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dae5c90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | 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'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def parse_args():
parser = argparse.ArgumentParser('Melanoma Classification Training')
parser.add_argument('--kaggle', action='store_true', default=False,
help='Runnin on kaggle')
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,
help='Path to the folder with images on Kaggle')
parser.add_argument('--skin_color_csv', default=None, type=str,
help='Path to the CSV file containing skin color labels')
parser.add_argument('--data_path', default='./isic2020_challenge', type=str,
help='Path to dataset with train/valid folders')
parser.add_argument('--output_dir', default='./melanoma_output', type=str,
help='Path to save model outputs')
parser.add_argument('--log_dir', default='./melanoma_logs', type=str,
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',
'convnextv2_atto', 'convnextv2_femto', 'convnextv2_pico', 'convnextv2_nano',
'convnextv2_tiny', 'convnextv2_small', 'convnextv2_base', 'convnextv2_large',
'convnextv2_xlarge'
],
help='Model architecture to use')
parser.add_argument('--num_classes', default=2, type=int,
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')
# Perform evaluation only
parser.add_argument('--test', action='store_true', default=False,
help='Perform evaluation only')
# Training parameter
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')
# Criterion parameters
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)
# Optimizer 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('--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)
# Augmentation parameters
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')
# Model EMA parameters
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')
# Misc parameters
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.')
# distributed training parameters
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')
# add config file
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 |