From 1d74fbcb2ca280a553e0ecdb018e97b710b0abe0 Mon Sep 17 00:00:00 2001
From: Anwai Archit <anwai.archit@gmail.com>
Date: Tue, 24 Sep 2024 18:05:58 +0200
Subject: [PATCH] Add docstring

---
 micro_sam/instance_segmentation.py | 18 ++++++++++++++++--
 micro_sam/precompute_state.py      |  4 ++--
 micro_sam/util.py                  |  2 +-
 3 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/micro_sam/instance_segmentation.py b/micro_sam/instance_segmentation.py
index 9fc5d33a..5c7e0d9d 100644
--- a/micro_sam/instance_segmentation.py
+++ b/micro_sam/instance_segmentation.py
@@ -1157,8 +1157,22 @@ def automatic_instance_segmentation(
     halo: Optional[Tuple[int, int]] = None,
     use_amg: bool = False,
     **generate_kwargs
-):
-    """
+) -> None:
+    """Run automatic segmentation for the input image.
+
+    Args:
+        input_path: input_path: The input image file(s). Can either be a single image file (e.g. tif or png),
+            or a container file (e.g. hdf5 or zarr).
+        output_path: The output path where the instance segmentations will be saved.
+        embedding_path: The path where the embeddings are cached already / will be saved.
+        model_type: The SegmentAnything model to use. Will use the standard vit_l model by default.
+        checkpoint_path: Path to a checkpoint for a custom model.
+        key: The key to the input file. This is needed for container files (eg. hdf5 or zarr)
+            or to load several images as 3d volume. Provide a glob patterm, eg. "*.tif", for this case.
+        ndim: The dimensionality of the data.
+        tile_shape: Shape of the tiles for tiled prediction. By default prediction is run without tiling.
+        halo: Overlap of the tiles for tiled prediction.
+        use_amg: Whether to use Automatic Mask Generation (AMG) as the automatic segmentation method.
     """
     predictor, state = util.get_sam_model(model_type=model_type, checkpoint_path=checkpoint_path, return_state=True)
 
diff --git a/micro_sam/precompute_state.py b/micro_sam/precompute_state.py
index e4a970b7..520f2bae 100644
--- a/micro_sam/precompute_state.py
+++ b/micro_sam/precompute_state.py
@@ -235,10 +235,10 @@ def precompute_state(
             a container file (e.g. hdf5 or zarr) or a folder with images files.
             In case of a container file the argument `key` must be given. In case of a folder
             it can be given to provide a glob pattern to subselect files from the folder.
-        output_path: The output path were the embeddings and other state will be saved.
+        output_path: The output path where the embeddings and other state will be saved.
         pattern: Glob pattern to select files in a folder. The embeddings will be computed
             for each of these files. To select all files in a folder pass "*".
-        model_type: The SegmentAnything model to use. Will use the standard vit_h model by default.
+        model_type: The SegmentAnything model to use. Will use the standard vit_l model by default.
         checkpoint_path: Path to a checkpoint for a custom model.
         key: The key to the input file. This is needed for contaner files (e.g. hdf5 or zarr)
             or to load several images as 3d volume. Provide a glob pattern, e.g. "*.tif", for this case.
diff --git a/micro_sam/util.py b/micro_sam/util.py
index af1d1dc6..6a4cbc9e 100644
--- a/micro_sam/util.py
+++ b/micro_sam/util.py
@@ -44,7 +44,7 @@
     from tqdm import tqdm
 
 # this is the default model used in micro_sam
-# currently set to the default vit_h
+# currently set to the default vit_l
 _DEFAULT_MODEL = "vit_l"
 
 # The valid model types. Each type corresponds to the architecture of the