# Logger
logger.configure_logging(
os.path.abspath(os.path.join(opt.experiment_dir, 'logbook.txt')))
# CUDA
device = 'cuda' if torch.cuda.is_available(
) and not opt.no_cuda else 'cpu'
opt.device = device
if torch.cuda.is_available() and device == 'cpu':
logging.info(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# Adjust scales
utils.adjust_scales2image(opt.img_size, opt)
# Initial parameters
opt.scale_idx = 0
opt.nfc_prev = 0
opt.Noise_Amps = []
# Date
dataset = SingleVideoDataset(opt)
data_loader = DataLoader(dataset,
shuffle=True,
drop_last=True,
batch_size=opt.batch_size,
num_workers=2)
opt.dataset = dataset
opt = parser.parse_args()
if not os.path.exists(opt.out):
os.makedirs(opt.out)
torch.cuda.set_device(opt.gpu_id)
opt.device = "cuda:%s" % opt.gpu_id
opt.niter_init = opt.niter
opt.noise_amp_init = opt.noise_amp_a
opt.nfc_init = opt.nfc
opt.min_nfc_init = opt.min_nfc
opt.scale_factor_init = opt.scale_factor
adjust_scales2image(opt.img_size, opt)
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if torch.cuda.is_available() and opt.gpu_id == -1:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
opt.print_interval = int(opt.print_interval / opt.num_images)
Gs_a = []
reals_a = []
