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import argparse
import numpy as np
def get_args_parser():
parser = argparse.ArgumentParser('Few-shot learning script', add_help=False)
# General
parser.add_argument('--batch-size', default=1, type=int)
parser.add_argument('--num_classes', default=1000, type=int)
parser.add_argument('--epochs', default=100, type=int)
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.set_defaults(fp16=True)
parser.add_argument('--output_dir', default='output/tmp',
help='path where to save, empty for no saving')
parser.add_argument('--device', default='cuda',
help='cuda:gpu_id for single GPU training')
parser.add_argument('--seed', default=0, type=int)
# Dataset parameters
parser.add_argument('--data-path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--pretrained-checkpoint-path', default='.', type=str,
help='path which contains the directories pretrained_ckpts and pretrained_ckpts_converted')
parser.add_argument("--dataset", choices=["cifar_fs_elite", "cifar_fs", "mini_imagenet",
"meta_dataset", "meta_dataset_h5", "imagenet_h5",
"full_meta_dataset", "imagewise_meta_dataset"],
default="cifar_fs",
help="Which few-shot dataset.")
# Few-shot parameters (Mini-ImageNet & CIFAR-FS)
parser.add_argument("--nClsEpisode", default=5, type=int,
help="Number of categories in each episode.")
parser.add_argument("--nSupport", default=1, type=int,
help="Number of samples per category in the support set.")
parser.add_argument("--nQuery", default=15, type=int,
help="Number of samples per category in the query set.")
parser.add_argument("--nValEpisode", default=120, type=int,
help="Number of episodes for validation.")
parser.add_argument("--nEpisode", default=2000, type=int,
help="Number of episodes for training / testing.")
# MetaDataset parameters
parser.add_argument('--image_size', type=int, default=128,
help='Images will be resized to this value')
parser.add_argument('--base_sources', nargs="+", default=['aircraft', 'cu_birds', 'dtd', 'fungi', 'ilsvrc_2012', 'omniglot', 'quickdraw', 'vgg_flower'],
help='List of datasets to use for training')
parser.add_argument('--val_sources', nargs="+", default=['aircraft', 'cu_birds', 'dtd', 'fungi', 'ilsvrc_2012', 'omniglot', 'quickdraw', 'vgg_flower'],
help='List of datasets to use for validation')
parser.add_argument('--test_sources', nargs="+", default=['traffic_sign', 'mscoco', 'ilsvrc_2012', 'omniglot', 'aircraft', 'cu_birds', 'dtd', 'quickdraw', 'fungi', 'vgg_flower'],
help='List of datasets to use for meta-testing')
parser.add_argument('--shuffle', type=bool, default=True,
help='Whether or not to shuffle data for TFRecordDataset')
parser.add_argument('--train_transforms', nargs="+", default=['random_resized_crop', 'jitter', 'random_flip', 'to_tensor', 'normalize'],
help='Transforms applied to training data',)
parser.add_argument('--test_transforms', nargs="+", default=['resize', 'center_crop', 'to_tensor', 'normalize'],
help='Transforms applied to test data',)
parser.add_argument('--num_ways', type=int, default=None,
help='Set it if you want a fixed # of ways per task')
parser.add_argument('--num_support', type=int, default=None,
help='Set it if you want a fixed # of support samples per class')
parser.add_argument('--num_query', type=int, default=None,
help='Set it if you want a fixed # of query samples per class')
parser.add_argument('--min_ways', type=int, default=5,
help='Minimum # of ways per task')
parser.add_argument('--max_ways_upper_bound', type=int, default=50,
help='Maximum # of ways per task')
parser.add_argument('--max_num_query', type=int, default=10,
help='Maximum # of query samples')
parser.add_argument('--max_support_set_size', type=int, default=500,
help='Maximum # of support samples')
parser.add_argument('--max_support_size_contrib_per_class', type=int, default=100,
help='Maximum # of support samples per class')
parser.add_argument('--min_examples_in_class', type=int, default=0,
help='Classes that have less samples will be skipped')
parser.add_argument('--min_log_weight', type=float, default=np.log(0.5),
help='Do not touch, used to randomly sample support set')
parser.add_argument('--max_log_weight', type=float, default=np.log(2),
help='Do not touch, used to randomly sample support set')
parser.add_argument('--ignore_bilevel_ontology', action='store_true',
help='Whether or not to use superclass for BiLevel datasets (e.g Omniglot)')
parser.add_argument('--ignore_dag_ontology', action='store_true',
help='Whether to ignore ImageNet DAG ontology when sampling \
classes from it. This has no effect if ImageNet is not \
part of the benchmark.')
parser.add_argument('--ignore_hierarchy_probability', type=float, default=0.,
help='if using a hierarchy, this flag makes the sampler \
ignore the hierarchy for this proportion of episodes \
and instead sample categories uniformly.')
# CDFSL parameters
parser.add_argument('--test_n_way' , default=5, type=int, help='class num to classify for testing (validation) ')
parser.add_argument('--n_shot' , default=5, type=int, help='number of labeled data in each class, same as n_support')
parser.add_argument('--cdfsl_domains', nargs="+", default=['EuroSAT', 'ISIC', 'CropDisease', 'ChestX'], help='CDFSL datasets')
# Model params
parser.add_argument('--arch', default='dino_base_patch16_224', type=str,
help='Architecture of the backbone.')
parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
parser.add_argument('--pretrained_weights', default='', type=str, help="Path to pretrained weights to evaluate.")
parser.add_argument("--checkpoint_key", default="teacher", type=str, help='Key to use in the checkpoint (example: "teacher")')
parser.add_argument('--unused_params', action='store_true')
parser.add_argument('--no-pretrain', action='store_true')
# Deployment params
parser.add_argument("--deploy", type=str, default="vanilla",
help="Which few-shot model to be deployed for meta-testing.")
parser.add_argument('--num_adapters', default=1, type=int, help='Number of adapter tokens')
parser.add_argument('--ada_steps', default=40, type=int, help='Number of feature adaptation steps')
parser.add_argument('--ada_lr', default=5e-2, type=float, help='Learning rate of feature adaptation')
parser.add_argument('--aug_prob', default=0.9, type=float, help='Probability of applying data augmentation during meta-testing')
parser.add_argument('--aug_types', nargs="+", default=['color', 'translation'],
help='color, offset, offset_h, offset_v, translation, cutout')
# Other model parameters
parser.add_argument('--img-size', default=224, type=int, help='images input size')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.)')
parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: 0.1)')
parser.add_argument('--model-ema', action='store_true')
parser.add_argument('--no-model-ema', action='store_false', dest='model_ema')
parser.set_defaults(model_ema=False)
parser.add_argument('--model-ema-decay', type=float, default=0.99996, help='')
parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, help='')
# 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('--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)')
# Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
help='LR scheduler (default: "cosine"')
parser.add_argument('--lr', type=float, default=5e-5, metavar='LR',
help='learning rate (default: 5e-4)')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
help='warmup learning rate (default: 1e-6)')
parser.add_argument('--min-lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
help='epoch interval to decay LR (step scheduler)')
parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
help='LR decay rate (default: 0.1)')
# 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=0.0, help='Label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
parser.add_argument('--repeated-aug', action='store_true')
# * 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', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
# * Mixup params
parser.add_argument('--mixup', type=float, default=0.,
help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
parser.add_argument('--cutmix', type=float, default=0.,
help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
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"')
# Distillation parameters
parser.add_argument('--teacher-model', default='regnety_160', type=str, metavar='MODEL',
help='Name of teacher model to train (default: "regnety_160"')
parser.add_argument('--teacher-path', type=str, default='')
parser.add_argument('--distillation-type', default='none', choices=['none', 'soft', 'hard'], type=str, help="")
parser.add_argument('--distillation-alpha', default=0.5, type=float, help="")
parser.add_argument('--distillation-tau', default=1.0, type=float, help="")
# Misc
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
parser.add_argument('--dist-eval', action='store_true', default=False, help='Enabling distributed evaluation')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin-mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
help='')
parser.set_defaults(pin_mem=True)
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
return parser
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