def main():
print('settings:\n', args)
#annotate training data for 1st time
if args.annotate:
train_annotation()
#change config
config_path = 'configs/restorers/srresnet_srgan/msrresnet_x4c64b16_g1_1000k_div2k.py'
cfg = change_config(config_path)
check_params(cfg)
# Initialize distributed training (only need to initialize once), comment it if have already run this part
os.environ['RANK'] = '0'
os.environ['WORLD_SIZE'] = '1'
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29500' #'50297'
init_dist('pytorch', **cfg.dist_params)
# Build dataset
datasets = [build_dataset(cfg.data.train)]
# Build the SRCNN model
model = build_model(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
# Create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# Meta information
meta = dict()
# if cfg.get('exp_name', None) is None:
# cfg['exp_name'] = osp.splitext(osp.basename(cfg.work_dir))[0]
meta['exp_name'] = '_'.join([
'bs' + str(args.bs), 'iter' + str(args.iter),
'block' + str(args.num_blocks), args.loss
])
meta['mmedit Version'] = mmedit.__version__
meta['seed'] = 0
meta['start_time'] = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
# Train the model
train_model(model,
datasets,
cfg,
distributed=True,
validate=True,
meta=meta)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# log env info
env_info_dict = collect_env.collect_env()
env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
# log some basic info
logger.info('Distributed training: {}'.format(distributed))
logger.info('mmedit Version: {}'.format(__version__))
logger.info('Config:\n{}'.format(cfg.text))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
model = build_model(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmedit_version=__version__,
config=cfg.text,
)
# meta information
meta = dict()
if cfg.get('exp_name', None) is None:
cfg['exp_name'] = osp.splitext(osp.basename(cfg.work_dir))[0]
meta['exp_name'] = cfg.exp_name
meta['mmedit Version'] = __version__
meta['seed'] = args.seed
meta['env_info'] = env_info
# add an attribute for visualization convenience
train_model(model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)