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train.py
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import os
import sys
sys.path.append('./')
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from Unsuper.configs.config import cfg, log_config_to_file, cfg_from_list, cfg_from_yaml_file
from Unsuper.utils import common_utils
from Unsuper.dataset import build_dataloader
from Unsuper.utils.train_utils import build_optimizer, build_scheduler
from symbols.model_base import ModelTemplate
from Unsuper.utils.train_utils import train_model
import torch.distributed as dist
os.environ['CUDA_VISIBLE_DEVICES'] = ""
from pathlib import Path
import argparse
import datetime
from symbols.get_model import get_sym
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default='./Unsuper/configs/UnsuperPoint_coco.yaml', help='specify the config for training')
# parser.add_argument('--batch_size', type=int, default=32, required=False, help='batch size for training')
# parser.add_argument('--epochs', type=int, default=20, required=False, help='number of epochs to train for')
parser.add_argument('--workers', type=int, default=12, help='number of workers for dataloader')
# parser.add_argument('--extra_tag', type=str, default='no_score', help='extra tag for this experiment')
parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model')
parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
parser.add_argument('--sync_bn', action='store_true', default=False, help='whether to use sync bn')
parser.add_argument('--fix_random_seed', action='store_true', default=True, help='')
parser.add_argument('--ckpt_save_interval', type=int, default=1, help='number of training epochs')
parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
parser.add_argument('--max_ckpt_save_num', type=int, default=30, help='max number of saved checkpoint')
parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
help='set extra config keys if needed')
parser.add_argument('--max_waiting_mins', type=int, default=0, help='max waiting minutes')
parser.add_argument('--start_epoch', type=int, default=0, help='')
parser.add_argument('--save_to_file', action='store_true', default=False, help='')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
# cfg.TAG = Path(args.cfg_file).stem
cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def main():
args, cfg = parse_config()
if args.launcher == 'none':
dist_train = False
args.batch_size = cfg['data']['batch_size']
else:
args.batch_size, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' % args.launcher)(
args.batch_size, args.tcp_port, args.local_rank, backend='nccl'
)
dist_train = True
if args.fix_random_seed:
common_utils.set_random_seed(233)
output_dir = cfg.ROOT_DIR / '../output' # / cfg.TAG / args.extra_tag
ckpt_dir = output_dir / 'ckpt'
output_dir.mkdir(parents=True, exist_ok=True)
ckpt_dir.mkdir(parents=True, exist_ok=True)
log_file = output_dir / ('log_train_%s.txt' % datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
# log to file
logger.info('**********************Start logging**********************')
gpu_list = os.environ['CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys() else 'ALL'
logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
if dist_train:
total_gpus = dist.get_world_size()
logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
if cfg.LOCAL_RANK == 0:
os.system('cp %s %s' % (args.cfg_file, output_dir))
# PS D:\Program Files\tf_env\Scripts> .\tensorboard.exe --logdir=X:\project\UnsuperPoint\output\tensorboard --host=127.0.0.1 --port=8888
# http://127.0.0.1:8888
tb_log = SummaryWriter(log_dir=str(output_dir / 'tensorboard')) if cfg.LOCAL_RANK == 0 else None
# tb_log = None
# -----------------------create dataloader & network & optimizer---------------------------
train_set, train_loader, train_sampler = build_dataloader(
dataset_cfg=cfg['data'],
batch_size=args.batch_size,
dist=dist_train, workers=args.workers,
logger=logger,
training=True
)
model = get_sym(model_config=cfg['MODEL'], image_shape=cfg['data']['IMAGE_SHAPE'], is_training=True) #build_network(cfg['MODEL'], cfg['data']['IMAGE_SHAPE'], False)
# model = build_LightningNetwork(cfg['MODEL'], cfg['data']['IMAGE_SHAPE'], False)
if args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda()
optimizer = build_optimizer(model, cfg['MODEL']['OPTIMIZATION'])
# load checkpoint if it is possible
start_epoch = it = 0
last_epoch = -1
if args.pretrained_model is not None:
model.load_params_from_file(filename=args.pretrained_model, to_cpu=dist, logger=logger)
if args.ckpt is not None:
it, start_epoch = model.load_params_with_optimizer(args.ckpt, to_cpu=dist, optimizer=optimizer, logger=logger)
last_epoch = start_epoch + 1
else:
# ckpt_list = glob.glob(str(ckpt_dir / '*checkpoint_epoch_*.pth'))
# if len(ckpt_list) > 0:
# ckpt_list.sort(key=os.path.getmtime)
# it, start_epoch = model.load_params_with_optimizer(
# ckpt_list[-1], to_cpu=dist, optimizer=optimizer, logger=logger
# )
# last_epoch = start_epoch + 1
# elif not dist_train:
# model.apply(ModelTemplate.init_weights)
model.apply(ModelTemplate.init_weights)
torch.nn.init.normal_(model.score[3].weight)
torch.nn.init.normal_(model.position[3].weight)
torch.nn.init.normal_(model.descriptor[3].weight)
model.train() # before wrap to DistributedDataParallel to support fixed some parameters
if dist_train:
model = nn.parallel.DistributedDataParallel(model, device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()])
logger.info(model)
total_iters_each_epoch = len(train_loader)
lr_scheduler, lr_warmup_scheduler = build_scheduler(
optimizer, total_iters_each_epoch=total_iters_each_epoch, total_epochs=cfg['MODEL']['OPTIMIZATION']['EPOCHS'],
last_epoch=last_epoch, optim_cfg=cfg['MODEL']['OPTIMIZATION']
)
# -----------------------start training---------------------------
logger.info('**********************Start training %s **********************' % (cfg.EXP_GROUP_PATH))
train_model(
model,
optimizer,
train_loader,
lr_scheduler=lr_scheduler,
optim_cfg=cfg['MODEL']['OPTIMIZATION'],
start_epoch=start_epoch,
total_epochs=cfg['MODEL']['OPTIMIZATION']['EPOCHS'],
start_iter=it,
rank=cfg.LOCAL_RANK,
tb_log=tb_log,
ckpt_save_dir=ckpt_dir,
train_sampler=train_sampler,
lr_warmup_scheduler=lr_warmup_scheduler,
ckpt_save_interval=args.ckpt_save_interval,
max_ckpt_save_num=args.max_ckpt_save_num,
cfg=cfg
)
logger.info('**********************End training %s **********************\n\n\n' % (cfg.EXP_GROUP_PATH))
if __name__ == '__main__':
main()