[Model] Support DINO & DETR and add PaddleDetectionModel class (#1837)

* 添加paddleclas模型

* 更新README_CN

* 更新README_CN

* 更新README

* update get_model.sh

* update get_models.sh

* update paddleseg models

* update paddle_seg models

* update paddle_seg models

* modified test resources

* update benchmark_gpu_trt.sh

* add paddle detection

* add paddledetection to benchmark

* modified benchmark cmakelists

* update benchmark scripts

* modified benchmark function calling

* modified paddledetection documents

* add PaddleDetectonModel

* reset examples/paddledetection

* resolve conflict

* update pybind

* resolve conflict

* fix bug

* delete debug mode

* update checkarch log

* update trt inputs example

* Update README.md

---------

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

python/fastdeploy/vision/detection/ppdet/__init__.py

@@ -800,6 +800,78 @@ class GFL(PPYOLOE):
         assert self.initialized, "GFL model initialize failed."
 
 
+class PaddleDetectionModel(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a PaddleDetectionModel model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g ppyoloe/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g ppyoloe/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+        :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(PaddleDetectionModel, self).__init__(runtime_option)
+
+        self._model = C.vision.detection.PaddleDetectionModel(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PaddleDetectionModel model initialize failed."
+
+    def predict(self, im):
+        """Detect an input image
+
+        :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :return: DetectionResult
+        """
+
+        assert im is not None, "The input image data is None."
+        return self._model.predict(im)
+
+    def batch_predict(self, images):
+        """Detect a batch of input image list
+
+        :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 DetectionResult
+        """
+
+        return self._model.batch_predict(images)
+
+    def clone(self):
+        """Clone PPYOLOE object
+
+        :return: a new PPYOLOE object
+        """
+
+        class PPYOLOEClone(PPYOLOE):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PPYOLOEClone(self._model.clone())
+        return clone_model
+
+    @property
+    def preprocessor(self):
+        """Get PaddleDetPreprocessor object of the loaded model
+
+        :return PaddleDetPreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get PaddleDetPostprocessor object of the loaded model
+
+        :return PaddleDetPostprocessor
+        """
+        return self._model.postprocessor
+
+
+
 class PPYOLOER(PPYOLOE):
     def __init__(self,
                  model_file,