mirror of
https://github.com/clearml/clearml
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77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
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import sys
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import random
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try:
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import numpy as np
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except Exception:
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np = None
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try:
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import cv2
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except Exception:
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cv2 = None
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def make_deterministic(seed=1337, cudnn_deterministic=False):
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"""
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Ensure deterministic behavior across PyTorch using the provided random seed.
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This function makes sure that torch, numpy and random use the same random seed.
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When using trains's task, call this function using the task's random seed like so:
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make_deterministic(task.get_random_seed())
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:param int seed: Seed number
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:param bool cudnn_deterministic: In order to make computations deterministic on your specific platform
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and PyTorch release, set this value to True. torch will only allow those CuDNN algorithms that are
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(believed to be) deterministic. This can have a performance impact (slower execution) depending on your model.
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"""
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seed = int(seed) & 0xFFFFFFFF
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torch = sys.modules.get("torch")
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tf = sys.modules.get("tensorflow")
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if cudnn_deterministic:
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try:
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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except Exception:
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pass
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random.seed(seed)
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if np is not None:
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np.random.seed(seed)
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if cv2 is not None:
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try:
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cv2.setRNGSeed(seed)
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except Exception:
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pass
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if torch is not None:
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try:
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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except Exception:
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pass
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if tf is not None:
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# reset graph state
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try:
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import tensorflow
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from tensorflow.python.eager.context import _context
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eager_mode_bypass = _context is None
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except Exception:
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eager_mode_bypass = False
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if not eager_mode_bypass:
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try:
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tf.set_random_seed(seed)
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except Exception:
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pass
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try:
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tf.random.set_random_seed(seed)
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except Exception:
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pass
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make_deterministic()
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