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import logging |
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import random |
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from typing import List, Optional, Tuple |
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import numpy as np |
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import torch |
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from mmengine.dist import get_rank, sync_random_seed |
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from mmengine.logging import print_log |
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from mmengine.utils import digit_version, is_list_of |
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from mmengine.utils.dl_utils import TORCH_VERSION |
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def calc_dynamic_intervals( |
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start_interval: int, |
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dynamic_interval_list: Optional[List[Tuple[int, int]]] = None |
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) -> Tuple[List[int], List[int]]: |
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"""Calculate dynamic intervals. |
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Args: |
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start_interval (int): The interval used in the beginning. |
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dynamic_interval_list (List[Tuple[int, int]], optional): The |
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first element in the tuple is a milestone and the second |
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element is a interval. The interval is used after the |
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corresponding milestone. Defaults to None. |
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Returns: |
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Tuple[List[int], List[int]]: a list of milestone and its corresponding |
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intervals. |
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""" |
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if dynamic_interval_list is None: |
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return [0], [start_interval] |
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assert is_list_of(dynamic_interval_list, tuple) |
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dynamic_milestones = [0] |
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dynamic_milestones.extend( |
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[dynamic_interval[0] for dynamic_interval in dynamic_interval_list]) |
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dynamic_intervals = [start_interval] |
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dynamic_intervals.extend( |
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[dynamic_interval[1] for dynamic_interval in dynamic_interval_list]) |
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return dynamic_milestones, dynamic_intervals |
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def set_random_seed(seed: Optional[int] = None, |
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deterministic: bool = False, |
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diff_rank_seed: bool = False) -> int: |
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"""Set random seed. |
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Args: |
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seed (int, optional): 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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Defaults to False. |
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diff_rank_seed (bool): Whether to add rank number to the random seed to |
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have different random seed in different threads. Defaults to False. |
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""" |
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if seed is None: |
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seed = sync_random_seed() |
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if diff_rank_seed: |
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rank = get_rank() |
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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_all(seed) |
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if deterministic: |
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if torch.backends.cudnn.benchmark: |
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print_log( |
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'torch.backends.cudnn.benchmark is going to be set as ' |
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'`False` to cause cuDNN to deterministically select an ' |
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'algorithm', |
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logger='current', |
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level=logging.WARNING) |
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torch.backends.cudnn.deterministic = True |
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torch.backends.cudnn.benchmark = False |
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if digit_version(TORCH_VERSION) >= digit_version('1.10.0'): |
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torch.use_deterministic_algorithms(True) |
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return seed |
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