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59 lines
2.3 KiB
59 lines
2.3 KiB
import logging
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import os
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from collections import OrderedDict
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import torch
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from .train_state import TrainState, serialize_train_state, deserialize_train_state
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_logger = logging.getLogger(__name__)
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def resume_train_checkpoint(
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train_state,
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checkpoint_path,
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resume_opt=True,
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deserialize_fn=deserialize_train_state,
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log_info=True):
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raise NotImplementedError
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# resume_epoch = None
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# if os.path.isfile(checkpoint_path):
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# checkpoint = torch.load(checkpoint_path, map_location='cpu')
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#
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# if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
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# if log_info:
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# _logger.info('Restoring model state from checkpoint...')
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# new_state_dict = OrderedDict()
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# for k, v in checkpoint['state_dict'].items():
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# name = k[7:] if k.startswith('module') else k
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# new_state_dict[name] = v
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# model.load_state_dict(new_state_dict)
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#
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# if optimizer is not None and 'optimizer' in checkpoint:
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# if log_info:
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# _logger.info('Restoring optimizer state from checkpoint...')
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# optimizer.load_state_dict(checkpoint['optimizer'])
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#
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# if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
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# if log_info:
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# _logger.info('Restoring AMP loss scaler state from checkpoint...')
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# loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
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#
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# if 'epoch' in checkpoint:
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# resume_epoch = checkpoint['epoch']
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# if 'version' in checkpoint and checkpoint['version'] > 1:
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# resume_epoch += 1 # start at the next epoch, old checkpoints incremented before save
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#
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# if log_info:
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# _logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
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# else:
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# model.load_state_dict(checkpoint)
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# if log_info:
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# _logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
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# return resume_epoch
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# else:
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# _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
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# raise FileNotFoundError()
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