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
