fix codestyle

python/examples/ocr/ocr_web_server.py

@@ -27,6 +27,7 @@ import time
 import re
 import base64
 
+
 class OCRService(WebService):
     def init_det_client(self, det_port, det_client_config):
         self.det_preprocess = Sequential([
@@ -46,7 +47,7 @@ class OCRService(WebService):
         ori_h, ori_w, _ = im.shape
         det_img = self.det_preprocess(im)
         det_out = self.det_client.predict(
-                feed={"image": det_img}, fetch=["concat_1.tmp_0"])
+            feed={"image": det_img}, fetch=["concat_1.tmp_0"])
         _, new_h, new_w = det_img.shape
         filter_func = FilterBoxes(10, 10)
         post_func = DBPostProcess({

python/paddle_serving_app/local_predict.py

@@ -130,5 +130,6 @@ class Debugger(object):
             fetch_map[name] = outputs[self.fetch_names_to_idx_[
                 name]].as_ndarray()
             if len(outputs[self.fetch_names_to_idx_[name]].lod) > 0:
-                fetch_map[name+".lod"] = outputs[self.fetch_names_to_idx_[name]].lod[0]
+                fetch_map[name + ".lod"] = outputs[self.fetch_names_to_idx_[
+                    name]].lod[0]
         return fetch_map

python/paddle_serving_app/reader/image_reader.py

@@ -781,10 +781,12 @@ class Transpose(object):
                         "({})".format(self.transpose_target)
         return format_string
 
+
 class SortedBoxes(object):
     """
     Sorted bounding boxes from Detection
     """
+
     def __init__(self):
         pass
 
@@ -798,12 +800,14 @@ class SortedBoxes(object):
                 tmp = _boxes[i]
                 _boxes[i] = _boxes[i + 1]
                 _boxes[i + 1] = tmp
-        return _boxes       
+        return _boxes
+
 
 class GetRotateCropImage(object):
     """
     Rotate and Crop image from OCR Det output
     """
+
     def __init__(self):
         pass
 

python/paddle_serving_app/reader/ocr_reader.py

@@ -120,7 +120,12 @@ class CharacterOps(object):
 
 
 class OCRReader(object):
-    def __init__(self, algorithm="CRNN", image_shape=[3,32,320], char_type="ch", batch_num=1, char_dict_path="./ppocr_keys_v1.txt"):
+    def __init__(self,
+                 algorithm="CRNN",
+                 image_shape=[3, 32, 320],
+                 char_type="ch",
+                 batch_num=1,
+                 char_dict_path="./ppocr_keys_v1.txt"):
         self.rec_image_shape = image_shape
         self.character_type = char_type
         self.rec_batch_num = batch_num
@@ -129,7 +134,7 @@ class OCRReader(object):
         char_ops_params["character_dict_path"] = char_dict_path
         char_ops_params['loss_type'] = 'ctc'
         self.char_ops = CharacterOps(char_ops_params)
-    
+
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         if self.character_type == "ch":
@@ -151,7 +156,6 @@ class OCRReader(object):
         padding_im[:, :, 0:resized_w] = resized_image
         return padding_im
 
-
     def preprocess(self, img_list):
         img_num = len(img_list)
         norm_img_batch = []
@@ -180,14 +184,15 @@ class OCRReader(object):
             end = rec_idx_lod[rno + 1]
             if isinstance(rec_idx_batch, list):
                 rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
-            else: #nd array
+            else:  #nd array
                 rec_idx_tmp = rec_idx_batch[beg:end, 0]
             preds_text = self.char_ops.decode(rec_idx_tmp)
             if with_score:
                 beg = predict_lod[rno]
                 end = predict_lod[rno + 1]
                 if isinstance(outputs["softmax_0.tmp_0"], list):
-                    outputs["softmax_0.tmp_0"] = np.array(outputs["softmax_0.tmp_0"]).astype(np.float32)
+                    outputs["softmax_0.tmp_0"] = np.array(outputs[
+                        "softmax_0.tmp_0"]).astype(np.float32)
                 probs = outputs["softmax_0.tmp_0"][beg:end, :]
                 ind = np.argmax(probs, axis=1)
                 blank = probs.shape[1]

python/paddle_serving_server_gpu/web_service.py

@@ -189,7 +189,8 @@ class WebService(object):
     def _launch_local_predictor(self, gpu):
         from paddle_serving_app.local_predict import Debugger
         self.client = Debugger()
-        self.client.load_model_config("{}".format(self.model_config), gpu=gpu, profile=False)
+        self.client.load_model_config(
+            "{}".format(self.model_config), gpu=gpu, profile=False)
 
     def run_web_service(self):
         self.app_instance.run(host="0.0.0.0",