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cfg.py
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cfg.py
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import argparse
from torchvision import models
from methods import METHOD_LIST
DS_LIST = ["in100","imagenet"]
def get_cfg():
""" generates configuration from user input in console """
parser = argparse.ArgumentParser(description="")
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
help='model architecture (e.g. resnet18, resnet50, resnet200, resnet50x2)')
parser.add_argument('-j', '--workers', default=24, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument(
"--bs", type=int, default=256, help="train bs",
)
parser.add_argument('--lr', '--learning-rate', default=0.5, type=float,
metavar='LR', help='initial (base) learning rate for train', dest='lr')
parser.add_argument('--wd', '--weight-decay', default=1e-5, type=float,
metavar='W', help='weight decay (default: 1e-5)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=100, type=int,
metavar='N', help='print frequency (default: 100)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--pretrained', default='', type=str, metavar='PATH',
help='path to checkpoint for evaluation(default: none)')
parser.add_argument('--world-size', default=1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=0, type=int,
help='node rank for distributed training')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--dist-url', default='tcp://localhost:10001', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument(
"--method", type=str, choices=METHOD_LIST, default="intl", help="loss type",
)
parser.add_argument(
"--env_name",
type=str,
default="INTL",
help="name of the run for wandb project",
)
parser.add_argument(
"--iters", type=int, default=4, help="ITN_iterations",
)
parser.add_argument(
"--warmup_eps",
type=int,
default=2,
help="epochs of learning rate warmup",
)
# data augmentation
parser.add_argument("--nmb_crops", type=int, default=[1, 1], nargs="+",
help="list of number of crops (example: [1, 1, 1, 1])")
parser.add_argument("--crops_size", type=int, default=[224, 224], nargs="+",
help="crops resolutions (example: [192, 160, 128, 96])")
parser.add_argument("--min_scale_crops", type=float, default=[0.08, 0.08], nargs="+",
help="argument in RandomResizedCrop (example: [0.2, 0.167, 0.133, 0.1])")
parser.add_argument("--max_scale_crops", type=float, default=[1, 1], nargs="+",
help="argument in RandomResizedCrop (example: [1.0, 0.833, 0.667, 0.5])")
parser.add_argument("--gaussian_prob", type=float, default=[1.0, 0.1], nargs="+",
help="gaussian_prob (example:[0.5, 0.5, 0.5, 0.5])")
parser.add_argument("--solarization_prob", type=float, default=[0.0, 0.2], nargs="+",
help="gaussian_prob (example: [0.1, 0.1, 0.1, 0.1])")
parser.add_argument('--multi-crop', dest="multi_crop", action="store_true", help='using multi-crop')
parser.add_argument(
"--projection_layers", type=int, default=3, help="number of FC layers in projection"
)
parser.add_argument(
"--projection_size", type=int, default=8192, help="size of FC layers in projection"
)
parser.add_argument("--emb", type=int, default=8192, help="embedding size")
parser.add_argument(
"--m", type=float, default=0.996, help="intl_momentum"
)
parser.add_argument("--dataset", type=str, choices=DS_LIST, default="imagenet")
parser.add_argument("--data_path", type=str, default='data/ImageNet/')
parser.add_argument("--axis",
type=int,
choices=[0,1],
default=0,
help='0 for channel whitening, 1 for batch whitening')
###lincls argument
parser.add_argument('--train-percent', default=100, type=int,
choices=(100, 10, 1),
help='size of traing set in percent')
parser.add_argument('--weights', default='freeze', type=str,
choices=('finetune', 'freeze'),
help='finetune or freeze resnet weights')
parser.add_argument('--schedule', default='step', type=str,
choices=('step', 'cos'),
help='learning rate scheduler')
parser.add_argument('--lr-backbone', default=0.004, type=float, metavar='LR',
help='backbone base learning rate')
parser.add_argument('--lr-classifier', default=0.2, type=float, metavar='LR',
help='classifier base learning rate')
return parser.parse_args()