fix tablerec-rare train error

ppocr/modeling/heads/table_att_head.py

@@ -82,7 +82,8 @@ class TableAttentionHead(nn.Layer):
         batch_size = fea.shape[0]
 
         hidden = paddle.zeros((batch_size, self.hidden_size))
-        output_hiddens = paddle.zeros((batch_size, self.max_text_length + 1, self.hidden_size))
+        output_hiddens = paddle.zeros(
+            (batch_size, self.max_text_length + 1, self.hidden_size))
         if self.training and targets is not None:
             structure = targets[0]
             for i in range(self.max_text_length + 1):
@@ -91,19 +92,13 @@ class TableAttentionHead(nn.Layer):
                 (outputs, hidden), alpha = self.structure_attention_cell(
                     hidden, fea, elem_onehots)
                 output_hiddens[:, i, :] = outputs
-                # output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
-            output = paddle.concat(output_hiddens, axis=1)
-            structure_probs = self.structure_generator(output)
-            if self.loc_type == 1:
-                loc_preds = self.loc_generator(output)
-                loc_preds = F.sigmoid(loc_preds)
-            else:
-                loc_fea = fea.transpose([0, 2, 1])
-                loc_fea = self.loc_fea_trans(loc_fea)
-                loc_fea = loc_fea.transpose([0, 2, 1])
-                loc_concat = paddle.concat([output, loc_fea], axis=2)
-                loc_preds = self.loc_generator(loc_concat)
-                loc_preds = F.sigmoid(loc_preds)
+            structure_probs = self.structure_generator(output_hiddens)
+            loc_fea = fea.transpose([0, 2, 1])
+            loc_fea = self.loc_fea_trans(loc_fea)
+            loc_fea = loc_fea.transpose([0, 2, 1])
+            loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
+            loc_preds = self.loc_generator(loc_concat)
+            loc_preds = F.sigmoid(loc_preds)
         else:
             temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
             structure_probs = None
@@ -118,17 +113,15 @@ class TableAttentionHead(nn.Layer):
                 (outputs, hidden), alpha = self.structure_attention_cell(
                     hidden, fea, elem_onehots)
                 output_hiddens[:, i, :] = outputs
-                # output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
                 structure_probs_step = self.structure_generator(outputs)
                 temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
 
-            output = output_hiddens
-            structure_probs = self.structure_generator(output)
+            structure_probs = self.structure_generator(output_hiddens)
             structure_probs = F.softmax(structure_probs)
             loc_fea = fea.transpose([0, 2, 1])
             loc_fea = self.loc_fea_trans(loc_fea)
             loc_fea = loc_fea.transpose([0, 2, 1])
-            loc_concat = paddle.concat([output, loc_fea], axis=2)
+            loc_concat = paddle.concat([output_hiddens, 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}
@@ -203,8 +196,10 @@ class SLAHead(nn.Layer):
         fea = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
 
         hidden = paddle.zeros((batch_size, self.hidden_size))
-        structure_preds = paddle.zeros((batch_size, self.max_text_length + 1, self.num_embeddings))
-        loc_preds = paddle.zeros((batch_size, self.max_text_length + 1, self.loc_reg_num))
+        structure_preds = paddle.zeros(
+            (batch_size, self.max_text_length + 1, self.num_embeddings))
+        loc_preds = paddle.zeros(
+            (batch_size, self.max_text_length + 1, self.loc_reg_num))
         structure_preds.stop_gradient = True
         loc_preds.stop_gradient = True
         if self.training and targets is not None: