o yi<@s UddlmZddlmZmZmZmZddlmZddl Z ddl m Z ddl m Z ddl mZiZeed<d e d ee je jfd ee e ffd d Zd e d ee je jfd ee e ffddZ dde de ded e dded ee e ff ddZde de d e fddZdS)) permutations)AnyCallableTupleUnion)warnN)Tensor)Literal)_SCIPY_AVAILABLE_ps_dict metric_mtx eval_funcreturncspddlm|}tfdd|D}||j}t|d|dddddf ddg}||fS) aSolves the linear sum assignment problem using scipy, and returns the best metric values and the corresponding permutations. Args: metric_mtx: the metric matrix, shape [batch_size, spk_num, spk_num] eval_func: the function to reduce the metric values of different the permutations Returns: best_metric: shape ``[batch]`` best_perm: shape ``[batch, spk]`` r)linear_sum_assignmentcs g|] }|tjkdqS))torchmax).0pwmr rU/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/audio/pit.py /s z<_find_best_perm_by_linear_sum_assignment..N) scipy.optimizerdetachcpurtensortodevicegathermean)r r mmtx best_perm best_metricrrr(_find_best_perm_by_linear_sum_assignments   *r'c Cs|jdd\}}t|t|j}|tvr*tjttt||jdj }|t|<nt|}|jd}|d |||}t |d|}|j dd} || dd\} } | } |j | ddf} | | fS)aSolves the linear sum assignment problem using exhaustive method, i.e. exhaustively calculates the metric values of all possible permutations, and returns the best metric values and the corresponding permutations. Args: metric_mtx: the metric matrix, shape ``[batch_size, spk_num, spk_num]`` eval_func: the function to reduce the metric values of different the permutations Returns: best_metric: shape ``[batch]`` best_perm: shape ``[batch, spk]`` Nr)r!r)N.r)dim)shapestrr!r rrlistrrangeTexpandr"r#r) r r batch_sizespk_numkeypsperm_numbpsmetric_of_ps_details metric_of_psr& best_indexesr%rrr$_find_best_perm_by_exhaustive_method5s   r8rpredstarget metric_funcrminkwargscKs|jdd|jddkrtd|dvrtd||jdkr/td|jd|jd|jdd\}}d }t|D]Y}t|D]R} |d uri||d d | d f|d d |d ffi||d d || f<qD||d d | d f|d d |d ffi|} tj|||f| j| jd }| |d d || f<qDq>|d krtj ntj } |d kst s|d krt st d|dt || \} } | | fSt|| \} } | | fS)aCalculates `Permutation invariant training`_ (PIT) that can evaluate models for speaker independent multi- talker speech separation in a permutation invariant way. Args: preds: float tensor with shape ``(batch_size,num_speakers,...)`` target: float tensor with shape ``(batch_size,num_speakers,...)`` metric_func: a metric function accept a batch of target and estimate, i.e. ``metric_func(preds[:, i, ...], target[:, j, ...])``, and returns a batch of metric tensors ``(batch,)`` eval_func: the function to find the best permutation, can be ``'min'`` or ``'max'``, i.e. the smaller the better or the larger the better. kwargs: Additional args for metric_func Returns: Tuple of two float tensors. First tensor with shape ``(batch,)`` contains the best metric value for each sample and second tensor with shape ``(batch,)`` contains the best permutation. Example: >>> from torchmetrics.functional.audio import scale_invariant_signal_distortion_ratio >>> # [batch, spk, time] >>> preds = torch.tensor([[[-0.0579, 0.3560, -0.9604], [-0.1719, 0.3205, 0.2951]]]) >>> target = torch.tensor([[[ 1.0958, -0.1648, 0.5228], [-0.4100, 1.1942, -0.5103]]]) >>> best_metric, best_perm = permutation_invariant_training( ... preds, target, scale_invariant_signal_distortion_ratio, 'max') >>> best_metric tensor([-5.1091]) >>> best_perm tensor([[0, 1]]) >>> pit_permutate(preds, best_perm) tensor([[[-0.0579, 0.3560, -0.9604], [-0.1719, 0.3205, 0.2951]]]) rrz_Predictions and targets are expected to have the same shape at the batch and speaker dimensionsr<z-eval_func can only be "max" or "min" but got z/Inputs must be of shape [batch, spk, ...], got z and z insteadN.)dtyper!rzIn pit metric for speaker-num z8>3, we recommend installing scipy for better performance)r) RuntimeError ValueErrorndimr,remptyr?r!rr=r rr8r')r9r:r;r r>r/r0r target_idx preds_idx first_eleopr&r%rrrpermutation_invariant_training`s<#    .  rIpermcCstddt||D}|S)a!Permutate estimate according to perm. Args: preds: the estimates you want to permutate, shape [batch, spk, ...] perm: the permutation returned from permutation_invariant_training, shape [batch, spk] Returns: Tensor: the permutated version of estimate cSsg|] \}}t|d|qS)r)r index_select)rpredprrrrsz!pit_permutate..)rstackzip)r9rJ preds_pmtedrrr pit_permutates rQ)r) itertoolsrtypingrrrrwarningsrrrtyping_extensionsr torchmetrics.utilities.importsr r dict__annotations__r=rr'r8rIrQrrrrsJ          ,  G