FIX: explicitly set weights_only to avoid FutureWarning (#193)

* FIX: explicitly set weights_only to avoid FutureWarning

- If I'm understanding the Torch 2.4 changelog correctly, you just need to explicitly pass a value to weights_only. Since the default is alraedy False, I am just explicitly setting it here so this should be backward compatible

* FIX: fix other uses of torch.load in codebase

* DOC: Update changelog

* Update mne_icalabel/iclabel/network/tests/test_network.py

* fix one more

* Change weights_only to True

---------

Co-authored-by: Mathieu Scheltienne <mathieu.scheltienne@gmail.com>
Co-authored-by: Mathieu Scheltienne <mathieu.scheltienne@fcbg.ch>

doc/changes/authors.inc

@@ -1,4 +1,5 @@
 .. _Adam Li: https://github.com/adam2392
 .. _Mathieu Scheltienne: https://github.com/mscheltienne
 .. _Jacob Feitelberg: https://github.com/jacobf18
-.. _Anand Saini: https://github.com/anandsaini024
+.. _Anand Saini: https://github.com/anandsaini024
+.. _Scott Huberty: https://github.com/scott-huberty

doc/changes/latest.rst

@@ -18,3 +18,4 @@ Version 0.7
 ===========
 
 - Raise helpful error message when montage is incomplete (:pr:`181` by `Mathieu Scheltienne`_)
+- Explicitly pass ``weights_only=True`` in all instances of ``torch.load`` used by mne-icalabel, both to suppress a warning in PyTorch 2.4 and to follow best security practices (:pr:`193` by `Scott Huberty`_)

mne_icalabel/iclabel/network/tests/test_network.py

@@ -36,7 +36,7 @@
 
 def test_weights_pytorch():
     """Compare the weights of pytorch model and matconvnet model."""
-    network_python = torch.load(torch_iclabel_path)
+    network_python = torch.load(torch_iclabel_path, weights_only=True)
     network_matlab = loadmat(matconvnet_iclabel_path)
 
     # load weights from matlab network
@@ -119,7 +119,7 @@ def test_network_outputs_pytorch():
 
     # run the forward pass on pytorch
     iclabel_net = ICLabelNet()
-    iclabel_net.load_state_dict(torch.load(torch_iclabel_path))
+    iclabel_net.load_state_dict(torch.load(torch_iclabel_path, weights_only=True))
     torch_labels = iclabel_net(images, psd, autocorr)
     torch_labels = torch_labels.detach().numpy()  # (30, 7)
 

mne_icalabel/iclabel/network/torch.py

@@ -198,7 +198,7 @@ def _run_iclabel(images: ArrayLike, psds: ArrayLike, autocorr: ArrayLike) -> NDA
     # load weights
     network_file = files("mne_icalabel.iclabel.network") / "assets" / "ICLabelNet.pt"
     iclabel_net = ICLabelNet()
-    iclabel_net.load_state_dict(torch.load(network_file))
+    iclabel_net.load_state_dict(torch.load(network_file, weights_only=True))
     # format inputs and run forward pass
     labels = iclabel_net(
         *_format_input_for_torch(*_format_input(images, psds, autocorr))