fix gap between table structure train model and inference model (#4565)

* add indent in pipeline_rpc_client.py

* fix gap in table structure train model and inference model

configs/table/table_mv3.yml

@@ -1,10 +1,10 @@
 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)