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 cfg.checkpoint_config is not None:
# save mmaction version in checkpoints as meta data
cfg.checkpoint_config.meta = dict(mmact_version=__version__,
config=cfg.text)
# 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)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
model = build_recognizer(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
train_dataset = get_trimmed_dataset(cfg.data.train)
val_dataset = get_trimmed_dataset(cfg.data.val)
datasets = []
for flow in cfg.workflow:
assert flow[0] in ['train', 'val']
if flow[0] == 'train':
datasets.append(train_dataset)
else:
datasets.append(val_dataset)
train_network(model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger)
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 is_valid(args.resume_from):
cfg.resume_from = args.resume_from
if is_valid(args.load_from):
cfg.load_from = args.load_from
if is_valid(args.load2d_from):
cfg.model.backbone.pretrained = args.load2d_from
cfg.model.backbone.pretrained2d = True
if args.num_videos is not None:
assert args.num_videos > 0
cfg.data.videos_per_gpu = args.num_videos
if cfg.checkpoint_config is not None:
cfg.checkpoint_config.meta = dict(mmact_version=__version__,
config=cfg.text)
if args.data_dir is not None:
cfg = update_data_paths(cfg, args.data_dir)
if hasattr(
cfg.model, 'masked_num'
) and cfg.model.masked_num is not None and cfg.model.masked_num > 0:
assert cfg.data.videos_per_gpu > cfg.model.masked_num
cfg.data.videos_per_gpu -= cfg.model.masked_num
# 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)
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}'.format(args.seed))
set_random_seed(args.seed)
train_dataset = get_trimmed_dataset(cfg.data.train)
ignores = ['num_batches_tracked']
if args.ignores is not None and len(args.ignores) > 0:
ignores += args.ignores
model = build_recognizer(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
if args.sync_bn:
logger.info('Enabled SyncBatchNorm')
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
train_network(model,
train_dataset,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
ignores=tuple(ignores))