all ready

configs/rec/rec_r50_fpn_srn.yml

@@ -3,7 +3,7 @@ Global:
   epoch_num: 72
   log_smooth_window: 20
   print_batch_step: 5
-  save_model_dir: ./output/rec/srn
+  save_model_dir: ./output/rec/srn_new
   save_epoch_step: 3
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [0, 5000]
@@ -25,8 +25,10 @@ Global:
 
 Optimizer:
   name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 10.0
   lr:
-    name: Cosine
     learning_rate: 0.0001
 
 Architecture:
@@ -58,7 +60,6 @@ Train:
   dataset:
     name: LMDBDataSet
     data_dir: ./train_data/srn_train_data_duiqi
-    #label_file_list: ["./train_data/ic15_data/1.txt"]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR
@@ -77,7 +78,7 @@ Train:
   loader:
     shuffle: False
     batch_size_per_card: 64
-    drop_last: True
+    drop_last: False
     num_workers: 4
 
 Eval:

ppocr/modeling/heads/self_attention.py

@@ -359,6 +359,7 @@ class PrepareDecoder(nn.Layer):
         self.emb0 = paddle.nn.Embedding(
             num_embeddings=src_vocab_size,
             embedding_dim=self.src_emb_dim,
+            padding_idx=bos_idx,
             weight_attr=paddle.ParamAttr(
                 name=word_emb_param_name,
                 initializer=nn.initializer.Normal(0., src_emb_dim**-0.5)))

ppocr/postprocess/rec_postprocess.py

@@ -182,14 +182,15 @@ class SRNLabelDecode(BaseRecLabelDecode):
 
         preds_prob = np.reshape(preds_prob, [-1, 25])
 
-        text = self.decode(preds_idx, preds_prob)
+        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
 
         if label is None:
+            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
             return text
-        label = self.decode(label, is_remove_duplicate=False)
+        label = self.decode(label, is_remove_duplicate=True)
         return text, label
 
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=True):
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
         """ convert text-index into text-label. """
         result_list = []
         ignored_tokens = self.get_ignored_tokens()

tools/program.py

@@ -242,6 +242,12 @@ def train(config,
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
+                model_average = paddle.optimizer.ModelAverage(
+                    0.15,
+                    parameters=model.parameters(),
+                    min_average_window=10000,
+                    max_average_window=15625)
+                model_average.apply()
                 cur_metirc = eval(model, valid_dataloader, post_process_class,
                                   eval_class)
                 cur_metirc_str = 'cur metirc, {}'.format(', '.join(
@@ -277,6 +283,7 @@ def train(config,
                                           best_model_dict[main_indicator],
                                           global_step)
             global_step += 1
+            optimizer.clear_grad()
             batch_start = time.time()
         if dist.get_rank() == 0:
             save_model(