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from pytorch_lightning.callbacks import ModelCheckpoint |
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import os |
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class UniversalCheckpoint(ModelCheckpoint): |
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@staticmethod |
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def add_argparse_args(parent_args): |
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parser = parent_args.add_argument_group('universal checkpoint callback') |
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parser.add_argument('--monitor', default='step', type=str) |
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parser.add_argument('--mode', default='max', type=str) |
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parser.add_argument('--save_ckpt_path', default='./ckpt/', type=str) |
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parser.add_argument('--load_ckpt_path', default='./ckpt/', type=str) |
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parser.add_argument( |
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'--filename', default='model-ep{epoch:02d}-st{step:d}', type=str) |
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parser.add_argument('--save_last', action='store_true', default=False) |
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parser.add_argument('--save_top_k', default=10, type=float) |
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parser.add_argument('--every_n_train_steps', default=None, type=float) |
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parser.add_argument('--save_weights_only', action='store_true', default=False) |
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parser.add_argument('--every_n_epochs', default=None, type=int) |
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parser.add_argument('--save_on_train_epoch_end', action='store_true', default=None) |
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return parent_args |
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def __init__(self, args): |
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super().__init__(monitor=args.monitor, |
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save_top_k=args.save_top_k, |
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mode=args.mode, |
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every_n_train_steps=args.every_n_train_steps, |
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save_weights_only=args.save_weights_only, |
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dirpath=args.save_ckpt_path, |
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filename=args.filename, |
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save_last=args.save_last, |
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every_n_epochs=args.every_n_epochs, |
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save_on_train_epoch_end=args.save_on_train_epoch_end) |
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if args.load_ckpt_path is not None and \ |
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not os.path.exists(args.load_ckpt_path): |
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print('--------warning no checkpoint found--------, remove args') |
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args.load_ckpt_path = None |
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