After model_dict.update(pretrained_dict), the model_dict may still have keys that pretrained_model doesn’t have, which will cause a error.
Assum following situation:
pretrained_dict: ['A', 'B', 'C', 'D']
model_dict: ['A', 'B', 'C', 'E']

After pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} and model_dict.update(pretrained_dict), they are:
pretrained_dict: ['A', 'B', 'C']
model_dict: ['A', 'B', 'C', 'E']

So when performing model.load_state_dict(pretrained_dict), model_dict still has key E that pretrained_dict doen’t have.
So how about using model.load_state_dict(model_dict) instead of model.load_state_dict(pretrained_dict)?
The complete snippet is therefore as follow:
pretrained_dict = ...
model_dict = model.state_dict()

# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict) 
# 3. load the new state dict
model.load_state_dict(model_dict)