refactor post processing seg

src/autoseg/postprocess/segment_mws.py

@@ -1,15 +1,26 @@
 # UTILIZES RUSTY_MWS, HGLOM FOR ALL SEGMENTATION, ORIGINALLY WRITTEN BY BRIAN REICHER (2023)
-
+import torch
 import rusty_mws
 import hglom
 
 from ..utils import neighborhood
 from ..predict.network_predictions import predict_task
+from ..networks.FLibUNet import setup_unet
+from ..models import *
+from ..losses import *
+
+
+def _setup_model(network=setup_unet(), model=MTLSDModel):
+    model = model(unet=network, num_fmaps=network.out_channels)
+    return model
 
 
 def get_validation_segmentation(
     segmentation_style: str = "mws",
+    model=_setup_model(),
+    model_type="MTLSD",
     iteration="latest",
+    model_path="./",
     raw_file="../../data/xpress-challenge.zarr",
     raw_dataset="volumes/validation_raw",
     out_file="./validation.zarr",
@@ -44,7 +55,10 @@ def get_validation_segmentation(
 
     if pred_affs:
         predict_task(  # Raw --> Affinities
+            model=model,
+            model_type=model_type,
             iteration=iteration,
+            model_path=model_path,
             raw_file=raw_file,
             raw_dataset=raw_dataset,
             out_file=out_file,

src/autoseg/predict/network_predictions.py

@@ -30,7 +30,7 @@ def predict_task(
     voxel_size: int = 33,
 ) -> None:
     """
-    Predict affinities using a trained deep learning model.
+    Predict affinities using a trained learning model.
 
     Parameters:
         model: