| import argparse
|
| import yaml
|
| import os
|
| import logging
|
| from copy import deepcopy
|
|
|
| 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'):
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| return True
|
| elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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| return False
|
| else:
|
| raise argparse.ArgumentTypeError('Boolean value expected.')
|
|
|
| def parse_args():
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| parser = argparse.ArgumentParser('Melanoma Classification Training')
|
|
|
| parser.add_argument('--kaggle', action='store_true', default=False,
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| help='Runnin on kaggle')
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| parser.add_argument('--kaggle_csv_file', default=r"/kaggle/input/siim-isic-melanoma-classification/train.csv", type=str,
|
| help='Path to the CSV file containing image names and labels on Kaggle')
|
| parser.add_argument('--kaggle_image_dir', default=r"/kaggle/input/siim-isic-melanoma-classification/jpeg/train", type=str,
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| help='Path to the folder with images on Kaggle')
|
| parser.add_argument('--skin_color_csv', default=None, type=str,
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| help='Path to the CSV file containing skin color labels')
|
|
|
| parser.add_argument('--data_path', default='./isic2020_challenge', type=str,
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| help='Path to dataset with train/valid folders')
|
| parser.add_argument('--output_dir', default='./melanoma_output', type=str,
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| help='Path to save model outputs')
|
| parser.add_argument('--log_dir', default='./melanoma_logs', type=str,
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| help='Path for tensorboard logs')
|
|
|
| parser.add_argument('--model', default='convnext_tiny', type=str,
|
| choices=['convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large',
|
| 'efficientnetv2_b0', 'efficientnetv2_b1', 'efficientnetv2_b2', 'efficientnetv2_b3',
|
| 'efficientnetv2_m', 'efficientnetv2_l', 'efficientnetv2_s', 'efficientnetv2_xl',
|
| 'dinov2_vit_base', 'dinov2_vit_small', 'dinov2_vit_large', 'dinov2_vit_giant2',
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| 'convnextv2_atto', 'convnextv2_femto', 'convnextv2_pico', 'convnextv2_nano',
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| 'convnextv2_tiny', 'convnextv2_small', 'convnextv2_base', 'convnextv2_large',
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| 'convnextv2_xlarge'
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| ],
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| help='Model architecture to use')
|
| parser.add_argument('--num_classes', default=2, type=int,
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| help='Number of output classes')
|
| parser.add_argument('--num_groups', default = 1, type=int,
|
| help = "Number of skin color groups")
|
| parser.add_argument('--pretrained', default=True, type=str2bool,
|
| help='Use pretrained weights')
|
| parser.add_argument('--freeze_model', default=False, type=str2bool,
|
| help='Freeze model weights')
|
| parser.add_argument('--in_22k', default=False, type=str2bool,
|
| help='Use pretrained weights on ImageNet22k')
|
| parser.add_argument('--input_size', default=224, type=int,
|
| help='Input image size')
|
| parser.add_argument('--drop_path', type=float, default=0.1,
|
| help='Drop path rate')
|
|
|
|
|
| parser.add_argument('--test', action='store_true', default=False,
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| help='Perform evaluation only')
|
|
|
|
|
| parser.add_argument('--batch_size', default=16, type=int,
|
| help='Batch size for training and validation')
|
| parser.add_argument('--epochs', default=30, type=int,
|
| help='Number of epochs to train')
|
| parser.add_argument('--start_epoch', default=0, type=int,
|
| help='Start epoch for resuming training')
|
| parser.add_argument('--update_freq', default=1, type=int,
|
| help='Gradient accumulation steps')
|
|
|
|
|
| parser.add_argument('--ohem', action='store_true', default=False,
|
| help='Use OHEM loss')
|
| parser.add_argument('--ifw', action='store_true', default=False,
|
| help='Use inverse frequency weighting.')
|
| parser.add_argument('--recall_ce', action='store_true', default=False,
|
| help='Use Recall Cross Entropy loss')
|
| parser.add_argument('--focal_loss', action='store_true', default=False,
|
| help='Use Focal loss')
|
| parser.add_argument('--domain_independent_loss', action='store_true', default=False,
|
| help='Use Domain Independent Loss')
|
| parser.add_argument('--domain_discriminative_loss', action='store_true', default=False,
|
| help='Use Domain Discriminative Loss')
|
| parser.add_argument('--conditional_accuracy', type=str2bool)
|
|
|
|
|
| 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('--lr', default=1e-4, type=float,
|
| help='Learning rate')
|
| parser.add_argument('--min_lr', type=float, default=1e-6,
|
| help='Lower LR bound for cyclic schedulers')
|
| parser.add_argument('--weight_decay', default=5e-3, type=float,
|
| help='Weight decay')
|
| parser.add_argument('--weight_decay_end', default=None, type=float,
|
| help='Final weight decay value')
|
| parser.add_argument('--clip_grad', type=float, default=None,
|
| help='Clip gradient norm value (None = no clipping)')
|
| parser.add_argument('--warmup_epochs', type=int, default=0,
|
| help='Epochs to warmup LR')
|
| parser.add_argument('--warmup_steps', type=int, default=-1,
|
| help='Steps to warmup LR (overrides warmup_epochs if > 0)')
|
| parser.add_argument('--layer_decay', type=float, default=1.0)
|
|
|
|
|
| parser.add_argument('--oversample_malignant', action='store_true', default=False,
|
| help='Oversample malignant lesions')
|
| parser.add_argument('--undersample_benign', action='store_true', default=False,
|
| help='Undersample benign lesions')
|
| parser.add_argument('--undersample_benign_ratio', type=float, default=-1,
|
| help='Undersample benign lesions')
|
|
|
| parser.add_argument('--smoothing', type=float, default=0.1,
|
| help='Label smoothing value')
|
| parser.add_argument('--mixup', type=float, default=0.0,
|
| help='mixup alpha, mixup enabled if > 0.')
|
| parser.add_argument('--cutmix', type=float, default=0.0,
|
| help='cutmix alpha, cutmix enabled if > 0.')
|
| parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
|
| help='cutmix min/max ratio, overrides alpha')
|
| parser.add_argument('--mixup_prob', type=float, default=1.0,
|
| help='Probability of applying mixup/cutmix')
|
| parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
|
| help='Probability of switching to cutmix when both enabled')
|
| parser.add_argument('--mixup_mode', type=str, default='batch',
|
| help='How to apply mixup/cutmix ["batch", "pair", "elem"]')
|
| parser.add_argument('--cielab', action='store_true', default=False,
|
| help='Load images to CIELab colorspace')
|
| parser.add_argument('--skin_former', action='store_true', default=False,
|
| help='Transform lighter skin types to darker ones')
|
| parser.add_argument('--segment_out_skin', type=str2bool, default=False,
|
| help='Use skin segmentation to remove background')
|
|
|
| parser.add_argument('--model_ema', type=str2bool, default=False,
|
| help='Enable tracking moving average of model weights')
|
| parser.add_argument('--model_ema_decay', type=float, default=0.9999,
|
| help='Factor for model weights moving average')
|
| parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False,
|
| help='Force EMA model weights to be stored on CPU')
|
|
|
|
|
| parser.add_argument('--save_ckpt', type=str2bool, default=True,
|
| help='Save checkpoints during training')
|
| parser.add_argument('--save_ckpt_freq', type=int, default=1,
|
| help='Frequency to save checkpoints (epochs)')
|
| parser.add_argument('--seed', default=0, type=int,
|
| help='Random seed for reproducibility')
|
| parser.add_argument('--device', default='cuda', type=str,
|
| help='Device to use for training')
|
| parser.add_argument('--num_workers', default=4, type=int,
|
| help='Number of workers for data loading')
|
| parser.add_argument('--pin_mem', type=str2bool, default=True,
|
| help='Pin CPU memory for data loading')
|
| parser.add_argument('--checkpoint', default='', type=str,
|
| help='Path to checkpoint training/evaluation.')
|
|
|
|
|
| parser.add_argument('--use_amp', type=str2bool, default=False,
|
| help='Use PyTorch automatic mixed precision')
|
| 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('--distributed', type=str2bool, default=False)
|
| parser.add_argument('--dist_url', default='env://',
|
| help='url used to set up distributed training')
|
|
|
|
|
| parser.add_argument('--config', default=None, type=str,
|
| help='Path to the config file')
|
|
|
| args = parser.parse_args()
|
| if args.weight_decay_end is None:
|
| args.weight_decay_end = args.weight_decay
|
|
|
| if args.config is not None and os.path.exists(args.config):
|
| config = load_yaml_config(args.config)
|
| args = update_args_with_config(args, config)
|
| return args
|
|
|
| def load_yaml_config(config_path):
|
| with open(config_path, 'r') as f:
|
| return yaml.safe_load(f)
|
|
|
| def update_args_with_config(args, config_dict):
|
| parser, explicit_args = args._get_kwargs(), {}
|
|
|
| for k, v in parser:
|
| if hasattr(args, f"__{k}"):
|
| explicit_args[k] = True
|
|
|
| args_copy = deepcopy(args)
|
| for k, v in config_dict.items():
|
| if k not in explicit_args and hasattr(args_copy, k):
|
| setattr(args_copy, k, v)
|
|
|
| return args_copy |
