aac_metrics.classes.cider_d module

class CIDErD(
return_all_scores: True = True,
*,
n: int = 4,
sigma: float = 6.0,
tokenizer: Callable[[str], list[str]] = str.split,
return_tfidf: bool = False,
scale: float = 10.0,
)[source]
class CIDErD(
return_all_scores: False,
*,
n: int = 4,
sigma: float = 6.0,
tokenizer: Callable[[str], list[str]] = str.split,
return_tfidf: bool = False,
scale: float = 10.0,
)

Bases: Generic[T_CIDErDOut], AACMetric[T_CIDErDOut]

Consensus-based Image Description Evaluation metric class.

For more information, see cider_d().

compute() → T_CIDErDOut[source]
extra_repr() → str[source]

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, ...][source]
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[source]
training : bool
update(
candidates: list[str],
mult_references: list[list[str]],
) → None[source]