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[CVCUDA] Add CV-CUDA support in PaddleSeg (#1761)

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* add cvcuda support in ppseg

* python and pybind

* add resize op, remove concat,std::move

* define resize op
This commit is contained in:
guxukai
2023-04-09 10:38:18 +08:00
committed by GitHub
parent c90aa7bd6f
commit ed19c759df
5 changed files with 128 additions and 96 deletions
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115
fastdeploy/vision/segmentation/ppseg/ppseg_pybind.cc
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@@ -15,44 +15,52 @@
namespace fastdeploy {
void BindPPSeg(pybind11::module& m) {
pybind11::class_<vision::segmentation::PaddleSegPreprocessor>(
m, "PaddleSegPreprocessor")
pybind11::class_<vision::segmentation::PaddleSegPreprocessor,
vision::ProcessorManager>(m, "PaddleSegPreprocessor")
.def(pybind11::init<std::string>())
.def("run",
[](vision::segmentation::PaddleSegPreprocessor& 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])));
}
images.push_back(vision::WrapMat(PyArrayToCvMat(im_list[i])));
}
// Record the shape of input images
std::map<std::string, std::vector<std::array<int, 2>>> imgs_info;
std::vector<FDTensor> outputs;
if (!self.Run(&images, &outputs, &imgs_info)) {
throw std::runtime_error("Failed to preprocess the input data in PaddleSegPreprocessor.");
self.SetImgsInfo(&imgs_info);
if (!self.Run(&images, &outputs)) {
throw std::runtime_error(
"Failed to preprocess the input data in "
"PaddleSegPreprocessor.");
}
for (size_t i = 0; i < outputs.size(); ++i) {
outputs[i].StopSharing();
}
return make_pair(outputs, imgs_info);;
return make_pair(outputs, imgs_info);
;
})
.def("disable_normalize", [](vision::segmentation::PaddleSegPreprocessor& self) {
self.DisableNormalize();
})
.def("disable_permute", [](vision::segmentation::PaddleSegPreprocessor& self) {
self.DisablePermute();
})
.def_property("is_vertical_screen",
&vision::segmentation::PaddleSegPreprocessor::GetIsVerticalScreen,
&vision::segmentation::PaddleSegPreprocessor::SetIsVerticalScreen);
.def("disable_normalize",
[](vision::segmentation::PaddleSegPreprocessor& self) {
self.DisableNormalize();
})
.def("disable_permute",
[](vision::segmentation::PaddleSegPreprocessor& self) {
self.DisablePermute();
})
.def_property(
"is_vertical_screen",
&vision::segmentation::PaddleSegPreprocessor::GetIsVerticalScreen,
&vision::segmentation::PaddleSegPreprocessor::SetIsVerticalScreen);
pybind11::class_<vision::segmentation::PaddleSegModel, FastDeployModel>(
m, "PaddleSegModel")
.def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
ModelFormat>())
.def("clone", [](vision::segmentation::PaddleSegModel& self) {
return self.Clone();
})
.def("clone",
[](vision::segmentation::PaddleSegModel& self) {
return self.Clone();
})
.def("predict",
[](vision::segmentation::PaddleSegModel& self,
pybind11::array& data) {
@@ -62,48 +70,61 @@ void BindPPSeg(pybind11::module& m) {
return res;
})
.def("batch_predict",
[](vision::segmentation::PaddleSegModel& self, std::vector<pybind11::array>& data) {
[](vision::segmentation::PaddleSegModel& 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]));
images.push_back(PyArrayToCvMat(data[i]));
}
std::vector<vision::SegmentationResult> results;
self.BatchPredict(images, &results);
return results;
})
.def_property_readonly("preprocessor", &vision::segmentation::PaddleSegModel::GetPreprocessor)
.def_property_readonly("postprocessor", &vision::segmentation::PaddleSegModel::GetPostprocessor);
.def_property_readonly(
"preprocessor",
&vision::segmentation::PaddleSegModel::GetPreprocessor)
.def_property_readonly(
"postprocessor",
&vision::segmentation::PaddleSegModel::GetPostprocessor);
pybind11::class_<vision::segmentation::PaddleSegPostprocessor>(
m, "PaddleSegPostprocessor")
.def(pybind11::init<std::string>())
.def("run",
[](vision::segmentation::PaddleSegPostprocessor& self,
.def("run",
[](vision::segmentation::PaddleSegPostprocessor& self,
std::vector<FDTensor>& inputs,
const std::map<std::string, std::vector<std::array<int, 2>>>& imgs_info) {
std::vector<vision::SegmentationResult> results;
if (!self.Run(inputs, &results, imgs_info)) {
throw std::runtime_error("Failed to postprocess the runtime result in PaddleSegPostprocessor.");
}
return results;
})
const std::map<std::string, std::vector<std::array<int, 2>>>&
imgs_info) {
std::vector<vision::SegmentationResult> results;
if (!self.Run(inputs, &results, imgs_info)) {
throw std::runtime_error(
"Failed to postprocess the runtime result in "
"PaddleSegPostprocessor.");
}
return results;
})
.def("run",
[](vision::segmentation::PaddleSegPostprocessor& self,
std::vector<pybind11::array>& input_array,
const std::map<std::string, std::vector<std::array<int, 2>>>& imgs_info) {
std::vector<vision::SegmentationResult> results;
std::vector<FDTensor> inputs;
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
if (!self.Run(inputs, &results, imgs_info)) {
throw std::runtime_error("Failed to postprocess the runtime result in PaddleSegPostprocessor.");
}
return results;
})
.def_property("apply_softmax",
&vision::segmentation::PaddleSegPostprocessor::GetApplySoftmax,
&vision::segmentation::PaddleSegPostprocessor::SetApplySoftmax)
.def_property("store_score_map",
&vision::segmentation::PaddleSegPostprocessor::GetStoreScoreMap,
&vision::segmentation::PaddleSegPostprocessor::SetStoreScoreMap);
const std::map<std::string, std::vector<std::array<int, 2>>>&
imgs_info) {
std::vector<vision::SegmentationResult> results;
std::vector<FDTensor> inputs;
PyArrayToTensorList(input_array, &inputs, /*share_buffer=*/true);
if (!self.Run(inputs, &results, imgs_info)) {
throw std::runtime_error(
"Failed to postprocess the runtime result in "
"PaddleSegPostprocessor.");
}
return results;
})
.def_property(
"apply_softmax",
&vision::segmentation::PaddleSegPostprocessor::GetApplySoftmax,
&vision::segmentation::PaddleSegPostprocessor::SetApplySoftmax)
.def_property(
"store_score_map",
&vision::segmentation::PaddleSegPostprocessor::GetStoreScoreMap,
&vision::segmentation::PaddleSegPostprocessor::SetStoreScoreMap);
}
} // namespace fastdeploy