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jblitzar
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github-python

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help='Random erase count (default: 1)')
parser.add_argument('--resplit', type=str2bool, default=False,
help='Do not random erase first (clean) augmentation split')
# * Mixup params
parser.add_argument('--mixup', type=float, default=0.8,
help='mixup alpha, mixup enabled if > 0.')
parser.add_argument('--cutmix', type=float, default=1.0,
help='cutmix alpha, cutmix enabled if > 0.')
parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup_prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup_mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
# * Finetuning params
parser.add_argument('--finetune', default='',
help='finetune from checkpoint')
parser.add_argument('--head_init_scale', default=1.0, type=float,
help='classifier head initial scale, typically adjusted in fine-tuning')
parser.add_argument('--model_key', default='model|module', type=str,
help='which key to load from saved state dict, usually model or model_ema')
parser.add_argument('--model_prefix', default='', type=str)
# Dataset parameters
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--eval_data_path', default=None, type=str,
help='dataset path for evaluation')
parser.add_argument('--nb_classes', default=1000, type=int,
help='number of the classification types')
parser.add_argument('--imagenet_default_mean_and_std', type=str2bool, default=True)
parser.add_argument('--data_set', default='IMNET', choices=['CIFAR10', 'CIFAR100', 'IMNET', 'image_folder'],
type=str, help='ImageNet dataset path')
parser.add_argument('--output_dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default=None,
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--auto_resume', type=str2bool, default=True)
parser.add_argument('--save_ckpt', type=str2bool, default=True)
parser.add_argument('--save_ckpt_freq', default=10, type=int)
parser.add_argument('--save_ckpt_num', default=10, type=int)
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', type=str2bool, default=False,
help='Perform evaluation only')
parser.add_argument('--dist_eval', type=str2bool, default=True,
help='Enabling distributed evaluation')
parser.add_argument('--disable_eval', type=str2bool, default=False,
help='Disabling evaluation during training')
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--pin_mem', type=str2bool, default=True,
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', type=str2bool, default=False)
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
parser.add_argument('--use_amp', type=str2bool, default=False,
help="Use PyTorch's AMP (Automatic Mixed Precision) or not")
# Weights and Biases arguments
parser.add_argument('--enable_wandb', type=str2bool, default=False,
help="enable logging to Weights and Biases")
parser.add_argument('--project', default='convnext', type=str,
help="The name of the W&B project where you're sending the new run.")
parser.add_argument('--wandb_ckpt', type=str2bool, default=False,
help="Save model checkpoints as W&B Artifacts.")
parser.add_argument('--show_flops', type=str2bool, default=True,
help="Display FLOPS at start of training")
return parser
def main(args):
utils.init_distributed_mode(args)
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
if args.disable_eval: