o yi:$@sddlmZddlZddlmZddlmZmZddlmZddl m Z m Z  ddeded ed ee d ee d e d efddZ       ddeded e d ee d ee dedee dee deedee d efddZdS))OptionalN)Tensor)_reduce_stat_scores_stat_scores_update)_input_squeeze) AverageMethodMDMCAverageMethodtpfpfnaverage mdmc_average zero_divisionreturnc Csd|}d|||}|tjkr(|tjkr(|||dk}||}||}|tjkrK|tjkrKt||B|Bdk} d|| df<d|| df<t|||dkrTdn|||||dS)a~Computes dice from the stat scores: true positives, false positives, false negatives. Args: tp: True positives fp: False positives fn: False negatives average: Defines the reduction that is applied mdmc_average: Defines how averaging is done for multi-dimensional multi-class inputs (on top of the ``average`` parameter) r.weightedN) numerator denominatorweightsr r r) rMACROr SAMPLEWISENONEtorchnonzerocpur) r r r r r rrrcondmeaningless_indecesr_/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/dice.py _dice_computes$    r microglobal?predstarget thresholdtop_k num_classes multiclass ignore_indexc  Csd} || vrtd| d|d|dvr#|r|dkr#td|dgd } || vr6td | d|d|rO| d urO| |krD|dkrOtd | d |d|d urct|tr\|dkrctd|t||\}}|dvrpdn|} t||| |||||| d \} }}}t| |||||S)aComputes `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). .. note:: What is considered a sample in the multi-dimensional multi-class case depends on the value of ``mdmc_average``. .. note:: 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. 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 import dice >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> dice(preds, target, average='micro') tensor(0.2500) )r!macrorsamplesnoneNzThe `average` has to be one of z, got .)r+rr-NzWhen you set `average` as z,, you have to provide the number of classes.)N samplewiser"z$The `mdmc_average` has to be one of NzThe `ignore_index` z is not valid for inputs with z classesrz4The `top_k` should be an integer larger than 0, got )rr-Nr+)reduce mdmc_reducer&r(r'r)r*) ValueError isinstanceintrrr )r$r%rr r r&r'r(r)r*allowed_averageallowed_mdmc_averager1r r _r rrrdiceBs4m r9)r)rr!r"r#NNNN)typingrrr2torchmetrics.functional.classification.stat_scoresrrtorchmetrics.utilities.checksrtorchmetrics.utilities.enumsrrstrr5r floatboolr9rrrrsj     -