Implement micro-sam training script

scripts/training/README.md

@@ -4,6 +4,7 @@ This folder contains the scripts for training a 3D U-Net for cell segmentation i
 It contains two relevant scripts:
 - `check_training_data.py`, which visualizes the training data and annotations in napari.
 - `train_distance_unet.py`, which trains the 3D U-Net.
+- `train_micro_sam.py`, which fine-tunes a micro-sam model on the data.
 
 Both scripts accept the argument `-i /path/to/data`, to specify the root folder with the training data. For example, run `python train_distance_unet.py -i /path/to/data` for training. The scripts will consider all tif files in the sub-folders of the root folder for training.
 They will load the **image data** according to the following rules:
@@ -12,3 +13,5 @@ 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/`.

scripts/training/train_distance_unet.py

@@ -46,7 +46,6 @@ def select_paths(image_paths, label_paths, split, filter_empty):
     assert len(image_paths) == len(label_paths)
 
     n_files = len(image_paths)
-
     train_fraction = 0.85
 
     n_train = int(train_fraction * n_files)

scripts/training/train_micro_sam.py

@@ -0,0 +1,72 @@
+import argparse
+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
+
+ROOT_CLUSTER = "/scratch-grete/usr/nimcpape/data/moser/lightsheet/training"
+
+
+def raw_transform(x):
+    x = x.astype("float32")
+    min_, max_ = np.percentile(x, 1), np.percentile(x, 99)
+    x -= min_
+    x /= max_
+    x = np.clip(x, 0, 1)
+    return x * 255
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--root", "-i", help="The root folder with the annotated training crops.",
+        default=ROOT_CLUSTER,
+    )
+    parser.add_argument(
+        "--name", help="Optional name for the model to be trained. If not given the current date is used."
+    )
+    parser.add_argument(
+        "--n_objects_per_batch", "-n", type=int, default=15,
+        help="The number of objects to use during training. Set it to a lower value if you run out of GPU memory."
+        "The default value is 15."
+    )
+    args = parser.parse_args()
+
+    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}"
+    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,
+        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,
+        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,
+    )
+
+
+if __name__ == "__main__":
+    main()