[Model] Add Paddle3D smoke model (#1766)

* add smoke model

* add 3d vis

* update code

* update doc

* mv paddle3d from detection to perception

* update result for velocity

* update code for CI

* add set input data for TRT backend

* add serving support for smoke model

* update code

* update code

* update code

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

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

@@ -0,0 +1,106 @@
+# 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 SmokePreprocessor:
+    def __init__(self, config_file):
+        """Create a preprocessor for Smoke
+        """
+        self._preprocessor = C.vision.perception.SmokePreprocessor(config_file)
+
+    def run(self, input_ims):
+        """Preprocess input images for Smoke
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims)
+
+
+class SmokePostprocessor:
+    def __init__(self):
+        """Create a postprocessor for Smoke
+        """
+        self._postprocessor = C.vision.perception.SmokePostprocessor()
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for Smoke
+
+        :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 Smoke(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a SMoke model exported by Smoke.
+
+        :param model_file: (str)Path of model file, e.g ./smoke.pdmodel
+        :param params_file: (str)Path of parameters file, e.g ./smoke.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(Smoke, self).__init__(runtime_option)
+
+        self._model = C.vision.perception.Smoke(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "Smoke initialize failed."
+
+    def predict(self, input_image):
+        """Detect an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param conf_threshold: confidence threshold for postprocessing, default is 0.25
+        :param nms_iou_threshold: iou threshold for NMS, default is 0.5
+        :return: PerceptionResult
+        """
+        return self._model.predict(input_image)
+
+    def batch_predict(self, images):
+        """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
+        :return list of PerceptionResult
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get SmokePreprocessor object of the loaded model
+
+        :return SmokePreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get SmokePostprocessor object of the loaded model
+
+        :return SmokePostprocessor
+        """
+        return self._model.postprocessor