o .wi @sddlmZmZmZddlZddlmZddlmZddl m Z dede dd efd d Z dd ededede dd ef ddZ dedede d eefddZddedeed eefddZdS))AnyListOptionalN)Tensor) functional)Literalx reduction)elementwise_meansumnoneNreturncCs@|dkr t|S|dks|dur|S|dkrt|Std)aBReduces a given tensor by a given reduction method. Args: x: the tensor, which shall be reduced reduction: a string specifying the reduction method ('elementwise_mean', 'none', 'sum') Return: reduced Tensor Raise: ValueError if an invalid reduction parameter was given r r Nr zReduction parameter unknown.)torchmeanr ValueError)rr r_/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/utilities/distributed.pyreduces  rr numdenomweightsclass_reductionmicromacroweightedr NcCsd}|dkrt|t|n||}d|||k<|dkr |S|dkr)t|S|dkr;t||t|S|dksC|durE|Std|d |) aReduce classification metrics of the form ``num / denom * weights``. For example for calculating standard accuracy the num would be number of true positives per class, denom would be the support per class, and weights would be a tensor of 1s. Args: num: numerator tensor denom: denominator tensor weights: weights for each class class_reduction: reduction method for multiclass problems: - ``'micro'``: calculate metrics globally (default) - ``'macro'``: calculate metrics for each label, and find their unweighted mean. - ``'weighted'``: calculate metrics for each label, and find their weighted mean. - ``'none'`` or ``None``: returns calculated metric per class Raises: ValueError: If ``class_reduction`` is none of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None``. rrrrrr NzReduction parameter z' unknown. Choose between one of these: )rr rfloatr)rrrrvalid_reductionfractionrrr class_reduce-s$  rresultgroup world_sizecsbtfddt|D}tj||Wdn1s"wY|tj|<|S)Ncg|]}tqSrr zeros_like.0_r rr ]z._simple_gather_all_tensors..)rno_gradrange distributed all_gatherget_rank)r r!r"gathered_resultrr)r_simple_gather_all_tensors[s r2c s|dur tjjj}|}tj|}tjj|d|jdkr%t|||Stj |j |j dfddt |D}tjj ||dt|jddjtfdd |D}|r_t|||StVg}}t|D]}|d||qrt||fd dt |D}tj ||t|D]\} } d d| D} || | || <qWdn1swY||tj|<|S) a5Gather all tensors from several ddp processes onto a list that is broadcast to all processes. Works on tensors that have the same number of dimensions, but where each dimension may differ. In this case tensors are padded, gathered and then trimmed to secure equal workload for all processes. Args: result: the value to sync group: the process group to gather results from. Defaults to all processes (world) Return: list with size equal to the process group where element i corresponds to result tensor from process i N)r!r)devicecr#rr$r&) local_sizerrr*r+z&gather_all_tensors..)dimc3s|] }t|kVqdSN)all)r'ls)max_sizerr sz%gather_all_tensors..cr#rr$r&) result_paddedrrr*r+cSsg|]}t|qSr)slice)r'dim_sizerrrr*s)rr.r!WORLD contiguousget_world_sizebarrierndimr2tensorshaper3r-r/stackmaxvaluesr7r,detachcpureversedappenditemFpad enumerater0) r r!r" local_sizesall_sizes_equalpad_dimspad_byvalr1idx item_size slice_paramr)r4r9r;rgather_all_tensorsds<          rX)r r6)typingrrrrrtorch.nnrrMtyping_extensionsrrrintr2rXrrrrs(     .$