def get_bootstrap_dataset_config() -> CN: _C = CN() _C.DATASET = "" # ratio used to mix data loaders _C.RATIO = 0.1 # image loader _C.IMAGE_LOADER = CN(new_allowed=True) _C.IMAGE_LOADER.TYPE = "" _C.IMAGE_LOADER.BATCH_SIZE = 4 _C.IMAGE_LOADER.NUM_WORKERS = 4 _C.IMAGE_LOADER.CATEGORIES = [] _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000 _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True) # inference _C.INFERENCE = CN() # batch size for model inputs _C.INFERENCE.INPUT_BATCH_SIZE = 4 # batch size to group model outputs _C.INFERENCE.OUTPUT_BATCH_SIZE = 2 # sampled data _C.DATA_SAMPLER = CN(new_allowed=True) _C.DATA_SAMPLER.TYPE = "" _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False # filter _C.FILTER = CN(new_allowed=True) _C.FILTER.TYPE = "" return _C
def get_bootstrap_dataset_config() -> CN: _C = CN() _C.DATASET = "" # ratio used to mix data loaders _C.RATIO = 0.1 # image loader _C.IMAGE_LOADER = CN(new_allowed=True) _C.IMAGE_LOADER.TYPE = "" _C.IMAGE_LOADER.BATCH_SIZE = 4 _C.IMAGE_LOADER.NUM_WORKERS = 4 # inference _C.INFERENCE = CN() # batch size for model inputs _C.INFERENCE.INPUT_BATCH_SIZE = 4 # batch size to group model outputs _C.INFERENCE.OUTPUT_BATCH_SIZE = 2 # sampled data _C.DATA_SAMPLER = CN(new_allowed=True) _C.DATA_SAMPLER.TYPE = "" # filter _C.FILTER = CN(new_allowed=True) _C.FILTER.TYPE = "" return _C