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import argparse
import logging
import os
import yaml
logger = logging.getLogger(__name__)
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "1"):
return True
elif v.lower() in ("no", "false", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
# fmt: off
def create_parser():
# 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_config = argparse.ArgumentParser(description='Training Config', add_help=False)
parser_config.add_argument('-c', '--config', type=str, default='',
help='YAML config file specifying default arguments (default="")')
# The main parser. It inherits the --config argument for better help information.
parser = argparse.ArgumentParser(description='ImageNet Training', parents=[parser_config])
# System parameters
group = parser.add_argument_group('System parameters')
group.add_argument('--mode', type=int, default=0,
help='Running in GRAPH_MODE(0) or PYNATIVE_MODE(1) (default=0)')
group.add_argument('--distribute', type=str2bool, nargs='?', const=True, default=False,
help='Run distribute (default=False)')
group.add_argument('--val_while_train', type=str2bool, nargs='?', const=True, default=False,
help='Verify accuracy while training (default=False)')
group.add_argument('--val_interval', type=int, default=1,
help='Interval for validation while training. Unit: epoch (default=1)')
group.add_argument('--log_interval', type=int, default=100,
help='Interval for print training log. Unit: step (default=100)')
group.add_argument('--seed', type=int, default=42,
help='Seed value for determining randomness in numpy, random, and mindspore (default=42)')
# Dataset parameters
group = parser.add_argument_group('Dataset parameters')
group.add_argument('--dataset', type=str, default='imagenet',
help='Type of dataset (default="imagenet")')
group.add_argument('--data_dir', type=str, default='./',
help='Path to dataset (default="./")')
group.add_argument('--train_split', type=str, default='train',
help='Dataset train split name (default="train")')
group.add_argument('--val_split', type=str, default='val',
help='Dataset validation split name (default="val")')
group.add_argument('--dataset_download', type=str2bool, nargs='?', const=True, default=False,
help='If downloading the dataset, only support Mnist, Cifar10 and Cifar100 (default=False)')
group.add_argument('--num_parallel_workers', type=int, default=8,
help='Number of parallel workers (default=8)')
group.add_argument('--shuffle', type=str2bool, nargs='?', const=True, default=True,
help='Whether or not to perform shuffle on the dataset (default=True)')
group.add_argument('--num_samples', type=int, default=None,
help='Number of elements to sample. None means sample all elements (default=None)')
group.add_argument('--batch_size', type=int, default=128,
help='Number of batch size (default=128)')
group.add_argument('--drop_remainder', type=str2bool, nargs='?', const=True, default=True,
help='Determines whether or not to drop the last block whose data '
'row number is less than batch size (default=True)')
# Augmentation parameters
group = parser.add_argument_group('Augmentation parameters')
group.add_argument('--image_resize', type=int, default=224,
help='Crop the size of the image (default=224)')
group.add_argument('--scale', type=tuple, default=(0.08, 1.0),
help='Random resize scale (default=(0.08, 1.0))')
group.add_argument('--ratio', type=tuple, default=(0.75, 1.333),
help='Random resize aspect ratio (default=(0.75, 1.333))')
group.add_argument('--hflip', type=float, default=0.5,
help='Horizontal flip training aug probability (default=0.5)')
group.add_argument('--vflip', type=float, default=0.0,
help='Vertical flip training aug probability (default=0.0)')
group.add_argument('--color_jitter', type=float, default=0.4,
help='Color jitter factor (default=0.4)')
group.add_argument('--interpolation', type=str, default='bilinear',
help='Image interpolation mode for resize operator(default="bilinear")')
group.add_argument('--auto_augment', type=str, default=None,
help='AutoAugment policy. "randaug" for RandAugment, "autoaug" for original AutoAugment, '
'"autoaugr" for AutoAugment with increasing posterize. and "3a" for AutoAugment with only 3'
'transformations. '
'"augmix" for AugmixAugment. '
'"trivialaugwide" for TrivialAugmentWide. '
'If apply, recommend for imagenet: randaug-m7-mstd0.5 (default=None).'
'Example: "randaug-m10-n2-w0-mstd0.5-mmax10-inc0", "autoaug-mstd0.5" or autoaugr-mstd0.5.')
group.add_argument('--aug_splits', type=int, default=0,
help='Number of augmentation splits (default: 0, valid: 3 (currently, only support 3 splits))'
'it should be set with one auto_augment')
group.add_argument('--re_prob', type=float, default=0.0,
help='Probability of performing erasing (default=0.0)')
group.add_argument('--re_scale', type=tuple, default=(0.02, 0.33),
help='Range of area scale of the erased area (default=(0.02, 0.33))')
group.add_argument('--re_ratio', type=tuple, default=(0.3, 3.3),
help='Range of aspect ratio of the erased area (default=(0.3, 3.3))')
group.add_argument('--re_value', default=0,
help='Pixel value used to pad the erased area (default=0),'
'please refer to mindspore.dataset.vision.RandomErasing.')
group.add_argument('--re_max_attempts', type=int, default=10,
help='The maximum number of attempts to propose a valid erased area, '
'beyond which the original image will be returned (default=10)')
group.add_argument('--mean', type=list, default=[0.485 * 255, 0.456 * 255, 0.406 * 255],
help='List or tuple of mean values for each channel, '
'with respect to channel order (default=[0.485 * 255, 0.456 * 255, 0.406 * 255])')
group.add_argument('--std', type=list, default=[0.229 * 255, 0.224 * 255, 0.225 * 255],
help='List or tuple of mean values for each channel, '
'with respect to channel order (default=[0.229 * 255, 0.224 * 255, 0.225 * 255])')
group.add_argument('--crop_pct', type=float, default=0.875,
help='Input image center crop percent (default=0.875)')
group.add_argument('--mixup', type=float, default=0.0,
help='Hyperparameter of beta distribution of mixup. '
'Recommended value is 0.2 for ImageNet (default=0.0)')
group.add_argument('--cutmix', type=float, default=0.0,
help='Hyperparameter of beta distribution of cutmix (default=0.0)')
group.add_argument('--cutmix_prob', type=float, default=1.0,
help='Probability of applying cutmix and/or mixup (default=1.0)')
group.add_argument('--aug_repeats', type=int, default=0,
help='Number of dataset repetition for repeated augmentation. '
'If 0 or 1, repeated augmentation is disabled. '
'Otherwise, repeated augmentation is enabled and the common choice is 3 (default=0)')
# Model parameters
group = parser.add_argument_group('Model parameters')
group.add_argument('--model', type=str, default='mobilenet_v2_035_224',
help='Name of model')
group.add_argument('--num_classes', type=int, default=None,
help='Number of label classes. If None, read from standard datasets (default=None)')
group.add_argument('--in_channels', type=int, default=3,
help='Input channels (default=3)')
group.add_argument('--drop_rate', type=float, default=None,
help='Drop rate (default=None)')
group.add_argument('--drop_path_rate', type=float, default=None,
help='Drop path rate (default=None)')
group.add_argument('--pretrained', type=str2bool, nargs='?', const=True, default=False,
help='Load pretrained model (default=False)')
group.add_argument('--ckpt_path', type=str, default='',
help='Initialize model from this checkpoint. '
'If resume training, specify the checkpoint path (default="")')
group.add_argument('--resume_opt', type=str2bool, nargs='?', const=True, default=False,
help='Resume optimizer state including LR (default=False)')
group.add_argument('--keep_checkpoint_max', type=int, default=10,
help='Max number of checkpoint files (default=10)')
group.add_argument('--ckpt_save_dir', type=str, default="./ckpt",
help='Path of checkpoint (default="./ckpt")')
group.add_argument('--ckpt_save_interval', type=int, default=1,
help='Checkpoint saving interval. Unit: epoch (default=1)')
group.add_argument('--ckpt_save_policy', type=str, default='latest_k',
help='Checkpoint saving strategy. The optional values is '
'None, "top_k" or "latest_k" (default="latest_k")')
group.add_argument('--epoch_size', type=int, default=90,
help='Train epoch size (default=90)')
group.add_argument('--dataset_sink_mode', type=str2bool, nargs='?', const=True, default=True,
help='The dataset sink mode (default=True)')
group.add_argument('--ema', type=str2bool, nargs='?', const=True, default=False,
help='Training with ema (default=False)')
group.add_argument('--ema_decay', type=float, default=0.9999,
help='EMA decay (default=0.9999)')
group.add_argument('--clip_grad', type=str2bool, nargs='?', const=True, default=False,
help='Whether use clip grad (default=False)')
group.add_argument('--clip_value', type=float, default=15.0,
help='Clip value (default=15.0)')
group.add_argument('--layer_decay', type=float, default=None,
help='layer-wise learning rate decay (default: None)')
group.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Accumulate the gradients of n batches before update.")
# Optimize parameters
group = parser.add_argument_group('Optimizer parameters')
group.add_argument('--opt', type=str, default='adam',
choices=['sgd', 'momentum', 'adam', 'adamw', 'lion', 'rmsprop', 'adagrad', 'lamb', "nadam"],
help='Type of optimizer (default="adam")')
group.add_argument('--momentum', type=float, default=0.9,
help='Hyperparameter of type float, means momentum for the moving average. '
'It must be at least 0.0 (default=0.9)')
group.add_argument('--weight_decay', type=float, default=1e-6,
help='Weight decay (default=1e-6)')
group.add_argument('--use_nesterov', type=str2bool, nargs='?', const=True, default=False,
help='Enables the Nesterov momentum (default=False)')
group.add_argument('--weight_decay_filter', type=str, default="disable",
choices=['disable', 'auto', 'norm_and_bias'],
help='filter parameters from weight_decay. '
'choice: "disable" - No parameters to filter from weight_decay; "auto" - In this case, '
'we do not apply weight decay filtering to any parameters. However, MindSpore currently '
'automatically filters norm parameters from weight decay. It is unclear whether there '
'will be any changes in future versions of MindSpore, so it is recommended to stay updated;'
'"norm_and_bias" - Filter the paramtersof Norm layer and Bias from weight decay')
group.add_argument('--eps', type=float, default=1e-10,
help='Term Added to the Denominator to Improve Numerical Stability (default=1e-10)')
# Scheduler parameters
group = parser.add_argument_group('Scheduler parameters')
group.add_argument('--scheduler', type=str, default='cosine_decay',
choices=['constant', 'cosine_decay', 'exponential_decay', 'step_decay',
'multi_step_decay', 'one_cycle', 'cyclic'],
help='Type of scheduler (default="cosine_decay")')
group.add_argument('--lr', type=float, default=0.001,
help='Learning rate (default=0.001)')
group.add_argument('--min_lr', type=float, default=1e-6,
help='The minimum value of learning rate if scheduler supports (default=1e-6)')
group.add_argument('--warmup_epochs', type=int, default=3,
help='Warmup epochs (default=3)')
group.add_argument('--warmup_factor', type=float, default=0.0,
help='Warmup factor of learning rate (default=0.0)')
group.add_argument('--decay_epochs', type=int, default=100,
help='Decay epochs (default=100)')
group.add_argument('--decay_rate', type=float, default=0.9,
help='LR decay rate if scheduler supports (default=0.9)')
group.add_argument('--multi_step_decay_milestones', type=list, default=[30, 60, 90],
help='List of epoch milestones for lr decay, which is ONLY effective for '
'the multi_step_decay scheduler. LR will be decay by decay_rate at the milestone epoch.')
group.add_argument('--lr_epoch_stair', type=str2bool, nargs='?', const=True, default=False,
help='If True, LR will be updated in the first step of each epoch and '
'LRs are the same in the remaining steps in the epoch. Otherwise, '
'learning rate is updated every step dynamically (default=False)')
group.add_argument('--num_cycles', type=int, default=1,
help='Num of cycles for cosine decay and cyclic (default=1)')
group.add_argument('--cycle_decay', type=float, default=1.0,
help='Decay rate of lr max in each cosine cycle (default=1.0)')
# Loss parameters
group = parser.add_argument_group('Loss parameters')
group.add_argument('--loss', type=str, default='CE', choices=['BCE', 'CE'],
help='Type of loss, BCE (BinaryCrossEntropy) or CE (CrossEntropy) (default="CE")')
group.add_argument('--label_smoothing', type=float, default=0.0,
help='Use label smoothing (default=0.0)')
group.add_argument('--aux_factor', type=float, default=0.0,
help='Aux loss factor (default=0.0)')
group.add_argument('--reduction', type=str, default='mean',
help='Type of reduction to be applied to loss (default="mean")')
# AMP parameters
group = parser.add_argument_group('Auto mixing precision parameters')
group.add_argument('--amp_level', type=str, default='O0',
help='Amp level - Auto Mixed Precision level for saving memory and acceleration. '
'Choice: O0 - all FP32, O1 - only cast ops in white-list to FP16, '
'O2 - cast all ops except for blacklist to FP16, '
'O3 - cast all ops to FP16. (default="O0").')
group.add_argument('--val_amp_level', type=str, default='O0',
help='Amp level used in validation '
'Choice: O0 - all FP32, O1 - only cast ops in white-list to FP16, '
'O2 - cast all ops except for blacklist to FP16, '
'O3 - cast all ops to FP16. (default="O0").')
group.add_argument('--amp_cast_list', type=str, default=None,
help='At the cell level, customize the black-list or white-list to cast cells to '
'FP16 based on the value of argument "amp_level". If None, use the built-in '
'black-list and white-list. (default=None) '
'If amp_level="O0" or "O3", this argument has no effect. '
'If amp_level="O1", cast all cells in the white-list to FP16. '
'If amp_level="O2", cast all cells except for the black-list to FP16. '
'Example: "[nn.Conv1d, nn.Conv2d]" or "[nn.BatchNorm1d, nn.BatchNorm2d]".')
group.add_argument('--loss_scale_type', type=str, default='fixed',
choices=['fixed', 'dynamic', 'auto'],
help='The type of loss scale (default="fixed")')
group.add_argument('--loss_scale', type=float, default=1.0,
help='Loss scale (default=1.0)')
group.add_argument('--drop_overflow_update', type=bool, default=False,
help='Whether to execute optimizer if there is an overflow (default=False)')
return parser_config, parser
# fmt: on
def _check_cfgs_in_parser(cfgs: dict, parser: argparse.ArgumentParser):
actions_dest = [action.dest for action in parser._actions]
defaults_key = parser._defaults.keys()
for k in cfgs.keys():
if k not in actions_dest and k not in defaults_key:
raise KeyError(f"{k} does not exist in ArgumentParser!")
def parse_args(args=None):
parser_config, parser = create_parser()
# Do we have a config file to parse?
args_config, remaining = parser_config.parse_known_args(args)
if args_config.config:
with open(args_config.config, "r") as f:
cfg = yaml.safe_load(f)
_check_cfgs_in_parser(cfg, parser)
parser.set_defaults(**cfg)
parser.set_defaults(config=args_config.config)
# The main arg parser parses the rest of the args, the usual
# defaults will have been overridden if config file specified.
args = parser.parse_args(remaining)
return args
def save_args(args: argparse.Namespace, filepath: str, rank: int = 0) -> None:
"""If in master process, save ``args`` to a YAML file. Otherwise, do nothing.
Args:
args (Namespace): The parsed arguments to be saved.
filepath (str): A filepath ends with ``.yaml``.
rank (int): Process rank in the distributed training. Defaults to 0.
"""
assert isinstance(args, argparse.Namespace)
assert filepath.endswith(".yaml")
if rank != 0:
return
os.makedirs(os.path.dirname(os.path.abspath(filepath)), exist_ok=True)
with open(filepath, "w") as f:
yaml.safe_dump(args.__dict__, f)
logger.info(f"Args is saved to {filepath}.")
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