# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import wraps
from typing import Callable, Optional, Sequence, Tuple
import torch
from pytorch_lightning.metrics.functional.auc import auc as __auc
from pytorch_lightning.metrics.functional.auroc import auroc as __auroc
from pytorch_lightning.metrics.functional.average_precision import average_precision as __ap
from pytorch_lightning.metrics.functional.iou import iou as __iou
from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve
from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve as __prc
from pytorch_lightning.metrics.functional.roc import roc as __roc
from pytorch_lightning.metrics.utils import class_reduce
from pytorch_lightning.metrics.utils import get_num_classes as __gnc
from pytorch_lightning.metrics.utils import reduce
from pytorch_lightning.metrics.utils import to_categorical as __tc
from pytorch_lightning.metrics.utils import to_onehot as __to
from pytorch_lightning.utilities import rank_zero_warn
def to_onehot(
tensor: torch.Tensor,
num_classes: Optional[int] = None,
) -> torch.Tensor:
"""
Converts a dense label tensor to one-hot format
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.to_onehot`
"""
rank_zero_warn(
"This `to_onehot` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.utils import to_onehot`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __to(tensor, num_classes)
def to_categorical(tensor: torch.Tensor, argmax_dim: int = 1) -> torch.Tensor:
"""
Converts a tensor of probabilities to a dense label tensor
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.to_categorical`
"""
rank_zero_warn(
"This `to_categorical` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.utils import to_categorical`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __tc(tensor)
def get_num_classes(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
) -> int:
"""
Calculates the number of classes for a given prediction and target tensor.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.utils.get_num_classes`
"""
rank_zero_warn(
"This `get_num_classes` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.utils import get_num_classes`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __gnc(pred, target, num_classes)
def stat_scores(
pred: torch.Tensor,
target: torch.Tensor,
class_index: int,
argmax_dim: int = 1,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Calculates the number of true positive, false positive, true negative
and false negative for a specific class
Args:
pred: prediction tensor
target: target tensor
class_index: class to calculate over
argmax_dim: if pred is a tensor of probabilities, this indicates the
axis the argmax transformation will be applied over
Return:
True Positive, False Positive, True Negative, False Negative, Support
Example:
>>> x = torch.tensor([1, 2, 3])
>>> y = torch.tensor([0, 2, 3])
>>> tp, fp, tn, fn, sup = stat_scores(x, y, class_index=1)
>>> tp, fp, tn, fn, sup
(tensor(0), tensor(1), tensor(2), tensor(0), tensor(0))
"""
if pred.ndim == target.ndim + 1:
pred = to_categorical(pred, argmax_dim=argmax_dim)
tp = ((pred == class_index) * (target == class_index)).to(torch.long).sum()
fp = ((pred == class_index) * (target != class_index)).to(torch.long).sum()
tn = ((pred != class_index) * (target != class_index)).to(torch.long).sum()
fn = ((pred != class_index) * (target == class_index)).to(torch.long).sum()
sup = (target == class_index).to(torch.long).sum()
return tp, fp, tn, fn, sup
# todo: remove in 1.4
[docs]def stat_scores_multiple_classes(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
argmax_dim: int = 1,
reduction: str = 'none',
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Calculates the number of true positive, false positive, true negative
and false negative for each class
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.stat_scores`
Raises:
ValueError:
If ``reduction`` is not one of ``"none"``, ``"sum"`` or ``"elementwise_mean"``.
"""
rank_zero_warn(
"This `stat_scores_multiple_classes` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrics.functional import stat_scores`."
" It will be removed in v1.4.0", DeprecationWarning
)
if pred.ndim == target.ndim + 1:
pred = to_categorical(pred, argmax_dim=argmax_dim)
num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes)
if pred.dtype != torch.bool:
pred = pred.clamp_max(max=num_classes)
if target.dtype != torch.bool:
target = target.clamp_max(max=num_classes)
possible_reductions = ('none', 'sum', 'elementwise_mean')
if reduction not in possible_reductions:
raise ValueError("reduction type %s not supported" % reduction)
if reduction == 'none':
pred = pred.view((-1, )).long()
target = target.view((-1, )).long()
tps = torch.zeros((num_classes + 1, ), device=pred.device)
fps = torch.zeros((num_classes + 1, ), device=pred.device)
fns = torch.zeros((num_classes + 1, ), device=pred.device)
sups = torch.zeros((num_classes + 1, ), device=pred.device)
match_true = (pred == target).float()
match_false = 1 - match_true
tps.scatter_add_(0, pred, match_true)
fps.scatter_add_(0, pred, match_false)
fns.scatter_add_(0, target, match_false)
tns = pred.size(0) - (tps + fps + fns)
sups.scatter_add_(0, target, torch.ones_like(match_true))
tps = tps[:num_classes]
fps = fps[:num_classes]
tns = tns[:num_classes]
fns = fns[:num_classes]
sups = sups[:num_classes]
elif reduction == 'sum' or reduction == 'elementwise_mean':
count_match_true = (pred == target).sum().float()
oob_tp, oob_fp, oob_tn, oob_fn, oob_sup = stat_scores(pred, target, num_classes, argmax_dim)
tps = count_match_true - oob_tp
fps = pred.nelement() - count_match_true - oob_fp
fns = pred.nelement() - count_match_true - oob_fn
tns = pred.nelement() * (num_classes + 1) - (tps + fps + fns + oob_tn)
sups = pred.nelement() - oob_sup.float()
if reduction == 'elementwise_mean':
tps /= num_classes
fps /= num_classes
fns /= num_classes
tns /= num_classes
sups /= num_classes
return tps.float(), fps.float(), tns.float(), fns.float(), sups.float()
def _confmat_normalize(cm):
""" Normalization function for confusion matrix """
cm = cm / cm.sum(-1, keepdim=True)
nan_elements = cm[torch.isnan(cm)].nelement()
if nan_elements != 0:
cm[torch.isnan(cm)] = 0
rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.')
return cm
# todo: remove in 1.4
def precision_recall(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
return_support: bool = False,
return_state: bool = False
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Computes precision and recall for different thresholds
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.precision_recall`.
Will be removed in v1.4.0.
Args:
pred: estimated probabilities
target: ground-truth labels
num_classes: number of classes
class_reduction: method to reduce metric score over labels
- ``'micro'``: calculate metrics globally (default)
- ``'macro'``: calculate metrics for each label, and find their unweighted mean.
- ``'weighted'``: calculate metrics for each label, and find their weighted mean.
- ``'none'``: returns calculated metric per class
return_support: returns the support for each class, need for fbeta/f1 calculations
return_state: returns a internal state that can be ddp reduced
before doing the final calculation
Return:
Tensor with precision and recall
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 2, 2, 2])
>>> precision_recall(x, y, class_reduction='macro')
(tensor(0.5000), tensor(0.3333))
"""
rank_zero_warn(
"This `precision_recall` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrcs.functional import precision_recall`."
" It will be removed in v1.4.0", DeprecationWarning
)
tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred=pred, target=target, num_classes=num_classes)
precision = class_reduce(tps, tps + fps, sups, class_reduction=class_reduction)
recall = class_reduce(tps, tps + fns, sups, class_reduction=class_reduction)
if return_state:
return {'tps': tps, 'fps': fps, 'fns': fns, 'sups': sups}
if return_support:
return precision, recall, sups
return precision, recall
# todo: remove in 1.4
def precision(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes precision score.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.recall`. Will be removed in v1.4.0.
Args:
pred: estimated probabilities
target: ground-truth labels
num_classes: number of classes
class_reduction: method to reduce metric score over labels
- ``'micro'``: calculate metrics globally (default)
- ``'macro'``: calculate metrics for each label, and find their unweighted mean.
- ``'weighted'``: calculate metrics for each label, and find their weighted mean.
- ``'none'``: returns calculated metric per class
Return:
Tensor with precision.
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> precision(x, y)
tensor(0.7500)
"""
rank_zero_warn(
"This `precision` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrics.functional import precision`."
" It will be removed in v1.4.0", DeprecationWarning
)
return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[0]
# todo: remove in 1.4
def recall(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes recall score.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.recall`. Will be removed in v1.4.0.
Args:
pred: estimated probabilities
target: ground-truth labels
num_classes: number of classes
class_reduction: method to reduce metric score over labels
- ``'micro'``: calculate metrics globally (default)
- ``'macro'``: calculate metrics for each label, and find their unweighted mean.
- ``'weighted'``: calculate metrics for each label, and find their weighted mean.
- ``'none'``: returns calculated metric per class
Return:
Tensor with recall.
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> recall(x, y)
tensor(0.7500)
"""
rank_zero_warn(
"This `recall` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrics.functional import recall`."
" It will be removed in v1.4.0", DeprecationWarning
)
return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[1]
# todo: remove in 1.3
def roc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Computes the Receiver Operating Characteristic (ROC). It assumes classifier is binary.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
"""
rank_zero_warn(
"This `multiclass_roc` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.functional.roc import roc`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __roc(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
# TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py
def _roc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Computes the Receiver Operating Characteristic (ROC). It assumes classifier is binary.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 1, 1])
>>> fpr, tpr, thresholds = _roc(x, y)
>>> fpr
tensor([0., 0., 0., 0., 1.])
>>> tpr
tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
>>> thresholds
tensor([4, 3, 2, 1, 0])
"""
rank_zero_warn(
"This `multiclass_roc` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.functional.roc import roc`."
" It will be removed in v1.3.0", DeprecationWarning
)
fps, tps, thresholds = _binary_clf_curve(pred, target, sample_weights=sample_weight, pos_label=pos_label)
# Add an extra threshold position
# to make sure that the curve starts at (0, 0)
tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps])
fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps])
thresholds = torch.cat([thresholds[0][None] + 1, thresholds])
if fps[-1] <= 0:
raise ValueError("No negative samples in targets, false positive value should be meaningless")
fpr = fps / fps[-1]
if tps[-1] <= 0:
raise ValueError("No positive samples in targets, true positive value should be meaningless")
tpr = tps / tps[-1]
return fpr, tpr, thresholds
# TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py
def multiclass_roc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
num_classes: Optional[int] = None,
) -> Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
"""
Computes the Receiver Operating Characteristic (ROC) for multiclass predictors.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.roc.roc`
Args:
pred: estimated probabilities
target: ground-truth labels
sample_weight: sample weights
num_classes: number of classes (default: None, computes automatically from data)
Return:
returns roc for each class.
Number of classes, false-positive rate (fpr), true-positive rate (tpr), thresholds
Example:
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
... [0.05, 0.85, 0.05, 0.05],
... [0.05, 0.05, 0.85, 0.05],
... [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> multiclass_roc(pred, target) # doctest: +NORMALIZE_WHITESPACE
((tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])),
(tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])),
(tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500])),
(tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500])))
"""
rank_zero_warn(
"This `multiclass_roc` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.functional.roc import roc`."
" It will be removed in v1.3.0", DeprecationWarning
)
num_classes = get_num_classes(pred, target, num_classes)
class_roc_vals = []
for c in range(num_classes):
pred_c = pred[:, c]
class_roc_vals.append(_roc(pred=pred_c, target=target, sample_weight=sample_weight, pos_label=c))
return tuple(class_roc_vals)
def auc(
x: torch.Tensor,
y: torch.Tensor,
) -> torch.Tensor:
"""
Computes Area Under the Curve (AUC) using the trapezoidal rule
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.auc.auc`. Will be removed
in v1.4.0.
Args:
x: x-coordinates
y: y-coordinates
Return:
Tensor containing AUC score (float)
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> auc(x, y)
tensor(4.)
"""
rank_zero_warn(
"This `auc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auc import auc`."
" It will be removed in v1.4.0", DeprecationWarning
)
return __auc(x, y)
# todo: remove in 1.4
def auc_decorator() -> Callable:
rank_zero_warn("This `auc_decorator` was deprecated in v1.2.0." " It will be removed in v1.4.0", DeprecationWarning)
def wrapper(func_to_decorate: Callable) -> Callable:
@wraps(func_to_decorate)
def new_func(*args, **kwargs) -> torch.Tensor:
x, y = func_to_decorate(*args, **kwargs)[:2]
return auc(x, y)
return new_func
return wrapper
# todo: remove in 1.4
def multiclass_auc_decorator() -> Callable:
rank_zero_warn(
"This `multiclass_auc_decorator` was deprecated in v1.2.0."
" It will be removed in v1.4.0", DeprecationWarning
)
def wrapper(func_to_decorate: Callable) -> Callable:
@wraps(func_to_decorate)
def new_func(*args, **kwargs) -> torch.Tensor:
results = []
for class_result in func_to_decorate(*args, **kwargs):
x, y = class_result[:2]
results.append(auc(x, y))
return torch.stack(results)
return new_func
return wrapper
# todo: remove in 1.4
def auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
max_fpr: float = None,
) -> torch.Tensor:
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.auroc.auroc`. Will be removed
in v1.4.0.
Args:
pred: estimated probabilities
target: ground-truth labels
sample_weight: sample weights
pos_label: the label for the positive class
max_fpr: If not ``None``, calculates standardized partial AUC over the
range [0, max_fpr]. Should be a float between 0 and 1.
Return:
Tensor containing ROCAUC score
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 1, 0])
>>> auroc(x, y)
tensor(0.5000)
"""
rank_zero_warn(
"This `auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning
)
return __auroc(
preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label, max_fpr=max_fpr, num_classes=1
)
# todo: remove in 1.4
def multiclass_auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
num_classes: Optional[int] = None,
) -> torch.Tensor:
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from multiclass
prediction scores
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.auroc.auroc`. Will be removed
in v1.4.0.
Args:
pred: estimated probabilities, with shape [N, C]
target: ground-truth labels, with shape [N,]
sample_weight: sample weights
num_classes: number of classes (default: None, computes automatically from data)
Return:
Tensor containing ROCAUC score
Raises:
ValueError:
If ``pred`` don't sum up to ``1`` over classes for ``Multiclass AUROC``.
ValueError:
If number of classes found in ``target`` does not equal the number of
columns in ``pred``.
ValueError:
If number of classes deduced from ``pred`` does not equal the number of
classes passed in ``num_classes``.
Example:
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
... [0.05, 0.85, 0.05, 0.05],
... [0.05, 0.05, 0.85, 0.05],
... [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> multiclass_auroc(pred, target, num_classes=4)
tensor(0.6667)
"""
rank_zero_warn(
"This `multiclass_auroc` was deprecated in v1.2.0 in favor of"
" `pytorch_lightning.metrics.functional.auroc import auroc`."
" It will be removed in v1.4.0", DeprecationWarning
)
if not torch.allclose(pred.sum(dim=1), torch.tensor(1.0)):
raise ValueError(
"Multiclass AUROC metric expects the target scores to be"
" probabilities, i.e. they should sum up to 1.0 over classes"
)
if torch.unique(target).size(0) != pred.size(1):
raise ValueError(
f"Number of classes found in in 'target' ({torch.unique(target).size(0)})"
f" does not equal the number of columns in 'pred' ({pred.size(1)})."
" Multiclass AUROC is not defined when all of the classes do not"
" occur in the target labels."
)
if num_classes is not None and num_classes != pred.size(1):
raise ValueError(
f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal"
f" the number of classes passed in 'num_classes' ({num_classes})."
)
return __auroc(preds=pred, target=target, sample_weights=sample_weight, num_classes=num_classes)
[docs]def dice_score(
pred: torch.Tensor,
target: torch.Tensor,
bg: bool = False,
nan_score: float = 0.0,
no_fg_score: float = 0.0,
reduction: str = 'elementwise_mean',
) -> torch.Tensor:
"""
Compute dice score from prediction scores
Args:
pred: estimated probabilities
target: ground-truth labels
bg: whether to also compute dice for the background
nan_score: score to return, if a NaN occurs during computation
no_fg_score: score to return, if no foreground pixel was found in target
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
Return:
Tensor containing dice score
Example:
>>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05],
... [0.05, 0.85, 0.05, 0.05],
... [0.05, 0.05, 0.85, 0.05],
... [0.05, 0.05, 0.05, 0.85]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> dice_score(pred, target)
tensor(0.3333)
"""
num_classes = pred.shape[1]
bg = (1 - int(bool(bg)))
scores = torch.zeros(num_classes - bg, device=pred.device, dtype=torch.float32)
for i in range(bg, num_classes):
if not (target == i).any():
# no foreground class
scores[i - bg] += no_fg_score
continue
tp, fp, tn, fn, sup = stat_scores(pred=pred, target=target, class_index=i)
denom = (2 * tp + fp + fn).to(torch.float)
# nan result
score_cls = (2 * tp).to(torch.float) / denom if torch.is_nonzero(denom) else nan_score
scores[i - bg] += score_cls
return reduce(scores, reduction=reduction)
# todo: remove in 1.4
def iou(
pred: torch.Tensor,
target: torch.Tensor,
ignore_index: Optional[int] = None,
absent_score: float = 0.0,
num_classes: Optional[int] = None,
reduction: str = 'elementwise_mean',
) -> torch.Tensor:
"""
Intersection over union, or Jaccard index calculation.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.iou.iou`. Will be removed in
v1.4.0.
Args:
pred: Tensor containing integer predictions, with shape [N, d1, d2, ...]
target: Tensor containing integer targets, with shape [N, d1, d2, ...]
ignore_index: optional int specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. Has no effect if given an int that is not in the
range [0, num_classes-1], where num_classes is either given or derived from pred and target. By default, no
index is ignored, and all classes are used.
absent_score: score to use for an individual class, if no instances of the class index were present in
`pred` AND no instances of the class index were present in `target`. For example, if we have 3 classes,
[0, 0] for `pred`, and [0, 2] for `target`, then class 1 would be assigned the `absent_score`. Default is
0.0.
num_classes: Optionally specify the number of classes
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
Return:
IoU score : Tensor containing single value if reduction is
'elementwise_mean', or number of classes if reduction is 'none'
Example:
>>> target = torch.randint(0, 2, (10, 25, 25))
>>> pred = torch.tensor(target)
>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
>>> iou(pred, target)
tensor(0.9660)
"""
rank_zero_warn(
"This `iou` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrics.functional.iou import iou`."
" It will be removed in v1.4.0", DeprecationWarning
)
return __iou(
pred=pred,
target=target,
ignore_index=ignore_index,
absent_score=absent_score,
threshold=0.5,
num_classes=num_classes,
reduction=reduction
)
# todo: remove in 1.3
def precision_recall_curve(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
):
"""
Computes precision-recall pairs for different thresholds.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.precision_recall_curve.precision_recall_curve`
"""
rank_zero_warn(
"This `precision_recall_curve` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __prc(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
# todo: remove in 1.3
def multiclass_precision_recall_curve(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
num_classes: Optional[int] = None,
):
"""
Computes precision-recall pairs for different thresholds given a multiclass scores.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.precision_recall_curve.precision_recall_curve`
"""
rank_zero_warn(
"This `multiclass_precision_recall_curve` was deprecated in v1.1.0 in favor of"
" `from pytorch_lightning.metrics.functional.precision_recall_curve import precision_recall_curve`."
" It will be removed in v1.3.0", DeprecationWarning
)
if num_classes is None:
num_classes = get_num_classes(pred, target, num_classes)
return __prc(preds=pred, target=target, sample_weights=sample_weight, num_classes=num_classes)
# todo: remove in 1.3
def average_precision(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
):
"""
Compute average precision from prediction scores.
.. warning :: Deprecated in favor of
:func:`~pytorch_lightning.metrics.functional.average_precision.average_precision`
"""
rank_zero_warn(
"This `average_precision` was deprecated in v1.1.0 in favor of"
" `pytorch_lightning.metrics.functional.average_precision import average_precision`."
" It will be removed in v1.3.0", DeprecationWarning
)
return __ap(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
