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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.backends.cudnn as cudnn
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import torchvision.transforms.functional as TF
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import numpy as np
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import os
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import math
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import random
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import logging
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import logging.handlers
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from matplotlib import pyplot as plt
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from scipy.ndimage import zoom
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import SimpleITK as sitk
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from medpy import metric
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def set_seed(seed):
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# for hash
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os.environ['PYTHONHASHSEED'] = str(seed)
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# for python and numpy
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random.seed(seed)
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np.random.seed(seed)
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# for cpu gpu
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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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# for cudnn
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cudnn.benchmark = False
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cudnn.deterministic = True
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def get_logger(name, log_dir):
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'''
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Args:
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name(str): name of logger
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log_dir(str): path of log
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'''
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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logger = logging.getLogger(name)
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logger.setLevel(logging.INFO)
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info_name = os.path.join(log_dir, '{}.info.log'.format(name))
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info_handler = logging.handlers.TimedRotatingFileHandler(info_name,
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when='D',
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encoding='utf-8')
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info_handler.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S')
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info_handler.setFormatter(formatter)
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logger.addHandler(info_handler)
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return logger
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def log_config_info(config, logger):
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config_dict = config.__dict__
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log_info = f'#----------Config info----------#'
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logger.info(log_info)
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for k, v in config_dict.items():
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if k[0] == '_':
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continue
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else:
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log_info = f'{k}: {v},'
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logger.info(log_info)
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def get_optimizer(config, model):
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assert config.opt in ['Adadelta', 'Adagrad', 'Adam', 'AdamW', 'Adamax', 'ASGD', 'RMSprop', 'Rprop', 'SGD'], 'Unsupported optimizer!'
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if config.opt == 'Adadelta':
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return torch.optim.Adadelta(
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model.parameters(),
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lr = config.lr,
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rho = config.rho,
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eps = config.eps,
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weight_decay = config.weight_decay
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)
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elif config.opt == 'Adagrad':
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return torch.optim.Adagrad(
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model.parameters(),
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lr = config.lr,
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lr_decay = config.lr_decay,
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eps = config.eps,
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weight_decay = config.weight_decay
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)
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elif config.opt == 'Adam':
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return torch.optim.Adam(
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model.parameters(),
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lr = config.lr,
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betas = config.betas,
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eps = config.eps,
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