File size: 10,371 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
# Copyright The 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 typing import Optional
import torch
from torch import Tensor
from torchmetrics.functional.classification.stat_scores import _reduce_stat_scores, _stat_scores_update
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.checks import _input_squeeze
from torchmetrics.utilities.enums import AverageMethod, MDMCAverageMethod
def _dice_compute(
tp: Tensor,
fp: Tensor,
fn: Tensor,
average: Optional[str],
mdmc_average: Optional[str],
zero_division: int = 0,
) -> Tensor:
"""Compute dice from the stat scores: true positives, false positives, false negatives.
Args:
tp: True positives
fp: False positives
fn: False negatives
average: Defines the reduction that is applied
mdmc_average: Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter)
zero_division: The value to use for the score if denominator equals zero.
"""
numerator = 2 * tp
denominator = 2 * tp + fp + fn
if average == AverageMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
cond = tp + fp + fn == 0
numerator = numerator[~cond]
denominator = denominator[~cond]
if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE:
# a class is not present if there exists no TPs, no FPs, and no FNs
meaningless_indices = torch.nonzero((tp | fn | fp) == 0).cpu()
numerator[meaningless_indices, ...] = -1
denominator[meaningless_indices, ...] = -1
return _reduce_stat_scores(
numerator=numerator,
denominator=denominator,
weights=None if average != "weighted" else tp + fn,
average=average,
mdmc_average=mdmc_average,
zero_division=zero_division,
)
def dice(
preds: Tensor,
target: Tensor,
zero_division: int = 0,
average: Optional[str] = "micro",
mdmc_average: Optional[str] = "global",
threshold: float = 0.5,
top_k: Optional[int] = None,
num_classes: Optional[int] = None,
multiclass: Optional[bool] = None,
ignore_index: Optional[int] = None,
) -> Tensor:
r"""Compute `Dice`_.
.. math:: \text{Dice} = \frac{\text{2 * TP}}{\text{2 * TP} + \text{FP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and
false negatives respecitively.
It is recommend set `ignore_index` to index of background class.
The reduction method (how the recall scores are aggregated) is controlled by the
``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
multi-dimensional multi-class case.
Args:
preds: Predictions from model (probabilities, logits or labels)
target: Ground truth values
zero_division: The value to use for the score if denominator equals zero
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, across all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics across classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics across classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
.. tip::
What is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
.. hint::
If ``'none'`` and a given class doesn't occur in the ``preds`` or ``target``,
the value for the class will be ``nan``.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
num_classes:
Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
threshold:
Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case
of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
top_k:
Number of the highest probability or logit score predictions considered finding the correct label,
relevant only for (multi-dimensional) multi-class inputs. The
default value (``None``) will be interpreted as 1 for these inputs.
Should be left at default (``None``) for all other types of inputs.
multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be.
.. warning::
The ``dice`` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will
be removed in v1.7.0. Please instead consider using ``f1score`` metric from the classification subpackage as it
provides the same functionality. Additionally, we are going to re-add the ``dice`` metric in the segmentation
domain in v1.6.0 with slight modifications to functionality.
Return:
The shape of the returned tensor depends on the ``average`` parameter
- If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned
- If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number of classes
Raises:
ValueError:
If ``average`` is not one of ``"micro"``, ``"macro"``, ``"weighted"``, ``"samples"``, ``"none"`` or ``None``
ValueError:
If ``mdmc_average`` is not one of ``None``, ``"samplewise"``, ``"global"``.
ValueError:
If ``average`` is set but ``num_classes`` is not provided.
ValueError:
If ``num_classes`` is set and ``ignore_index`` is not in the range ``[0, num_classes)``.
Example:
>>> from torchmetrics.functional.classification import dice
>>> preds = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> dice(preds, target, average='micro')
tensor(0.2500)
"""
rank_zero_warn(
"The `dice` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will"
" removed in v1.7.0. Please instead consider using `f1score` metric from the classification subpackage as it"
" provides the same functionality. Additionally, we are going to re-add the `dice` metric in the segmentation"
" domain in v1.6.0 with slight modifications to functionality.",
DeprecationWarning,
)
allowed_average = ("micro", "macro", "weighted", "samples", "none", None)
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1):
raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.")
allowed_mdmc_average = [None, "samplewise", "global"]
if mdmc_average not in allowed_mdmc_average:
raise ValueError(f"The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.")
if num_classes and ignore_index is not None and (not ignore_index < num_classes or num_classes == 1):
raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes")
if top_k is not None and (not isinstance(top_k, int) or top_k <= 0):
raise ValueError(f"The `top_k` should be an integer larger than 0, got {top_k}")
preds, target = _input_squeeze(preds, target)
reduce = "macro" if average in ("weighted", "none", None) else average
tp, fp, _, fn = _stat_scores_update(
preds,
target,
reduce=reduce,
mdmc_reduce=mdmc_average,
threshold=threshold,
num_classes=num_classes,
top_k=top_k,
multiclass=multiclass,
ignore_index=ignore_index,
)
return _dice_compute(tp, fp, fn, average, mdmc_average, zero_division)
|