[Model] Refactor PaddleClas module (#505)

* Refactor the PaddleClas module

* fix bug

* remove debug code

* clean unused code

* support pybind

* Update fd_tensor.h

* Update fd_tensor.cc

* temporary revert python api

* fix ci error

* fix code style problem
This commit is contained in:
Jason
2022-11-07 19:33:47 +08:00
committed by GitHub
parent a0a8ace174
commit 3589c0fa94
15 changed files with 527 additions and 142 deletions

View File

@@ -15,16 +15,62 @@
namespace fastdeploy {
void BindPaddleClas(pybind11::module& m) {
pybind11::class_<vision::classification::PaddleClasPreprocessor>(
m, "PaddleClasPreprocessor")
.def(pybind11::init<std::string>())
.def("run", [](vision::classification::PaddleClasPreprocessor& 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)) {
pybind11::eval("raise Exception('Failed to preprocess the input data in PaddleClasPreprocessor.')");
}
return outputs;
});
pybind11::class_<vision::classification::PaddleClasPostprocessor>(
m, "PaddleClasPostprocessor")
.def(pybind11::init<int>())
.def("run", [](vision::classification::PaddleClasPostprocessor& self, std::vector<FDTensor>& inputs) {
std::vector<vision::ClassifyResult> results;
if (!self.Run(inputs, &results)) {
pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleClasPostprocessor.')");
}
return results;
})
.def("run", [](vision::classification::PaddleClasPostprocessor& self, std::vector<pybind11::array>& input_array) {
std::vector<vision::ClassifyResult> results;
std::vector<FDTensor> inputs;
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
if (!self.Run(inputs, &results)) {
pybind11::eval("raise Exception('Failed to postprocess the runtime result in PaddleClasPostprocessor.')");
}
return results;
})
.def_property("topk", &vision::classification::PaddleClasPostprocessor::GetTopk, &vision::classification::PaddleClasPostprocessor::SetTopk);
pybind11::class_<vision::classification::PaddleClasModel, FastDeployModel>(
m, "PaddleClasModel")
.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
ModelFormat>())
.def("predict", [](vision::classification::PaddleClasModel& self,
pybind11::array& data, int topk = 1) {
auto mat = PyArrayToCvMat(data);
vision::ClassifyResult res;
self.Predict(&mat, &res, topk);
return res;
});
.def("predict", [](vision::classification::PaddleClasModel& self, pybind11::array& data) {
cv::Mat im = PyArrayToCvMat(data);
vision::ClassifyResult result;
self.Predict(im, &result);
return result;
})
.def("batch_predict", [](vision::classification::PaddleClasModel& 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::ClassifyResult> results;
self.BatchPredict(images, &results);
return results;
})
.def_property_readonly("preprocessor", &vision::classification::PaddleClasModel::GetPreprocessor)
.def_property_readonly("postprocessor", &vision::classification::PaddleClasModel::GetPostprocessor);
}
} // namespace fastdeploy