o  i\@s^ddlmZddlZddlmZmZddlmZddlm Z Gddde Z ddddZ dS)) annotationsN)do_metric_reductionignore_background)MetricReduction)CumulativeIterationMetriccsFeZdZdZdejddfdfd d ZdddZ ddddZZ S)MeanIoUag Compute average Intersection over Union (IoU) score between two tensors. 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` should be in one-hot format. You can use suitable transforms in ``monai.transforms.post`` first to achieve binarized values. 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_pred` and `y` can be a list of channel-first Tensor (CHW[D]) or a batch-first Tensor (BCHW[D]). Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`. Args: include_background: whether to include IoU 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. TFinclude_backgroundbool reductionMetricReduction | str get_not_nans ignore_emptyreturnNonecs&t||_||_||_||_dSN)super__init__r r r r)selfr r r r __class__W/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/metrics/meaniou.pyr3s  zMeanIoU.__init__y_pred torch.TensorycCs4|}|dkrtd|dt|||j|jdS)a 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 IoU metric. It must be one-hot format and first dim is batch. The values should be binarized. Raises: ValueError: when `y_pred` has less than three dimensions. zHy_pred should have at least 3 dimensions (batch, channel, spatial), got .)rrr r) ndimension ValueError compute_iour r)rrrdimsrrr_compute_tensor@s   zMeanIoU._compute_tensorNMetricReduction | str | None0torch.Tensor | tuple[torch.Tensor, torch.Tensor]cCsB|}t|tjstdt||p|j\}}|jr||fS|S)a Execute reduction logic for the output of `compute_iou`. 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. z-the data to aggregate must be PyTorch Tensor.) get_buffer isinstancetorchTensorrrr r )rr datafnot_nansrrr aggregateTs  zMeanIoU.aggregate) r r r r r r rr rr)rrrrrrr)r r#rr$) __name__ __module__ __qualname____doc__rMEANrr"r, __classcell__rrrrrs rTrrrr r rrc Cs|s t||d\}}|j|jkrtd|jd|jdt|j}ttd|}tj|||d}t||}tj||d}|||} |rZt|dk|| tj t d|j d St| dk|| tj d |j d S) aComputes Intersection over Union (IoU) score metric from a batch of predictions. 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 IoU metric. It must be one-hot format and first dim is batch. The values should be binarized. include_background: whether to include IoU 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. Returns: IoU scores per batch and per class, (shape [batch_size, num_classes]). Raises: ValueError: when `y_pred` and `y` have different shapes. )rrz*y_pred and y should have same shapes, got z and r)dimrnan)deviceg?) rshaperlenlistranger'sumwheretensorfloatr6) rrr rn_len reduce_axis intersectiony_oy_pred_ounionrrrr is    &"r )TT) rrrrr r rr rr) __future__rr'monai.metrics.utilsrr monai.utilsrmetricrrr rrrrs   T