_, _, loader = iPERLoader(data_root=args.path, batch=args.batch_size, transform=transform).data_load()
# model for image
model_img = AppVQVAE().to(device)
model_img = nn.DataParallel(model_img).to(device)
if is_load_model_img is True:
print('Loading model_img ...', end='')
model_img.load_state_dict(torch.load(args.model_img_path))
model_img.eval()
print('Done')
else:
print('model_img Initialized.')
optimizer_img = optim.Adam(model_img.parameters(), lr=args.lr)
# model for condition
model_cond = VQVAE().to(device)
model_cond = nn.DataParallel(model_cond).cuda()
if is_load_model_cond is True:
print('Loading model_cond ...', end='')
model_cond.load_state_dict(torch.load(args.model_cond_path))
model_cond.eval()
print('Done')
else:
print('model_cond Initialized.')
optimizer_cond = optim.Adam(model_cond.parameters(), lr=args.lr)
# transfer model
model_transfer = TransferModel().to(device)
model_transfer = nn.DataParallel(model_transfer).to(device)
if is_load_model_transfer is True:
print('Loading model_transfer ...', end='')
transform=transform).data_load()
# model for image
model_img = AppVQVAE().to(device)
model_img = nn.DataParallel(model_img).to(device)
if is_load_model_img is True:
print('Loading model_img ...', end='')
model_img.load_state_dict(torch.load(args.model_img_path))
model_img.eval()
print('Done')
else:
print('model_img Initialized.')
optimizer_img = optim.Adam(model_img.parameters(), lr=args.lr)
# model for condition
model_cond = VQVAE().to(device)
model_cond = nn.DataParallel(model_cond).cuda()
if is_load_model_cond is True:
print('Loading model_cond ...', end='')
model_cond.load_state_dict(torch.load(args.model_cond_path))
model_cond.eval()
print('Done')
else:
print('model_cond Initialized.')
optimizer_cond = optim.Adam(model_cond.parameters(), lr=args.lr)
# transfer model
model_transfer = TransferModel().to(device)
model_transfer = nn.DataParallel(model_transfer).to(device)
if is_load_model_transfer is True:
print('Loading model_transfer ...', end='')