def __init__(
self,
num_classes: int,
ignore_index: Optional[int] = None,
absent_score: float = 0.0,
threshold: float = 0.5,
reduction: str = "elementwise_mean",
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.IoU`.
.. deprecated::
Use :class:`~torchmetrics.IoU`. Will be removed in v1.5.0.
"""
void(
num_classes,
ignore_index,
absent_score,
threshold,
reduction,
compute_on_step,
dist_sync_on_step,
process_group,
)
def __init__(
self,
threshold: float = 0.5,
top_k: Optional[int] = None,
reduce: str = "micro",
num_classes: Optional[int] = None,
ignore_index: Optional[int] = None,
mdmc_reduce: Optional[str] = None,
is_multiclass: Optional[bool] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.StatScores`.
.. deprecated::
Use :class:`~torchmetrics.StatScores`. Will be removed in v1.5.0.
"""
void(
threshold,
top_k,
reduce,
num_classes,
ignore_index,
mdmc_reduce,
is_multiclass,
compute_on_step,
dist_sync_on_step,
process_group,
dist_sync_fn,
)
def __init__(self, metrics: Union[List[Metric], Tuple[Metric],
Dict[str, Metric]]):
"""
.. deprecated::
Use :class:`torchmetrics.MetricCollection`. Will be removed in v1.5.0.
"""
void(metrics)
def __init__(
self,
operator: Callable,
metric_a: Union[Metric, int, float, torch.Tensor],
metric_b: Union[Metric, int, float, torch.Tensor, None],
):
"""
.. deprecated::
Use :class:`torchmetrics.metric.CompositionalMetric`. Will be removed in v1.5.0.
"""
void(operator, metric_a, metric_b)
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
r"""
.. deprecated::
Use :class:`torchmetrics.Metric`. Will be removed in v1.5.0.
"""
void(compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.MeanAbsoluteError`.
.. deprecated::
Use :class:`~torchmetrics.MeanAbsoluteError`. Will be removed in v1.5.0.
"""
void(compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
def __init__(
self,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.ROC`.
.. deprecated::
Use :class:`~torchmetrics.ROC`. Will be removed in v1.5.0.
"""
void(num_classes, pos_label, compute_on_step, dist_sync_on_step, process_group)
def __init__(
self,
threshold: float = 0.5,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.HammingDistance`.
.. deprecated::
Use :class:`~torchmetrics.HammingDistance`. Will be removed in v1.5.0.
"""
void(threshold, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
def mean_squared_log_error(preds: torch.Tensor,
target: torch.Tensor) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.mean_squared_log_error`. Will be removed in v1.5.0.
"""
return void(preds, target)
def __init__(
self,
multioutput: str = "uniform_average",
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.ExplainedVariance`.
.. deprecated::
Use :class:`~torchmetrics.ExplainedVariance`. Will be removed in v1.5.0.
"""
void(multioutput, compute_on_step, dist_sync_on_step, process_group,
dist_sync_fn)
def hamming_distance(preds: torch.Tensor,
target: torch.Tensor,
threshold: float = 0.5) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.hamming_distance`. Will be removed in v1.5.0.
"""
return void(preds, target, threshold)
def __init__(
self,
data_range: Optional[float] = None,
base: float = 10.0,
reduction: str = "elementwise_mean",
dim: Optional[Union[int, Tuple[int, ...]]] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.PSNR`.
.. deprecated::
Use :class:`~torchmetrics.PSNR`. Will be removed in v1.5.0.
"""
void(data_range, base, reduction, dim, compute_on_step, dist_sync_on_step, process_group)
def auc(x: torch.Tensor,
y: torch.Tensor,
reorder: bool = False) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.auc`. Will be removed in v1.5.0.
"""
return void(x, y, reorder)
def __init__(
self,
num_classes: int,
normalize: Optional[str] = None,
threshold: float = 0.5,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.ConfusionMatrix`.
.. deprecated::
Use :class:`~torchmetrics.ConfusionMatrix`. Will be removed in v1.5.0.
"""
void(num_classes, normalize, threshold, compute_on_step,
dist_sync_on_step, process_group)
def r2score(preds: torch.Tensor,
target: torch.Tensor,
adjusted: int = 0,
multioutput: str = "uniform_average") -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.r2score`. Will be removed in v1.5.0.
"""
return void(preds, target, adjusted, multioutput)
def __init__(
self,
threshold: float = 0.5,
top_k: Optional[int] = None,
subset_accuracy: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.Accuracy`.
.. deprecated::
Use :class:`~torchmetrics.Accuracy`. Will be removed in v1.5.0.
"""
void(threshold, top_k, subset_accuracy, compute_on_step,
dist_sync_on_step, process_group, dist_sync_fn)
def __init__(
self,
num_classes: int,
threshold: float = 0.5,
average: str = "micro",
multilabel: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.F1`.
.. deprecated::
Use :class:`~torchmetrics.F1`. Will be removed in v1.5.0.
"""
void(num_classes, threshold, average, multilabel, compute_on_step,
dist_sync_on_step, process_group)
def embedding_similarity(batch: torch.Tensor,
similarity: str = "cosine",
reduction: str = "none",
zero_diagonal: bool = True) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.embedding_similarity`. Will be removed in v1.5.0.
"""
return void(batch, similarity, reduction, zero_diagonal)
def bleu_score(translate_corpus: Sequence[str],
reference_corpus: Sequence[str],
n_gram: int = 4,
smooth: bool = False) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.bleu_score`. Will be removed in v1.5.0.
"""
return void(translate_corpus, reference_corpus, n_gram, smooth)
def __init__(
self,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
average: Optional[str] = 'macro',
max_fpr: Optional[float] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
"""
This implementation refers to :class:`~torchmetrics.AUROC`.
.. deprecated::
Use :class:`~torchmetrics.AUROC`. Will be removed in v1.5.0.
"""
void(num_classes, pos_label, average, max_fpr, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
def explained_variance(
preds: torch.Tensor,
target: torch.Tensor,
multioutput: str = 'uniform_average',
) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
"""
.. deprecated::
Use :func:`torchmetrics.functional.explained_variance`. Will be removed in v1.5.0.
"""
return void(preds, target, multioutput)
def confusion_matrix(preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
normalize: Optional[str] = None,
threshold: float = 0.5) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.confusion_matrix`. Will be removed in v1.5.0.
"""
return void(preds, target, num_classes, normalize, threshold)
def __init__(
self,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: str = "elementwise_mean",
data_range: Optional[float] = None,
k1: float = 0.01,
k2: float = 0.03,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
"""
This implementation refers to :class:`~torchmetrics.SSIM`.
.. deprecated::
Use :class:`~torchmetrics.SSIM`. Will be removed in v1.5.0.
"""
void(kernel_size, sigma, reduction, data_range, k1, k2,
compute_on_step, dist_sync_on_step, process_group)
def f1(preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
threshold: float = 0.5,
average: str = "micro",
multilabel: Optional[bool] = None) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.f1`. Will be removed in v1.5.0.
"""
return void(preds, target, num_classes, threshold, average, multilabel)
def average_precision(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[List[torch.Tensor], torch.Tensor]:
"""
.. deprecated::
Use :func:`torchmetrics.functional.average_precision`. Will be removed in v1.5.0.
"""
return void(preds, target, num_classes, pos_label, sample_weights)
def accuracy(
preds: torch.Tensor,
target: torch.Tensor,
threshold: float = 0.5,
top_k: Optional[int] = None,
subset_accuracy: bool = False,
) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.accuracy`. Will be removed in v1.5.0.
"""
return void(preds, target, threshold, top_k, subset_accuracy)
def precision_recall_curve(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[List[torch.Tensor], List[torch.Tensor],
List[torch.Tensor]], ]:
"""
.. deprecated::
Use :func:`torchmetrics.functional.accuracy`. Will be removed in v1.5.0.
"""
return void(preds, target, num_classes, pos_label, sample_weights)
def roc(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor],
List[Tensor]]]:
"""
.. deprecated::
Use :func:`torchmetrics.functional.roc`. Will be removed in v1.5.0.
"""
return void(preds, target, num_classes, pos_label, sample_weights)
def psnr(
preds: torch.Tensor,
target: torch.Tensor,
data_range: Optional[float] = None,
base: float = 10.0,
reduction: str = 'elementwise_mean',
dim: Optional[Union[int, Tuple[int, ...]]] = None,
) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.psnr`. Will be removed in v1.5.0.
"""
return void(preds, target, data_range, base, reduction, dim)
def iou(
pred: torch.Tensor,
target: torch.Tensor,
ignore_index: Optional[int] = None,
absent_score: float = 0.0,
threshold: float = 0.5,
num_classes: Optional[int] = None,
reduction: str = "elementwise_mean",
) -> torch.Tensor:
"""
.. deprecated::
Use :func:`torchmetrics.functional.iou`. Will be removed in v1.5.0.
"""
return void(pred, target, ignore_index, absent_score, threshold,
num_classes, reduction)
