aac_metrics.classes.cider_d module

class CIDErD(return_all_scores: bool = True, *, n: int = 4, sigma: float = 6.0, tokenizer: ~typing.Callable[[str], list[str]] = <method 'split' of 'str' objects>, return_tfidf: bool = False, scale: float = 10.0)

Bases: AACMetric[tuple[CIDErDScores, CIDErDScores] | Tensor]

Consensus-based Image Description Evaluation metric class.

• Paper: https://arxiv.org/pdf/1411.5726.pdf

For more information, see cider_d().

compute() → tuple[CIDErDScores, CIDErDScores] | Tensor

extra_repr() → str

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

full_state_update: ClassVar[bool | None] = False

get_output_names() → tuple[str, ...]

higher_is_better: ClassVar[bool | None] = True

is_differentiable: ClassVar[bool | None] = False

max_value: ClassVar[float] = 10.0

min_value: ClassVar[float] = 0.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]],

) → None