geowatch.utils.util_torchmetrics module

class geowatch.utils.util_torchmetrics.BinaryF1Score(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: int | None = None, num_labels: int | None = None, average: Literal['micro', 'macro', 'weighted', 'none'] | None = 'micro', multidim_average: Literal['global', 'samplewise'] | None = 'global', top_k: int | None = 1, ignore_index: int | None = None, validate_args: bool = True, **kwargs: Any)

Bases: StatScores

Initialize task metric.

compute()

class geowatch.utils.util_torchmetrics.BinaryOverallAccuracy(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: int | None = None, num_labels: int | None = None, average: Literal['micro', 'macro', 'weighted', 'none'] | None = 'micro', multidim_average: Literal['global', 'samplewise'] | None = 'global', top_k: int | None = 1, ignore_index: int | None = None, validate_args: bool = True, **kwargs: Any)

Bases: StatScores

Initialize task metric.

compute()

class geowatch.utils.util_torchmetrics.BinaryBalancedAccuracy(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: int | None = None, num_labels: int | None = None, average: Literal['micro', 'macro', 'weighted', 'none'] | None = 'micro', multidim_average: Literal['global', 'samplewise'] | None = 'global', top_k: int | None = 1, ignore_index: int | None = None, validate_args: bool = True, **kwargs: Any)

Bases: StatScores

Initialize task metric.

compute()

class geowatch.utils.util_torchmetrics.BinaryTruePositiveRate(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: int | None = None, num_labels: int | None = None, average: Literal['micro', 'macro', 'weighted', 'none'] | None = 'micro', multidim_average: Literal['global', 'samplewise'] | None = 'global', top_k: int | None = 1, ignore_index: int | None = None, validate_args: bool = True, **kwargs: Any)

Bases: StatScores

Initialize task metric.

compute()