# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
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from __future__ import annotations
import torch
from monai.metrics.utils import do_metric_reduction
from monai.utils import MetricReduction
from .metric import CumulativeIterationMetric
__all__ = ["DiceMetric", "compute_dice", "DiceHelper"]
[docs]
class DiceMetric(CumulativeIterationMetric):
"""
Compute average Dice score for a set of pairs of prediction-groundtruth segmentations.
It supports both multi-classes and multi-labels tasks.
Input `y_pred` is compared with ground truth `y`.
`y_pred` is expected to have binarized predictions and `y` can be single-channel class indices or in the
one-hot format. The `include_background` parameter can be set to ``False`` to exclude
the first category (channel index 0) which is by convention assumed to be background. If the non-background
segmentations are small compared to the total image size they can get overwhelmed by the signal from the
background. `y_preds` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]),
`y` can also be in the format of `B1HW[D]`.
Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
Args:
include_background: whether to include Dice computation on the first channel of
the predicted output. Defaults to ``True``.
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
Here `not_nans` count the number of not nans for the metric, thus its shape equals to the shape of the metric.
ignore_empty: whether to ignore empty ground truth cases during calculation.
If `True`, NaN value will be set for empty ground truth cases.
If `False`, 1 will be set if the predictions of empty ground truth cases are also empty.
num_classes: number of input channels (always including the background). When this is None,
``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
single-channel class indices and the number of classes is not automatically inferred from data.
return_with_label: whether to return the metrics with label, only works when reduction is "mean_batch".
If `True`, use "label_{index}" as the key corresponding to C channels; if 'include_background' is True,
the index begins at "0", otherwise at "1". It can also take a list of label names.
The outcome will then be returned as a dictionary.
"""
def __init__(
self,
include_background: bool = True,
reduction: MetricReduction | str = MetricReduction.MEAN,
get_not_nans: bool = False,
ignore_empty: bool = True,
num_classes: int | None = None,
return_with_label: bool | list[str] = False,
) -> None:
super().__init__()
self.include_background = include_background
self.reduction = reduction
self.get_not_nans = get_not_nans
self.ignore_empty = ignore_empty
self.num_classes = num_classes
self.return_with_label = return_with_label
self.dice_helper = DiceHelper(
include_background=self.include_background,
reduction=MetricReduction.NONE,
get_not_nans=False,
softmax=False,
ignore_empty=self.ignore_empty,
num_classes=self.num_classes,
)
def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor: # type: ignore[override]
"""
Args:
y_pred: input data to compute, typical segmentation model output.
It must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32]. The values
should be binarized.
y: ground truth to compute mean Dice metric. `y` can be single-channel class indices or
in the one-hot format.
Raises:
ValueError: when `y_pred` has less than three dimensions.
"""
dims = y_pred.ndimension()
if dims < 3:
raise ValueError(f"y_pred should have at least 3 dimensions (batch, channel, spatial), got {dims}.")
# compute dice (BxC) for each channel for each batch
return self.dice_helper(y_pred=y_pred, y=y) # type: ignore
[docs]
def aggregate(
self, reduction: MetricReduction | str | None = None
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
"""
Execute reduction and aggregation logic for the output of `compute_dice`.
Args:
reduction: define mode of reduction to the metrics, will only apply reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to `self.reduction`. if "none", will not do reduction.
"""
data = self.get_buffer()
if not isinstance(data, torch.Tensor):
raise ValueError(f"the data to aggregate must be PyTorch Tensor, got {type(data)}.")
# do metric reduction
f, not_nans = do_metric_reduction(data, reduction or self.reduction)
if self.reduction == MetricReduction.MEAN_BATCH and self.return_with_label:
_f = {}
if isinstance(self.return_with_label, bool):
for i, v in enumerate(f):
_label_key = f"label_{i+1}" if not self.include_background else f"label_{i}"
_f[_label_key] = round(v.item(), 4)
else:
for key, v in zip(self.return_with_label, f):
_f[key] = round(v.item(), 4)
f = _f
return (f, not_nans) if self.get_not_nans else f
def compute_dice(
y_pred: torch.Tensor,
y: torch.Tensor,
include_background: bool = True,
ignore_empty: bool = True,
num_classes: int | None = None,
) -> torch.Tensor:
"""Computes Dice score metric for a batch of predictions.
Args:
y_pred: input data to compute, typical segmentation model output.
`y_pred` can be single-channel class indices or in the one-hot format.
y: ground truth to compute mean dice metric. `y` can be single-channel class indices or in the one-hot format.
include_background: whether to include Dice computation on the first channel of
the predicted output. Defaults to True.
ignore_empty: whether to ignore empty ground truth cases during calculation.
If `True`, NaN value will be set for empty ground truth cases.
If `False`, 1 will be set if the predictions of empty ground truth cases are also empty.
num_classes: number of input channels (always including the background). When this is None,
``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
single-channel class indices and the number of classes is not automatically inferred from data.
Returns:
Dice scores per batch and per class, (shape: [batch_size, num_classes]).
"""
return DiceHelper( # type: ignore
include_background=include_background,
reduction=MetricReduction.NONE,
get_not_nans=False,
softmax=False,
ignore_empty=ignore_empty,
num_classes=num_classes,
)(y_pred=y_pred, y=y)
[docs]
class DiceHelper:
"""
Compute Dice score between two tensors `y_pred` and `y`.
`y_pred` and `y` can be single-channel class indices or in the one-hot format.
Example:
.. code-block:: python
import torch
from monai.metrics import DiceHelper
n_classes, batch_size = 5, 16
spatial_shape = (128, 128, 128)
y_pred = torch.rand(batch_size, n_classes, *spatial_shape).float() # predictions
y = torch.randint(0, n_classes, size=(batch_size, 1, *spatial_shape)).long() # ground truth
score, not_nans = DiceHelper(include_background=False, sigmoid=True, softmax=True)(y_pred, y)
print(score, not_nans)
"""
[docs]
def __init__(
self,
include_background: bool | None = None,
sigmoid: bool = False,
softmax: bool | None = None,
activate: bool = False,
get_not_nans: bool = True,
reduction: MetricReduction | str = MetricReduction.MEAN_BATCH,
ignore_empty: bool = True,
num_classes: int | None = None,
) -> None:
"""
Args:
include_background: whether to include the score on the first channel
(default to the value of `sigmoid`, False).
sigmoid: whether ``y_pred`` are/will be sigmoid activated outputs. If True, thresholding at 0.5
will be performed to get the discrete prediction. Defaults to False.
softmax: whether ``y_pred`` are softmax activated outputs. If True, `argmax` will be performed to
get the discrete prediction. Defaults to the value of ``not sigmoid``.
activate: whether to apply sigmoid to ``y_pred`` if ``sigmoid`` is True. Defaults to False.
This option is only valid when ``sigmoid`` is True.
get_not_nans: whether to return the number of not-nan values.
reduction: define mode of reduction to the metrics
ignore_empty: if `True`, NaN value will be set for empty ground truth cases.
If `False`, 1 will be set if the Union of ``y_pred`` and ``y`` is empty.
num_classes: number of input channels (always including the background). When this is None,
``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
single-channel class indices and the number of classes is not automatically inferred from data.
"""
self.sigmoid = sigmoid
self.reduction = reduction
self.get_not_nans = get_not_nans
self.include_background = sigmoid if include_background is None else include_background
self.softmax = not sigmoid if softmax is None else softmax
self.activate = activate
self.ignore_empty = ignore_empty
self.num_classes = num_classes
def compute_channel(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
""""""
y_o = torch.sum(y)
if y_o > 0:
return (2.0 * torch.sum(torch.masked_select(y, y_pred))) / (y_o + torch.sum(y_pred))
if self.ignore_empty:
return torch.tensor(float("nan"), device=y_o.device)
denorm = y_o + torch.sum(y_pred)
if denorm <= 0:
return torch.tensor(1.0, device=y_o.device)
return torch.tensor(0.0, device=y_o.device)
def __call__(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
"""
Args:
y_pred: input predictions with shape (batch_size, num_classes or 1, spatial_dims...).
the number of channels is inferred from ``y_pred.shape[1]`` when ``num_classes is None``.
y: ground truth with shape (batch_size, num_classes or 1, spatial_dims...).
"""
_softmax, _sigmoid = self.softmax, self.sigmoid
if self.num_classes is None:
n_pred_ch = y_pred.shape[1] # y_pred is in one-hot format or multi-channel scores
else:
n_pred_ch = self.num_classes
if y_pred.shape[1] == 1 and self.num_classes > 1: # y_pred is single-channel class indices
_softmax = _sigmoid = False
if _softmax:
if n_pred_ch > 1:
y_pred = torch.argmax(y_pred, dim=1, keepdim=True)
elif _sigmoid:
if self.activate:
y_pred = torch.sigmoid(y_pred)
y_pred = y_pred > 0.5
first_ch = 0 if self.include_background else 1
data = []
for b in range(y_pred.shape[0]):
c_list = []
for c in range(first_ch, n_pred_ch) if n_pred_ch > 1 else [1]:
x_pred = (y_pred[b, 0] == c) if (y_pred.shape[1] == 1) else y_pred[b, c].bool()
x = (y[b, 0] == c) if (y.shape[1] == 1) else y[b, c]
c_list.append(self.compute_channel(x_pred, x))
data.append(torch.stack(c_list))
data = torch.stack(data, dim=0).contiguous() # type: ignore
f, not_nans = do_metric_reduction(data, self.reduction) # type: ignore
return (f, not_nans) if self.get_not_nans else f