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from pytorch_lightning.callbacks import ModelCheckpoint
class UniversalCheckpoint(ModelCheckpoint):
@staticmethod
def add_argparse_args(parent_args):
parser = parent_args.add_argument_group('universal checkpoint callback')
parser.add_argument('--monitor', default='train_loss', type=str)
parser.add_argument('--mode', default='min', type=str)
parser.add_argument('--save_ckpt_path', default='./ckpt/', type=str)
parser.add_argument('--load_ckpt_path', default='./ckpt/', type=str)
parser.add_argument(
'--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
parser.add_argument('--save_last', action='store_true', default=False)
parser.add_argument('--save_top_k', default=3, type=float)
parser.add_argument('--every_n_train_steps', default=None, type=float)
parser.add_argument('--save_weights_only', action='store_true', default=False)
parser.add_argument('--every_n_epochs', default=None, type=int)
parser.add_argument('--save_on_train_epoch_end', action='store_true', default=None)
return parent_args
def __init__(self, args):
super().__init__(monitor=args.monitor,
save_top_k=args.save_top_k,
mode=args.mode,
every_n_train_steps=args.every_n_train_steps,
save_weights_only=args.save_weights_only,
dirpath=args.save_ckpt_path,
filename=args.filename,
save_last=args.save_last,
every_n_epochs=args.every_n_epochs,
save_on_train_epoch_end=args.save_on_train_epoch_end)
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