PaddleX/deploy/ultra-infer/ultra_infer/vision/faceid/contrib/adaface/adaface_pybind.cc

// 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 "ultra_infer/pybind/main.h"

namespace ultra_infer {
void BindAdaFace(pybind11::module &m) {
  pybind11::class_<vision::faceid::AdaFacePreprocessor>(m,
                                                        "AdaFacePreprocessor")
      .def(pybind11::init())
      .def("run",
           [](vision::faceid::AdaFacePreprocessor &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 AdaFacePreprocessor.");
             }
             for (size_t i = 0; i < outputs.size(); ++i) {
               outputs[i].StopSharing();
             }
             return outputs;
           })
      .def_property("permute", &vision::faceid::AdaFacePreprocessor::GetPermute,
                    &vision::faceid::AdaFacePreprocessor::SetPermute)
      .def_property("alpha", &vision::faceid::AdaFacePreprocessor::GetAlpha,
                    &vision::faceid::AdaFacePreprocessor::SetAlpha)
      .def_property("beta", &vision::faceid::AdaFacePreprocessor::GetBeta,
                    &vision::faceid::AdaFacePreprocessor::SetBeta)
      .def_property("size", &vision::faceid::AdaFacePreprocessor::GetSize,
                    &vision::faceid::AdaFacePreprocessor::SetSize);

  pybind11::class_<vision::faceid::AdaFacePostprocessor>(m,
                                                         "AdaFacePostprocessor")
      .def(pybind11::init())
      .def("run",
           [](vision::faceid::AdaFacePostprocessor &self,
              std::vector<FDTensor> &inputs) {
             std::vector<vision::FaceRecognitionResult> results;
             if (!self.Run(inputs, &results)) {
               throw std::runtime_error("Failed to postprocess the runtime "
                                        "result in AdaFacePostprocessor.");
             }
             return results;
           })
      .def("run",
           [](vision::faceid::AdaFacePostprocessor &self,
              std::vector<pybind11::array> &input_array) {
             std::vector<vision::FaceRecognitionResult> 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 AdaFacePostprocessor.");
             }
             return results;
           })
      .def_property("l2_normalize",
                    &vision::faceid::AdaFacePostprocessor::GetL2Normalize,
                    &vision::faceid::AdaFacePostprocessor::SetL2Normalize);

  pybind11::class_<vision::faceid::AdaFace, UltraInferModel>(m, "AdaFace")
      .def(pybind11::init<std::string, std::string, RuntimeOption,
                          ModelFormat>())
      .def("predict",
           [](vision::faceid::AdaFace &self, pybind11::array &data) {
             cv::Mat im = PyArrayToCvMat(data);
             vision::FaceRecognitionResult result;
             self.Predict(im, &result);
             return result;
           })
      .def("batch_predict",
           [](vision::faceid::AdaFace &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::FaceRecognitionResult> results;
             self.BatchPredict(images, &results);
             return results;
           })
      .def_property_readonly("preprocessor",
                             &vision::faceid::AdaFace::GetPreprocessor)
      .def_property_readonly("postprocessor",
                             &vision::faceid::AdaFace::GetPostprocessor);
}
} // namespace ultra_infer