Source code for monai.metrics.meandice

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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