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68 lines
1.4 KiB
68 lines
1.4 KiB
2 years ago
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"""
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Misc utilities
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"""
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import json
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import random
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import sys
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import torch
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import numpy as np
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from uniter_model.utils.logger import LOGGER
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class NoOp(object):
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""" useful for distributed training No-Ops """
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def __getattr__(self, name):
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return self.noop
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def noop(self, *args, **kwargs):
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return
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def parse_with_config(parser):
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args = parser.parse_args()
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if args.config is not None:
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config_args = json.load(open(args.config))
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override_keys = {arg[2:].split('=')[0] for arg in sys.argv[1:]
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if arg.startswith('--')}
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for k, v in config_args.items():
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if k not in override_keys:
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setattr(args, k, v)
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del args.config
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return args
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VE_ENT2IDX = {
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'contradiction': 0,
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'entailment': 1,
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'neutral': 2
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}
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VE_IDX2ENT = {
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0: 'contradiction',
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1: 'entailment',
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2: 'neutral'
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}
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class Struct(object):
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def __init__(self, dict_):
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self.__dict__.update(dict_)
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def set_dropout(model, drop_p):
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for name, module in model.named_modules():
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# we might want to tune dropout for smaller dataset
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if isinstance(module, torch.nn.Dropout):
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if module.p != drop_p:
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module.p = drop_p
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LOGGER.info(f'{name} set to {drop_p}')
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def set_random_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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