mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
yeliang2258
a509dd8ec1
* add smoke model * add 3d vis * update code * update doc * mv paddle3d from detection to perception * update result for velocity * update code for CI * add set input data for TRT backend * add serving support for smoke model * update code * update code * update code --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
93 lines
3.8 KiB
C++
93 lines
3.8 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 BindSmoke(pybind11::module& m) {
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pybind11::class_<vision::perception::SmokePreprocessor,
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vision::ProcessorManager>(m, "SmokePreprocessor")
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.def(pybind11::init<std::string>())
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.def("run", [](vision::perception::SmokePreprocessor& self,
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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(
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"Failed to preprocess the input data in SmokePreprocessor.");
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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::perception::SmokePostprocessor>(m,
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"SmokePostprocessor")
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.def(pybind11::init<>())
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.def("run",
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[](vision::perception::SmokePostprocessor& self,
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std::vector<FDTensor>& inputs) {
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std::vector<vision::PerceptionResult> results;
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if (!self.Run(inputs, &results)) {
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throw std::runtime_error(
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"Failed to postprocess the runtime result in "
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"SmokePostprocessor.");
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}
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return results;
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})
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.def("run", [](vision::perception::SmokePostprocessor& self,
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std::vector<pybind11::array>& input_array) {
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std::vector<vision::PerceptionResult> 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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throw std::runtime_error(
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"Failed to postprocess the runtime result in "
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"SmokePostprocessor.");
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}
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return results;
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});
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pybind11::class_<vision::perception::Smoke, FastDeployModel>(m, "Smoke")
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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",
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[](vision::perception::Smoke& self, pybind11::array& data) {
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auto mat = PyArrayToCvMat(data);
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vision::PerceptionResult 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",
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[](vision::perception::Smoke& self,
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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::PerceptionResult> 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",
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&vision::perception::Smoke::GetPreprocessor)
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.def_property_readonly("postprocessor",
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&vision::perception::Smoke::GetPostprocessor);
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}
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} // namespace fastdeploy
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