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- // Copyright (c) 2022 PaddlePaddle Authors. 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.
- #include "ultra_infer/pybind/main.h"
- namespace ultra_infer {
- void BindCaddn(pybind11::module &m) {
- pybind11::class_<vision::perception::CaddnPreprocessor,
- vision::ProcessorManager>(m, "CaddnPreprocessor")
- .def(pybind11::init<std::string>())
- .def("run",
- [](vision::perception::CaddnPreprocessor &self,
- std::vector<pybind11::array> &im_list,
- std::vector<float> &cam_data, std::vector<float> &lidar_data) {
- std::vector<vision::FDMat> images;
- for (size_t i = 0; i < im_list.size(); ++i) {
- images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
- }
- std::vector<FDTensor> outputs;
- if (!self.Run(&images, cam_data, lidar_data, &outputs)) {
- throw std::runtime_error(
- "Failed to preprocess the input data in CaddnPreprocessor.");
- }
- for (size_t i = 0; i < outputs.size(); ++i) {
- outputs[i].StopSharing();
- }
- return outputs;
- });
- pybind11::class_<vision::perception::CaddnPostprocessor>(m,
- "CaddnPostprocessor")
- .def(pybind11::init<>())
- .def("run",
- [](vision::perception::CaddnPostprocessor &self,
- std::vector<FDTensor> &inputs) {
- std::vector<vision::PerceptionResult> results;
- if (!self.Run(inputs, &results)) {
- throw std::runtime_error(
- "Failed to postprocess the runtime result in "
- "CaddnPostprocessor.");
- }
- return results;
- })
- .def("run", [](vision::perception::CaddnPostprocessor &self,
- std::vector<pybind11::array> &input_array) {
- std::vector<vision::PerceptionResult> results;
- std::vector<FDTensor> inputs;
- PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
- if (!self.Run(inputs, &results)) {
- throw std::runtime_error(
- "Failed to postprocess the runtime result in "
- "CaddnPostprocessor.");
- }
- return results;
- });
- pybind11::class_<vision::perception::Caddn, UltraInferModel>(m, "Caddn")
- .def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
- ModelFormat>())
- .def("predict",
- [](vision::perception::Caddn &self, pybind11::array &data,
- std::vector<float> &cam_data, std::vector<float> &lidar_data) {
- auto mat = PyArrayToCvMat(data);
- vision::PerceptionResult res;
- self.Predict(mat, cam_data, lidar_data, &res);
- return res;
- })
- .def("batch_predict",
- [](vision::perception::Caddn &self,
- std::vector<pybind11::array> &data, std::vector<float> &cam_data,
- std::vector<float> &lidar_data) {
- std::vector<cv::Mat> images;
- for (size_t i = 0; i < data.size(); ++i) {
- images.push_back(PyArrayToCvMat(data[i]));
- }
- std::vector<vision::PerceptionResult> results;
- self.BatchPredict(images, cam_data, lidar_data, &results);
- return results;
- })
- .def_property_readonly("preprocessor",
- &vision::perception::Caddn::GetPreprocessor)
- .def_property_readonly("postprocessor",
- &vision::perception::Caddn::GetPostprocessor);
- }
- } // namespace ultra_infer
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