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facenet_pytorch/models/inception_resnet_v1.py

@@ -292,7 +292,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-def load_weights(mdl, name):
+def load_weights(mdl: torch.nn.Module, name: str) -> None:
     """Download pretrained state_dict and load into model.
 
     Arguments:
@@ -304,8 +304,10 @@ def load_weights(mdl, name):
     """
     if name == "vggface2":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt"
+        hash_prefix = "281cebca8662831adb987a874bdcb36e73f5b1c6dc5ee5878f305e985625d99b"
     elif name == "casia-webface":
         path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt"
+        hash_prefix = "7a67afdbbc995fce5e10128675e318799a70698c2f433ba75dd7eb9a2f096e7d"
     else:
         msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'"
         raise ValueError(msg)
@@ -315,7 +317,7 @@ def load_weights(mdl, name):
 
     cached_file = model_dir / Path(path).name
     if not cached_file.exists():
-        download_url_to_file(path, cached_file)
+        download_url_to_file(path, cached_file, hash_prefix)
 
     state_dict = torch.load(cached_file)
     mdl.load_state_dict(state_dict)