When I trained at ubutun: python train_voc.sh
train_voc.sh
CUDA_VISIBLE_DEVICES=0,1,2,3
python train.py
–backbone resnet --lr 0.007
–workers 4
–use-sbd True
–epochs 50
–batch-size 16
–gpu-ids 0,1,2,3
–checkname deeplab-resnet
–eval-interval 1
–dataset pascal
train.py:error:unrecognized arguments:True
train.py:Parameter Settings
def main():
parser = argparse.ArgumentParser(description=“PyTorch DeeplabV3Plus Training”)
parser.add_argument(’–backbone’, type=str, default=‘xception’,
choices=[‘resnet’, ‘xception’, ‘drn’, ‘mobilenet’],
help=‘backbone name (default: resnet)’)
parser.add_argument(’–out-stride’, type=int, default=16,
help=‘network output stride (default: 8)’)
parser.add_argument(’–dataset’, type=str, default=‘pascal’,
choices=[‘pascal’, ‘coco’, ‘cityscapes’],
help=‘dataset name (default: pascal)’)
parser.add_argument(’–use-sbd’, action=‘store_true’, default=True,
help=‘whether to use SBD dataset (default: True)’)
parser.add_argument(’–workers’, type=int, default=2,
metavar=‘N’, help=‘dataloader threads’)
parser.add_argument(’–base-size’, type=int, default=513,
help=‘base image size’)
parser.add_argument(’–crop-size’, type=int, default=513,
help=‘crop image size’)
parser.add_argument(’–sync-bn’, type=bool, default=None,
help=‘whether to use sync bn (default: auto)’)
parser.add_argument(’–freeze-bn’, type=bool, default=False,
help=‘whether to freeze bn parameters (default: False)’)
parser.add_argument(’–loss-type’, type=str, default=‘ce’,
choices=[‘ce’, ‘focal’],
help=‘loss func type (default: ce)’)
# training hyper params
parser.add_argument(’–epochs’, type=int, default=None, metavar=‘N’,
help=‘number of epochs to train (default: auto)’)
parser.add_argument(’–start_epoch’, type=int, default=0,
metavar=‘N’, help=‘start epochs (default:0)’)
parser.add_argument(’–batch-size’, type=int, default=None,
metavar=‘N’, help=‘input batch size for
training (default: auto)’)
parser.add_argument(’–test-batch-size’, type=int, default=None,
metavar=‘N’, help=‘input batch size for
testing (default: auto)’)
parser.add_argument(’–use-balanced-weights’, action=‘store_true’, default=False,
help=‘whether to use balanced weights (default: False)’)
# optimizer params
parser.add_argument(’–lr’, type=float, default=None, metavar=‘LR’,
help=‘learning rate (default: auto)’)
parser.add_argument(’–lr-scheduler’, type=str, default=‘poly’,
choices=[‘poly’, ‘step’, ‘cos’],
help=‘lr scheduler mode: (default: poly)’)
parser.add_argument(’–momentum’, type=float, default=0.9,
metavar=‘M’, help=‘momentum (default: 0.9)’)
parser.add_argument(’–weight-decay’, type=float, default=5e-4,
metavar=‘M’, help=‘w-decay (default: 5e-4)’)
parser.add_argument(’–nesterov’, action=‘store_true’, default=False,
help=‘whether use nesterov (default: False)’)
# cuda, seed and logging
parser.add_argument(’–no-cuda’, action=‘store_true’, default=
False, help=‘disables CUDA training’)
parser.add_argument(’–gpu-ids’, type=str, default=‘0’,
help=‘use which gpu to train, must be a
comma-separated list of integers only (default=0)’)
parser.add_argument(’–seed’, type=int, default=1, metavar=‘S’,
help=‘random seed (default: 1)’)
# checking point
parser.add_argument(’–resume’, type=str, default=None,
help=‘put the path to resuming file if needed’)
parser.add_argument(’–checkname’, type=str, default=None,
help=‘set the checkpoint name’)
# finetuning pre-trained models
parser.add_argument(’–ft’, action=‘store_true’, default=False,
help=‘finetuning on a different dataset’)
# evaluation option
parser.add_argument(’–eval-interval’, type=int, default=1,
help=‘evaluuation interval (default: 1)’)
parser.add_argument(’–no-val’, action=‘store_true’, default=False,
help=‘skip validation during training’)
This seems to be a parameter setting issue, but I have all the parameters set, as required in the README