:jddlmZddlZddlmZddlmZddlmZ ddededed ee d e eeeff d Z d ed eded efdZ ddededed ee d ef dZ y))OptionalN)Tensor)_check_same_shape _safe_dividepredstarget thresholdkeep_sequence_dimreturnc t||d}nYdcxkr|jksntd|jdtfdt |jD}||k\j }||k\j }yt j||zj}t j||z |zj}t j||z |zj} n~t j||z|j}t j||z |z|j}t j||z |z|j} ||| fS)a%Update and return variables required to compute Critical Success Index. Checks for same shape of tensors. Args: preds: Predicted tensor target: Ground truth tensor threshold: Values above or equal to threshold are replaced with 1, below by 0 keep_sequence_dim: Index of the sequence dimension if the inputs are sequences of images. If specified, the score will be calculated separately for each image in the sequence. If ``None``, the score will be calculated across all dimensions. Nrz.Expected keep_sequence dim to be in range [0, z ] but got c3.K|] }|k7s |ywN).0ir s {/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/regression/csi.py z1_critical_success_index_update..,sPq>O9OPs )dim) rndim ValueErrortuplerangebooltorchsumint) rr r r sum_dims preds_bin target_binhitsmisses false_alarmss ` r_critical_success_index_updater$sbeV$  # 0ejj 0I%**U_`q_rsttPE%**$5PP)#))+II%++-J yyZ/0446I 2j@AEEGyy)j"8I!EFJJL yyZ/X>BBDI 2j@hOSSUyy)j"8I!E8TXXZ  %%r!r"r#c&t|||z|zS)aCompute critical success index. Args: hits: Number of true positives after binarization misses: Number of false negatives after binarization false_alarms: Number of false positives after binarization Returns: If input tensors are 5-dimensional and ``keep_sequence_dim=True``, the metric returns a ``(S,)`` vector with CSI scores for each image in the sequence. Otherwise, it returns a scalar tensor with the CSI score. r)r!r"r#s r_critical_success_index_computer'=s dVml: ;;r%c@t||||\}}}t|||S)aYCompute critical success index. Args: preds: Predicted tensor target: Ground truth tensor threshold: Values above or equal to threshold are replaced with 1, below by 0 keep_sequence_dim: Index of the sequence dimension if the inputs are sequences of images. If specified, the score will be calculated separately for each image in the sequence. If ``None``, the score will be calculated across all dimensions. Returns: If ``keep_sequence_dim`` is specified, the metric returns a vector of with CSI scores for each image in the sequence. Otherwise, it returns a scalar tensor with the CSI score. Example: >>> import torch >>> from torchmetrics.functional.regression import critical_success_index >>> x = torch.Tensor([[0.2, 0.7], [0.9, 0.3]]) >>> y = torch.Tensor([[0.4, 0.2], [0.8, 0.6]]) >>> critical_success_index(x, y, 0.5) tensor(0.3333) Example: >>> import torch >>> from torchmetrics.functional.regression import critical_success_index >>> x = torch.Tensor([[[0.2, 0.7], [0.9, 0.3]], [[0.2, 0.7], [0.9, 0.3]]]) >>> y = torch.Tensor([[[0.4, 0.2], [0.8, 0.6]], [[0.4, 0.2], [0.8, 0.6]]]) >>> critical_success_index(x, y, 0.5, keep_sequence_dim=0) tensor([0.3333, 0.3333]) )r$r')rr r r r!r"r#s rcritical_success_indexr)Ms-D"@vyZk!lD&, *4 FFr%r)typingrrrtorchmetrics.utilities.checksrtorchmetrics.utilities.computerfloatrrr$r'r)rr%rr.s ;7Y]#& #&!#&.3#&HPQT #& 666 !"#&L <& <&