updated dataset configs

conf/dataset/default.yaml

@@ -2,4 +2,5 @@ irl_pc: /media/faizan/2418a65c-11bc-4f64-bf23-ecd60d93cf53/faizan/Faizan_thesis/
 laptop_pc: "E:/Brats23 Data/Dataset/BraTS23_mapped/dataset"
 colab: "/gdrive/MyDrive/BraTS2023/"
 sines_pc: /drive/faizanai.rrl/datasets
-dataset_folder: # specify your dataset path here if any of the above does not relate to you
+dataset_folder: # specify your dataset path here if any of the above does not relate to you
+version:  brats2023

conf/model/default.yaml

@@ -1,3 +1,3 @@
 roi: 128
 infer_overlap: 0.6
-architecture: scfe_net
+architecture: segres_net

conf/training/default.yaml

@@ -10,7 +10,7 @@ solver_name: Adam
 loss_type: dice
 pretrained: False
 resume: False
-exp_name: scfe_net_runs
+exp_name: segres_net_runs
 seed: 123
 colab: False
 irl: True

test.py

@@ -44,7 +44,7 @@
     from thesis.models.v3.model import SCFENet
 except ModuleNotFoundError:
     print('model not available, please train with other models')
-    sys.exit(1)
+    # sys.exit(1)
 
 from functools import partial
 
@@ -143,7 +143,7 @@ def test(args, data_loader, model):
 def main(cfg: DictConfig):
     # Select model
 
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     # Efficient training
     torch.backends.cudnn.benchmark = True
@@ -267,13 +267,14 @@ def main(cfg: DictConfig):
     batch_size = cfg.test.batch
     workers = cfg.test.workers
     dataset_folder = cfg.dataset.irl_pc
+    dataset_version = cfg.dataset.version
 
     # Load checkpoints
-    model.load_state_dict(torch.load(cfg.test.weights, weights_only=True))
+    model.load_state_dict(torch.load(cfg.test.weights, weights_only=True, map_location=device))
     model.eval()
 
     # Load dataset
-    test_loader = get_datasets(dataset_folder=dataset_folder, mode="test", target_size=(128, 128, 128))
+    test_loader = get_datasets(dataset_folder=dataset_folder, mode="test", target_size=(128, 128, 128), version=dataset_version)
     test_loader = torch.utils.data.DataLoader(test_loader, 
                                             batch_size=batch_size, 
                                             shuffle=False, num_workers=workers, 

utils/all_utils.py

@@ -40,7 +40,7 @@ def save_seg_csv(csv, args):
         columns = ['id', 'et_dice', 'tc_dice', 'wt_dice', 'et_hd', 'tc_hd', 'wt_hd', 'et_sens', 'tc_sens', 'wt_sens', 'et_spec', 'tc_spec', 'wt_spec']
         save_path = os.path.join(args.training.exp_name, "csv")
         os.makedirs(save_path, exist_ok= True)
-        csv_path = os.path.join(save_path, "test_metrics.csv")
+        csv_path = os.path.join(save_path, f"{args.model.architecture}_test_metrics.csv")
         val_metrics.to_csv(csv_path, index=False, columns=columns)
     except KeyboardInterrupt:
         print("Save CSV File Error!")