def load_model_and_optimizer_loc(self,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer = None,
model_location=None):
with open(model_location, 'r') as f:
model_checkpoint = torch.load(f)
model.load_state_dict(model_checkpoint['state_dict'])
if optimizer is not None:
optimizer.load_state_dict(model_checkpoint['optimizer'])
return model, optimizer, model_checkpoint['acc']
