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https://github.com/PaddlePaddle/FastDeploy.git
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d74e1209ae
* Update Inpaint pipeline * Update concat * Add GaussianRandomKernel * Update GaussianRandom * Add vae endoder * Add unet infer * Add vae decoder predict * add PrepareMaskAndMaskedImage * Add imwrite * Add time counter * Fix pipeline * use FDTensor move * Fix scaled_linear dpm solver * Add RGB2BGR
111 lines
3.4 KiB
C++
111 lines
3.4 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/tile.h"
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#include "fastdeploy/function/eigen.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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void TileFunctor(const FDTensor& x,
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const std::vector<int64_t>& origin_repeat_times,
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FDTensor* out) {
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auto x_shape = x.Shape();
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auto repeat_times = origin_repeat_times;
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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FDASSERT(repeat_times[i] > 0,
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"All elements of the input 'repeat_times' "
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"for tile op must be positive integers, but "
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"the value received is %d.",
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repeat_times[i]);
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}
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if (repeat_times.size() < x_shape.size()) {
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int diff = x_shape.size() - repeat_times.size();
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repeat_times.insert(repeat_times.begin(), diff, 1);
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} else {
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int diff = repeat_times.size() - x_shape.size();
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x_shape.insert(x_shape.begin(), diff, 1);
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}
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FDASSERT(repeat_times.size() == x_shape.size(),
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"The rank (%d) of the input 'x' and the rank (%d) of the input "
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"'repeat_times' for tile op must match after promotion.",
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x_shape.size(), repeat_times.size());
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if (Rank == 0) {
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// Deep copy
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*out = x;
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return;
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}
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FDTensor out_tmp;
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Eigen::DSizes<Eigen::DenseIndex, Rank> bcast_dims;
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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bcast_dims[i] = repeat_times[i];
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}
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std::vector<int64_t> out_shape(x_shape);
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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out_shape[i] *= repeat_times[i];
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}
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out_tmp.Allocate(out_shape, x.Dtype());
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auto eigen_x = EigenTensor<T, Rank>::From(x, x_shape);
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auto eigen_out = EigenTensor<T, Rank>::From(out_tmp, out_shape);
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const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
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eigen_out.device(dev) = eigen_x.broadcast(bcast_dims);
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*out = std::move(out_tmp);
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}
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template <typename T>
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void TileKernel(const FDTensor& x, const std::vector<int64_t>& repeat_times,
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FDTensor* out) {
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auto rank = x.Shape().size();
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auto repeat_times_size = repeat_times.size();
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rank = (std::max)(rank, repeat_times_size);
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switch (rank) {
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case 0:
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*out = x;
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break;
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case 1:
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TileFunctor<T, 1>(x, repeat_times, out);
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break;
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case 2:
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TileFunctor<T, 2>(x, repeat_times, out);
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break;
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case 3:
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TileFunctor<T, 3>(x, repeat_times, out);
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break;
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case 4:
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TileFunctor<T, 4>(x, repeat_times, out);
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break;
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case 5:
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TileFunctor<T, 5>(x, repeat_times, out);
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break;
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case 6:
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TileFunctor<T, 6>(x, repeat_times, out);
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break;
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}
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}
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void Tile(const FDTensor& x, const std::vector<int64_t>& repeat_times,
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FDTensor* out) {
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FD_VISIT_ALL_TYPES(x.dtype, "TileKernel",
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([&] { TileKernel<data_t>(x, repeat_times, out); }));
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}
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} // namespace function
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} // namespace fastdeploy
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