def create_cfg_from_cli_args(args, default_cfg):
"""
Instead of loading from defaults.py, this binary only includes necessary
configs building from scratch, and overrides them from args. There're two
levels of config:
_C: the config system used by this binary, which is a sub-set of training
config, override by configurable_cfg. It can also be override by
args.opts for convinience.
configurable_cfg: common configs that user should explicitly specify
in the args.
"""
_C = CN()
_C.INPUT = default_cfg.INPUT
_C.DATASETS = default_cfg.DATASETS
_C.DATALOADER = default_cfg.DATALOADER
_C.TEST = default_cfg.TEST
if hasattr(default_cfg, "D2GO_DATA"):
_C.D2GO_DATA = default_cfg.D2GO_DATA
if hasattr(default_cfg, "TENSORBOARD"):
_C.TENSORBOARD = default_cfg.TENSORBOARD
# NOTE configs below might not be necessary, but must add to make code work
_C.MODEL = CN()
_C.MODEL.META_ARCHITECTURE = default_cfg.MODEL.META_ARCHITECTURE
_C.MODEL.MASK_ON = default_cfg.MODEL.MASK_ON
_C.MODEL.KEYPOINT_ON = default_cfg.MODEL.KEYPOINT_ON
_C.MODEL.LOAD_PROPOSALS = default_cfg.MODEL.LOAD_PROPOSALS
assert _C.MODEL.LOAD_PROPOSALS is False, "caffe2 model doesn't support"
_C.OUTPUT_DIR = args.output_dir
configurable_cfg = [
"DATASETS.TEST",
args.datasets,
"INPUT.MIN_SIZE_TEST",
args.min_size,
"INPUT.MAX_SIZE_TEST",
args.max_size,
]
cfg = _C.clone()
cfg.merge_from_list(configurable_cfg)
cfg.merge_from_list(args.opts)
return cfg
def get_default_config():
cfg = CfgNode()
cfg.D2GO_DATA = CfgNode()
cfg.D2GO_DATA.AUG_OPS = CfgNode()
return cfg
