/* * Copyright (c) 2018 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 SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings) { DataType dst_data_type = src.data_type(); TensorShape orig_shape = src.shape(); std::vector paddings_extended = paddings; for(size_t i = paddings.size(); i < TensorShape::num_max_dimensions; i++) { paddings_extended.emplace_back(PaddingInfo{ 0, 0 }); } TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(orig_shape, paddings); SimpleTensor dst(padded_shape, dst_data_type); // Reference algorithm: loop over the different dimension of the input. for(int idx = 0; idx < dst.num_elements(); idx++) { 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]; std::array dims = { 0, 1, 2, 3, 4, 5 }; std::array 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); }; // If the tuple [i,j,k,l,m] is in the padding area, then seimply set the value if(std::any_of(dims.begin(), dims.end(), is_padding_area)) { dst[idx] = T(0); } else { // If the tuple[i,j,k,l,m] is not in the padding area, then copy the input into the output 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 pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); template SimpleTensor pad_layer(const SimpleTensor &src, const PaddingList &paddings); } // namespace reference } // namespace validation } // namespace test } // namespace arm_compute