| import torch | |
| from videoretalking.models.DNet import DNet | |
| from videoretalking.models.LNet import LNet | |
| from videoretalking.models.ENet import ENet | |
| def _load(checkpoint_path): | |
| map_location=None if torch.cuda.is_available() else torch.device('cpu') | |
| checkpoint = torch.load(checkpoint_path, map_location=map_location) | |
| return checkpoint | |
| def load_checkpoint(path, model): | |
| print("Load checkpoint from: {}".format(path)) | |
| checkpoint = _load(path) | |
| s = checkpoint["state_dict"] if 'arcface' not in path else checkpoint | |
| new_s = {} | |
| for k, v in s.items(): | |
| if 'low_res' in k: | |
| continue | |
| else: | |
| new_s[k.replace('module.', '')] = v | |
| model.load_state_dict(new_s, strict=False) | |
| return model | |
| def load_network(LNet_path,ENet_path): | |
| L_net = LNet() | |
| L_net = load_checkpoint(LNet_path, L_net) | |
| E_net = ENet(lnet=L_net) | |
| model = load_checkpoint(ENet_path, E_net) | |
| return model.eval() | |
| def load_DNet(DNet_path): | |
| D_Net = DNet() | |
| print("Load checkpoint from: {}".format(DNet_path)) | |
| checkpoint = torch.load(DNet_path, map_location=lambda storage, loc: storage) | |
| D_Net.load_state_dict(checkpoint['net_G_ema'], strict=False) | |
| return D_Net.eval() |