#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import argparse
import random
import warnings
from loguru import logger
import torch
import torch.backends.cudnn as cudnn
from yolox.core import launch
from yolox.exp import Exp, check_exp_value, get_exp
from yolox.utils import configure_module, configure_nccl, configure_omp, get_num_devices
def make_parser():
parser = argparse.ArgumentParser("YOLOX train parser")
parser.add_argument("-expn", "--experiment-name", type=str, default=None)
parser.add_argument("-n", "--name", type=str, default=None, help="model name")
# distributed
parser.add_argument(
"--dist-backend", default="nccl", type=str, help="distributed backend"
)
parser.add_argument(
"--dist-url",
default=None,
type=str,
help="url used to set up distributed training",
)
parser.add_argument("-b", "--batch-size", type=int, default=64, help="batch size")
parser.add_argument(
"-d", "--devices", default=None, type=int, help="device for training"
)
parser.add_argument(
"-f",
"--exp_file",
default=None,
type=str,
help="plz input your experiment description file",
)
parser.add_argument(
"--resume", default=False, action="store_true", help="resume training"
)
parser.add_argument("-c", "--ckpt", default=None, type=str, help="checkpoint file")
parser.add_argument(
"-e",
"--start_epoch",
default=None,
type=int,
help="resume training start epoch",
)
parser.add_argument(
"--num_machines", default=1, type=int, help="num of node for training"
)
parser.add_argument(
"--machine_rank", default=0, type=int, help="node rank for multi-node training"
)
parser.add_argument(
"--fp16",
dest="fp16",
default=False,
action="store_true",
help="Adopting mix precision training.",
)
parser.add_argument(
"--cache",
type=str,
nargs="?",
const="ram",
help="Caching imgs to ram/disk for fast training.",
)
parser.add_argument(
"-o",
"--occupy",
dest="occupy",
default=False,
action="store_true",
help="occupy GPU memory first for training.",
)
parser.add_argument(
"-l",
"--logger",
type=str,
help="Logger to be used for metrics. \
Implemented loggers include `tensorboard` and `wandb`.",
default="tensorboard"
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
return parser
@logger.catch
def main(exp: Exp, args):
if exp.seed is not None:
random.seed(exp.seed)
torch.manual_seed(exp.seed)
cudnn.deterministic = True
warnings.warn(
"You have chosen to seed training. This will turn on the CUDNN deterministic setting, "
"which can slow down your training considerably! You may see unexpected behavior "
"when restarting from checkpoints."
)
# set environment variables for distributed training
configure_nccl()
configure_omp()
cudnn.benchmark = True
trainer = exp.get_trainer(args)
trainer.train()
if __name__ == "__main__":
configure_module()
args = make_parser().parse_args()
exp = get_exp(args.exp_file, args.name)
exp.merge(args.opts)
check_exp_value(exp)
if not args.experiment_name:
args.experiment_name = exp.exp_name
num_gpu = get_num_devices() if args.devices is None else args.devices
assert num_gpu <= get_num_devices()
if args.cache is not None:
exp.dataset = exp.get_dataset(cache=True, cache_type=args.cache)
dist_url = "auto" if args.dist_url is None else args.dist_url
launch(
main,
num_gpu,
args.num_machines,
args.machine_rank,
backend=args.dist_backend,
dist_url=dist_url,
args=(exp, args),
) 利用这个代码，不使用预训练权重，只使用我们的目标任务数据训练学生模型权重，用于后面的蒸馏可以实现吗
本文介绍了least函数的功能及用法，该函数用于返回多个表达式中值最小的一个，并支持不同类型间的隐式转换。通过示例展示了如何在SQL查询中使用least函数。
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