Merge pull request #740 from MissPenguin/develop

disable_se & add params for cls in hub serving 

configs/det/det_mv3_db.yml

@@ -49,6 +49,6 @@ 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

configs/det/det_mv3_db_v1.1.yml

+Global:
+  algorithm: DB
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/det_db/
+  save_epoch_step: 200
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
+  train_batch_size_per_card: 16
+  test_batch_size_per_card: 16
+  image_shape: [3, 640, 640]
+  reader_yml: ./configs/det/det_db_icdar15_reader.yml
+  pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
+  checkpoints:
+  save_res_path: ./output/det_db/predicts_db.txt
+  save_inference_dir:
+  
+Architecture:
+  function: ppocr.modeling.architectures.det_model,DetModel
+
+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
+  model_name: large
+  k: 50
+  inner_channels: 96
+  out_channels: 2
+
+Loss:
+  function: ppocr.modeling.losses.det_db_loss,DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+
+Optimizer:
+  function: ppocr.optimizer,AdamDecay
+  base_lr: 0.001
+  beta1: 0.9
+  beta2: 0.999
+
+PostProcess:
+  function: ppocr.postprocess.db_postprocess,DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5

deploy/hubserving/ocr_system/params.py

@@ -10,7 +10,7 @@ class Config(object):
 
 def read_params():
     cfg = Config()
-    
+
     #params for text detector
     cfg.det_algorithm = "DB"
     cfg.det_model_dir = "./inference/ch_det_mv3_db/"
@@ -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(