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https://github.com/open-mmlab/mmcv.git
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ef48a47389
* improve digit_version & use it for version_checking * more testing for digit_version * setuptools >= 50 is needed * fix CI * add debuging log * >= to == * fix lint * remove * add failure case * replace * fix * consider TORCH_VERSION == 'parrots' * add unittest * digit_version do not deal with the case if 'parrots' in version name.
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import os
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import random
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import numpy as np
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import torch
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from mmcv.runner import set_random_seed
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from mmcv.utils import TORCH_VERSION, digit_version
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is_rocm_pytorch = False
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if digit_version(TORCH_VERSION) >= digit_version('1.5'):
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from torch.utils.cpp_extension import ROCM_HOME
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is_rocm_pytorch = True if ((torch.version.hip is not None) and
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(ROCM_HOME is not None)) else False
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def test_set_random_seed():
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set_random_seed(0)
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a_random = random.randint(0, 10)
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a_np_random = np.random.rand(2, 2)
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a_torch_random = torch.rand(2, 2)
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assert torch.backends.cudnn.deterministic is False
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assert torch.backends.cudnn.benchmark is False
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assert os.environ['PYTHONHASHSEED'] == str(0)
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set_random_seed(0, True)
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b_random = random.randint(0, 10)
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b_np_random = np.random.rand(2, 2)
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b_torch_random = torch.rand(2, 2)
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assert torch.backends.cudnn.deterministic is True
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if is_rocm_pytorch:
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assert torch.backends.cudnn.benchmark is True
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else:
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assert torch.backends.cudnn.benchmark is False
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assert a_random == b_random
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assert np.equal(a_np_random, b_np_random).all()
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assert torch.equal(a_torch_random, b_torch_random)
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