disable se & add cls params for hub serving

configs/det/det_mv3_db.yml

@@ -24,6 +24,7 @@ Backbone:
   function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
   scale: 0.5
   model_name: large
+  disable_se: true
 
 Head:
   function: ppocr.modeling.heads.det_db_head,DBHead
@@ -49,6 +50,6 @@ Optimizer:
 PostProcess:
   function: ppocr.postprocess.db_postprocess,DBPostProcess
   thresh: 0.3
-  box_thresh: 0.7
+  box_thresh: 0.6
   max_candidates: 1000
-  unclip_ratio: 2.0
+  unclip_ratio: 1.5

deploy/hubserving/ocr_system/params.py

@@ -38,6 +38,13 @@ def read_params():
     cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
     cfg.use_space_char = True
 
+    #params for text classifier
+    cfg.use_angle_cls = False
+    cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v1.1_cls_infer/"
+    cfg.cls_image_shape = "3, 48, 192"
+    cfg.label_list = ['0', '180']
+    cfg.cls_batch_num = 30
+
     cfg.use_zero_copy_run = False
 
     return cfg

ppocr/modeling/backbones/det_mobilenet_v3.py

@@ -79,6 +79,8 @@ class MobileNetV3():
         assert self.scale in supported_scale, \
             "supported scale are {} but input scale is {}".format(supported_scale, self.scale)
 
+        self.disable_se = params.get('disable_se', False)
+        
     def __call__(self, input):
         scale = self.scale
         inplanes = self.inplanes
@@ -232,7 +234,7 @@ class MobileNetV3():
             num_groups=num_mid_filter,
             use_cudnn=False,
             name=name + '_depthwise')
-        if use_se:
+        if use_se and not self.disable_se:
             conv1 = self.se_block(
                 input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
 

ppocr/modeling/heads/det_db_head.py

@@ -123,6 +123,13 @@ class DBHead(object):
         return fluid.layers.reciprocal(1 + fluid.layers.exp(-self.k * (x - y)))
 
     def __call__(self, conv_features, mode="train"):
+        """
+        Fuse different levels of feature map from backbone in the FPN manner. 
+        Args:
+            conv_features(list): feature maps from backbone
+            mode(str): runtime mode, can be "train", "eval" or "test"
+        Return: predicts
+        """
         c2, c3, c4, c5 = conv_features
         param_attr = fluid.initializer.MSRAInitializer(uniform=False)
         in5 = fluid.layers.conv2d(