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use argparse groups to group arguments

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pull/1265/head
Jakub Kaczmarzyk 3 years ago
parent e1e4c9bbae
commit dcad288fd6
1 changed files with 118 additions and 109 deletions
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train.py
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@ -71,238 +71,247 @@ parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# Dataset parameters # Dataset parameters
group = parser.add_argument_group('Dataset parameters')
# Keep this argument outside of the dataset group because it is positional.
parser.add_argument('data_dir', metavar='DIR', parser.add_argument('data_dir', metavar='DIR',
help='path to dataset') help='path to dataset')
parser.add_argument('--dataset', '-d', metavar='NAME', default='', group.add_argument('--dataset', '-d', metavar='NAME', default='',
help='dataset type (default: ImageFolder/ImageTar if empty)') help='dataset type (default: ImageFolder/ImageTar if empty)')
parser.add_argument('--train-split', metavar='NAME', default='train', group.add_argument('--train-split', metavar='NAME', default='train',
help='dataset train split (default: train)') help='dataset train split (default: train)')
parser.add_argument('--val-split', metavar='NAME', default='validation', group.add_argument('--val-split', metavar='NAME', default='validation',
help='dataset validation split (default: validation)') help='dataset validation split (default: validation)')
parser.add_argument('--dataset-download', action='store_true', default=False, group.add_argument('--dataset-download', action='store_true', default=False,
help='Allow download of dataset for torch/ and tfds/ datasets that support it.') help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
parser.add_argument('--class-map', default='', type=str, metavar='FILENAME', group.add_argument('--class-map', default='', type=str, metavar='FILENAME',
help='path to class to idx mapping file (default: "")') help='path to class to idx mapping file (default: "")')
# Model parameters # Model parameters
parser.add_argument('--model', default='resnet50', type=str, metavar='MODEL', group = parser.add_argument_group('Model parameters')
group.add_argument('--model', default='resnet50', type=str, metavar='MODEL',
help='Name of model to train (default: "resnet50"') help='Name of model to train (default: "resnet50"')
parser.add_argument('--pretrained', action='store_true', default=False, group.add_argument('--pretrained', action='store_true', default=False,
help='Start with pretrained version of specified network (if avail)') help='Start with pretrained version of specified network (if avail)')
parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH', group.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
help='Initialize model from this checkpoint (default: none)') help='Initialize model from this checkpoint (default: none)')
parser.add_argument('--resume', default='', type=str, metavar='PATH', group.add_argument('--resume', default='', type=str, metavar='PATH',
help='Resume full model and optimizer state from checkpoint (default: none)') help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--no-resume-opt', action='store_true', default=False, group.add_argument('--no-resume-opt', action='store_true', default=False,
help='prevent resume of optimizer state when resuming model') help='prevent resume of optimizer state when resuming model')
parser.add_argument('--num-classes', type=int, default=None, metavar='N', group.add_argument('--num-classes', type=int, default=None, metavar='N',
help='number of label classes (Model default if None)') help='number of label classes (Model default if None)')
parser.add_argument('--gp', default=None, type=str, metavar='POOL', group.add_argument('--gp', default=None, type=str, metavar='POOL',
help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.') help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
parser.add_argument('--img-size', type=int, default=None, metavar='N', group.add_argument('--img-size', type=int, default=None, metavar='N',
help='Image patch size (default: None => model default)') help='Image patch size (default: None => model default)')
parser.add_argument('--input-size', default=None, nargs=3, type=int, group.add_argument('--input-size', default=None, nargs=3, type=int,
metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
parser.add_argument('--crop-pct', default=None, type=float, group.add_argument('--crop-pct', default=None, type=float,
metavar='N', help='Input image center crop percent (for validation only)') metavar='N', help='Input image center crop percent (for validation only)')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', group.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override mean pixel value of dataset') help='Override mean pixel value of dataset')
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', group.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
help='Override std deviation of dataset') help='Override std deviation of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME', group.add_argument('--interpolation', default='', type=str, metavar='NAME',
help='Image resize interpolation type (overrides model)') help='Image resize interpolation type (overrides model)')
parser.add_argument('-b', '--batch-size', type=int, default=128, metavar='N', group.add_argument('-b', '--batch-size', type=int, default=128, metavar='N',
help='Input batch size for training (default: 128)') help='Input batch size for training (default: 128)')
parser.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N', group.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N',
help='Validation batch size override (default: None)') help='Validation batch size override (default: None)')
parser.add_argument('--channels-last', action='store_true', default=False, group.add_argument('--channels-last', action='store_true', default=False,
help='Use channels_last memory layout') help='Use channels_last memory layout')
parser.add_argument('--torchscript', dest='torchscript', action='store_true', group.add_argument('--torchscript', dest='torchscript', action='store_true',
help='torch.jit.script the full model') help='torch.jit.script the full model')
parser.add_argument('--fuser', default='', type=str, group.add_argument('--fuser', default='', type=str,
help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
parser.add_argument('--grad-checkpointing', action='store_true', default=False, group.add_argument('--grad-checkpointing', action='store_true', default=False,
help='Enable gradient checkpointing through model blocks/stages') help='Enable gradient checkpointing through model blocks/stages')
# Optimizer parameters # Optimizer parameters
parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', group = parser.add_argument_group('Optimizer parameters')
group.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "sgd"') help='Optimizer (default: "sgd"')
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON', group.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: None, use opt default)') help='Optimizer Epsilon (default: None, use opt default)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA', group.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)') help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M', group.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='Optimizer momentum (default: 0.9)') help='Optimizer momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=2e-5, group.add_argument('--weight-decay', type=float, default=2e-5,
help='weight decay (default: 2e-5)') help='weight decay (default: 2e-5)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM', group.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)') help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--clip-mode', type=str, default='norm', group.add_argument('--clip-mode', type=str, default='norm',
help='Gradient clipping mode. One of ("norm", "value", "agc")') help='Gradient clipping mode. One of ("norm", "value", "agc")')
parser.add_argument('--layer-decay', type=float, default=None, group.add_argument('--layer-decay', type=float, default=None,
help='layer-wise learning rate decay (default: None)') help='layer-wise learning rate decay (default: None)')
# Learning rate schedule parameters # Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER', group = parser.add_argument_group('Learning rate schedule parameters')
group.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
help='LR scheduler (default: "step"') help='LR scheduler (default: "step"')
parser.add_argument('--lr', type=float, default=0.05, metavar='LR', group.add_argument('--lr', type=float, default=0.05, metavar='LR',
help='learning rate (default: 0.05)') help='learning rate (default: 0.05)')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', group.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
help='learning rate noise on/off epoch percentages') help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', group.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
help='learning rate noise limit percent (default: 0.67)') help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', group.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
help='learning rate noise std-dev (default: 1.0)') help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT', group.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
help='learning rate cycle len multiplier (default: 1.0)') help='learning rate cycle len multiplier (default: 1.0)')
parser.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT', group.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
help='amount to decay each learning rate cycle (default: 0.5)') help='amount to decay each learning rate cycle (default: 0.5)')
parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N', group.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
help='learning rate cycle limit, cycles enabled if > 1') help='learning rate cycle limit, cycles enabled if > 1')
parser.add_argument('--lr-k-decay', type=float, default=1.0, group.add_argument('--lr-k-decay', type=float, default=1.0,
help='learning rate k-decay for cosine/poly (default: 1.0)') help='learning rate k-decay for cosine/poly (default: 1.0)')
parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR', group.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
help='warmup learning rate (default: 0.0001)') help='warmup learning rate (default: 0.0001)')
parser.add_argument('--min-lr', type=float, default=1e-6, metavar='LR', group.add_argument('--min-lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epochs', type=int, default=300, metavar='N', group.add_argument('--epochs', type=int, default=300, metavar='N',
help='number of epochs to train (default: 300)') help='number of epochs to train (default: 300)')
parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N', group.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).') help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
parser.add_argument('--start-epoch', default=None, type=int, metavar='N', group.add_argument('--start-epoch', default=None, type=int, metavar='N',
help='manual epoch number (useful on restarts)') help='manual epoch number (useful on restarts)')
parser.add_argument('--decay-milestones', default=[30, 60], type=int, nargs='+', metavar="MILESTONES", group.add_argument('--decay-milestones', default=[30, 60], type=int, nargs='+', metavar="MILESTONES",
help='list of decay epoch indices for multistep lr. must be increasing') help='list of decay epoch indices for multistep lr. must be increasing')
parser.add_argument('--decay-epochs', type=float, default=100, metavar='N', group.add_argument('--decay-epochs', type=float, default=100, metavar='N',
help='epoch interval to decay LR') help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N', group.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
help='epochs to warmup LR, if scheduler supports') help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', group.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
help='epochs to cooldown LR at min_lr, after cyclic schedule ends') help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', group.add_argument('--patience-epochs', type=int, default=10, metavar='N',
help='patience epochs for Plateau LR scheduler (default: 10') help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', group.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
help='LR decay rate (default: 0.1)') help='LR decay rate (default: 0.1)')
# Augmentation & regularization parameters # Augmentation & regularization parameters
parser.add_argument('--no-aug', action='store_true', default=False, group = parser.add_argument_group('Augmentation and regularization parameters')
group.add_argument('--no-aug', action='store_true', default=False,
help='Disable all training augmentation, override other train aug args') help='Disable all training augmentation, override other train aug args')
parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT', group.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
help='Random resize scale (default: 0.08 1.0)') help='Random resize scale (default: 0.08 1.0)')
parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO', group.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
help='Random resize aspect ratio (default: 0.75 1.33)') help='Random resize aspect ratio (default: 0.75 1.33)')
parser.add_argument('--hflip', type=float, default=0.5, group.add_argument('--hflip', type=float, default=0.5,
help='Horizontal flip training aug probability') help='Horizontal flip training aug probability')
parser.add_argument('--vflip', type=float, default=0., group.add_argument('--vflip', type=float, default=0.,
help='Vertical flip training aug probability') help='Vertical flip training aug probability')
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', group.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)') help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default=None, metavar='NAME', group.add_argument('--aa', type=str, default=None, metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". (default: None)'), help='Use AutoAugment policy. "v0" or "original". (default: None)'),
parser.add_argument('--aug-repeats', type=float, default=0, group.add_argument('--aug-repeats', type=float, default=0,
help='Number of augmentation repetitions (distributed training only) (default: 0)') help='Number of augmentation repetitions (distributed training only) (default: 0)')
parser.add_argument('--aug-splits', type=int, default=0, group.add_argument('--aug-splits', type=int, default=0,
help='Number of augmentation splits (default: 0, valid: 0 or >=2)') help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
parser.add_argument('--jsd-loss', action='store_true', default=False, group.add_argument('--jsd-loss', action='store_true', default=False,
help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.') help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
parser.add_argument('--bce-loss', action='store_true', default=False, group.add_argument('--bce-loss', action='store_true', default=False,
help='Enable BCE loss w/ Mixup/CutMix use.') help='Enable BCE loss w/ Mixup/CutMix use.')
parser.add_argument('--bce-target-thresh', type=float, default=None, group.add_argument('--bce-target-thresh', type=float, default=None,
help='Threshold for binarizing softened BCE targets (default: None, disabled)') help='Threshold for binarizing softened BCE targets (default: None, disabled)')
parser.add_argument('--reprob', type=float, default=0., metavar='PCT', group.add_argument('--reprob', type=float, default=0., metavar='PCT',
help='Random erase prob (default: 0.)') help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='pixel', group.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")') help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1, group.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)') help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False, group.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split') help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.0, group.add_argument('--mixup', type=float, default=0.0,
help='mixup alpha, mixup enabled if > 0. (default: 0.)') help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix', type=float, default=0.0, group.add_argument('--cutmix', type=float, default=0.0,
help='cutmix alpha, cutmix enabled if > 0. (default: 0.)') help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None, group.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (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, group.add_argument('--mixup-prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled') help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup-switch-prob', type=float, default=0.5, group.add_argument('--mixup-switch-prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled') help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup-mode', type=str, default='batch', group.add_argument('--mixup-mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N', group.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
help='Turn off mixup after this epoch, disabled if 0 (default: 0)') help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1, group.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)') help='Label smoothing (default: 0.1)')
parser.add_argument('--train-interpolation', type=str, default='random', group.add_argument('--train-interpolation', type=str, default='random',
help='Training interpolation (random, bilinear, bicubic default: "random")') help='Training interpolation (random, bilinear, bicubic default: "random")')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', group.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.)') help='Dropout rate (default: 0.)')
parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT', group.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
help='Drop connect rate, DEPRECATED, use drop-path (default: None)') help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
parser.add_argument('--drop-path', type=float, default=None, metavar='PCT', group.add_argument('--drop-path', type=float, default=None, metavar='PCT',
help='Drop path rate (default: None)') help='Drop path rate (default: None)')
parser.add_argument('--drop-block', type=float, default=None, metavar='PCT', group.add_argument('--drop-block', type=float, default=None, metavar='PCT',
help='Drop block rate (default: None)') help='Drop block rate (default: None)')
# Batch norm parameters (only works with gen_efficientnet based models currently) # Batch norm parameters (only works with gen_efficientnet based models currently)
parser.add_argument('--bn-momentum', type=float, default=None, group = parser.add_argument_group('Batch norm parameters', 'Only works with gen_efficientnet based models currently.')
group.add_argument('--bn-momentum', type=float, default=None,
help='BatchNorm momentum override (if not None)') help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None, group.add_argument('--bn-eps', type=float, default=None,
help='BatchNorm epsilon override (if not None)') help='BatchNorm epsilon override (if not None)')
parser.add_argument('--sync-bn', action='store_true', group.add_argument('--sync-bn', action='store_true',
help='Enable NVIDIA Apex or Torch synchronized BatchNorm.') help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
parser.add_argument('--dist-bn', type=str, default='reduce', group.add_argument('--dist-bn', type=str, default='reduce',
help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")') help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
parser.add_argument('--split-bn', action='store_true', group.add_argument('--split-bn', action='store_true',
help='Enable separate BN layers per augmentation split.') help='Enable separate BN layers per augmentation split.')
# Model Exponential Moving Average # Model Exponential Moving Average
parser.add_argument('--model-ema', action='store_true', default=False, group = parser.add_argument_group('Model exponential moving average parameters')
group.add_argument('--model-ema', action='store_true', default=False,
help='Enable tracking moving average of model weights') help='Enable tracking moving average of model weights')
parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, group.add_argument('--model-ema-force-cpu', action='store_true', default=False,
help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.') help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
parser.add_argument('--model-ema-decay', type=float, default=0.9998, group.add_argument('--model-ema-decay', type=float, default=0.9998,
help='decay factor for model weights moving average (default: 0.9998)') help='decay factor for model weights moving average (default: 0.9998)')
# Misc # Misc
parser.add_argument('--seed', type=int, default=42, metavar='S', group = parser.add_argument_group('Miscellaneous parameters')
group.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)') help='random seed (default: 42)')
parser.add_argument('--worker-seeding', type=str, default='all', group.add_argument('--worker-seeding', type=str, default='all',
help='worker seed mode (default: all)') help='worker seed mode (default: all)')
parser.add_argument('--log-interval', type=int, default=50, metavar='N', group.add_argument('--log-interval', type=int, default=50, metavar='N',
help='how many batches to wait before logging training status') help='how many batches to wait before logging training status')
parser.add_argument('--recovery-interval', type=int, default=0, metavar='N', group.add_argument('--recovery-interval', type=int, default=0, metavar='N',
help='how many batches to wait before writing recovery checkpoint') help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('--checkpoint-hist', type=int, default=10, metavar='N', group.add_argument('--checkpoint-hist', type=int, default=10, metavar='N',
help='number of checkpoints to keep (default: 10)') help='number of checkpoints to keep (default: 10)')
parser.add_argument('-j', '--workers', type=int, default=4, metavar='N', group.add_argument('-j', '--workers', type=int, default=4, metavar='N',
help='how many training processes to use (default: 4)') help='how many training processes to use (default: 4)')
parser.add_argument('--save-images', action='store_true', default=False, group.add_argument('--save-images', action='store_true', default=False,
help='save images of input bathes every log interval for debugging') help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False, group.add_argument('--amp', action='store_true', default=False,
help='use NVIDIA Apex AMP or Native AMP for mixed precision training') help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
parser.add_argument('--apex-amp', action='store_true', default=False, group.add_argument('--apex-amp', action='store_true', default=False,
help='Use NVIDIA Apex AMP mixed precision') help='Use NVIDIA Apex AMP mixed precision')
parser.add_argument('--native-amp', action='store_true', default=False, group.add_argument('--native-amp', action='store_true', default=False,
help='Use Native Torch AMP mixed precision') help='Use Native Torch AMP mixed precision')
parser.add_argument('--no-ddp-bb', action='store_true', default=False, group.add_argument('--no-ddp-bb', action='store_true', default=False,
help='Force broadcast buffers for native DDP to off.') help='Force broadcast buffers for native DDP to off.')
parser.add_argument('--pin-mem', action='store_true', default=False, group.add_argument('--pin-mem', action='store_true', default=False,
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-prefetcher', action='store_true', default=False, group.add_argument('--no-prefetcher', action='store_true', default=False,
help='disable fast prefetcher') help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH', group.add_argument('--output', default='', type=str, metavar='PATH',
help='path to output folder (default: none, current dir)') help='path to output folder (default: none, current dir)')
parser.add_argument('--experiment', default='', type=str, metavar='NAME', group.add_argument('--experiment', default='', type=str, metavar='NAME',
help='name of train experiment, name of sub-folder for output') help='name of train experiment, name of sub-folder for output')
parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC', group.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
help='Best metric (default: "top1"') help='Best metric (default: "top1"')
parser.add_argument('--tta', type=int, default=0, metavar='N', group.add_argument('--tta', type=int, default=0, metavar='N',
help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)') help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
parser.add_argument("--local_rank", default=0, type=int) group.add_argument("--local_rank", default=0, type=int)
parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False, group.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
help='use the multi-epochs-loader to save time at the beginning of every epoch') help='use the multi-epochs-loader to save time at the beginning of every epoch')
parser.add_argument('--log-wandb', action='store_true', default=False, group.add_argument('--log-wandb', action='store_true', default=False,
help='log training and validation metrics to wandb') help='log training and validation metrics to wandb')

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