polish srn anno

ppocr/modeling/heads/rec_srn_all_head.py

@@ -28,6 +28,13 @@ gradient_clip = 10
 
 
 class SRNPredict(object):
+    """
+    SRN:
+        see arxiv: https://arxiv.org/abs/2003.12294
+    args:
+        params(dict): the super parameters for network build
+    """
+
     def __init__(self, params):
         super(SRNPredict, self).__init__()
         self.char_num = params['char_num']
@@ -39,7 +46,15 @@ class SRNPredict(object):
         self.hidden_dims = params['hidden_dims']
 
     def pvam(self, inputs, others):
+        """
+        Parallel visual attention module model
 
+        args:
+            inputs(variable):  Feature map extracted from backbone network
+            others(list):  Other location information variables
+
+        return: pvam_features
+        """
         b, c, h, w = inputs.shape
         conv_features = fluid.layers.reshape(x=inputs, shape=[-1, c, h * w])
         conv_features = fluid.layers.transpose(x=conv_features, perm=[0, 2, 1])
@@ -98,6 +113,15 @@ class SRNPredict(object):
         return pvam_features
 
     def gsrm(self, pvam_features, others):
+        """
+        Global Semantic Reasonging Module
+
+        args:
+            pvam_features(variable):  Feature map extracted from pvam
+            others(list):  Other location information variables
+
+        return: gsrm_features, word_out, gsrm_out
+        """
 
         #===== GSRM Visual-to-semantic embedding block =====
         b, t, c = pvam_features.shape
@@ -190,7 +214,15 @@ class SRNPredict(object):
         return gsrm_features, word_out, gsrm_out
 
     def vsfd(self, pvam_features, gsrm_features):
+        """
+        Visual-Semantic Fusion Decoder Module
 
+        args:
+            pvam_features(variable):  Feature map extracted from pvam
+            gsrm_features(list):  Feature map extracted from gsrm
+
+        return: fc_out
+        """
         #===== Visual-Semantic Fusion Decoder Module =====
         b, t, c1 = pvam_features.shape
         b, t, c2 = gsrm_features.shape