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"""
@Date: 2021/08/15
@description:
"""
import random
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import os
import cv2
def init_env(seed, deterministic=False, loader_work_num=0):
# Fix seed
# Python & NumPy
np.random.seed(seed)
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
# PyTorch
torch.manual_seed(seed) # 为CPU设置随机种子
if torch.cuda.is_available():
torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(seed) # 为所有GPU设置随机种子
# cuDNN
if deterministic:
# 复现
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True # 将这个 flag 置为 True 的话，每次返回的卷积算法将是确定的，即默认算法
else:
cudnn.benchmark = True # 如果网络的输入数据维度或类型上变化不大，设置true
torch.backends.cudnn.deterministic = False
# Using multiple threads in Opencv can cause deadlocks
if loader_work_num != 0:
cv2.setNumThreads(0)
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