[Sync][Internal] sync some internal paddle3d codes (#2108)

python/fastdeploy/vision/perception/__init__.py

@@ -16,3 +16,4 @@ from __future__ import absolute_import
 from .paddle3d.smoke import *
 from .paddle3d.petr import *
 from .paddle3d.centerpoint import *
+from .paddle3d.caddn import *

python/fastdeploy/vision/perception/paddle3d/caddn.py

@@ -0,0 +1,108 @@
+# 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.
+
+from __future__ import absolute_import
+import logging
+from .... import FastDeployModel, ModelFormat
+from .... import c_lib_wrap as C
+
+
+class CaddnPreprocessor:
+    def __init__(self, config_file):
+        """Create a preprocessor for Caddn
+        """
+        self._preprocessor = C.vision.perception.CaddnPreprocessor(config_file)
+
+    def run(self, input_ims, cam_data, lidar_data):
+        """Preprocess input images for Caddn
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims, cam_data, lidar_data)
+
+
+class CaddnPostprocessor:
+    def __init__(self):
+        """Create a postprocessor for Caddn
+        """
+        self._postprocessor = C.vision.perception.CaddnPostprocessor()
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for Caddn
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :return: list of PerceptionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
+        """
+        return self._postprocessor.run(runtime_results)
+
+
+class Caddn(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a Caddn model exported by Caddn.
+
+        :param model_file: (str)Path of model file, e.g ./Caddn.pdmodel
+        :param params_file: (str)Path of parameters file, e.g ./Caddn.pdiparams
+        :param config_file: (str)Path of config file, e.g ./infer_cfg.yaml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+        super(Caddn, self).__init__(runtime_option)
+
+        self._model = C.vision.perception.Caddn(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "Caddn initialize failed."
+
+    def predict(self, input_image, cam_data, lidar_data):
+        """Detect an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param: cam_data: (list)The input camera data
+        :param: lidar_data: (list)The input lidar data
+        :return: PerceptionResult
+        """
+        return self._model.predict(input_image, cam_data, lidar_data)
+
+    def batch_predict(self, images, cam_data, lidar_data):
+        """Classify a batch of input image
+
+        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :param: cam_data: (list)The input camera data
+        :param: lidar_data: (list)The input lidar data
+        :return list of PerceptionResult
+        """
+
+        return self._model.batch_predict(images, cam_data, lidar_data)
+
+    @property
+    def preprocessor(self):
+        """Get CaddnPreprocessor object of the loaded model
+
+        :return CaddnPreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get CaddnPostprocessor object of the loaded model
+
+        :return CaddnPostprocessor
+        """
+        return self._model.postprocessor