rec_r45_abinet.yml add max_length and image_size (#10744)

* rec_r45_abinet.yml add max_length and image_shape

* image_shape to image_size

configs/rec/rec_r45_abinet.yml

@@ -16,7 +16,7 @@ Global:
   # for data or label process
   character_dict_path:
   character_type: en
-  max_text_length: 25
+  max_text_length: &max_text_length 25
   infer_mode: False
   use_space_char: False
   save_res_path: ./output/rec/predicts_abinet.txt
@@ -45,6 +45,8 @@ Architecture:
     name: ABINetHead
     use_lang: True
     iter_size: 3
+    max_length: *max_text_length
+    image_size: [ &h 32, &w 128 ]  # [ h, w ]
 
 
 Loss:
@@ -70,7 +72,7 @@ Train:
       - ABINetLabelEncode: # Class handling label
           ignore_index: *ignore_index
       - ABINetRecResizeImg:
-          image_shape: [3, 32, 128]
+          image_shape: [3, *h, *w]
       - KeepKeys:
           keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
   loader:
@@ -90,7 +92,7 @@ Eval:
       - ABINetLabelEncode: # Class handling label
           ignore_index: *ignore_index
       - ABINetRecResizeImg:
-          image_shape: [3, 32, 128]
+          image_shape: [3, *h, *w]
       - KeepKeys:
           keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
   loader:

ppocr/modeling/heads/rec_abinet_head.py

@@ -182,11 +182,13 @@ class ABINetHead(nn.Layer):
                  dropout=0.1,
                  max_length=25,
                  use_lang=False,
-                 iter_size=1):
+                 iter_size=1,
+                 image_size=(32, 128)):
         super().__init__()
         self.max_length = max_length + 1
+        h, w = image_size[0] // 4, image_size[1] // 4
         self.pos_encoder = PositionalEncoding(
-            dropout=0.1, dim=d_model, max_len=8 * 32)
+            dropout=0.1, dim=d_model, max_len=h * w)
         self.encoder = nn.LayerList([
             TransformerBlock(
                 d_model=d_model,
@@ -199,7 +201,7 @@ class ABINetHead(nn.Layer):
         ])
         self.decoder = PositionAttention(
             max_length=max_length + 1,  # additional stop token
-            mode='nearest', )
+            mode='nearest', h=h, w=w)
         self.out_channels = out_channels
         self.cls = nn.Linear(d_model, self.out_channels)
         self.use_lang = use_lang