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import argparse
class TrainOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
gen = self.parser.add_argument_group('General')
gen.add_argument(
'--resume',
dest='resume',
default=False,
action='store_true',
help='Resume from checkpoint (Use latest checkpoint by default')
io = self.parser.add_argument_group('io')
io.add_argument('--log_dir',
default='logs',
help='Directory to store logs')
io.add_argument(
'--pretrained_checkpoint',
default=None,
help='Load a pretrained checkpoint at the beginning training')
train = self.parser.add_argument_group('Training Options')
train.add_argument('--num_epochs',
type=int,
default=200,
help='Total number of training epochs')
train.add_argument('--regressor',
type=str,
choices=['hmr', 'pymaf_net'],
default='pymaf_net',
help='Name of the SMPL regressor.')
train.add_argument('--cfg_file',
type=str,
default='./configs/pymaf_config.yaml',
help='config file path for PyMAF.')
train.add_argument(
'--img_res',
type=int,
default=224,
help='Rescale bounding boxes to size [img_res, img_res] before feeding them in the network'
)
train.add_argument(
'--rot_factor',
type=float,
default=30,
help='Random rotation in the range [-rot_factor, rot_factor]')
train.add_argument(
'--noise_factor',
type=float,
default=0.4,
help='Randomly multiply pixel values with factor in the range [1-noise_factor, 1+noise_factor]'
)
train.add_argument(
'--scale_factor',
type=float,
default=0.25,
help='Rescale bounding boxes by a factor of [1-scale_factor,1+scale_factor]'
)
train.add_argument(
'--openpose_train_weight',
default=0.,
help='Weight for OpenPose keypoints during training')
train.add_argument('--gt_train_weight',
default=1.,
help='Weight for GT keypoints during training')
train.add_argument('--eval_dataset',
type=str,
default='h36m-p2-mosh',
help='Name of the evaluation dataset.')
train.add_argument('--single_dataset',
default=False,
action='store_true',
help='Use a single dataset')
train.add_argument('--single_dataname',
type=str,
default='h36m',
help='Name of the single dataset.')
train.add_argument('--eval_pve',
default=False,
action='store_true',
help='evaluate PVE')
train.add_argument('--overwrite',
default=False,
action='store_true',
help='overwrite the latest checkpoint')
train.add_argument('--distributed',
action='store_true',
help='Use distributed training')
train.add_argument('--dist_backend',
default='nccl',
type=str,
help='distributed backend')
train.add_argument('--dist_url',
default='tcp://127.0.0.1:10356',
type=str,
help='url used to set up distributed training')
train.add_argument('--world_size',
default=1,
type=int,
help='number of nodes for distributed training')
train.add_argument("--local_rank", default=0, type=int)
train.add_argument('--rank',
default=0,
type=int,
help='node rank for distributed training')
train.add_argument(
'--multiprocessing_distributed',
action='store_true',
help='Use multi-processing distributed training to launch '
'N processes per node, which has N GPUs. This is the '
'fastest way to use PyTorch for either single node or '
'multi node data parallel training')
misc = self.parser.add_argument_group('Misc Options')
misc.add_argument('--misc',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
return
def parse_args(self):
"""Parse input arguments."""
self.args = self.parser.parse_args()
self.save_dump()
return self.args
def save_dump(self):
"""Store all argument values to a json file.
The default location is logs/expname/args.json.
"""
pass
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