Enable user to load multigradicon.

src/unigradicon/__init__.py

@@ -169,12 +169,30 @@ def make_sim(similarity):
     else:
         raise ValueError(f"Similarity measure {similarity} not recognized. Choose from [lncc, lncc2, mind].")
 
+def get_multigradicon(loss_fn=icon.LNCC(sigma=5)):
+    net = make_network(input_shape, include_last_step=True, loss_fn=loss_fn)
+    from os.path import exists
+    weights_location = "network_weights/multigradicon1.0/Step_2_final.trch"
+    if not exists(weights_location):
+        print("Downloading pretrained multigradicon model")
+        import urllib.request
+        import os
+        download_path = "https://github.com/uncbiag/uniGradICON/releases/download/multigradicon_weights/Step_2_final.trch"
+        os.makedirs("network_weights/multigradicon1.0/", exist_ok=True)
+        urllib.request.urlretrieve(download_path, weights_location)
+    print(f"Loading weights from {weights_location}")
+    trained_weights = torch.load(weights_location, map_location=torch.device("cpu"))
+    net.regis_net.load_state_dict(trained_weights)
+    net.to(config.device)
+    net.eval()
+    return net
+
 def get_unigradicon(loss_fn=icon.LNCC(sigma=5)):
     net = make_network(input_shape, include_last_step=True, loss_fn=loss_fn)
     from os.path import exists
     weights_location = "network_weights/unigradicon1.0/Step_2_final.trch"
     if not exists(weights_location):
-        print("Downloading pretrained model")
+        print("Downloading pretrained unigradicon model")
         import urllib.request
         import os
         download_path = "https://github.com/uncbiag/uniGradICON/releases/download/unigradicon_weights/Step_2_final.trch"
@@ -186,6 +204,14 @@ def get_unigradicon(loss_fn=icon.LNCC(sigma=5)):
     net.eval()
     return net
 
+def get_model_from_model_zoo(model_name="unigradicon", loss_fn=icon.LNCC(sigma=5)):
+    if model_name == "unigradicon":
+        return get_unigradicon(loss_fn)
+    elif model_name == "multigradicon":
+        return get_multigradicon(loss_fn)
+    else:
+        raise ValueError(f"Model {model_name} not recognized. Choose from [unigradicon, multigradicon].")
+
 def quantile(arr: torch.Tensor, q):
     arr = arr.flatten()
     l = len(arr)
@@ -252,10 +278,12 @@ def main():
                          default="50", help="The number of IO iterations. Default is 50. Set to 'None' to disable IO.")
     parser.add_argument("--io_sim", required=False,
                          default="lncc", help="The similarity measure used in IO. Default is LNCC. Choose from [lncc, lncc2, mind].")
+    parser.add_argument("--model", required=False,
+                         default="unigradicon", help="The model to load. Default is unigradicon. Choose from [unigradicon, multigradicon].")
 
     args = parser.parse_args()
 
-    net = get_unigradicon(make_sim(args.io_sim))
+    net = get_model_from_model_zoo(args.model, make_sim(args.io_sim))
 
     fixed = itk.imread(args.fixed)
     moving = itk.imread(args.moving)

tests/test_command_arguments.py

@@ -5,6 +5,7 @@
 
 import subprocess
 import os
+import torch
 
 
 class TestCommandInterface(unittest.TestCase):
@@ -14,6 +15,7 @@ def __init__(self, methodName: str = "runTest") -> None:
         self.test_data_dir = icon_registration.test_utils.TEST_DATA_DIR
         self.test_temp_dir = f"{self.test_data_dir}/temp"
         os.makedirs(self.test_temp_dir, exist_ok=True)
+        self.device = torch.cuda.current_device()
 
     def test_register_unigradicon_inference(self):
         subprocess.run([
@@ -58,8 +60,8 @@ def test_register_unigradicon_inference(self):
 
         # remove temp file
         os.remove(f"{self.test_temp_dir}/transform.hdf5")
-
-    def test_register_unigradicon_io(self):
+    
+    def test_register_multigradicon_inference(self):
         subprocess.run([
             "unigradicon-register",
             "--fixed", f"{self.test_data_dir}/lung_test_data/copd1_highres_EXP_STD_COPD_img.nii.gz",
@@ -68,7 +70,9 @@ def test_register_unigradicon_io(self):
             "--moving", f"{self.test_data_dir}/lung_test_data/copd1_highres_INSP_STD_COPD_img.nii.gz",
             "--moving_modality", "ct",
             "--moving_segmentation", f"{self.test_data_dir}/lung_test_data/copd1_highres_INSP_STD_COPD_label.nii.gz",
-            "--transform_out", f"{self.test_temp_dir}/transform.hdf5"
+            "--transform_out", f"{self.test_temp_dir}/transform.hdf5",
+            "--io_iterations", "None",
+            "--model", "multigradicon"
         ])
 
         # load transform
@@ -97,8 +101,10 @@ def test_register_unigradicon_io(self):
             )
             dists.append(np.sqrt(np.sum((px - py) ** 2)))
         print(np.mean(dists))
-        self.assertLess(np.mean(dists), 1.5)
+        self.assertLess(np.mean(dists), 3.8)
 
         # remove temp file
         os.remove(f"{self.test_temp_dir}/transform.hdf5")
+
+