[Model] Support PP-ShiTuV2 models for PaddleClas (#1900)

* [cmake] add faiss.cmake -> pp-shituv2

* [PP-ShiTuV2] Support PP-ShituV2-Det model

* [PP-ShiTuV2] Support PP-ShiTuV2-Det model

* [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support

* [PP-ShiTuV2] Add PPShiTuV2Recognizer c++&python support

* [Bug Fix] fix ppshitu_pybind error

* [benchmark] Add ppshituv2-det c++ benchmark

* [examples] Add PP-ShiTuV2 det & rec examples

* [vision] Update vision classification result

* [Bug Fix] fix trt shapes setting errors

python/fastdeploy/vision/classification/__init__.py

@@ -15,6 +15,10 @@ from __future__ import absolute_import
 
 from .contrib.yolov5cls import YOLOv5Cls
 from .ppcls import *
+from .ppshitu import PPShiTuV2Detector
+from .ppshitu import PPShiTuV2Recognizer
+from .ppshitu import PPShiTuV2RecognizerPreprocessor
+from .ppshitu import PPShiTuV2RecognizerPostprocessor
 from .contrib.resnet import ResNet
 
 PPLCNet = PaddleClasModel

python/fastdeploy/vision/classification/ppshitu/__init__.py

@@ -0,0 +1,131 @@
+from __future__ import absolute_import
+import logging
+from .... import FastDeployModel, ModelFormat
+from .... import c_lib_wrap as C
+from ...common import ProcessorManager
+from ...detection.ppdet import PicoDet
+
+
+class PPShiTuV2Detector(PicoDet):
+    """Detect main body from an input image.
+    """
+    ...
+
+
+class PPShiTuV2RecognizerPreprocessor(ProcessorManager):
+    def __init__(self, config_file):
+        """Create a preprocessor for PPShiTuV2Recognizer from configuration file
+
+        :param config_file: (str)Path of configuration file, e.g PPLCNet/inference_cls.yaml
+        """
+        super(PPShiTuV2RecognizerPreprocessor, self).__init__()
+        self._manager = C.vision.classification.PPShiTuV2RecognizerPreprocessor(
+            config_file)
+
+    def disable_normalize(self):
+        """
+        This function will disable normalize in preprocessing step.
+        """
+        self._manager.disable_normalize()
+
+    def disable_permute(self):
+        """
+        This function will disable hwc2chw in preprocessing step.
+        """
+        self._manager.disable_permute()
+
+    def initial_resize_on_cpu(self, v):
+        """
+        When the initial operator is Resize, and input image size is large,
+        maybe it's better to run resize on CPU, because the HostToDevice memcpy
+        is time consuming. Set this True to run the initial resize on CPU.
+        :param: v: True or False
+        """
+        self._manager.initial_resize_on_cpu(v)
+
+
+class PPShiTuV2RecognizerPostprocessor:
+    def __init__(self, topk=1):
+        """Create a postprocessor for PPShiTuV2Recognizer
+
+        """
+        self._postprocessor = C.vision.classification.PPShiTuV2RecognizerPostprocessor(
+        )
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for PPShiTuV2Recognizer
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :return: list of ClassifyResult, the feature vector is ClassifyResult.feature (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 PPShiTuV2Recognizer(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a image PPShiTuV2Recognizer model exported by PaddleClas.
+
+        :param model_file: (str)Path of model file, e.g PPLCNet/inference.pdmodel
+        :param params_file: (str)Path of parameters file, e.g PPLCNet/inference.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 deploy, e.g PPLCNet/inference_cls.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(PPShiTuV2Recognizer, self).__init__(runtime_option)
+        self._model = C.vision.classification.PPShiTuV2Recognizer(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PPShiTuV2Recognizer model initialize failed."
+
+    def clone(self):
+        """Clone PPShiTuV2Recognizer object
+
+        :return: a new PPShiTuV2Recognizer object
+        """
+
+        class PPShiTuV2RecognizerCloneModel(PPShiTuV2Recognizer):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PPShiTuV2RecognizerCloneModel(self._model.clone())
+        return clone_model
+
+    def predict(self, im):
+        """Extract feature from an input image
+
+        :param im: (numpy.ndarray) The input image data, a 3-D array with layout HWC, BGR format
+        :return: ClassifyResult
+        """
+
+        return self._model.predict(im)
+
+    def batch_predict(self, images):
+        """Extract features from 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 ClassifyResult, the feature vector is ClassifyResult.feature
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get PPShiTuV2RecognizerPreprocessor object of the loaded model
+
+        :return PPShiTuV2RecognizerPreprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get PPShiTuV2RecognizerPostprocessor object of the loaded model
+
+        :return PPShiTuV2RecognizerPostprocessor
+        """
+        return self._model.postprocessor