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parser.add_argument(
"--use_fp16",
type=utils.bool_flag,
default=True,
help="Whether or not to use mixed precision for training.",
)
parser.add_argument(
"--clip_grad",
type=float,
default=0.0,
help="Gradient clipping value.",
)
parser.add_argument(
"--batch_size_per_gpu",
default=128,
type=int,
help="Batch size per GPU. Total batch size is proportional to the number of GPUs.",
)
parser.add_argument(
"--epochs",
default=200,
type=int,
help="Number of training epochs.",
)
parser.add_argument(
"--optim_args",
type=str,
default="{'betas':(0.9, 0.99), 'eps':1e-8}",
help="Dictionary of keyword arguments for the optimizer.",
)
parser.add_argument(
"--wd",
type=float,
default=3e-2,
help="Weight decay for the SGD optimizer.",
)
parser.add_argument(
"--lr",
default=3e-4,
type=float,
help="Maximum learning rate at the end of linear warmup.",
)
parser.add_argument(
"--min_lr",
type=float,
default=1e-6,
help="Minimum learning rate at the end of training.",
)
parser.add_argument(
"--warmup_epochs",
default=10,
type=int,
help="Number of training epochs for the learning-rate-warm-up phase.",
)
parser.add_argument(
"--output_dir",
default="./output",
type=str,
help="Path to the output folder to save logs and checkpoints.",
)
parser.add_argument(
"--saveckpt_freq",
default=20,
type=int,
help="Frequency of intermediate checkpointing.",
)
parser.add_argument(
"--seed",
default=22,
type=int,
help="Random seed",
)
parser.add_argument(
"--num_workers",
default=12,
type=int,
help="Number of data loading workers per GPU.",
)
parser.add_argument(
"--dist_url",
default="env://",
type=str,
help="Url used to set up distributed training.",
)
parser.add_argument(
"--local_rank",
default=0,
type=int,
help="Please ignore this argument; No need to set it manually.",
)
args = parser.parse_args()
args.teachers = sorted(args.teachers.split(","))
args.num_cpus = len(os.sched_getaffinity(0))
os.makedirs(args.output_dir, exist_ok=True)