Update segmentation related documentation and ignore model loading warnings

Author: constantinpape

flamingo_tools/segmentation/unet_prediction.py

@@ -1,5 +1,6 @@
 import multiprocessing as mp
 import os
+import warnings
 from concurrent import futures
 
 import imageio.v3 as imageio
@@ -37,10 +38,12 @@ def ndim(self):
 
 
 def prediction_impl(input_path, input_key, output_folder, model_path, scale, block_shape, halo):
-    if os.path.isdir(model_path):
-        model = load_model(model_path)
-    else:
-        model = torch.load(model_path)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        if os.path.isdir(model_path):
+            model = load_model(model_path)
+        else:
+            model = torch.load(model_path)
 
     mask_path = os.path.join(output_folder, "mask.zarr")
     image_mask = z5py.File(mask_path, "r")["mask"]

scripts/README.md

@@ -36,7 +36,7 @@ You can run it like this for an input volume that is stored in n5, e.g. the fuse
 python run_prediction_distance_unet.py -i /path/to/volume.n5 -k setup0/timepoint0/s0 -m /path/to/model -o /path/to/output_folder
 ```
 Here, `-i` specifies the input filepath, `-o` the folder where the results are saved and `-k` the internal path in the n5 file.
-The `-m` argument specifies the model to use for prediction. You need to give the path to the folder that contains the checkpoint (the `best.pt` file).
+The `-m` argument specifies the filepath to the model for prediction. You need to give the path to the folder that contains the checkpoint (the `best.pt` file).
 
 You can also run the script for a tif file. In this case you don't need the `-k` parameter:
 ```