Finsih micro-sam training iteration

scripts/training/README.md

@@ -14,4 +14,4 @@ They will load the **image data** according to the following rules:
 The training script will save the trained model in `checkpoints/cochlea_distance_unet_<CURRENT_DATE>`, e.g. `checkpoints/cochlea_distance_unet_20250115`.
 For further options for the scripts run `python check_training_data.py -h` / `python train_distance_unet.py -h`.
 
-The script `train_micro_sam.py` works similar to the U-Net training script. It saves the finetuned model for annotation with `micro_sam` to `checkpoints/`.
+The script `train_micro_sam.py` works similar to the U-Net training script. It saves the finetuned model for annotation with `micro_sam` to `checkpoints/cochlea_micro_sam_<CURRENT_DATE>`.

scripts/training/train_micro_sam.py

@@ -2,7 +2,6 @@
 from datetime import datetime
 
 import numpy as np
-import torch_em
 from micro_sam.training import default_sam_loader, train_sam
 from train_distance_unet import get_image_and_label_paths, select_paths
 
@@ -36,35 +35,35 @@ def main():
 
     root = args.root
     run_name = datetime.now().strftime("%Y%m%d") if args.name is None else args.name
-    name = f"cochlea_distance_unet_{run_name}"
+    name = f"cochlea_micro_sam_{run_name}"
     n_objects_per_batch = args.n_objects_per_batch
 
     image_paths, label_paths = get_image_and_label_paths(root)
     train_image_paths, train_label_paths = select_paths(image_paths, label_paths, split="train", filter_empty=True)
     val_image_paths, val_label_paths = select_paths(image_paths, label_paths, split="val", filter_empty=True)
 
     patch_shape = (1, 256, 256)
-    sampler = torch_em.data.sampler.MinInstanceSampler(2, min_size=10)
     max_sampling_attempts = 2500
 
     train_loader = default_sam_loader(
         raw_paths=train_image_paths, raw_key=None, label_paths=train_label_paths, label_key=None,
         patch_shape=patch_shape, with_segmentation_decoder=True,
-        raw_transform=raw_transform, sampler=sampler, min_size=10,
+        raw_transform=raw_transform,
         num_workers=6, batch_size=1, is_train=True,
         max_sampling_attempts=max_sampling_attempts,
     )
     val_loader = default_sam_loader(
         raw_paths=val_image_paths, raw_key=None, label_paths=val_label_paths, label_key=None,
         patch_shape=patch_shape, with_segmentation_decoder=True,
-        raw_transform=raw_transform, sampler=sampler, min_size=10,
+        raw_transform=raw_transform,
         num_workers=6, batch_size=1, is_train=False,
         max_sampling_attempts=max_sampling_attempts,
     )
 
     train_sam(
         name=name, model_type="vit_b_lm", train_loader=train_loader, val_loader=val_loader,
         n_epochs=50, n_objects_per_batch=n_objects_per_batch,
+        save_root=".",
     )