[Other]Refactor PaddleSeg with preprocessor && postprocessor && support batch (#639)

* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

Co-authored-by: Jason <jiangjiajun@baidu.com>

python/fastdeploy/vision/segmentation/ppseg/__init__.py

@@ -41,35 +41,55 @@ class PaddleSegModel(FastDeployModel):
             model_format)
         assert self.initialized, "PaddleSeg model initialize failed."
 
-    def predict(self, input_image):
+    def predict(self, image):
         """Predict the segmentation result for an input image
 
         :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
         :return: SegmentationResult
         """
-        return self._model.predict(input_image)
+        return self._model.predict(image)
 
-    def disable_normalize_and_permute(self):
-        return self._model.disable_normalize_and_permute()
+    def batch_predict(self, image_list):
+        """Predict the segmentation results for a batch of input image
+        :param image_list: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return list of SegmentationResult
+        """
+        return self._model.batch_predict(image_list)
 
     @property
-    def apply_softmax(self):
-        """Atrribute of PaddleSeg model. Stating Whether applying softmax operator in the postprocess, default value is False
-
-        :return: value of apply_softmax(bool)
+    def preprocessor(self):
+        """Get PaddleSegPreprocessor object of the loaded model
+        :return PaddleSegPreprocessor
         """
-        return self._model.apply_softmax
+        return self._model.preprocessor
 
-    @apply_softmax.setter
-    def apply_softmax(self, value):
-        """Set attribute apply_softmax of PaddleSeg model.
-
-        :param value: (bool)The value to set apply_softmax
+    @property
+    def postprocessor(self):
+        """Get PaddleSegPostprocessor object of the loaded model
+        :return PaddleSegPostprocessor
         """
-        assert isinstance(
-            value,
-            bool), "The value to set `apply_softmax` must be type of bool."
-        self._model.apply_softmax = value
+        return self._model.postprocessor
+
+
+class PaddleSegPreprocessor:
+    def __init__(self, config_file):
+        """Create a preprocessor for PaddleSegModel from configuration file
+        :param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
+        """
+        self._preprocessor = C.vision.segmentation.PaddleSegPreprocessor(
+            config_file)
+
+    def run(self, input_ims):
+        """Preprocess input images for PaddleSegModel
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims)
+
+    def disable_normalize_and_permute(self):
+        """To disable normalize and hwc2chw in preprocessing step.
+        """
+        return self._preprocessor.disable_normalize_and_permute()
 
     @property
     def is_vertical_screen(self):
@@ -77,7 +97,7 @@ class PaddleSegModel(FastDeployModel):
 
         :return: value of is_vertical_screen(bool)
         """
-        return self._model.is_vertical_screen
+        return self._preprocessor.is_vertical_screen
 
     @is_vertical_screen.setter
     def is_vertical_screen(self, value):
@@ -88,4 +108,59 @@ class PaddleSegModel(FastDeployModel):
         assert isinstance(
             value,
             bool), "The value to set `is_vertical_screen` must be type of bool."
-        self._model.is_vertical_screen = value
+        self._preprocessor.is_vertical_screen = value
+
+
+class PaddleSegPostprocessor:
+    def __init__(self, config_file):
+        """Create a postprocessor for PaddleSegModel from configuration file
+        :param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
+        """
+        self._postprocessor = C.vision.segmentation.PaddleSegPostprocessor(
+            config_file)
+
+    def run(self, runtime_results, imgs_info):
+        """Postprocess the runtime results for PaddleSegModel
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :param: imgs_info: The original input images shape info map, key is "shape_info", value is [[image_height, image_width]]
+        :return: list of SegmentationResult(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, imgs_info)
+
+    @property
+    def apply_softmax(self):
+        """Atrribute of PaddleSeg model. Stating Whether applying softmax operator in the postprocess, default value is False
+
+        :return: value of apply_softmax(bool)
+        """
+        return self._postprocessor.apply_softmax
+
+    @apply_softmax.setter
+    def apply_softmax(self, value):
+        """Set attribute apply_softmax of PaddleSeg model.
+
+        :param value: (bool)The value to set apply_softmax
+        """
+        assert isinstance(
+            value,
+            bool), "The value to set `apply_softmax` must be type of bool."
+        self._postprocessor.apply_softmax = value
+
+    @property
+    def store_score_map(self):
+        """Atrribute of PaddleSeg model. Stating Whether storing score map in the SegmentationResult, default value is False
+
+        :return: value of store_score_map(bool)
+        """
+        return self._postprocessor.store_score_map
+
+    @store_score_map.setter
+    def store_score_map(self, value):
+        """Set attribute store_score_map of PaddleSeg model.
+
+        :param value: (bool)The value to set store_score_map
+        """
+        assert isinstance(
+            value,
+            bool), "The value to set `store_score_map` must be type of bool."
+        self._postprocessor.store_score_map = value