updates on train 3d without decoer

development/train_3d_model_with_lucchi_without_decoder.py

@@ -177,7 +177,7 @@ def train(args):
     #label_transform = torch_em.transform.label.BoundaryTransform(add_binary_target=False)
     label_transform = torch_em.transform.label.MinSizeLabelTransform
     ndim = 2
-    min_size = 50
+    min_size = 100
     max_sampling_attempts = 5000
 
     if with_rois:
@@ -198,7 +198,8 @@ def train(args):
         #rois=np.s_[64:, :, :],
         #n_samples=200,
     )
-    train_ds.max_sampling_attempts = max_sampling_attempts
+    for ds in train_ds.datasets:
+        ds.max_sampling_attempts = max_sampling_attempts
     train_loader = get_data_loader(train_ds, shuffle=True, batch_size=2)
 
     val_ds = default_sam_dataset(
@@ -212,7 +213,8 @@ def train(args):
         is_train=False, 
         #n_samples=25,
     )
-    val_ds.max_sampling_attempts = max_sampling_attempts
+    for ds in val_ds.datasets:
+        ds.max_sampling_attempts = max_sampling_attempts
     val_loader = get_data_loader(val_ds, shuffle=True, batch_size=1)
 
     #check_loader(train_loader, n_samples=3)
@@ -221,7 +223,7 @@ def train(args):
     # breakpoint()
 
     train_sam(
-        name="mito_model", model_type="vit_b",
+        name=args.exp_name, model_type="vit_b",
         train_loader=train_loader, val_loader=val_loader,
         n_epochs=50, n_objects_per_batch=10,
         with_segmentation_decoder=False,
@@ -252,7 +254,7 @@ def main():
         help="The filepath to where the logs and the checkpoints will be saved."
     )
     parser.add_argument(
-        "--exp_name", default="vitb_3d_lora4-microsam-hypam-lucchi",
+        "--exp_name", default="vitb_3d-mitotomo",
         help="The filepath to where the logs and the checkpoints will be saved."
     )
     parser.add_argument("--without_rois", action="store_true", help="Train without Regions Of Interest (ROI)")