add rec_nrtr

ppocr/utils/dict_99.txt

+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+<
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+\
+]
+^
+_
+`
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+{
+|
+}
+~
+ 
\ No newline at end of file

tools/eval.py

@@ -22,6 +22,7 @@ import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
 from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
@@ -30,6 +31,7 @@ from ppocr.utils.save_load import init_model
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
+
 def main():
     global_config = config['Global']
     # build dataloader

tools/program.py

@@ -186,7 +186,7 @@ def train(config,
     model.train()
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
-
+    use_nrtr = config['Architecture']['algorithm'] == "NRTR"
     if 'start_epoch' in best_model_dict:
         start_epoch = best_model_dict['start_epoch']
     else:
@@ -211,6 +211,9 @@ def train(config,
                 others = batch[-4:]
                 preds = model(images, others)
                 model_average = True
+            elif use_nrtr:
+                max_len = batch[2].max()
+                preds = model(images, batch[1][:,:2+max_len])
             else:
                 preds = model(images)
             loss = loss_class(preds, batch)
@@ -350,13 +353,11 @@ def eval(model, valid_dataloader, post_process_class, eval_class,
                 break
             images = batch[0]
             start = time.time()
-
             if use_srn:
                 others = batch[-4:]
                 preds = model(images, others)
             else:
                 preds = model(images)
-
             batch = [item.numpy() for item in batch]
             # Obtain usable results from post-processing methods
             post_result = post_process_class(preds, batch[1])
@@ -386,7 +387,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-        'CLS', 'PGNet', 'Distillation'
+        'CLS', 'PGNet', 'Distillation','NRTR'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'