vision_unlearning.metrics.image

Classes

MetricImage	Based only on the image itself
MetricPaintingStyle	Based only on the image itself
MetricRace	Race classification using Hugging Face model:
MetricGender	Based only on the image itself
MetricQuality	Based only on the image itself

Module Contents

class vision_unlearning.metrics.image.MetricImage(/, **data: Any)

Bases: vision_unlearning.metrics.base.Metric, abc.ABC

Based only on the image itself e.g., image quality, painting style

abstract score(image: PIL.Image.Image) → Dict[str, Any]

class vision_unlearning.metrics.image.MetricPaintingStyle(/, **data: Any)

Bases: MetricImage

Based only on the image itself e.g., image quality, painting style

metrics: List[Literal['is_desired_style', 'desired_style_confidence']] = []

desired_style: str

top_k: int = 5

model_path: str

device: torch.device | str | int | None = 'cuda'

_pipeline: transformers.pipelines.image_classification.ImageClassificationPipeline | None = None

model_post_init(__context: dict | None = None) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

score(image: PIL.Image.Image) → Dict[str, bool | float]

class vision_unlearning.metrics.image.MetricRace(/, **data: Any)

Bases: MetricImage

Race classification using Hugging Face model: syntheticbot/clip-face-attribute-classifier

Requires the following additional dependencies: * tf_keras = “~2.19.0” * tensorrt = “~10.13.2” * blinker = “~1.9.0”

model_post_init(__context: dict | None = None) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

score(image: PIL.Image.Image) → Dict[str, str]

class vision_unlearning.metrics.image.MetricGender(/, **data: Any)

Bases: MetricImage

Based only on the image itself e.g., image quality, painting style

device: torch.device | str | int | None = 'cpu'

_model_name: str = 'prithivMLmods/Realistic-Gender-Classification'

_id2label

_model: Any

_processor: Any

model_post_init(__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

score(image: PIL.Image.Image) → Dict[str, Literal['male', 'female'] | float]

class vision_unlearning.metrics.image.MetricQuality(/, **data: Any)

Bases: MetricImage

Based only on the image itself e.g., image quality, painting style

_load_image(img: PIL.Image.Image | numpy.ndarray | str) → torch.Tensor

score(image: PIL.Image.Image | numpy.ndarray | str) → Dict[str, float]

score_batch(images: List[PIL.Image.Image | numpy.ndarray | str]) → List[Dict[str, float]]