fix gap in table structure train model and inference model (#4566)

configs/table/table_mv3.yml

 Global:
   use_gpu: true
-  epoch_num: 50
+  epoch_num: 400
   log_smooth_window: 20
   print_batch_step: 5
   save_model_dir: ./output/table_mv3/
-  save_epoch_step: 5
+  save_epoch_step: 3
   # evaluation is run every 400 iterations after the 0th iteration
   eval_batch_step: [0, 400]
   cal_metric_during_train: True
@@ -12,18 +12,17 @@ Global:
   checkpoints: 
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/table/table.jpg
   # for data or label process
   character_dict_path: ppocr/utils/dict/table_structure_dict.txt
   character_type: en
   max_text_length: 100
-  max_elem_length: 500
+  max_elem_length: 800
   max_cell_num: 500
   infer_mode: False
   process_total_num: 0
   process_cut_num: 0
 
-
 Optimizer:
   name: Adam
   beta1: 0.9
@@ -41,13 +40,15 @@ Architecture:
   Backbone:
     name: MobileNetV3
     scale: 1.0
-    model_name: small
-    disable_se: True
+    model_name: large
   Head:
     name: TableAttentionHead
     hidden_size: 256
     l2_decay: 0.00001
     loc_type: 2
+    max_text_length: 100
+    max_elem_length: 800
+    max_cell_num: 500
 
 Loss:
   name: TableAttentionLoss

ppocr/modeling/heads/table_att_head.py

@@ -23,14 +23,22 @@ import numpy as np
 
 
 class TableAttentionHead(nn.Layer):
-    def __init__(self, in_channels, hidden_size, loc_type, in_max_len=488, **kwargs):
+    def __init__(self,
+                 in_channels,
+                 hidden_size,
+                 loc_type,
+                 in_max_len=488,
+                 max_text_length=100,
+                 max_elem_length=800,
+                 max_cell_num=500,
+                 **kwargs):
         super(TableAttentionHead, self).__init__()
         self.input_size = in_channels[-1]
         self.hidden_size = hidden_size
         self.elem_num = 30
-        self.max_text_length = 100
-        self.max_elem_length = 500
-        self.max_cell_num = 500
+        self.max_text_length = max_text_length
+        self.max_elem_length = max_elem_length
+        self.max_cell_num = max_cell_num
 
         self.structure_attention_cell = AttentionGRUCell(
             self.input_size, hidden_size, self.elem_num, use_gru=False)
@@ -42,11 +50,11 @@ class TableAttentionHead(nn.Layer):
             self.loc_generator = nn.Linear(hidden_size, 4)
         else:
             if self.in_max_len == 640:
-                self.loc_fea_trans = nn.Linear(400, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1)
             elif self.in_max_len == 800:
-                self.loc_fea_trans = nn.Linear(625, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1)
             else:
-                self.loc_fea_trans = nn.Linear(256, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1)
             self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
 
     def _char_to_onehot(self, input_char, onehot_dim):
@@ -69,7 +77,7 @@ class TableAttentionHead(nn.Layer):
         output_hiddens = []
         if self.training and targets is not None:
             structure = targets[0]
-            for i in range(self.max_elem_length+1):
+            for i in range(self.max_elem_length + 1):
                 elem_onehots = self._char_to_onehot(
                     structure[:, i], onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer):
             alpha = None
             max_elem_length = paddle.to_tensor(self.max_elem_length)
             i = 0
-            while i < max_elem_length+1:
+            while i < max_elem_length + 1:
                 elem_onehots = self._char_to_onehot(
                     temp_elem, onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -119,7 +127,7 @@ class TableAttentionHead(nn.Layer):
                 loc_concat = paddle.concat([output, loc_fea], axis=2)
                 loc_preds = self.loc_generator(loc_concat)
                 loc_preds = F.sigmoid(loc_preds)
-        return {'structure_probs':structure_probs, 'loc_preds':loc_preds}
+        return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
 
 
 class AttentionGRUCell(nn.Layer):