added pgt_coeff to argparser

run_pgt_train.py

@@ -10,34 +10,24 @@ import torch
 
 def main(args):
     model = YOLOv10PGT('yolov10n.pt')
-    model.train(    
-                data=args.data_yaml, 
-                epochs=args.epochs, 
-                batch=args.batch_size,
-                # amp=False,
-                pgt_coeff=3.0,
-                # cfg='pgt_train.yaml',  # Load and train model with the config file
-                )
+
+    if args.pgt_coeff is None:
+        model.train(data=args.data_yaml, epochs=args.epochs, batch=args.batch_size)
+    else:
+        model.train(    
+                    data=args.data_yaml, 
+                    epochs=args.epochs, 
+                    batch=args.batch_size,
+                    # amp=False,
+                    pgt_coeff=args.pgt_coeff,
+                    # cfg='pgt_train.yaml',  # Load and train model with the config file
+                    )
     # If you want to finetune the model with pretrained weights, you could load the 
     # pretrained weights like below 
     # model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
     # or
     # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
     # model = YOLOv10('yolov10n.pt', task='segment')
-    # model = YOLOv10('yolov10n.pt', task='segment')
-
-    # args_dict = dict(
-    #             model='yolov10n.pt', 
-    #             data=args.data_yaml, 
-    #             epochs=args.epochs, batch=args.batch_size,
-    #             # pgt_coeff=5.0,
-    #             # cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
-    #             )
-    # trainer = PGTSegmentationTrainer(overrides=args_dict)
-    # trainer.train(
-    #             # debug=True, 
-    #             # args = dict(pgt_coeff=0.1), # Should add later to config
-    #             )
 
     # Create a directory to save model weights if it doesn't exist
     model_weights_dir = 'model_weights'
@@ -63,6 +53,7 @@ if __name__ == "__main__":
     parser.add_argument('--batch_size', type=int, default=32, help='Batch size for training')
     parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
     parser.add_argument('--data_yaml', type=str, default='coco.yaml', help='Path to the data YAML file')
+    parser.add_argument('--pgt_coeff', type=float, default=None, help='Coefficient for PGT')
     args = parser.parse_args()
 
     # Set CUDA device (only needed for multi-gpu machines)