mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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3589c0fa94
* Refactor the PaddleClas module * fix bug * remove debug code * clean unused code * support pybind * Update fd_tensor.h * Update fd_tensor.cc * temporary revert python api * fix ci error * fix code style problem
77 lines
3.6 KiB
C++
77 lines
3.6 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 BindPaddleClas(pybind11::module& m) {
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pybind11::class_<vision::classification::PaddleClasPreprocessor>(
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m, "PaddleClasPreprocessor")
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.def(pybind11::init<std::string>())
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.def("run", [](vision::classification::PaddleClasPreprocessor& 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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pybind11::eval("raise Exception('Failed to preprocess the input data in PaddleClasPreprocessor.')");
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}
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return outputs;
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});
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pybind11::class_<vision::classification::PaddleClasPostprocessor>(
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m, "PaddleClasPostprocessor")
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.def(pybind11::init<int>())
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.def("run", [](vision::classification::PaddleClasPostprocessor& self, std::vector<FDTensor>& inputs) {
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std::vector<vision::ClassifyResult> results;
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if (!self.Run(inputs, &results)) {
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pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleClasPostprocessor.')");
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}
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return results;
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})
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.def("run", [](vision::classification::PaddleClasPostprocessor& self, std::vector<pybind11::array>& input_array) {
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std::vector<vision::ClassifyResult> results;
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std::vector<FDTensor> inputs;
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PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
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if (!self.Run(inputs, &results)) {
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pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleClasPostprocessor.')");
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}
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return results;
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})
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.def_property("topk", &vision::classification::PaddleClasPostprocessor::GetTopk, &vision::classification::PaddleClasPostprocessor::SetTopk);
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pybind11::class_<vision::classification::PaddleClasModel, FastDeployModel>(
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m, "PaddleClasModel")
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.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def("predict", [](vision::classification::PaddleClasModel& self, pybind11::array& data) {
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cv::Mat im = PyArrayToCvMat(data);
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vision::ClassifyResult result;
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self.Predict(im, &result);
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return result;
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})
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.def("batch_predict", [](vision::classification::PaddleClasModel& 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<vision::ClassifyResult> results;
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self.BatchPredict(images, &results);
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return results;
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})
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.def_property_readonly("preprocessor", &vision::classification::PaddleClasModel::GetPreprocessor)
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.def_property_readonly("postprocessor", &vision::classification::PaddleClasModel::GetPostprocessor);
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}
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} // namespace fastdeploy
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