aac_metrics.classes.spice module

class SPICE(

return_all_scores: bool = True,

*,

cache_path: str | Path | None = None,

java_path: str | Path | None = None,

tmp_path: str | Path | None = None,

n_threads: int | None = None,

java_max_memory: str = '8G',

timeout: None | int | Iterable[int] = None,

separate_cache_dir: bool = True,

use_shell: bool | None = None,

verbose: int = 0,

)

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

Semantic Propositional Image Caption Evaluation class.

• Paper: https://arxiv.org/pdf/1607.08822.pdf

For more information, see spice().

compute() → tuple[SPICEScores, SPICEScores] | 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] = 1.0

min_value: ClassVar[float] = 0.0

reset() → None

training: bool

update(

candidates: list[str],

mult_references: list[list[str]],

) → None