o .wi# @s$ddlZddlmZddlmZmZmZmZddlZddl m Z ddlm Z ddl m Z ddlmZmZddlmZd Zd ee ee fd e fd d Zd e d e fddZd e d e fddZd e d e fddZd e d e fddZd ed efddZd ed eeeffddZ d?de deed e fddZ d@d e ded ed e fd!d"Z!d@d#e d$ed ed e fd%d&Z"dAd e d'ed e fd(d)Z#d e d e fd*d+Z$d,ed efd-d.Z%d?d e d/eed e fd0d1Z&dBd e d eed2eej'd e fd3d4Z(d e d e fd5d6Z)d7e d8e d efd9d:Z*d e d;e dZ+dS)CN)Sequence)AnyListOptionalUnion)apply_to_collection)Tensor)TorchMetricsUserWarning)_TORCH_LESS_THAN_2_6_XLA_AVAILABLE)rank_zero_warngư>xreturncCs8t|tjr|Sdd|D}|stdtj|ddS)z'Concatenation along the zero dimension.cSs0g|]}|dkr|jdkr|dn|qS)r)numelndim unsqueeze).0yrX/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/utilities/data.py !s0z dim_zero_cat..zNo samples to concatenaterdim) isinstancetorchr ValueErrorcatr rrr dim_zero_cats rcCtj|ddS)z#Summation along the zero dimension.rr)rsumrrrr dim_zero_sum'r"cCr )z!Average along the zero dimension.rr)rmeanrrrr dim_zero_mean,r#r%cCtj|ddjS)zMax along the zero dimension.rr)rmaxvaluesrrrr dim_zero_max1r)cCr&)zMin along the zero dimension.rr)rminr(rrrr dim_zero_min6r*r,cCsdd|DS)z&Flatten list of list into single list.cSsg|] }|D]}|qqSrr)rsublistitemrrrr=sz_flatten..rrrrr_flatten;r#r/cCshi}d}|D]'\}}t|tr%|D]\}}||vrd}|||<qq||vr+d}|||<q||fS)zYFlatten dict of dicts into single dict and checking for duplicates in keys along the way.FT)itemsrdict)r new_dict duplicateskeyvaluekvrrr _flatten_dict@s   r8 label_tensor num_classescCsr|durt|d}tj|jd|g|jddR|j|jd}| d |}| d|dS)aConvert a dense label tensor to one-hot format. Args: label_tensor: dense label tensor, with shape [N, d1, d2, ...] num_classes: number of classes C Returns: A sparse label tensor with shape [N, C, d1, d2, ...] Example: >>> x = torch.tensor([1, 2, 3]) >>> to_onehot(x) tensor([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) Nrr)dtypedevice?) intr'detachr.rzerosshaper;r<longr expand_asscatter_)r9r: tensor_onehotindexrrr to_onehotQs rGrr6rcCsF|jtjkr|jstj||dd|}||d|S|j||djS)z3torch.top_k does not support half precision on CPU.T)rstablerr6r) r;rhalfis_cudaargsortflipnarrowtopkindices)r r6ridxrrr"_top_k_with_half_precision_supporttsrR prob_tensorrOcCsXtj|tjd}|dkr|||j|ddd|S||t|||dd|S)awConvert a probability tensor to binary by selecting top-k the highest entries. Args: prob_tensor: dense tensor of shape ``[..., C, ...]``, where ``C`` is in the position defined by the ``dim`` argument topk: number of the highest entries to turn into 1s dim: dimension on which to compare entries Returns: A binary tensor of the same shape as the input tensor of type ``torch.int32`` Example: >>> x = torch.tensor([[1.1, 2.0, 3.0], [2.0, 1.0, 0.5]]) >>> select_topk(x, topk=2) tensor([[0, 1, 1], [1, 1, 0]], dtype=torch.int32) r;rT)rkeepdimr=rI)r zeros_liker>rDargmaxrR)rSrOr topk_tensorrrr select_topk|s rY argmax_dimcCstj||dS)awConvert a tensor of probabilities to a dense label tensor. Args: x: probabilities to get the categorical label [N, d1, d2, ...] argmax_dim: dimension to apply Return: A tensor with categorical labels [N, d2, ...] Example: >>> x = torch.tensor([[0.2, 0.5], [0.9, 0.1]]) >>> to_categorical(x) tensor([1, 0]) r)rrW)r rZrrrto_categoricalsr[cCs|dkr |S|S)Nr)rsqueezerrrr_squeeze_scalar_element_tensorsr]datacCs t|ttSN)rrr])r^rrr_squeeze_if_scalars r` minlengthcCsn|dur tt|}tsts|jr0tj||jdt|d}t | dd|j ddStj ||dS)aImplement custom bincount. PyTorch currently does not support ``torch.bincount`` when running in deterministic mode on GPU or when running MPS devices or when running on XLA device. This implementation therefore falls back to using a combination of `torch.arange` and `torch.eq` in these scenarios. A small performance hit can expected and higher memory consumption as `[batch_size, mincount]` tensor needs to be initialized compared to native ``torch.bincount``. Args: x: tensor to count minlength: minimum length to count Returns: Number of occurrences for each unique element in x Example: >>> x = torch.tensor([0,0,0,1,1,2,2,2,2]) >>> _bincount(x, minlength=3) tensor([3, 2, 4]) N)r<rrrra) lenrunique$are_deterministic_algorithms_enabledr is_mpsaranger<repeateqreshaper!bincount)r rameshrrr _bincounts rnr;cCs\to |jo |}tr&|r&tjdkr&tdt| j ||d |j Stj |||dS)z\Implement custom cumulative summation for Torch versions which does not support it natively.win32zYou are trying to use a metric in deterministic mode on GPU that uses `torch.cumsum`, which is currently not supported. The tensor will be copied to the CPU memory to compute it and then copied back to GPU. Expect some slowdowns.)rr;) rrfrKis_floating_pointr sysplatformr r cpucumsumtor<)r rr;is_cuda_fp_deterministicrrr_cumsumsrwcCs4||}t|}t|t|dd}||S)zSimilar to `_bincount`, but works also with tensor that do not contain continuous values. Args: x: tensor to count Returns: Number of occurrences for each unique element in x rrc)r+rrernr')r unique_xoutputrrr_flexible_bincounts rztensor1tensor2cCs&|j|jkr |j|jd}t||S)z:Wrap torch.allclose to be robust towards dtype difference.rT)r;rurallclose)r{r|rrrr}s  r}xpfpcCst|}||}||}|dd|dd|dd|dd}t||d}t|dt|d}|||||||S)zInterpolation function comparable to numpy.interp. Args: x: x-coordinates where to evaluate the interpolated values xp: x-coordinates of the data points fp: y-coordinates of the data points rNrbr)rrL searchsortedclamprd)r r~rsorted_indicesslopesrPrrrinterps 0rr_)rr)r)rN),rqcollections.abcrtypingrrrrrlightning_utilitiesrr!torchmetrics.utilities.exceptionsr torchmetrics.utilities.importsr r torchmetrics.utilities.printsr METRIC_EPSrr"r%r)r,listr/r1tupleboolr8r>rGrRrYr[r]r`rnr;rwrzr}rrrrrsF       #&