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fix: Set torch.load weights_only to True for security #133

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fix: Set torch.load weights_only to True for security #133

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2 changes: 1 addition & 1 deletion demo/gazesam/load_demo_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ def load_depth_model(mode, precision):

encoder = "vitb" # or 'vitb', 'vits'
model = DepthAnything(model_configs[encoder])
model.load_state_dict(torch.load(f"{PYTORCH_MODEL_DIR}/depth_anything_{encoder}14.pth"))
model.load_state_dict(torch.load(f"{PYTORCH_MODEL_DIR}/depth_anything_{encoder}14.pth", weights_only=True))
model = model.eval().cuda()
return model

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2 changes: 1 addition & 1 deletion efficientvit/apps/trainer/base.py
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Expand Up @@ -242,7 +242,7 @@ def sync_model(self):
print("Sync model")
self.save_model(model_name="sync.pt")
dist_barrier()
checkpoint = torch.load(os.path.join(self.checkpoint_path, "sync.pt"), map_location="cpu")
checkpoint = torch.load(os.path.join(self.checkpoint_path, "sync.pt"), map_location="cpu", weights_only=True)
dist_barrier()
if is_master():
os.remove(os.path.join(self.checkpoint_path, "sync.pt"))
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2 changes: 1 addition & 1 deletion efficientvit/models/utils/network.py
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Expand Up @@ -67,7 +67,7 @@ def build_kwargs_from_config(config: dict, target_func: callable) -> dict[str, a

def load_state_dict_from_file(file: str, only_state_dict=True) -> dict[str, torch.Tensor]:
file = os.path.realpath(os.path.expanduser(file))
checkpoint = torch.load(file, map_location="cpu")
checkpoint = torch.load(file, map_location="cpu", weights_only=True)
if only_state_dict and "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
return checkpoint