Removed LR Scheduler, updated args

entcl/cl.py

@@ -71,14 +71,6 @@ def cl_session(args, session_dataset: torch.utils.data.Dataset, model: torch.nn.
         weight_decay=args.weight_decay,
     )
     
-    if not args.no_sched:
-        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer=optimiser,
-            T_max=args.cl_epochs,
-            eta_min=args.lr * 1e-3,
-            verbose=args.verbose,
-        )
-    
     criterion = torch.nn.CrossEntropyLoss()
     
     results = None
@@ -128,8 +120,6 @@ def cl_session(args, session_dataset: torch.utils.data.Dataset, model: torch.nn.
         results.to_csv(f"{args.exp_dir}/results_s{args.current_session}.csv", index=False)
         logger.debug(f"Session {args.current_session} Results saved to {args.exp_dir}/results_s{args.current_session}.csv")
         
-        if not args.no_sched:
-            scheduler.step()
         
     return model
 

entcl/pretrain.py

@@ -33,17 +33,6 @@ def pretrain(args, model):
         weight_decay=args.weight_decay,
     )
     
-    if not args.no_sched:
-        logger.debug("Using Cosine Annealing LR Scheduler")
-        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
-            optimizer=optimiser,
-            T_max=args.pretrain_epochs,
-            eta_min=args.lr * 1e-3,
-            verbose=args.verbose,
-        )
-    else:
-        logger.debug("Not Using Scheduler")
-    
     criterion = torch.nn.CrossEntropyLoss()
     
     results = None
@@ -88,9 +77,6 @@ def pretrain(args, model):
             results.to_csv(f"{args.exp_dir}/results_s0.csv", index=False)
             logger.debug(f"Epoch {epoch} Finished. Pretrain Results Saved to {args.exp_dir}/results_s0.csv")
             
-            if not args.no_sched:
-                scheduler.step()
-            
         return model
     else:
         raise ValueError(f"No Model to load and mode is not pretrain or both. Mode: {args.mode}, Pretrain Load: {args.pretrain_load}")

entcl/run.py

@@ -77,11 +77,10 @@ if __name__ == "__main__":
     parser.add_argument('--dataset', type=str, default='cifar100', help='Dataset to use', choices=['cifar100'])
     
     # optimiser args
-    parser.add_argument('--lr', type=float, default=0.1, help='Learning Rate for all optimisers')
-    parser.add_argument('--gamma', type=float, default=0.1, help='Gamma for all optimisers')
-    parser.add_argument('--momentum', type=float, default=0.9, help='Momentum for all optimisers')
-    parser.add_argument('--weight_decay', type=float, default=5e-5, help='Weight Decay for all optimisers')
-    parser.add_argument('--no_sched', action='store_true', help='Do not use a scheduler')
+    parser.add_argument('--pretrain_lr', type=float, default=1e-3, help='Learning Rate for Pretraining Optimiser')
+    parser.add_argument('--cl_lr', type=float, default=1e-6, help='Learning Rate for CL Optimiser')
+    parser.add_argument('--momentum', type=float, default=0, help='Momentum for all optimisers')
+    parser.add_argument('--weight_decay', type=float, default=0, help='Weight Decay for all optimisers')
     
     # cl args
     parser.add_argument('--known', type=int, default=50, help='Number of known classes. The rest are novel classes')
@@ -96,21 +95,19 @@ if __name__ == "__main__":
     
     # pretrain args
     parser.add_argument('--pretrain_epochs', type=int, default=100, help='Number of epochs for pretraining')
-    parser.add_argument('--pretrain_sel_strat', type=str, default='last', choices=['last', 'best'], help='Pretrain Model Selection Strategy')
     parser.add_argument('--pretrain_load', type=str, default=None, help='Path to a pretrained model to load')
     
-    parser.add_argument('--retain_all', action='store_true', default=False, help='Keep all model checkpoints')
-    
     # model args
     parser.add_argument('--head', type=str, default='linear', help='Classification head to use', choices=['linear','mlp', 'dino_head'])
     parser.add_argument("--backbone", type=int, default=1, help="Version of DINO to use", choices=[1, 2])
     
     # ood args
-    parser.add_argument('--ood_score', type=str, default='entropy', help='Changes the metric(s) to base OOD detection on', choices=['entropy', 'energy', 'both'])
+    parser.add_argument('--ood_score', type=str, default='entropy', help='Changes the metric(s) to base OOD detection on', choices=['entropy', 'energy', 'both', 'cheat'])
     parser.add_argument('--ood_eps', type=float, default=1e-8, help='Epsilon value for computing entropy in OOD detection')
     
     # ncd args
-    parser.add_argument('--ncd_findk_method', type=str, default='cheat', help='Method to use for finding the number of novel classes', choices=['elbow', 'silhouette', 'gap', 'cheat'])
+    parser.add_argument('--cheat_ncd', action='store_true', default=False, help='Cheat NCD. Use the true labels for NCD')
+    
     args = parser.parse_args()