mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
866d044898
* model done, CLA fix * remove letter_box and ConvertAndPermute, use resize hwc2chw and convert in preprocess * remove useless values in preprocess * remove useless values in preprocess * fix reviewed problem * fix reviewed problem pybind * fix reviewed problem pybind * postprocess fix * add test_fastestdet.py, coco_val2017_500 fixed done, ready to review * fix reviewed problem * python/.../fastestdet.py * fix infer.cc, preprocess, python/fastestdet.py * fix examples/python/infer.py
86 lines
4.2 KiB
C++
86 lines
4.2 KiB
C++
// 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 "fastdeploy/pybind/main.h"
|
|
|
|
namespace fastdeploy {
|
|
void BindFastestDet(pybind11::module& m) {
|
|
pybind11::class_<vision::detection::FastestDetPreprocessor>(
|
|
m, "FastestDetPreprocessor")
|
|
.def(pybind11::init<>())
|
|
.def("run", [](vision::detection::FastestDetPreprocessor& self, std::vector<pybind11::array>& im_list) {
|
|
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;
|
|
std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
|
|
if (!self.Run(&images, &outputs, &ims_info)) {
|
|
throw std::runtime_error("raise Exception('Failed to preprocess the input data in FastestDetPreprocessor.')");
|
|
}
|
|
for (size_t i = 0; i < outputs.size(); ++i) {
|
|
outputs[i].StopSharing();
|
|
}
|
|
return make_pair(outputs, ims_info);
|
|
})
|
|
.def_property("size", &vision::detection::FastestDetPreprocessor::GetSize, &vision::detection::FastestDetPreprocessor::SetSize);
|
|
|
|
pybind11::class_<vision::detection::FastestDetPostprocessor>(
|
|
m, "FastestDetPostprocessor")
|
|
.def(pybind11::init<>())
|
|
.def("run", [](vision::detection::FastestDetPostprocessor& self, std::vector<FDTensor>& inputs,
|
|
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
|
|
std::vector<vision::DetectionResult> results;
|
|
if (!self.Run(inputs, &results, ims_info)) {
|
|
throw std::runtime_error("raise Exception('Failed to postprocess the runtime result in FastestDetPostprocessor.')");
|
|
}
|
|
return results;
|
|
})
|
|
.def("run", [](vision::detection::FastestDetPostprocessor& self, std::vector<pybind11::array>& input_array,
|
|
const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
|
|
std::vector<vision::DetectionResult> results;
|
|
std::vector<FDTensor> inputs;
|
|
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
|
|
if (!self.Run(inputs, &results, ims_info)) {
|
|
throw std::runtime_error("raise Exception('Failed to postprocess the runtime result in FastestDetPostprocessor.')");
|
|
}
|
|
return results;
|
|
})
|
|
.def_property("conf_threshold", &vision::detection::FastestDetPostprocessor::GetConfThreshold, &vision::detection::FastestDetPostprocessor::SetConfThreshold)
|
|
.def_property("nms_threshold", &vision::detection::FastestDetPostprocessor::GetNMSThreshold, &vision::detection::FastestDetPostprocessor::SetNMSThreshold);
|
|
|
|
pybind11::class_<vision::detection::FastestDet, FastDeployModel>(m, "FastestDet")
|
|
.def(pybind11::init<std::string, std::string, RuntimeOption,
|
|
ModelFormat>())
|
|
.def("predict",
|
|
[](vision::detection::FastestDet& self, pybind11::array& data) {
|
|
auto mat = PyArrayToCvMat(data);
|
|
vision::DetectionResult res;
|
|
self.Predict(mat, &res);
|
|
return res;
|
|
})
|
|
.def("batch_predict", [](vision::detection::FastestDet& self, std::vector<pybind11::array>& data) {
|
|
std::vector<cv::Mat> images;
|
|
for (size_t i = 0; i < data.size(); ++i) {
|
|
images.push_back(PyArrayToCvMat(data[i]));
|
|
}
|
|
std::vector<vision::DetectionResult> results;
|
|
self.BatchPredict(images, &results);
|
|
return results;
|
|
})
|
|
.def_property_readonly("preprocessor", &vision::detection::FastestDet::GetPreprocessor)
|
|
.def_property_readonly("postprocessor", &vision::detection::FastestDet::GetPostprocessor);
|
|
}
|
|
} // namespace fastdeploy
|