removed hard coded class number

Signed-off-by: elitap <[email protected]>

Author: elitap

training/example_train_script.sh

@@ -8,4 +8,5 @@ python main_2pt5d.py --max_epochs 100 --val_every 1 --optim_lr 0.000005 \
 --logdir finetune_ckpt_example --point_prompt --label_prompt --distributed --seed 12346 \
 --iterative_training_warm_up_epoch 50 --reuse_img_embedding \
 --label_prompt_warm_up_epoch 25 \
---checkpoint ./runs/9s_2dembed_model.pt
+--checkpoint ./runs/9s_2dembed_model.pt \
+--num_classes 105

training/main_2pt5d.py

@@ -113,6 +113,8 @@
 parser.add_argument("--skip_bk", action="store_true", help="skip background (0) during training")
 parser.add_argument("--patch_embed_3d", action="store_true", help="using 3d patch embedding layer")
 
+parser.add_argument("--num_classes", default=105, type=int, help="number of output classes")
+
 
 def start_tb(log_dir):
     cmd = ["tensorboard", "--logdir", log_dir]
@@ -123,6 +125,10 @@ def main():
     args = parser.parse_args()
     args.amp = not args.noamp
     args.logdir = "./runs/" + args.logdir
+    
+    if args.num_classes == 0:
+        warnings.warn("consider setting the correct number of classes")
+     
     # start_tb(args.logdir)
     if args.seed > -1:
         set_determinism(seed=args.seed)
@@ -135,6 +141,9 @@ def main():
         main_worker(gpu=0, args=args)
 
 
+
+
+
 def main_worker(gpu, args):
     if args.distributed:
         torch.multiprocessing.set_start_method("fork", force=True)
@@ -162,7 +171,7 @@ def main_worker(gpu, args):
 
     dice_loss = DiceCELoss(sigmoid=True)
 
-    post_label = AsDiscrete(to_onehot=105)
+    post_label = AsDiscrete(to_onehot=args.num_classes)
     post_pred = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
     dice_acc = DiceMetric(include_background=False, reduction=MetricReduction.MEAN, get_not_nans=True)
 

training/trainer_2pt5d.py

@@ -129,7 +129,7 @@ def prepare_sam_training_input(inputs, labels, args, model):
         unique_labels = unique_labels[: args.num_prompt]
 
     # add 4 background labels to every batch
-    background_labels = list(set([i for i in range(1, 105)]) - set(unique_labels.cpu().numpy()))
+    background_labels = list(set([i for i in range(1, args.num_classes)]) - set(unique_labels.cpu().numpy()))
     random.shuffle(background_labels)
     unique_labels = torch.cat([unique_labels, torch.tensor(background_labels[:4]).cuda(args.rank)])
 
@@ -375,7 +375,7 @@ def train_epoch_iterative(model, loader, optimizer, scaler, epoch, loss_func, ar
 
 
 def prepare_sam_test_input(inputs, labels, args, previous_pred=None):
-    unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
+    unique_labels = torch.tensor([i for i in range(1, args.num_classes)]).cuda(args.rank)
 
     # preprocess make the size of lable same as high_res_logit
     batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
@@ -400,7 +400,7 @@ def prepare_sam_test_input(inputs, labels, args, previous_pred=None):
 
 def prepare_sam_val_input_cp_only(inputs, labels, args):
     # Don't exclude background in val but will ignore it in metric calculation
-    unique_labels = torch.tensor([i for i in range(1, 105)]).cuda(args.rank)
+    unique_labels = torch.tensor([i for i in range(1, args.num_classes)]).cuda(args.rank)
 
     # preprocess make the size of lable same as high_res_logit
     batch_labels = torch.stack([labels == unique_labels[i] for i in range(len(unique_labels))], dim=0).float()
@@ -460,7 +460,7 @@ def val_epoch(model, loader, epoch, acc_func, args, iterative=False, post_label=
                 acc_batch = compute_dice(y_pred=y_pred, y=target)
                 acc_sum, not_nans = (
                     torch.nansum(acc_batch).item(),
-                    104 - torch.sum(torch.isnan(acc_batch).float()).item(),
+                    (args.num_classes-1) - torch.sum(torch.isnan(acc_batch).float()).item(),
                 )
                 acc_sum_total += acc_sum
                 not_nans_total += not_nans