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parameter.py
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parameter.py
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import argparse
def str2bool(v):
"""
Converts string to bool type; enables command line
arguments in the format of '--arg1 true --arg2 false'
"""
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def get_args_parser():
parser = argparse.ArgumentParser('ConvNeXt training and evaluation script for image classification', add_help=False)
parser.add_argument('--batch_size', default=4, type=int,
help='Per GPU batch size')
parser.add_argument('--epochs', default=300, type=int)
parser.add_argument('--experiment', default="country", type=str)
parser.add_argument('--update_freq', default=1, type=int, help='gradient accumulation steps')
# Model parameters
parser.add_argument('--model', default='BiT-M-R101x3', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--drop_path', type=float, default=0, metavar='PCT',
help='Drop path rate (default: 0.0)')
parser.add_argument('--input_size', default=224, type=int,
help='image input size')
parser.add_argument('--layer_scale_init_value', default=1e-6, type=float,
help="Layer scale initial values")
parser.add_argument('--dis_criterion', type=str, default="L1",
help='criterion for distance prediction')
# Dataset parameters
parser.add_argument('--data_path', default='./data', type=str,
help='dataset path')
parser.add_argument('--eval_data_path', default="", 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='GeoGuessr', choices=['CIFAR', 'IMNET', 'image_folder', 'GeoGuessr'],
type=str, help='ImageNet dataset path')
parser.add_argument('--output_dir', default='./logs/ConvNextB_dis',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default="./logs/ConvNextB_dis",
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=False)
parser.add_argument('--save_ckpt', type=str2bool, default=True)
parser.add_argument('--save_ckpt_freq', default=1, type=int)
parser.add_argument('--save_ckpt_num', default=3, 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=False,
help='Enabling distributed evaluation')
parser.add_argument('--disable_eval', type=str2bool, default=True,
help='Disabling evaluation during training')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', type=str2bool, default=False,
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
# EMA related parameters
parser.add_argument('--model_ema', type=str2bool, default=False)
parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False, help='')
parser.add_argument('--model_ema_eval', type=str2bool, default=False, help='Using ema to eval during training.')
# Optimization parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
weight decay. We use a cosine schedule for WD and using a larger decay by
the end of training improves performance for ViTs.""")
parser.add_argument('--lr', type=float, default=1e-3, metavar='LR',
help='learning rate (default: 4e-3), with total batch size 4096')
parser.add_argument('--layer_decay', type=float, default=1.0)
parser.add_argument('--min_lr', type=float, default=1e-4, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
# Augmentation parameters
parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
parser.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)')
parser.add_argument('--train_interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
# Evaluation parameters
parser.add_argument('--crop_pct', type=float, default=None)
# * Random Erase params
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
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)
# distributed training parameters
parser.add_argument('--world_size', default=2, 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.")
return parser