RuntimeError about deep-text-recognition-benchmark HOT 1 OPEN

I added the "--workers" to execute the code to solve this issue, but a new error message appeared.

dataset_root: lmdb/training
opt.select_data: ['MJ', 'ST']
opt.batch_ratio: ['0.5', '0.5']

dataset_root: lmdb/training dataset: MJ
sub-directory: /MJ num samples: 1000
num total samples of MJ: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of MJ per batch: 192 x 0.5 (batch_ratio) = 96

dataset_root: lmdb/training dataset: ST
sub-directory: /ST num samples: 1000
num total samples of ST: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of ST per batch: 192 x 0.5 (batch_ratio) = 96

Total_batch_size: 96+96 = 192

dataset_root: lmdb/validation dataset: /
sub-directory: /MJ num samples: 1000
sub-directory: /ST num samples: 1000

No Transformation module specified
model input parameters 32 100 20 1 512 256 63 25 None VGG BiLSTM CTC
loading pretrained model from None-VGG-BiLSTM-CTC.pth
Traceback (most recent call last):
File "train.py", line 317, in
train(opt)
File "train.py", line 84, in train
model.load_state_dict(torch.load(opt.saved_model, map_location=torch.device('cpu')), strict=False)
File "C:\Users\user\anaconda3\envs\EasyOCR\lib\site-packages\torch\nn\modules\module.py", line 1672, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DataParallel:
size mismatch for module.Prediction.weight: copying a param with shape torch.Size([37, 256]) from checkpoint, the shape in current model is torch.Size([63, 256]).
size mismatch for module.Prediction.bias: copying a param with shape torch.Size([37]) from checkpoint, the shape in current model is torch.Size([63]).

How can I resolve this issue?

from deep-text-recognition-benchmark.