jblitzar/github-python-corpus · Datasets at Hugging Face

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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.')
parser.add_argument('--cutmix', type=float, default=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 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"')

# * Finetuning params
parser.add_argument('--finetune', default='',
help='finetune from checkpoint')
parser.add_argument('--global_pool', action='store_true')
parser.set_defaults(global_pool=True)
parser.add_argument('--cls_token', action='store_false', dest='global_pool',
help='Use class token instead of global pool for classification')

# Dataset parameters
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--nb_classes', default=1000, type=int,
help='number of the classification types')

parser.add_argument('--output_dir', default='./output_dir',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default='./output_dir',
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
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 (recommended during training for faster monitor')
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')
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('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')

return parser


def main(args):
misc.init_distributed_mode(args)

print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
print("{}".format(args).replace(', ', ',\n'))

device = torch.device(args.device)

# fix the seed for reproducibility
seed = args.seed + misc.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)

cudnn.benchmark = True

dataset_train = build_dataset(is_train=True, args=args)
dataset_val = build_dataset(is_train=False, args=args)

if True: # args.distributed:
num_tasks = misc.get_world_size()
global_rank = misc.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)

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.')

parser.add_argument('--cutmix', type=float, default=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 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"')

# * Finetuning params

parser.add_argument('--finetune', default='',

help='finetune from checkpoint')

parser.add_argument('--global_pool', action='store_true')

parser.set_defaults(global_pool=True)

parser.add_argument('--cls_token', action='store_false', dest='global_pool',

help='Use class token instead of global pool for classification')

# Dataset parameters

parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,

help='dataset path')

parser.add_argument('--nb_classes', default=1000, type=int,

help='number of the classification types')

parser.add_argument('--output_dir', default='./output_dir',

help='path where to save, empty for no saving')

parser.add_argument('--log_dir', default='./output_dir',

help='path where to tensorboard log')

parser.add_argument('--device', default='cuda',

help='device to use for training / testing')

parser.add_argument('--seed', default=0, type=int)

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 (recommended during training for faster monitor')

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')

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('--local_rank', default=-1, type=int)

parser.add_argument('--dist_on_itp', action='store_true')

parser.add_argument('--dist_url', default='env://',

help='url used to set up distributed training')

return parser

def main(args):

misc.init_distributed_mode(args)

print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))

print("{}".format(args).replace(', ', ',\n'))

device = torch.device(args.device)

# fix the seed for reproducibility

seed = args.seed + misc.get_rank()

torch.manual_seed(seed)

np.random.seed(seed)

cudnn.benchmark = True

dataset_train = build_dataset(is_train=True, args=args)

dataset_val = build_dataset(is_train=False, args=args)

if True: # args.distributed:

num_tasks = misc.get_world_size()

global_rank = misc.get_rank()

sampler_train = torch.utils.data.DistributedSampler(

dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True

)

print("Sampler_train = %s" % str(sampler_train))

if args.dist_eval:

if len(dataset_val) % num_tasks != 0:

print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '

'This will slightly alter validation results as extra duplicate entries are added to achieve '

'equal num of samples per-process.')

sampler_val = torch.utils.data.DistributedSampler(

dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias

else:

sampler_val = torch.utils.data.SequentialSampler(dataset_val)