def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "auc",
activation: str = "Sigmoid",
class_args: List[str] = None,
**kwargs,
):
"""
Args:
input_key: input key to use for auc calculation
specifies our ``y_true``.
output_key: output key to use for auc calculation;
specifies our ``y_pred``.
prefix: key for the metric's name
multiplier: scale factor for the metric.
activation: An torch.nn activation applied to the outputs.
Must be one of ``'none'``, ``'Sigmoid'``, or ``'Softmax'``
class_args: class names to display in the logs.
If None, defaults to indices for each class, starting from 0
"""
metric_fn = wrap_metric_fn_with_activation(metric_fn=auc,
activation=activation)
metric_fn = wrap_class_metric2dict(metric_fn, class_args=class_args)
super().__init__(
prefix=prefix,
metric_fn=metric_fn,
input_key=input_key,
output_key=output_key,
**kwargs,
)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "multilabel_accuracy",
activation: str = "Sigmoid",
threshold: float = None,
**kwargs,
):
"""
Args:
input_key: input key to use for accuracy calculation;
specifies our `y_true`
output_key: output key to use for accuracy calculation;
specifies our `y_pred`
prefix: key for the metric's name
activation: An torch.nn activation applied to the outputs.
Must be one of ``"none"``, ``"Sigmoid"``, or ``"Softmax"``
threshold: threshold for for model output
**kwargs: key-value params to pass to the metric
.. note::
For `**kwargs` info, please follow
`catalyst.metrics.accuracy.multilabel_accuracy` docs
"""
super().__init__(
prefix=prefix,
metric_fn=wrap_metric_fn_with_activation(
metric_fn=multilabel_accuracy, activation=activation),
input_key=input_key,
output_key=output_key,
threshold=threshold,
**kwargs,
)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "dice",
activation: str = "Sigmoid",
**kwargs,
):
"""
Args:
input_key: input key to use for iou calculation
specifies our ``y_true``
output_key: output key to use for iou calculation;
specifies our ``y_pred``
prefix: key to store in logs
activation: An torch.nn activation applied to the model outputs.
Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
eps: epsilon to avoid zero division
threshold: threshold for outputs binarization
"""
super().__init__(
prefix=prefix,
metric_fn=wrap_metric_fn_with_activation(metric_fn=dice,
activation=activation),
input_key=input_key,
output_key=output_key,
**kwargs,
)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "multilabel_accuracy",
activation: str = "Sigmoid",
threshold: float = None,
):
"""
Args:
input_key: input key to use for accuracy calculation;
specifies our `y_true`
output_key: output key to use for accuracy calculation;
specifies our `y_pred`
prefix: key for the metric's name
threshold: threshold for for model output
activation: An torch.nn activation applied to the outputs.
Must be one of ``"none"``, ``"Sigmoid"``, or ``"Softmax"``
"""
super().__init__(
prefix=prefix,
metric_fn=wrap_metric_fn_with_activation(
metric_fn=multilabel_accuracy, activation=activation),
input_key=input_key,
output_key=output_key,
threshold=threshold,
)
def __init__(
self,
eps: float = 1e-7,
threshold: float = None,
activation: str = "Sigmoid",
):
"""@TODO: Docs. Contribution is welcome."""
super().__init__()
metric_fn = wrap_metric_fn_with_activation(metric_fn=dice,
activation=activation)
self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)
def __init__(
self,
eps: float = 1e-7,
threshold: float = None,
activation: str = "Sigmoid",
):
"""
Args:
eps: epsilon to avoid zero division
threshold: threshold for outputs binarization
activation: An torch.nn activation applied to the outputs.
Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
"""
super().__init__()
metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
activation=activation)
self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "iou",
activation: str = "Sigmoid",
per_class: bool = False,
class_args: List[str] = None,
**kwargs,
):
"""
Args:
input_key: input key to use for iou calculation
specifies our ``y_true``
output_key: output key to use for iou calculation;
specifies our ``y_pred``
prefix: key to store in logs
eps: epsilon to avoid zero division
threshold: threshold for outputs binarization
activation: An torch.nn activation applied to the outputs.
Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
per_class: boolean flag to log per class metrics,
or use mean/macro statistics otherwise
class_args: class names to display in the logs.
If None, defaults to indices for each class, starting from 0
**kwargs: key-value params to pass to the metric
.. note::
For ``**kwargs`` info, please follow
``catalyst.callbacks.metric.BatchMetricCallback`` and
``catalyst.metrics.iou.iou`` docs
"""
metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
activation=activation)
metric_fn = wrap_class_metric2dict(metric_fn,
per_class=per_class,
class_args=class_args)
super().__init__(
prefix=prefix,
metric_fn=metric_fn,
input_key=input_key,
output_key=output_key,
**kwargs,
)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "average_precision",
activation: str = "Sigmoid",
per_class: bool = False,
class_args: List[str] = None,
**kwargs,
):
"""
Args:
input_key: input key to use for
calculation mean average precision;
specifies our `y_true`.
output_key: output key to use for
calculation mean average precision;
specifies our `y_pred`.
prefix: key for the metric's name
activation: An torch.nn activation applied to the outputs.
Must be one of ``'none'``, ``'Sigmoid'``, or ``'Softmax'``
per_class: boolean flag to log per class metrics,
or use mean/macro statistics otherwise
class_args: class names to display in the logs.
If None, defaults to indices for each class, starting from 0
**kwargs: key-value params to pass to the metric
.. note::
For ``**kwargs`` info, please follow
``catalyst.callbacks.metric.LoaderMetricCallback`` and
``catalyst.metrics.precision.average_precision`` docs
"""
metric_fn = wrap_metric_fn_with_activation(metric_fn=average_precision,
activation=activation)
metric_fn = wrap_class_metric2dict(metric_fn,
per_class=per_class,
class_args=class_args)
super().__init__(
prefix=prefix,
metric_fn=metric_fn,
input_key=input_key,
output_key=output_key,
**kwargs,
)
def __init__(
self,
input_key: str = "targets",
output_key: str = "logits",
prefix: str = "dice",
activation: str = "Sigmoid",
class_args: List[str] = None,
**kwargs,
):
"""
Args:
input_key: input key to use for iou calculation
specifies our ``y_true``
output_key: output key to use for iou calculation;
specifies our ``y_pred``
prefix: key to store in logs
activation: An torch.nn activation applied to the outputs.
Must be one of ``'none'``, ``'Sigmoid'``, or ``'Softmax'``
class_args: class names to display in the logs.
If None, defaults to indices for each class, starting from 0
**kwargs: key-value params to pass to the metric
.. note::
For `**kwargs` info, please follow
`catalyst.metrics.dice.dice` docs
"""
metric_fn = wrap_metric_fn_with_activation(metric_fn=dice,
activation=activation)
metric_fn = wrap_class_metric2dict(metric_fn, class_args=class_args)
super().__init__(
prefix=prefix,
metric_fn=metric_fn,
input_key=input_key,
output_key=output_key,
**kwargs,
)
