a  d@sddlmZmZmZmZmZmZddlmZm Z ddl m Z ddl m Z ddlmZddlmZddlmZmZdd giZesd gZGd ddeZd S) )AnyCallableDictOptionalSequenceUnion)Tensortensor)Literal)permutation_invariant_training)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEPermutationInvariantTrainingZpit!PermutationInvariantTraining.plotcseZdZUdZdZeed<dZeed<eed<eed<dZ e e ed <dZ e e ed <de ed ededdfdd ZeeddddZedddZdeeeedfe eedddZZS)raxCalculate `Permutation invariant training`_ (PIT). This metric can evaluate models for speaker independent multi-talker speech separation in a permutation invariant way. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)`` - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(batch_size,num_speakers,...)`` As output of `forward` and `compute` the metric returns the following output - ``pesq`` (:class:`~torch.Tensor`): float scalar tensor with average PESQ value over samples Args: metric_func: a metric function accept a batch of target and estimate. if `mode`==`'speaker-wise'`, then ``metric_func(preds[:, i, ...], target[:, j, ...])`` is called and expected to return a batch of metric tensors ``(batch,)``; if `mode`==`'permutation-wise'`, then ``metric_func(preds[:, p, ...], target[:, :, ...])`` is called, where `p` is one possible permutation, e.g. [0,1] or [1,0] for 2-speaker case, and expected to return a batch of metric tensors ``(batch,)``; mode: can be `'speaker-wise'` or `'permutation-wise'`. 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 keyword arguments for either the ``metric_func`` or distributed communication, see :ref:`Metric kwargs` for more info. Example: >>> import torch >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio >>> _ = torch.manual_seed(42) >>> preds = torch.randn(3, 2, 5) # [batch, spk, time] >>> target = torch.randn(3, 2, 5) # [batch, spk, time] >>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio, ... mode="speaker-wise", eval_func="max") >>> pit(preds, target) tensor(-2.1065) Ffull_state_updateTis_differentiablesum_pit_metrictotalNplot_lower_boundplot_upper_bound speaker-wisemax)rzpermutation-wise)rmin) metric_funcmode eval_funckwargsreturnc sz|dd|dd|ddd}tjfi|||_||_||_||_|jdtddd |jd td dd dS) Ndist_sync_on_stepF process_group dist_sync_fn)r r!r"rgsum)defaultZdist_reduce_fxrr) popsuper__init__rrrrZ add_stater )selfrrrrZ base_kwargs __class___/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/audio/pit.pyr'Ts   z%PermutationInvariantTraining.__init__)predstargetrcCsLt|||j|j|jfi|jd}|j|7_|j|7_dS)z*Update state with predictions and targets.rN) r rrrrrr#rZnumel)r(r-r.Z pit_metricr+r+r,updateisz#PermutationInvariantTraining.update)rcCs |j|jS)zCompute metric.)rr)r(r+r+r,computersz$PermutationInvariantTraining.compute)valaxrcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio >>> preds = torch.randn(3, 2, 5) # [batch, spk, time] >>> target = torch.randn(3, 2, 5) # [batch, spk, time] >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio, ... mode="speaker-wise", eval_func="max") >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio >>> preds = torch.randn(3, 2, 5) # [batch, spk, time] >>> target = torch.randn(3, 2, 5) # [batch, spk, time] >>> metric = PermutationInvariantTraining(scale_invariant_signal_noise_ratio, ... mode="speaker-wise", eval_func="max") >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) )Z_plot)r(r1r2r+r+r,plotvs.r)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrfloatrrr rr'r/r0rrrrr3 __classcell__r+r+r)r,rs& .   N)typingrrrrrrZtorchrr Ztyping_extensionsr Z!torchmetrics.functional.audio.pitr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotrrZ__doctest_requires__Z__doctest_skip__rr+r+r+r,s