in references/classification/train.py [0:0]
def get_args_parser(add_help=True):
import argparse
parser = argparse.ArgumentParser(description="PyTorch Classification Training", add_help=add_help)
parser.add_argument("--data-path", default="/datasets01/imagenet_full_size/061417/", type=str, help="dataset path")
parser.add_argument("--model", default="resnet18", type=str, help="model name")
parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
parser.add_argument(
"-b", "--batch-size", default=32, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
)
parser.add_argument("--epochs", default=90, type=int, metavar="N", help="number of total epochs to run")
parser.add_argument(
"-j", "--workers", default=16, type=int, metavar="N", help="number of data loading workers (default: 16)"
)
parser.add_argument("--opt", default="sgd", type=str, help="optimizer")
parser.add_argument("--lr", default=0.1, type=float, help="initial learning rate")
parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
parser.add_argument(
"--wd",
"--weight-decay",
default=1e-4,
type=float,
metavar="W",
help="weight decay (default: 1e-4)",
dest="weight_decay",
)
parser.add_argument(
"--norm-weight-decay",
default=None,
type=float,
help="weight decay for Normalization layers (default: None, same value as --wd)",
)
parser.add_argument(
"--label-smoothing", default=0.0, type=float, help="label smoothing (default: 0.0)", dest="label_smoothing"
)
parser.add_argument("--mixup-alpha", default=0.0, type=float, help="mixup alpha (default: 0.0)")
parser.add_argument("--cutmix-alpha", default=0.0, type=float, help="cutmix alpha (default: 0.0)")
parser.add_argument("--lr-scheduler", default="steplr", type=str, help="the lr scheduler (default: steplr)")
parser.add_argument("--lr-warmup-epochs", default=0, type=int, help="the number of epochs to warmup (default: 0)")
parser.add_argument(
"--lr-warmup-method", default="constant", type=str, help="the warmup method (default: constant)"
)
parser.add_argument("--lr-warmup-decay", default=0.01, type=float, help="the decay for lr")
parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
parser.add_argument(
"--cache-dataset",
dest="cache_dataset",
help="Cache the datasets for quicker initialization. It also serializes the transforms",
action="store_true",
)
parser.add_argument(
"--sync-bn",
dest="sync_bn",
help="Use sync batch norm",
action="store_true",
)
parser.add_argument(
"--test-only",
dest="test_only",
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
help="Use pre-trained models from the modelzoo",
action="store_true",
)
parser.add_argument("--auto-augment", default=None, type=str, help="auto augment policy (default: None)")
parser.add_argument("--random-erase", default=0.0, type=float, help="random erasing probability (default: 0.0)")
# Mixed precision training parameters
parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
# distributed training parameters
parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
parser.add_argument(
"--model-ema", action="store_true", help="enable tracking Exponential Moving Average of model parameters"
)
parser.add_argument(
"--model-ema-steps",
type=int,
default=32,
help="the number of iterations that controls how often to update the EMA model (default: 32)",
)
parser.add_argument(
"--model-ema-decay",
type=float,
default=0.99998,
help="decay factor for Exponential Moving Average of model parameters (default: 0.99998)",
)
parser.add_argument(
"--use-deterministic-algorithms", action="store_true", help="Forces the use of deterministic algorithms only."
)
parser.add_argument(
"--interpolation", default="bilinear", type=str, help="the interpolation method (default: bilinear)"
)
parser.add_argument(
"--val-resize-size", default=256, type=int, help="the resize size used for validation (default: 256)"
)
parser.add_argument(
"--val-crop-size", default=224, type=int, help="the central crop size used for validation (default: 224)"
)
parser.add_argument(
"--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
)
parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
parser.add_argument("--ra-sampler", action="store_true", help="whether to use Repeated Augmentation in training")
parser.add_argument(
"--ra-reps", default=3, type=int, help="number of repetitions for Repeated Augmentation (default: 3)"
)
# Prototype models only
parser.add_argument(
"--prototype",
dest="prototype",
help="Use prototype model builders instead those from main area",
action="store_true",
)
parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
return parser