# 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.average_precision import average_precision as __ap
from pytorch_lightning.metrics.functional.f_beta import fbeta as __fb, f1 as __f1
from pytorch_lightning.metrics.functional.precision_recall_curve import _binary_clf_curve, precision_recall_curve as __prc
from pytorch_lightning.metrics.functional.roc import roc as __roc
from pytorch_lightning.metrics.utils import (
to_categorical as __tc,
to_onehot as __to,
get_num_classes as __gnc,
reduce,
class_reduce,
)
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)
[docs]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
[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
Args:
pred: prediction tensor
target: target tensor
num_classes: number of classes if known
argmax_dim: if pred is a tensor of probabilities, this indicates the
axis the argmax transformation will be applied over
reduction: a method to reduce metric score over labels (default: none)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum: add elements
Return:
True Positive, False Positive, True Negative, False Negative, Support
Example:
>>> x = torch.tensor([1, 2, 3])
>>> y = torch.tensor([0, 2, 3])
>>> tps, fps, tns, fns, sups = stat_scores_multiple_classes(x, y)
>>> tps
tensor([0., 0., 1., 1.])
>>> fps
tensor([0., 1., 0., 0.])
>>> tns
tensor([2., 2., 2., 2.])
>>> fns
tensor([1., 0., 0., 0.])
>>> sups
tensor([1., 0., 1., 1.])
"""
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)
tns = 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()
[docs]def accuracy(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
return_state: bool = False
) -> torch.Tensor:
"""
Computes the accuracy classification score
Args:
pred: predicted labels
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_state: returns a internal state that can be ddp reduced
before doing the final calculation
Return:
A Tensor with the accuracy score.
Example:
>>> x = torch.tensor([0, 1, 2, 3])
>>> y = torch.tensor([0, 1, 2, 2])
>>> accuracy(x, y)
tensor(0.7500)
"""
tps, fps, tns, fns, sups = stat_scores_multiple_classes(
pred=pred, target=target, num_classes=num_classes)
if return_state:
return {'tps': tps, 'sups': sups}
return class_reduce(tps, sups, sups, class_reduction=class_reduction)
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
[docs]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
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))
"""
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
[docs]def precision(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes precision score.
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)
"""
return precision_recall(pred=pred, target=target,
num_classes=num_classes, class_reduction=class_reduction)[0]
[docs]def recall(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes recall score.
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)
"""
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)
[docs]def auc(
x: torch.Tensor,
y: torch.Tensor,
) -> torch.Tensor:
"""
Computes Area Under the Curve (AUC) using the trapezoidal rule
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.)
"""
dx = x[1:] - x[:-1]
if (dx < 0).any():
if (dx <= 0).all():
direction = -1.
else:
raise ValueError(f"The 'x' array is neither increasing or decreasing: {x}. Reorder is not supported.")
else:
direction = 1.
return direction * torch.trapz(y, x)
def auc_decorator() -> Callable:
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
def multiclass_auc_decorator() -> Callable:
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
[docs]def auroc(
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None,
pos_label: int = 1.,
) -> torch.Tensor:
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores
Args:
pred: estimated probabilities
target: ground-truth labels
sample_weight: sample weights
pos_label: the label for the positive class
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)
"""
if any(target > 1):
raise ValueError('AUROC metric is meant for binary classification, but'
' target tensor contains value different from 0 and 1.'
' Use `multiclass_auroc` for multi class classification.')
@auc_decorator()
def _auroc(pred, target, sample_weight, pos_label):
return _roc(pred, target, sample_weight, pos_label)
return _auroc(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label)
[docs]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
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
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)
"""
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}).")
@multiclass_auc_decorator()
def _multiclass_auroc(pred, target, sample_weight, num_classes):
return multiclass_roc(pred, target, sample_weight, num_classes)
class_aurocs = _multiclass_auroc(pred=pred, target=target,
sample_weight=sample_weight,
num_classes=num_classes)
return torch.mean(class_aurocs)
[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)
[docs]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.
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)
"""
if pred.size() != target.size():
raise ValueError(f"'pred' shape ({pred.size()}) must equal 'target' shape ({target.size()})")
if not torch.allclose(pred.float(), pred.int().float()):
raise ValueError("'pred' must contain integer targets.")
num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes)
tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred, target, num_classes)
scores = torch.zeros(num_classes, device=pred.device, dtype=torch.float32)
for class_idx in range(num_classes):
if class_idx == ignore_index:
continue
tp = tps[class_idx]
fp = fps[class_idx]
fn = fns[class_idx]
sup = sups[class_idx]
# If this class is absent in the target (no support) AND absent in the pred (no true or false
# positives), then use the absent_score for this class.
if sup + tp + fp == 0:
scores[class_idx] = absent_score
continue
denom = tp + fp + fn
# Note that we do not need to worry about division-by-zero here since we know (sup + tp + fp != 0) from above,
# which means ((tp+fn) + tp + fp != 0), which means (2tp + fp + fn != 0). Since all vars are non-negative, we
# can conclude (tp + fp + fn > 0), meaning the denominator is non-zero for each class.
score = tp.to(torch.float) / denom
scores[class_idx] = score
# Remove the ignored class index from the scores.
if ignore_index is not None and ignore_index >= 0 and ignore_index < num_classes:
scores = torch.cat([
scores[:ignore_index],
scores[ignore_index + 1:],
])
return reduce(scores, 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)
# todo: remove in 1.2
def fbeta_score(
pred: torch.Tensor,
target: torch.Tensor,
beta: float,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes the F-beta score which is a weighted harmonic mean of precision and recall.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.f_beta.fbeta`
"""
rank_zero_warn(
"This `average_precision` was deprecated in v1.0.x in favor of"
" `from pytorch_lightning.metrics.functional.f_beta import fbeta`."
" It will be removed in v1.2.0", DeprecationWarning
)
if num_classes is None:
num_classes = get_num_classes(pred, target)
return __fb(preds=pred, target=target, beta=beta, num_classes=num_classes, average=class_reduction)
# todo: remove in 1.2
def f1_score(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
class_reduction: str = 'micro',
) -> torch.Tensor:
"""
Computes the F1-score (a.k.a F-measure), which is the harmonic mean of the precision and recall.
.. warning :: Deprecated in favor of :func:`~pytorch_lightning.metrics.functional.f_beta.f1`
"""
rank_zero_warn(
"This `average_precision` was deprecated in v1.0.x in favor of"
" `from pytorch_lightning.metrics.functional.f_beta import f1`."
" It will be removed in v1.2.0", DeprecationWarning
)
if num_classes is None:
num_classes = get_num_classes(pred, target)
return __f1(preds=pred, target=target, num_classes=num_classes, average=class_reduction)
