47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
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# Copyright 2019 Mobvoi Inc. All Rights Reserved.
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# Author: binbinzhang@mobvoi.com (Binbin Zhang)
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import logging
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import os
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import re
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import yaml
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import torch
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def load_checkpoint(model: torch.nn.Module, path: str) -> dict:
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if torch.cuda.is_available():
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logging.info('Checkpoint: loading from checkpoint %s for GPU' % path)
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checkpoint = torch.load(path)
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else:
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logging.info('Checkpoint: loading from checkpoint %s for CPU' % path)
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checkpoint = torch.load(path, map_location='cpu')
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model.load_state_dict(checkpoint)
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info_path = re.sub('.pt$', '.yaml', path)
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configs = {}
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if os.path.exists(info_path):
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with open(info_path, 'r') as fin:
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configs = yaml.load(fin, Loader=yaml.FullLoader)
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return configs
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def save_checkpoint(model: torch.nn.Module, path: str, infos=None):
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'''
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Args:
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infos (dict or None): any info you want to save.
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'''
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logging.info('Checkpoint: save to checkpoint %s' % path)
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if isinstance(model, torch.nn.DataParallel):
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state_dict = model.module.state_dict()
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elif isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save(state_dict, path)
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info_path = re.sub('.pt$', '.yaml', path)
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if infos is None:
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infos = {}
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with open(info_path, 'w') as fout:
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data = yaml.dump(infos)
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fout.write(data)
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