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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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