Datasets:
jblitzar
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github-python-corpus

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help="The index of the label to ignore during the training.")
parser.add_argument("--is-training", action="store_true",
help="Whether to updates the running means and variances during the training.")
parser.add_argument("--learning-rate", type=float, default=LEARNING_RATE,
help="Base learning rate for training with polynomial decay.")
parser.add_argument("--learning-rate-D", type=float, default=LEARNING_RATE_D,
help="Base learning rate for discriminator.")
parser.add_argument("--lambda-seg", type=float, default=LAMBDA_SEG,
help="lambda_seg.")
parser.add_argument("--lambda-adv-target1", type=float, default=LAMBDA_ADV_TARGET1,
help="lambda_adv for adversarial training.")
parser.add_argument("--lambda-adv-target2", type=float, default=LAMBDA_ADV_TARGET2,
help="lambda_adv for adversarial training.")
parser.add_argument("--momentum", type=float, default=MOMENTUM,
help="Momentum component of the optimiser.")
parser.add_argument("--not-restore-last", action="store_true",
help="Whether to not restore last (FC) layers.")
parser.add_argument("--num-classes", type=int, default=NUM_CLASSES,
help="Number of classes to predict (including background).")
parser.add_argument("--num-steps", type=int, default=NUM_STEPS,
help="Number of training steps.")
parser.add_argument("--num-steps-stop", type=int, default=NUM_STEPS_STOP,
help="Number of training steps for early stopping.")
parser.add_argument("--power", type=float, default=POWER,
help="Decay parameter to compute the learning rate.")
parser.add_argument("--random-mirror", action="store_true",
help="Whether to randomly mirror the inputs during the training.")
parser.add_argument("--random-scale", action="store_true",
help="Whether to randomly scale the inputs during the training.")
parser.add_argument("--random-seed", type=int, default=RANDOM_SEED,
help="Random seed to have reproducible results.")
parser.add_argument("--save-num-images", type=int, default=SAVE_NUM_IMAGES,
help="How many images to save.")
parser.add_argument("--save-pred-every", type=int, default=SAVE_PRED_EVERY,
help="Save summaries and checkpoint every often.")
parser.add_argument("--snapshot-dir", type=str, default=SNAPSHOT_DIR,
help="Where to save snapshots of the model.")
parser.add_argument("--weight-decay", type=float, default=WEIGHT_DECAY,
help="Regularisation parameter for L2-loss.")
parser.add_argument("--cpu", action='store_true', help="choose to use cpu device.")
parser.add_argument("--tensorboard", action='store_true', help="choose whether to use tensorboard.")
parser.add_argument("--log-dir", type=str, default=LOG_DIR,
help="Path to the directory of log.")
parser.add_argument("--set", type=str, default=SET,
help="choose adaptation set.")
parser.add_argument("--gpus", type=str, default="0,1", help="selected gpus")
parser.add_argument("--dist", action="store_true", help="DDP")
parser.add_argument("--ngpus_per_node", type=int, default=1, help='number of gpus in each node')
parser.add_argument("--print-every", type=int, default=20, help='output message every n iterations')
parser.add_argument("--src_dataset", type=str, default="gta5", help='training source dataset')
parser.add_argument("--tgt_dataset", type=str, default="cityscapes_train", help='training target dataset')
parser.add_argument("--tgt_val_dataset", type=str, default="cityscapes_val", help='training target dataset')
parser.add_argument("--noaug", action="store_true", help="augmentation")
parser.add_argument('--resize', type=int, default=2200, help='resize long size')
parser.add_argument("--clrjit_params", type=str, default="0.5,0.5,0.5,0.2", help='brightness,contrast,saturation,hue')
parser.add_argument('--rcrop', type=str, default='896,512', help='rondom crop size')
parser.add_argument('--hflip', type=float, default=0.5, help='random flip probility')
parser.add_argument('--src_rootpath', type=str, default='datasets/gta5')
parser.add_argument('--tgt_rootpath', type=str, default='datasets/cityscapes')
parser.add_argument('--noshuffle', action='store_true', help='do not use shuffle')
parser.add_argument('--no_droplast', action='store_true')
parser.add_argument('--pseudo_labels_folder', type=str, default='')
parser.add_argument("--batch_size_val", type=int, default=4, help='batch_size for validation')
parser.add_argument("--resume", type=str, default=RESUME, help='resume weight')
parser.add_argument("--freeze_bn", action="store_true", help="augmentation")
parser.add_argument("--hidden_dim", type=int, default=128, help='number of selected negative samples')
parser.add_argument("--layer", type=int, default=1, help='separate from which layer')
parser.add_argument("--output_folder", type=str, default="", help='output folder')
return parser.parse_args()
args = get_arguments()
def main_worker(gpu, world_size, dist_url):
"""Create the model and start the training."""
if gpu == 0:
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
logFilename = os.path.join(args.snapshot_dir, str(time.time()))
logging.basicConfig(
level = logging.INFO,
format ='%(asctime)s-%(levelname)s-%(message)s',
datefmt = '%y-%m-%d %H:%M',
filename = logFilename,
filemode = 'w+')
filehandler = logging.FileHandler(logFilename, encoding='utf-8')
logger = logging.getLogger()
logger.addHandler(filehandler)
handler = logging.StreamHandler()
logger.addHandler(handler)
logger.info(args)
np.random.seed(args.random_seed)
random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
# torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed_all(args.random_seed) # if you are using multi-GPU.
# torch.backends.cudnn.enabled = False