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Diffstat (limited to 'compute/cker/include/cker/operation/Pad.h')
| -rw-r--r-- | compute/cker/include/cker/operation/Pad.h | 224 |
1 files changed, 224 insertions, 0 deletions
diff --git a/compute/cker/include/cker/operation/Pad.h b/compute/cker/include/cker/operation/Pad.h new file mode 100644 index 000000000..af432f3a8 --- /dev/null +++ b/compute/cker/include/cker/operation/Pad.h @@ -0,0 +1,224 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_PAD_H__ +#define __NNFW_CKER_PAD_H__ + +#include "cker/Shape.h" +#include "cker/Types.h" +#include "cker/Utils.h" +#include <stdexcept> +#include <iostream> +namespace nnfw +{ +namespace cker +{ +inline void Pad(const int32_t *padding_data, int32_t pad_rank, const Shape &input_shape, + const float *input_data, const Shape &output_shape, float *output_data, + const float *constant_value_data) +{ + // Note, this is pad with mode=`CONSTANT`: it doesn't support `REFLECT` and `SYMMETRIC` + // TODO: come up with more subtle solution that uses subtensors like arm compute + // TODO: Check if it works for all layouts + + using PaddingInfo = std::pair<int32_t, int32_t>; + /** List of padding information */ + using PaddingList = std::vector<PaddingInfo>; + + auto constant_value = constant_value_data ? *constant_value_data : 0; + assert(output_shape.DimensionsCount() == input_shape.DimensionsCount()); + + PaddingList padding_list(pad_rank); + for (int32_t n = 0; n < pad_rank; ++n) + { + const int32_t *from = padding_data + (n * 2); + padding_list[n] = {from[0], from[1]}; + } + for (int32_t i = 0; i < pad_rank; ++i) + { + assert(output_shape.Dims(i) == + input_shape.Dims(i) + padding_list[i].first + padding_list[i].second); + } + /* Use pad_rank since given input/output shapes are expanded to 4d before calling all cker + functions: + 1. to prevent access violation in padding_list; + 2. handling as 4d is slower than as 2d/3d. + */ + switch (pad_rank) + { + case 0: + case 1: + { + const int32_t in_row_len = input_shape.Dims(0); + std::fill_n(output_data, padding_list[0].first, constant_value); + std::memcpy(output_data + padding_list[0].first, input_data, in_row_len * sizeof(float)); + std::fill_n(output_data + padding_list[0].first + in_row_len, padding_list[0].second, + constant_value); + break; + } + case 2: // HW + { + const int32_t in_row_len = input_shape.Dims(1); + const int32_t out_row_size = output_shape.Dims(1); + + // prepend padding rows + std::fill_n(output_data, padding_list[0].first * out_row_size, constant_value); + + const auto r_h_inp_lim = input_shape.Dims(0) + padding_list[0].first; + for (auto i = padding_list[0].first, j = 0; i < r_h_inp_lim; ++i, ++j) + { + auto out_offset = i * out_row_size; + const auto in_offset = j * in_row_len; + + // prepend padding values + std::fill_n(output_data + out_offset, padding_list[1].first, constant_value); + + out_offset += padding_list[1].first; + + // copy a row of input data + memcpy(output_data + out_offset, input_data + in_offset, in_row_len * sizeof(float)); + + out_offset += in_row_len; + + // append padding values + std::fill_n(output_data + out_offset, padding_list[1].second, constant_value); + } + + // append padding rows + std::fill_n(output_data + r_h_inp_lim * out_row_size, padding_list[0].second * out_row_size, + constant_value); + break; + } + case 3: // HWC + { + const int32_t in_row_len = input_shape.Dims(2); + const int32_t out_row_size = output_shape.Dims(2); + const auto plain_size = out_row_size * output_shape.Dims(1); + + // prepend padding plains + std::fill_n(output_data, padding_list[0].first * plain_size, constant_value); + + const auto r_h_inp_lim = input_shape.Dims(0) + padding_list[0].first; + for (auto i = padding_list[0].first, i_inp = 0; i < r_h_inp_lim; ++i, ++i_inp) + { + const auto out_w_offset = (i * output_shape.Dims(1) + 0) * output_shape.Dims(2); + + // prepend padding rows + std::fill_n(output_data + out_w_offset, padding_list[1].first * out_row_size, + constant_value); + + const auto r_w_inp_lim = input_shape.Dims(1) + padding_list[1].first; + for (auto j = padding_list[1].first, j_inp = 0; j < r_w_inp_lim; ++j, ++j_inp) + { + auto out_offset = (i * output_shape.Dims(1) + j) * output_shape.Dims(2); + const auto in_offset = (i_inp * input_shape.Dims(1) + j_inp) * input_shape.Dims(2); + + // prepend padding values + std::fill_n(output_data + out_offset, padding_list[2].first, constant_value); + + out_offset += padding_list[2].first; + + // copy a row of input data + memcpy(output_data + out_offset, input_data + in_offset, in_row_len * sizeof(float)); + + out_offset += in_row_len; + + // append padding values + std::fill_n(output_data + out_offset, padding_list[2].second, constant_value); + } + + // append padding rows + std::fill_n(output_data + out_w_offset + r_w_inp_lim * out_row_size, + padding_list[1].second * out_row_size, constant_value); + } + + // append padding plains + std::fill_n(output_data + r_h_inp_lim * plain_size, padding_list[0].second * plain_size, + constant_value); + break; + } + case 4: + { + auto get_offset = [](const Shape &shape, int32_t n, int32_t h, int32_t w) -> int32_t { + return ((n * shape.Dims(1) + h) * shape.Dims(2) + w) * shape.Dims(3); + }; + const int32_t in_row_len = input_shape.Dims(3); + const int32_t out_row_size = output_shape.Dims(3); + const auto plain_size = out_row_size * output_shape.Dims(2); + const auto parallelepiped_size = plain_size * output_shape.Dims(1); + + // prepend padding parallelepipeds + std::fill_n(output_data, padding_list[0].first * parallelepiped_size, constant_value); + + const auto r_b_inp_lim = input_shape.Dims(0) + padding_list[0].first; + for (auto i = padding_list[0].first, i_inp = 0; i < r_b_inp_lim; ++i, ++i_inp) + { + const auto out_h_offset = get_offset(output_shape, i, 0, 0); + // prepend padding plains + std::fill_n(output_data + out_h_offset, padding_list[1].first * plain_size, constant_value); + + const auto r_h_inp_lim = input_shape.Dims(1) + padding_list[1].first; + for (auto j = padding_list[1].first, j_inp = 0; j < r_h_inp_lim; ++j, ++j_inp) + { + const auto out_w_offset = get_offset(output_shape, i, j, 0); + + // prepend padding rows + std::fill_n(output_data + out_w_offset, padding_list[2].first * out_row_size, + constant_value); + + const auto r_w_inp_lim = input_shape.Dims(2) + padding_list[2].first; + for (auto k = padding_list[2].first, k_inp = 0; k < r_w_inp_lim; ++k, ++k_inp) + { + auto out_c_offset = get_offset(output_shape, i, j, k); + const auto in_offset = get_offset(input_shape, i_inp, j_inp, k_inp); + + // prepend padding values + std::fill_n(output_data + out_c_offset, padding_list[3].first, constant_value); + + out_c_offset += padding_list[3].first; + + // copy a row of input data + memcpy(output_data + out_c_offset, input_data + in_offset, in_row_len * sizeof(float)); + + out_c_offset += in_row_len; + + // append padding values + std::fill_n(output_data + out_c_offset, padding_list[3].second, constant_value); + } + + // append padding rows + std::fill_n(output_data + out_w_offset + r_w_inp_lim * out_row_size, + padding_list[2].second * out_row_size, constant_value); + } + + // append padding plains + std::fill_n(output_data + out_h_offset + r_h_inp_lim * plain_size, + padding_list[1].second * plain_size, constant_value); + } + // append padding parallelepipeds + std::fill_n(output_data + r_b_inp_lim * parallelepiped_size, + padding_list[0].second * parallelepiped_size, constant_value); + break; + } + default: + throw std::runtime_error("Padding for rank > 4 NYI"); + break; + } +} +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_PAD_H__ |