def load_model(path):
model = Wav2Lip()
print("Load checkpoint from: {}".format(path))
model.load_state_dict(
torch.load(path, map_location=lambda storage, loc: storage))
model = model.to(device)
return model.eval()
def load_model(path, device):
model = Wav2Lip()
print("Load checkpoint from: {}".format(path))
checkpoint = __load__(path, device)
s = checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
model.load_state_dict(new_s)
model = model.to(device)
return model.eval()
train_dataset = Dataset('train')
test_dataset = Dataset('val')
train_data_loader = data_utils.DataLoader(train_dataset,
batch_size=hparams.batch_size,
shuffle=True,
num_workers=hparams.num_workers)
test_data_loader = data_utils.DataLoader(test_dataset,
batch_size=hparams.batch_size,
num_workers=4)
device = torch.device("cuda" if use_cuda else "cpu")
# Model
model = Wav2Lip().to(device)
print('total trainable params {}'.format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
lr=hparams.initial_learning_rate)
if args.checkpoint_path is not None:
load_checkpoint(args.checkpoint_path,
model,
optimizer,
reset_optimizer=False)
load_checkpoint(args.syncnet_checkpoint_path,
syncnet,
None,
batch_size=hparams.batch_size,
shuffle=True,
num_workers=hparams.num_workers)
val_data_loader = data_utils.DataLoader(val_dataset,
batch_size=hparams.batch_size,
num_workers=4)
test_data_loader = data_utils.DataLoader(val_dataset,
batch_size=hparams.batch_size,
num_workers=4)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Model
model = Wav2Lip()
disc = SeqNetDisc()
model.to(device)
disc.to(device)
print('total trainable params {}'.format(
sum(p.numel() for p in model.parameters() if p.requires_grad)))
print('total DISC trainable params {}'.format(
sum(p.numel() for p in disc.parameters() if p.requires_grad)))
optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
lr=hparams.initial_learning_rate,
betas=(0.5, 0.999))
disc_optimizer = optim.Adam(
[p for p in disc.parameters() if p.requires_grad],
lr=hparams.disc_initial_learning_rate,
# Dataset and Dataloader setup
train_dataset_left = Dataset('train') # Left stereo image training dataset
test_dataset_left = Dataset('val') # Left Stereo image validation dataset
train_data_loader_Left = data_utils.DataLoader(
train_dataset_left,
batch_size=hparams.batch_size,
shuffle=True,
num_workers=hparams.num_workers)
test_data_loader_Left = data_utils.DataLoader(
test_dataset_left, batch_size=hparams.batch_size, num_workers=4)
device = torch.device("cuda" if use_cuda else "cpu")
model_Wav2Lip = Wav2Lip().to(device)
print('total trainable params {}'.format(
sum(p.numel() for p in model_Wav2Lip.parameters() if p.requires_grad)))
optimizer = optim.Adam(
[p for p in model_Wav2Lip.parameters() if p.requires_grad],
lr=hparams.initial_learning_rate)
if args.checkpoint_path is not None:
load_checkpoint(args.checkpoint_path,
model_Wav2Lip,
optimizer,
reset_optimizer=False)
load_checkpoint(args.syncnet_checkpoint_path,
syncnet,