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opt.py
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import argparse
def get_opts():
parser = argparse.ArgumentParser()
parser.add_argument('--num_gpus', type=int, default=1,
help='number of gpus')
parser.add_argument('--batch', type=int, default=2,
help='number of batch to be used in training')
parser.add_argument('--n_classes', type=int, default=8,
help='number of classes to be used in training')
# parser.add_argument('--num_gpus', type=int, default=1,
# help='number of gpus')
# parser.add_argument('--batch', type=int, default=4,
# help='number of batch to be used in training')
# parser.add_argument('--ckpt_path', type=str,
# default='/home/sj/workspace/jupyter/data/lightning-ce-net/ckpts/u-net/epoch=03.ckpt',
# help='pretrained checkpoint path to load')
parser.add_argument('--ckpt_path', type=str,
default='',
help='pretrained checkpoint path to load')
parser.add_argument('--load_ckpt', type=str,
default='',
choices=['params', 'model'],
help='which type to load .pth')
parser.add_argument('--imgs_dir', type=str,
# default='/home/sj/workspace/m/MA_NET/LITS/train/train_small/',
default='/opt/data/private/data/chao_data/Train_Sets/',
# default='/home/sj/workspace/data/chao_data/Train_Sets/',
# default='/home/sj/workspace/data/LITS-Challenge-Train-Data/data/',
help='image directory of history dataset')
parser.add_argument('--masks_dir', type=str,
# default='/home/sj/workspace/m/MA_NET/LITS/train/target_small/',
default='/opt/data/private/data/chao_data/Train_Sets/',
# default='/home/sj/workspace/data/chao_data/Train_Sets/',
# default='/home/sj/workspace/data/LITS-Challenge-Train-Data/label/',
help='image directory of history masks dataset')
parser.add_argument('--prefixes_to_ignore', nargs='+', type=str, default=['loss'],
help='the prefixes to ignore in the checkpoint state dict')
parser.add_argument('--val_percent', type=float, default=0.1,
help='number of validation percent to be used in training')
parser.add_argument('--lr', type=float, default=1e-3,
help='number of lr to be used in training')
parser.add_argument('--momentum', type=float, default=0.9,
help='learning rate momentum')
parser.add_argument('--num_epochs', type=int, default=2000,
help='number of training epochs')
parser.add_argument('--exp_name', type=str, default='scaa',
help='experiment name')
parser.add_argument('--use_amp', default=False, action="store_true",
help='use mixed precision training (NOT SUPPORTED!)')
# parser.add_argument('--dataset_name', type=str, default='dtu',
# choices=['dtu', 'blendedmvs'],
# help='which dataset to history/val')
# parser.add_argument('--n_views', type=int, default=3,
# help='number of views (including ref) to be used in training')
# parser.add_argument('--levels', type=int, default=3, choices=[3],
# help='number of FPN levels (fixed to be 3!)')
# parser.add_argument('--depth_interval', type=float, default=2.65,
# help='depth interval for the finest level, unit in mm')
# parser.add_argument('--n_depths', nargs='+', type=int, default=[8, 32, 48],
# help='number of depths in each level')
# parser.add_argument('--interval_ratios', nargs='+', type=float, default=[1.0, 2.0, 4.0],
# help='depth interval ratio to multiply with --depth_interval in each level')
# parser.add_argument('--num_groups', type=int, default=1, choices=[1, 2, 4, 8],
# help='number of groups in groupwise correlation, must be a divisor of 8')
# parser.add_argument('--loss_type', type=str, default='sl1',
# choices=['sl1'],
# help='loss to use')
#
# parser.add_argument('--batch_size', type=int, default=1,
# help='batch size')
#
#
#
# parser.add_argument('--optimizer', type=str, default='sgd',
# help='optimizer type',
# choices=['sgd', 'adam', 'radam', 'ranger'])
# parser.add_argument('--lr', type=float, default=1e-3,
# help='learning rate')
# parser.add_argument('--momentum', type=float, default=0.9,
# help='learning rate momentum')
# parser.add_argument('--weight_decay', type=float, default=1e-5,
# help='weight decay')
# parser.add_argument('--lr_scheduler', type=str, default='steplr',
# help='scheduler type',
# choices=['steplr', 'cosine', 'poly'])
# #### params for warmup, only applied when optimizer == 'sgd' or 'adam'
# parser.add_argument('--warmup_multiplier', type=float, default=1.0,
# help='lr is multiplied by this factor after --warmup_epochs')
# parser.add_argument('--warmup_epochs', type=int, default=0,
# help='Gradually warm-up(increasing) learning rate in optimizer')
# ###########################
# #### params for steplr ####
# parser.add_argument('--decay_step', nargs='+', type=int, default=[20],
# help='scheduler decay step')
# parser.add_argument('--decay_gamma', type=float, default=0.1,
# help='learning rate decay amount')
# ###########################
# #### params for poly ####
# parser.add_argument('--poly_exp', type=float, default=0.9,
# help='exponent for polynomial learning rate decay')
# ###########################
return parser.parse_args()