testing dataset

trainer/trainer.py

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+import os
+import torch.backends.cudnn as cudnn
+import yaml
+from train import train
+from utils import AttrDict
+import pandas as pd
+cudnn.benchmark = True
+cudnn.deterministic = False
+def get_config(file_path):
+    with open(file_path, 'r', encoding="utf8") as stream:
+        opt = yaml.safe_load(stream)
+    opt = AttrDict(opt)
+    if opt.lang_char == 'None':
+        characters = ''
+        for data in opt['select_data'].split('-'):
+            csv_path = os.path.join(opt['train_data'], data, 'labels.csv')
+            df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)
+            all_char = ''.join(df['words'])
+            characters += ''.join(set(all_char))
+        characters = sorted(set(characters))
+        opt.character= ''.join(characters)
+    else:
+        opt.character = opt.number + opt.symbol + opt.lang_char
+    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)
+    return opt
+
+if __name__ == "__main__":
+    # Load configuration
+    opt = get_config("config_files/en_filtered_config.yaml")
+    train(opt, amp=False)