torchbenchmark/models/timm_efficientdet/args.py

import yaml
import argparse
from timm.utils import add_bool_arg

def get_args(config_file=None):
    def _parse_args():
        if config_file:
            with open(config_file, 'r') as f:
                cfg = yaml.safe_load(f)
                parser.set_defaults(**cfg)

        # There may be remaining unrecognized options
        # The main arg parser parses the rest of the args, the usual
        # defaults will have been overridden if config file specified.
        args, _ = parser.parse_known_args()

        # Cache the args as a text string to save them in the output dir later
        args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
        return args, args_text
    # The first arg parser parses out only the --config argument, this argument is used to
    # load a yaml file containing key-values that override the defaults for the main parser below
    parser = argparse.ArgumentParser(description='Training Config', add_help=False)
    parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                        help='YAML config file specifying default arguments')


    parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
    # Dataset / Model parameters
    # parser.add_argument('root', metavar='DIR',
    #                     help='path to dataset')
    parser.add_argument('--dataset', default='coco', type=str, metavar='DATASET',
                        help='Name of dataset to train (default: "coco"')
    parser.add_argument('--model', default='tf_efficientdet_d1', type=str, metavar='MODEL',
                        help='Name of model to train (default: "tf_efficientdet_d1"')
    add_bool_arg(parser, 'redundant-bias', default=None, help='override model config for redundant bias')
    add_bool_arg(parser, 'soft-nms', default=None, help='override model config for soft-nms')
    parser.add_argument('--val-skip', type=int, default=0, metavar='N',
                        help='Skip every N validation samples.')
    parser.add_argument('--num-classes', type=int, default=None, metavar='N',
                        help='Override num_classes in model config if set. For fine-tuning from pretrained.')
    parser.add_argument('--pretrained', action='store_true', default=False,
                        help='Start with pretrained version of specified network (if avail)')
    parser.add_argument('--no-pretrained-backbone', action='store_true', default=False,
                        help='Do not start with pretrained backbone weights, fully random.')
    parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
                        help='Initialize model from this checkpoint (default: none)')
    parser.add_argument('--resume', default='', type=str, metavar='PATH',
                        help='Resume full model and optimizer state from checkpoint (default: none)')
    parser.add_argument('--no-resume-opt', action='store_true', default=False,
                        help='prevent resume of optimizer state when resuming model')
    parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                        help='Override mean pixel value of dataset')
    parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
                        help='Override std deviation of of dataset')
    parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                        help='Image resize interpolation type (overrides model)')
    parser.add_argument('--fill-color', default=None, type=str, metavar='NAME',
                        help='Image augmentation fill (background) color ("mean" or int)')
    parser.add_argument('--batch-size', type=int, default=32, metavar='N',
                        help='input batch size for training (default: 32)')
    parser.add_argument('--clip-grad', type=float, default=10.0, metavar='NORM',
                        help='Clip gradient norm (default: 10.0)')

    # Optimizer parameters
    parser.add_argument('--opt', default='momentum', type=str, metavar='OPTIMIZER',
                        help='Optimizer (default: "momentum"')
    parser.add_argument('--opt-eps', default=1e-3, type=float, metavar='EPSILON',
                        help='Optimizer Epsilon (default: 1e-3)')
    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=4e-5,
                        help='weight decay (default: 0.00004)')

    # Learning rate schedule parameters
    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                        help='LR scheduler (default: "step"')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    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('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                        help='learning rate cycle len multiplier (default: 1.0)')
    parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
                        help='learning rate cycle limit')
    parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                        help='warmup learning rate (default: 0.0001)')
    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
    parser.add_argument('--epochs', type=int, default=300, metavar='N',
                        help='number of epochs to train (default: 2)')
    parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                        help='manual epoch number (useful on restarts)')
    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                        help='epoch interval to decay LR')
    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=None, metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". (default: None)'),
    parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                        help='Random erase prob (default: 0.)')
    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('--train-interpolation', type=str, default='random',
                        help='Training interpolation (random, bilinear, bicubic default: "random")')

    # loss
    parser.add_argument('--smoothing', type=float, default=None, help='override model config label smoothing')
    add_bool_arg(parser, 'jit-loss', default=None, help='override model config for torchscript jit loss fn')
    add_bool_arg(parser, 'legacy-focal', default=None, help='override model config to use legacy focal loss')

    # Model Exponential Moving Average
    parser.add_argument('--model-ema', action='store_true', default=False,
                        help='Enable tracking moving average of model weights')
    parser.add_argument('--model-ema-decay', type=float, default=0.9998,
                        help='decay factor for model weights moving average (default: 0.9998)')

    # Misc
    parser.add_argument('--sync-bn', action='store_true',
                        help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
    parser.add_argument('--dist-bn', type=str, default='',
                        help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
    parser.add_argument('--seed', type=int, default=42, metavar='S',
                        help='random seed (default: 42)')
    parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
                        help='how many batches to wait before writing recovery checkpoint')
    parser.add_argument('-j', '--workers', type=int, default=0, metavar='N',
                        help='how many training processes to use (default: 0)')
    parser.add_argument('--save-images', action='store_true', default=False,
                        help='save images of input bathes every log interval for debugging')
    parser.add_argument('--amp', action='store_true', default=False,
                        help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
    parser.add_argument('--apex-amp', action='store_true', default=False,
                        help='Use NVIDIA Apex AMP mixed precision')
    parser.add_argument('--native-amp', action='store_true', default=False,
                        help='Use Native Torch AMP mixed precision')
    parser.add_argument('--channels-last', action='store_true', default=False,
                        help='Use channels_last memory layout')
    parser.add_argument('--pin-mem', action='store_true', default=False,
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no-prefetcher', action='store_true', default=False,
                        help='disable fast prefetcher')
    parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                        help='convert model torchscript for inference')
    add_bool_arg(parser, 'bench-labeler', default=False,
                help='label targets in model bench, increases GPU load at expense of loader processes')
    parser.add_argument('--output', default='', type=str, metavar='PATH',
                        help='path to output folder (default: none, current dir)')
    parser.add_argument('--eval-metric', default='map', type=str, metavar='EVAL_METRIC',
                        help='Best metric (default: "map"')
    parser.add_argument('--tta', type=int, default=0, metavar='N',
                        help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
    parser.add_argument("--local_rank", default=0, type=int)

    # Evaluation parameters
    parser.add_argument('--eval-interpolation', default='bilinear', type=str, metavar='NAME',
                help='Image resize interpolation type (overrides model)')
    parser.add_argument('--img-size', default=None, type=int,
                metavar='N', help='Input image dimension, uses model default if empty')
    parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
                help='path to latest checkpoint (default: none)')
    parser.add_argument('--use-ema', dest='use_ema', action='store_true',
                help='use ema version of weights if present')

    args, _ = _parse_args()
    return args