Upload directory

aligners/differentiable_face_aligner/dfa/utils/model_utils.py

@@ -0,0 +1,36 @@
+import torch
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+