mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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312e1b097d
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs Co-authored-by: Jason <jiangjiajun@baidu.com>
89 lines
4.5 KiB
C++
Executable File
89 lines
4.5 KiB
C++
Executable File
// 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 BindYOLOv5(pybind11::module& m) {
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pybind11::class_<vision::detection::YOLOv5Preprocessor>(
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m, "YOLOv5Preprocessor")
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.def(pybind11::init<>())
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.def("run", [](vision::detection::YOLOv5Preprocessor& 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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std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
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if (!self.Run(&images, &outputs, &ims_info)) {
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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 make_pair(outputs, ims_info);
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})
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.def_property("size", &vision::detection::YOLOv5Preprocessor::GetSize, &vision::detection::YOLOv5Preprocessor::SetSize)
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.def_property("padding_value", &vision::detection::YOLOv5Preprocessor::GetPaddingValue, &vision::detection::YOLOv5Preprocessor::SetPaddingValue)
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.def_property("is_scale_up", &vision::detection::YOLOv5Preprocessor::GetScaleUp, &vision::detection::YOLOv5Preprocessor::SetScaleUp);
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pybind11::class_<vision::detection::YOLOv5Postprocessor>(
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m, "YOLOv5Postprocessor")
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.def(pybind11::init<>())
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.def("run", [](vision::detection::YOLOv5Postprocessor& self, std::vector<FDTensor>& inputs,
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const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
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std::vector<vision::DetectionResult> results;
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if (!self.Run(inputs, &results, ims_info)) {
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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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.def("run", [](vision::detection::YOLOv5Postprocessor& self, std::vector<pybind11::array>& input_array,
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const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
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std::vector<vision::DetectionResult> 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, ims_info)) {
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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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.def_property("conf_threshold", &vision::detection::YOLOv5Postprocessor::GetConfThreshold, &vision::detection::YOLOv5Postprocessor::SetConfThreshold)
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.def_property("nms_threshold", &vision::detection::YOLOv5Postprocessor::GetNMSThreshold, &vision::detection::YOLOv5Postprocessor::SetNMSThreshold)
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.def_property("multi_label", &vision::detection::YOLOv5Postprocessor::GetMultiLabel, &vision::detection::YOLOv5Postprocessor::SetMultiLabel);
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pybind11::class_<vision::detection::YOLOv5, FastDeployModel>(m, "YOLOv5")
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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::detection::YOLOv5& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::DetectionResult res;
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self.Predict(mat, &res);
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return res;
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})
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.def("batch_predict", [](vision::detection::YOLOv5& 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::DetectionResult> 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::detection::YOLOv5::GetPreprocessor)
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.def_property_readonly("postprocessor", &vision::detection::YOLOv5::GetPostprocessor);
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}
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} // namespace fastdeploy
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