CSI/common/common.py

from argparse import ArgumentParser


def parse_args(default=False):
    """Command-line argument parser for training."""

    parser = ArgumentParser(description='Pytorch implementation of CSI')

    parser.add_argument('--dataset', help='Dataset',
                        choices=['cifar10', 'cifar100', 'imagenet', 'CNMC', 'CNMC_grayscale'], type=str)
    parser.add_argument('--one_class_idx', help='None: multi-class, Not None: one-class',
                        default=None, type=int)
    parser.add_argument('--model', help='Model',
                        choices=['resnet18', 'resnet18_imagenet'], type=str)
    parser.add_argument('--mode', help='Training mode',
                        default='simclr', type=str)
    parser.add_argument('--simclr_dim', help='Dimension of simclr layer',
                        default=128, type=int)

    parser.add_argument('--shift_trans_type', help='shifting transformation type', default='none',
                        choices=['rotation', 'cutperm', 'blur', 'randpers', 'sharp', 'blur_randpers',
                                 'blur_sharp', 'randpers_sharp', 'blur_randpers_sharp', 'noise', 'none'], type=str)

    parser.add_argument("--local_rank", type=int,
                        default=0, help='Local rank for distributed learning')
    parser.add_argument('--resume_path', help='Path to the resume checkpoint',
                        default=None, type=str)
    parser.add_argument('--load_path', help='Path to the loading checkpoint',
                        default=None, type=str)
    parser.add_argument("--no_strict", help='Do not strictly load state_dicts',
                        action='store_true')
    parser.add_argument('--suffix', help='Suffix for the log dir',
                        default=None, type=str)
    parser.add_argument('--error_step', help='Epoch steps to compute errors',
                        default=5, type=int)
    parser.add_argument('--save_step', help='Epoch steps to save models',
                        default=10, type=int)

    ##### Training Configurations #####
    parser.add_argument('--epochs', help='Epochs',
                        default=1000, type=int)
    parser.add_argument('--optimizer', help='Optimizer',
                        choices=['sgd', 'lars'],
                        default='lars', type=str)
    parser.add_argument('--lr_scheduler', help='Learning rate scheduler',
                        choices=['step_decay', 'cosine'],
                        default='cosine', type=str)
    parser.add_argument('--warmup', help='Warm-up epochs',
                        default=10, type=int)
    parser.add_argument('--lr_init', help='Initial learning rate',
                        default=1e-1, type=float)
    parser.add_argument('--weight_decay', help='Weight decay',
                        default=1e-6, type=float)
    parser.add_argument('--batch_size', help='Batch size',
                        default=128, type=int)
    parser.add_argument('--test_batch_size', help='Batch size for test loader',
                        default=100, type=int)
    
    parser.add_argument('--blur_sigma', help='Distortion grade',
                        default=2.0, type=float)
    parser.add_argument('--color_distort', help='Color distortion grade',
                        default=0.5, type=float)
    parser.add_argument('--distortion_scale', help='Perspective distortion grade',
                        default=0.6, type=float)
    parser.add_argument('--sharpness_factor', help='Sharpening or blurring factor of image. '
                                                   'Can be any non negative number. 0 gives a blurred image, '
                                                   '1 gives the original image while 2 increases the sharpness '
                                                   'by a factor of 2.',
                        default=2, type=float)
    parser.add_argument('--noise_mean', help='mean',
                        default=0, type=float)   
    parser.add_argument('--noise_std', help='std',
                        default=0.3, type=float)
                        

    ##### Objective Configurations #####
    parser.add_argument('--sim_lambda', help='Weight for SimCLR loss',
                        default=1.0, type=float)
    parser.add_argument('--temperature', help='Temperature for similarity',
                        default=0.5, type=float)

    ##### Evaluation Configurations #####
    parser.add_argument("--ood_dataset", help='Datasets for OOD detection',
                        default=None, nargs="*", type=str)
    parser.add_argument("--ood_score", help='score function for OOD detection',
                        default=['norm_mean'], nargs="+", type=str)
    parser.add_argument("--ood_layer", help='layer for OOD scores',
                        choices=['penultimate', 'simclr', 'shift'],
                        default=['simclr', 'shift'], nargs="+", type=str)
    parser.add_argument("--ood_samples", help='number of samples to compute OOD score',
                        default=1, type=int)
    parser.add_argument("--ood_batch_size", help='batch size to compute OOD score',
                        default=100, type=int)
    parser.add_argument("--resize_factor", help='resize scale is sampled from [resize_factor, 1.0]',
                        default=0.08, type=float)
    parser.add_argument("--resize_fix", help='resize scale is fixed to resize_factor (not (resize_factor, 1.0])',
                        action='store_true')

    parser.add_argument("--print_score", help='print quantiles of ood score',
                        action='store_true')
    parser.add_argument("--save_score", help='save ood score for plotting histogram',
                        action='store_true')

    ##### Process configuration option #####
    parser.add_argument("--proc_step", help='choose process to initiate.',
                        choices=['eval', 'train'],
                        default=None, type=str)
    parser.add_argument("--res", help='resolution of dataset',
                        default="32px", type=str)

    if default:
        return parser.parse_args('')  # empty string
    else:
        return parser.parse_args()