);
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If axis == 0, each slice will have the shape (Y, Z, W) and there will be X slices
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If axis == 1, each slice will have the shape (X, Z, W) and there will be Y slices.
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*/
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Coordinates expanded_coord;
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expanded_coord.set_num_dimensions(num_dimensions);
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expanded_coord.set(axis, slice);
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for(size_t k = 0; k < axis; ++k)
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{
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expanded_coord.set(k, in_coord[k]);
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}
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for(size_t k = axis + 1; k < num_dimensions; ++k)
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{
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expanded_coord.set(k, in_coord[k - 1]);
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}
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return expanded_coord;
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}
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template <typename T>
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SimpleTensor<T> get_slice(const SimpleTensor<T> &input_tensor, size_t axis, size_t slice)
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{
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TensorShape out_shape = input_tensor.shape();
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out_shape.remove_dimension(axis);
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const size_t unpacked_num_dimensions(input_tensor.shape().num_dimensions());
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SimpleTensor<T> output{ out_shape, input_tensor.data_type() };
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Window win;
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win.use_tensor_dimensions(out_shape);
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execute_window_loop(win, [&](const Coordinates & id)
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{
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const Coordinates input_coords = expand_coordinates(id, axis, slice, unpacked_num_dimensions);
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*reinterpret_cast<T *>(output(id)) = *reinterpret_cast<const T *>(input_tensor(input_coords));
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});
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return output;
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}
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} // namespace
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template <typename T>
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std::vector<SimpleTensor<T>> unstack(const SimpleTensor<T> &input_tensor, std::vector<SimpleTensor<T>> &output_tensors, int axis)
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{
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// Wrap around negative values
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const unsigned int axis_u = wrap_around(axis, static_cast<int>(input_tensor.shape().num_dimensions()));
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ARM_COMPUTE_ERROR_ON(axis_u >= input_tensor.shape().num_dimensions());
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for(size_t k = 0; k < output_tensors.size(); ++k)
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{
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SimpleTensor<T> &output = output_tensors[k];
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const SimpleTensor<T> kth_slice = get_slice(input_tensor, axis_u, k);
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output = copy_tensor<T>(kth_slice);
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}
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return output_tensors;
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}
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template std::vector<SimpleTensor<float>> unstack(const SimpleTensor<float> &input_tensor, std::vector<SimpleTensor<float>> &output_tensors, int axis);
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template std::vector<SimpleTensor<half>> unstack(const SimpleTensor<half> &input_tensor, std::vector<SimpleTensor<half>> &output_tensors, int axis);
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template std::vector<SimpleTensor<uint8_t>> unstack(const SimpleTensor<uint8_t> &input_tensor, std::vector<SimpleTensor<uint8_t>> &output_tensors, int axis);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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