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import os
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import random
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import sys
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import time
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import warnings
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from getpass import getuser
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from socket import gethostname
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import numpy as np
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import torch
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import annotator.uniformer.mmcv as mmcv
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def get_host_info():
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"""Get hostname and username.
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Return empty string if exception raised, e.g. ``getpass.getuser()`` will
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lead to error in docker container
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"""
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host = ''
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try:
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host = f'{getuser()}@{gethostname()}'
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except Exception as e:
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warnings.warn(f'Host or user not found: {str(e)}')
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finally:
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return host
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def get_time_str():
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return time.strftime('%Y%m%d_%H%M%S', time.localtime())
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def obj_from_dict(info, parent=None, default_args=None):
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"""Initialize an object from dict.
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The dict must contain the key "type", which indicates the object type, it
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can be either a string or type, such as "list" or ``list``. Remaining
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fields are treated as the arguments for constructing the object.
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Args:
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info (dict): Object types and arguments.
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parent (:class:`module`): Module which may containing expected object
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classes.
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default_args (dict, optional): Default arguments for initializing the
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object.
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Returns:
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any type: Object built from the dict.
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"""
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assert isinstance(info, dict) and 'type' in info
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assert isinstance(default_args, dict) or default_args is None
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args = info.copy()
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obj_type = args.pop('type')
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if mmcv.is_str(obj_type):
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if parent is not None:
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obj_type = getattr(parent, obj_type)
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else:
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obj_type = sys.modules[obj_type]
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elif not isinstance(obj_type, type):
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raise TypeError('type must be a str or valid type, but '
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f'got {type(obj_type)}')
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if default_args is not None:
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for name, value in default_args.items():
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args.setdefault(name, value)
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return obj_type(**args)
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def set_random_seed(seed, deterministic=False, use_rank_shift=False):
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"""Set random seed.
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Args:
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seed (int): Seed to be used.
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deterministic (bool): Whether to set the deterministic option for
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CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
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to True and `torch.backends.cudnn.benchmark` to False.
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Default: False.
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rank_shift (bool): Whether to add rank number to the random seed to
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have different random seed in different threads. Default: False.
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"""
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if use_rank_shift:
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rank, _ = mmcv.runner.get_dist_info()
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seed += rank
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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(seed)
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torch.cuda.manual_seed_all(seed)
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os.environ['PYTHONHASHSEED'] = str(seed)
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if deterministic:
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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