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FastDeploy/fastdeploy/vision/detection/contrib/rknpu2/rkyolo_pybind.cc
Zheng-Bicheng 6a3ac91057 [Model] Update rkyolo pybind (#1294)
更新rkyolo pybind
2023-02-11 09:09:53 +08:00

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4.5 KiB
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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 "fastdeploy/pybind/main.h"
namespace fastdeploy {
void BindRKYOLO(pybind11::module& m) {
pybind11::class_<vision::detection::RKYOLOPreprocessor>(
m, "RKYOLOPreprocessor")
.def(pybind11::init<>())
.def("run", [](vision::detection::RKYOLOPreprocessor& 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;
if (!self.Run(&images, &outputs)) {
throw std::runtime_error("Failed to preprocess the input data in PaddleClasPreprocessor.");
}
for (size_t i = 0; i < outputs.size(); ++i) {
outputs[i].StopSharing();
}
return outputs;
})
.def_property("size", &vision::detection::RKYOLOPreprocessor::GetSize,
&vision::detection::RKYOLOPreprocessor::SetSize)
.def_property("padding_value", &vision::detection::RKYOLOPreprocessor::GetPaddingValue,
&vision::detection::RKYOLOPreprocessor::SetPaddingValue)
.def_property("is_scale_up", &vision::detection::RKYOLOPreprocessor::GetScaleUp,
&vision::detection::RKYOLOPreprocessor::SetScaleUp);
pybind11::class_<vision::detection::RKYOLOPostprocessor>(
m, "RKYOLOPostprocessor")
.def(pybind11::init<>())
.def("run", [](vision::detection::RKYOLOPostprocessor& self,
std::vector<FDTensor>& inputs) {
std::vector<vision::DetectionResult> results;
if (!self.Run(inputs, &results)) {
throw std::runtime_error("Failed to postprocess the runtime result in RKYOLOV5Postprocessor.");
}
return results;
})
.def("run", [](vision::detection::RKYOLOPostprocessor& self,
std::vector<pybind11::array>& input_array) {
std::vector<vision::DetectionResult> 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 RKYOLOV5Postprocessor.");
}
return results;
})
.def_property("conf_threshold", &vision::detection::RKYOLOPostprocessor::GetConfThreshold,
&vision::detection::RKYOLOPostprocessor::SetConfThreshold)
.def_property("nms_threshold", &vision::detection::RKYOLOPostprocessor::GetNMSThreshold,
&vision::detection::RKYOLOPostprocessor::SetNMSThreshold)
.def_property("class_num", &vision::detection::RKYOLOPostprocessor::GetClassNum,
&vision::detection::RKYOLOPostprocessor::SetClassNum);
pybind11::class_<vision::detection::RKYOLOV5, FastDeployModel>(m, "RKYOLOV5")
.def(pybind11::init<std::string,
RuntimeOption,
ModelFormat>())
.def("predict",
[](vision::detection::RKYOLOV5& self,
pybind11::array& data) {
auto mat = PyArrayToCvMat(data);
vision::DetectionResult res;
self.Predict(mat, &res);
return res;
})
.def("batch_predict", [](vision::detection::RKYOLOV5& 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::RKYOLOV5::GetPreprocessor)
.def_property_readonly("postprocessor", &vision::detection::RKYOLOV5::GetPostprocessor);
}
} // namespace fastdeploy