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Fix pretrained checkpoint loading under torch < 2.4 (weights_only rejects hyper_parameters) #4

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Fix pretrained checkpoint loading under torch < 2.4 (weights_only rejects hyper_parameters) #4

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22 changes: 16 additions & 6 deletions models/checkpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,12 +38,22 @@ def load_checkpoint_state_dict(
torch.serialization.add_safe_globals(
[argparse.Namespace, pathlib.PosixPath, pathlib.WindowsPath]
)

checkpoint = torch.load(
checkpoint_path,
map_location="cpu",
weights_only=True,
)
checkpoint = torch.load(
checkpoint_path,
map_location="cpu",
weights_only=True,
)
else:
# torch < 2.4 has no add_safe_globals, so the weights_only unpickler
# cannot be extended to allow the argparse.Namespace that Lightning
# stores under hyper_parameters. A pretraining checkpoint therefore
# fails to load with weights_only=True. Fall back to a full load; the
# checkpoint is a local, trusted file produced by pretrain.py.
checkpoint = torch.load(
checkpoint_path,
map_location="cpu",
weights_only=False,
)
if not isinstance(checkpoint, Mapping):
raise TypeError("Checkpoint must contain a mapping")

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