def changelog(signum, frame):
for handler in logger.handlers[:]:
logger.removeHandler(handler)
logfile = "{0}/app.log.{1}".format(settings["log_path"],
time.strftime("%Y%m%d"))
fh = logging.FileHandler(filename=logfile)
fh.setFormatter(settings["log_format"])
logger.setLevel(logging.INFO)
logger.addHandler(fh)
def initLog(level):
"""初始化日志"""
logger.setLevel(level)
# 创建一个handler，用于写入日志文件
fh = logging.FileHandler('webtest.log')
fh.setLevel(level)
# 再创建一个handler，用于输出到控制台
ch = logging.StreamHandler()
ch.setLevel(level)
# 定义handler的输出格式
formatter = logging.Formatter('[%(asctime)s-%(name)s-%(levelname)s] %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# 给logger添加handler
logger.addHandler(fh)
logger.addHandler(ch)
trainer.train(1)
trainer.update_model()
times.append(time.time()-s)
if i % display == 0 and i > 0:
time_per_iter = np.mean(times)
logger.info('Time per iteration including communication: %f. Speed: %f images/s', time_per_iter, batch_size * nsteps_update / time_per_iter)
times = []
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Single trainer")
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--nsteps-update', type=int, default=1)
parser.add_argument('--dataset', type=str, default='imagenet', choices=_support_datasets, help='Specify the dataset for training')
parser.add_argument('--dnn', type=str, default='resnet50', choices=_support_dnns, help='Specify the neural network for training')
parser.add_argument('--data-dir', type=str, default='./data', help='Specify the data root path')
parser.add_argument('--lr', type=float, default=0.1, help='Default learning rate')
parser.add_argument('--max-epochs', type=int, default=settings.MAX_EPOCHS, help='Default maximum epochs to train')
parser.add_argument('--num-steps', type=int, default=35)
args = parser.parse_args()
batch_size = args.batch_size * args.nsteps_update
prefix = settings.PREFIX
relative_path = './logs/singlegpu-%s/%s-n%d-bs%d-lr%.4f-ns%d' % (prefix, args.dnn, 1, batch_size, args.lr, args.nsteps_update)
utils.create_path(relative_path)
logfile = os.path.join(relative_path, settings.hostname+'.log')
hdlr = logging.FileHandler(logfile)
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)
logger.info('Configurations: %s', args)
train_with_single(args.dnn, args.dataset, args.data_dir, 1, args.lr, args.batch_size, args.nsteps_update, args.max_epochs, args.num_steps)
# parser.add_option("-t", "--threads", dest="threads",
# help="threads number used for scan")
(opt, args) = parser.parse_args()
if not opt.filename and not opt.ip:
parser.print_help()
exit(1)
if opt.verbose:
logger.setLevel(logging.INFO)
if opt.output:
fh = logging.FileHandler(opt.output)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
logger.addHandler(fh)
if opt.filename:
with open(opt.filename, "rb") as f:
for line in f:
if line.lstrip(" ").startswith("#"):
continue
ipSegmentScan(line)
if opt.ip:
ipSegmentScan(opt.ip)
