mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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128 lines
4.1 KiB
C++
128 lines
4.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/function/pad.h"
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#include <cstdlib>
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#include "fastdeploy/function/eigen.h"
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#include "fastdeploy/utils/utils.h"
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namespace fastdeploy {
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namespace function {
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template <typename T, int Rank>
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struct PadEigen {
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using Array = std::array<std::pair<int64_t, int64_t>, Rank>;
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using Array32Bit = std::array<std::pair<int, int>, Rank>;
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using InType = Eigen::TensorMap<
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Eigen::Tensor<const T, Rank, Eigen::RowMajor, Eigen::DenseIndex>>;
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using InType32BitIndex =
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Eigen::TensorMap<Eigen::Tensor<const T, Rank, Eigen::RowMajor, int>,
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Eigen::Aligned>;
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using OutType = Eigen::TensorMap<
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Eigen::Tensor<T, Rank, Eigen::RowMajor, Eigen::DenseIndex>>;
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using OutType32BitIndex =
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Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int>,
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Eigen::Aligned>;
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static void Eval(const Eigen::DefaultDevice& dev,
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OutType out,
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const InType& in,
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const Array& padding,
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const T value) {
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out.device(dev) = in.pad(padding, value);
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}
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static void Eval32(const Eigen::DefaultDevice& dev,
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OutType32BitIndex out,
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const InType32BitIndex& in,
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const Array32Bit& padding,
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const T value) {
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out.device(dev) = in.pad(padding, value);
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}
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};
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template <typename T, size_t D>
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void PadFunction(const std::vector<int>& pads,
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const FDTensor& src,
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T pad_value,
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FDTensor* out) {
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std::array<std::pair<int64_t, int64_t>, D> paddings;
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for (size_t i = 0; i < paddings.size(); ++i) {
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paddings[i].first = pads[i * 2];
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paddings[i].second = pads[i * 2 + 1];
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}
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auto src_tensor = EigenTensor<T, D>::From(src);
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auto out_tensor = EigenTensor<T, D>::From(*out);
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const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
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PadEigen<T, D>::Eval(
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dev, out_tensor, src_tensor, paddings, pad_value);
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}
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template <typename T>
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void PaddingFunctor(int rank, const std::vector<int>& pads, T pad_value, const FDTensor& src, FDTensor* out) {
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switch (rank) {
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case 1:
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PadFunction<T, 1>(pads, src, pad_value, out);
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break;
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case 2:
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PadFunction<T, 2>(pads, src, pad_value, out);
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break;
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case 3:
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PadFunction<T, 3>(pads, src, pad_value, out);
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break;
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case 4:
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PadFunction<T, 4>(pads, src, pad_value, out);
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break;
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case 5:
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PadFunction<T, 5>(pads, src, pad_value, out);
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break;
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case 6:
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PadFunction<T, 6>(pads, src, pad_value, out);
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break;
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default:
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FDASSERT(false, "Pad only support tensors with no more than 6 dimensions currently.");
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}
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}
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template <typename T>
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void PadKernel(const FDTensor& x,
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const std::vector<int>& paddings,
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const T& pad_value,
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FDTensor* out) {
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std::vector<int64_t> new_shape(x.shape.size());
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for (size_t i = 0; i < x.shape.size(); ++i) {
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new_shape[i] = x.shape[i] + paddings[2 * i] + paddings[2 * i + 1];
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}
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out->Allocate(new_shape, x.dtype);
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PaddingFunctor<T>(x.shape.size(), paddings, pad_value, x, out);
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}
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void Pad(const FDTensor& x, FDTensor* out, const std::vector<int>& pads, float value) {
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FDASSERT(pads.size() == x.shape.size() * 2, "Size of pads:%zu must be 2 times of rank:%zu.", pads.size(), x.shape.size());
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FDTensor out_tmp;
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FD_VISIT_ALL_TYPES(x.dtype, "PadKernel",
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([&] { PadKernel<data_t>(x, pads, value, &out_tmp); }));
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*out = std::move(out_tmp);
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}
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} // namespace function
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} // namespace fastdeploy
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