removed unused packages

model/train.py

@@ -18,7 +18,6 @@ from torch.optim.lr_scheduler import ExponentialLR
 from torch.utils.data import DataLoader
 
 from model.src.utils import VCDADataset, get_class
-from shared.datastore import Datastore
 
 
 def parse_arguments():
@@ -57,19 +56,16 @@ optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
 loss_fn = nn.CrossEntropyLoss()
 
 # create datasets and loaders
-datastore = Datastore()
-datastore.connect()
 with open('model/src/dataset/train.csv', encoding='utf-8') as fh:
     train_data_name_list = fh.read().split('\n')
 train_dataset = VCDADataset(
-    datastore=datastore,
     data_name_list=train_data_name_list,
 )
 train_loader = DataLoader(dataset=train_dataset, num_workers=args.loader_workers)
 
 with open('model/src/dataset/val.csv', encoding='utf-8') as fh:
     val_data_name_list = fh.read().split('\n')
-val_dataset = VCDADataset(datastore=datastore, data_name_list=val_data_name_list)
+val_dataset = VCDADataset(data_name_list=val_data_name_list)
 val_loader = DataLoader(dataset=val_dataset, num_workers=args.loader_workers)
 
 # create trainer and evaluator
@@ -141,7 +137,7 @@ to_save = {
 }
 checkpoint_handler = Checkpoint(
     to_save,
-    f"runs/checkpoints/{run_name}",
+    f'runs/checkpoints/{run_name}',
     n_saved=3,
     filename_prefix='best',
     score_function=lambda engine: -engine.state.metrics['loss'],
@@ -155,7 +151,7 @@ if args.checkpoint:
 
 # save model config
 os.makedirs('runs/configs/', exist_ok=True)
-with open(f"runs/configs/{run_name}.json", 'w', encoding='utf-8') as fh:
+with open(f'runs/configs/{run_name}.json', 'w', encoding='utf-8') as fh:
     json.dump(config, fh)
 
 # start training