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87bcb5df21
* add style transfer model * add examples for generation model * add unit test * add speed comparison * add speed comparison * add variable for constant * add preprocessor and postprocessor * add preprocessor and postprocessor * fix * fix according to review Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
78 lines
3.3 KiB
C++
78 lines
3.3 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/pybind/main.h"
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namespace fastdeploy {
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void BindAnimeGAN(pybind11::module& m) {
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pybind11::class_<vision::generation::AnimeGAN, FastDeployModel>(m, "AnimeGAN")
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.def(pybind11::init<std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def("predict",
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[](vision::generation::AnimeGAN& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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cv::Mat res;
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self.Predict(mat, &res);
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auto ret = pybind11::array_t<unsigned char>(
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{res.rows, res.cols, res.channels()}, res.data);
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return ret;
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})
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.def("batch_predict",
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[](vision::generation::AnimeGAN& self, std::vector<pybind11::array>& data) {
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std::vector<cv::Mat> images;
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for (size_t i = 0; i < data.size(); ++i) {
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images.push_back(PyArrayToCvMat(data[i]));
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}
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std::vector<cv::Mat> results;
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self.BatchPredict(images, &results);
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std::vector<pybind11::array_t<unsigned char>> ret;
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for(size_t i = 0; i < results.size(); ++i){
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ret.push_back(pybind11::array_t<unsigned char>(
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{results[i].rows, results[i].cols, results[i].channels()}, results[i].data));
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}
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return ret;
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})
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.def_property_readonly("preprocessor", &vision::generation::AnimeGAN::GetPreprocessor)
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.def_property_readonly("postprocessor", &vision::generation::AnimeGAN::GetPostprocessor);
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pybind11::class_<vision::generation::AnimeGANPreprocessor>(
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m, "AnimeGANPreprocessor")
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.def(pybind11::init<>())
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.def("run", [](vision::generation::AnimeGANPreprocessor& self, std::vector<pybind11::array>& im_list) {
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std::vector<vision::FDMat> images;
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for (size_t i = 0; i < im_list.size(); ++i) {
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images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
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}
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std::vector<FDTensor> outputs;
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if (!self.Run(images, &outputs)) {
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throw std::runtime_error("Failed to preprocess the input data in PaddleClasPreprocessor.");
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}
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for (size_t i = 0; i < outputs.size(); ++i) {
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outputs[i].StopSharing();
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}
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return outputs;
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});
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pybind11::class_<vision::generation::AnimeGANPostprocessor>(
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m, "AnimeGANPostprocessor")
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.def(pybind11::init<>())
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.def("run", [](vision::generation::AnimeGANPostprocessor& self, std::vector<FDTensor>& inputs) {
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std::vector<cv::Mat> results;
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if (!self.Run(inputs, &results)) {
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throw std::runtime_error("Failed to postprocess the runtime result in YOLOv5Postprocessor.");
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}
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return results;
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});
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}
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} // namespace fastdeploy
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