o .wi;@sddlmZddlmZmZmZddlmZmZddl m Z m Z m Z ddl mZddlmZddlmZmZdd giZesAgd ZGd ddeZGd d d eZGdddeZdS))Sequence)AnyOptionalUnion)Tensortensor)'scale_invariant_signal_distortion_ratiosignal_distortion_ratio)source_aggregated_signal_distortion_ratio)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPESignalDistortionRatio fast_bss_eval)SignalDistortionRatio.plot(ScaleInvariantSignalDistortionRatio.plot*SourceAggregatedSignalDistortionRatio.plotc seZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e e ed <d Z e e ed <  dd e ededede e dedd f fdd Zdededd fddZdefddZd deeeed fde edefddZZS)!ra Calculate Signal to Distortion Ratio (SDR) metric. See `SDR ref1`_ and `SDR ref2`_ for details on the metric. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` As output of `forward` and `compute` the metric returns the following output - ``sdr`` (:class:`~torch.Tensor`): float scalar tensor with average SDR value over samples .. note: The metric currently does not seem to work with Pytorch v1.11 and specific GPU hardware. Args: use_cg_iter: If provided, conjugate gradient descent is used to solve for the distortion filter coefficients instead of direct Gaussian elimination, which requires that ``fast-bss-eval`` is installed and pytorch version >= 1.8. This can speed up the computation of the metrics in case the filters are long. Using a value of 10 here has been shown to provide good accuracy in most cases and is sufficient when using this loss to train neural separation networks. filter_length: The length of the distortion filter allowed zero_mean: When set to True, the mean of all signals is subtracted prior to computation of the metrics load_diag: If provided, this small value is added to the diagonal coefficients of the system metrics when solving for the filter coefficients. This can help stabilize the metric in the case where some reference signals may sometimes be zero kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import randn >>> from torchmetrics.audio import SignalDistortionRatio >>> preds = randn(8000) >>> target = randn(8000) >>> sdr = SignalDistortionRatio() >>> sdr(preds, target) tensor(-11.9930) >>> # use with pit >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import signal_distortion_ratio >>> preds = randn(4, 2, 8000) # [batch, spk, time] >>> target = randn(4, 2, 8000) >>> pit = PermutationInvariantTraining(signal_distortion_ratio, ... mode="speaker-wise", eval_func="max") >>> pit(preds, target) tensor(-11.7277) sum_sdrtotalFfull_state_updateTis_differentiablehigher_is_betterNplot_lower_boundplot_upper_bound use_cg_iter filter_length zero_mean load_diagkwargsreturnc sVtjdi|||_||_||_||_|jdtddd|jdtddddS)Nrsumdefaultdist_reduce_fxrr)super__init__rrrr add_stater)selfrrrrr  __class__r'S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/audio/sdr.pyr)eszSignalDistortionRatio.__init__predstargetcCsBt|||j|j|j|j}|j|7_|j|7_dS*Update state with predictions and targets.N) r rrrrrr#rnumel)r+r/r0 sdr_batchr'r'r.updatews zSignalDistortionRatio.updatecC |j|jSzCompute metric.)rrr+r'r'r.compute zSignalDistortionRatio.computevalaxcC |||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 SignalDistortionRatio >>> metric = SignalDistortionRatio() >>> metric.update(torch.rand(8000), torch.rand(8000)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import SignalDistortionRatio >>> metric = SignalDistortionRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(8000), torch.rand(8000))) >>> fig_, ax_ = metric.plot(values) _plotr+r;r<r'r'r.plot &r)NrFNNN)__name__ __module__ __qualname____doc__r__annotations__rboolrrrrfloatrintrr)r5r9rrr rrA __classcell__r'r'r,r.r&s: 6    2cseZdZUdZdZdZeed<eed<dZe e ed<dZ e e ed< dd e d e d dffd d Zdeded dfddZd efddZ dde eeeefde ed efddZZS)#ScaleInvariantSignalDistortionRatioa`Scale-invariant signal-to-distortion ratio`_ (SI-SDR). The SI-SDR value is in general considered an overall measure of how good a source sound. As input to `forward` and `update` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` As output of `forward` and `compute` the metric returns the following output - ``si_sdr`` (:class:`~torch.Tensor`): float scalar tensor with average SI-SDR value over samples Args: zero_mean: if to zero mean target and preds or not kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: TypeError: if target and preds have a different shape Example: >>> from torch import tensor >>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio >>> target = tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = tensor([2.5, 0.0, 2.0, 8.0]) >>> si_sdr = ScaleInvariantSignalDistortionRatio() >>> si_sdr(preds, target) tensor(18.4030) T sum_si_sdrrNrrFrr r!c sDtjdi|||_|jdtddd|jdtddddS)NrNr"r#r$rrr')r(r)rr*r)r+rr r,r'r.r)sz,ScaleInvariantSignalDistortionRatio.__init__r/r0cCs8t|||jd}|j|7_|j|7_dS)r2)r/r0rN)rrrNr#rr3)r+r/r0 si_sdr_batchr'r'r.r5sz*ScaleInvariantSignalDistortionRatio.updatecCr6r7)rNrr8r'r'r.r9r:z+ScaleInvariantSignalDistortionRatio.computer;r<cCr=)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 ScaleInvariantSignalDistortionRatio >>> target = torch.randn(5) >>> preds = torch.randn(5) >>> metric = ScaleInvariantSignalDistortionRatio() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import ScaleInvariantSignalDistortionRatio >>> target = torch.randn(5) >>> preds = torch.randn(5) >>> metric = ScaleInvariantSignalDistortionRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) r>r@r'r'r.rAs ,r)FrC)rDrErFrGrrrrHrrrJrrIrr)r5r9rrr rrArLr'r'r,r.rMs4   rMc seZdZUdZeed<eed<dZeed<dZeed<dZ eed<d Z e e ed <d Z e e ed <  dd ed ededd ffdd Zdededd fddZdefddZddeeeed fde edefddZZS)%SourceAggregatedSignalDistortionRatioaL`Source-aggregated signal-to-distortion ratio`_ (SA-SDR). The SA-SDR is proposed to provide a stable gradient for meeting style source separation, where one-speaker and multiple-speaker scenes coexist. As input to ``forward`` and ``update`` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(..., spk, time)`` - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(..., spk, time)`` As output of `forward` and `compute` the metric returns the following output - ``sa_sdr`` (:class:`~torch.Tensor`): float scalar tensor with average SA-SDR value over samples Args: preds: float tensor with shape ``(..., spk, time)`` target: float tensor with shape ``(..., spk, time)`` scale_invariant: if True, scale the targets of different speakers with the same alpha zero_mean: If to zero mean target and preds or not kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import randn >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio >>> preds = randn(2, 8000) # [..., spk, time] >>> target = randn(2, 8000) >>> sasdr = SourceAggregatedSignalDistortionRatio() >>> sasdr(preds, target) tensor(-50.8171) >>> # use with pit >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import source_aggregated_signal_distortion_ratio >>> preds = randn(4, 2, 8000) # [batch, spk, time] >>> target = randn(4, 2, 8000) >>> pit = PermutationInvariantTraining(source_aggregated_signal_distortion_ratio, ... mode="permutation-wise", eval_func="max") >>> pit(preds, target) tensor(-43.9780) msummnumFrTrrNrrscale_invariantrr r!c sztjd i|t|tstd|||_t|ts$td|||_|jdtddd|jdtddddS) Nz9Expected argument `scale_invarint` to be a bool, but got z4Expected argument `zero_mean` to be a bool, but got rQr"r#r$rRrr') r(r) isinstancerI ValueErrorrSrr*r)r+rSrr r,r'r.r)Ls  z.SourceAggregatedSignalDistortionRatio.__init__r/r0cCs:t|||j|j}|j|7_|j|7_dSr1)r rSrrQr#rRr3)r+r/r0mbatchr'r'r.r5^sz,SourceAggregatedSignalDistortionRatio.updatecCr6r7)rQrRr8r'r'r.r9er:z-SourceAggregatedSignalDistortionRatio.computer;r<cCr=)afPlot 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 SourceAggregatedSignalDistortionRatio >>> metric = SourceAggregatedSignalDistortionRatio() >>> metric.update(torch.rand(2,8000), torch.rand(2,8000)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio >>> metric = SourceAggregatedSignalDistortionRatio() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(2,8000), torch.rand(2,8000))) >>> fig_, ax_ = metric.plot(values) r>r@r'r'r.rAirBr)TFrC)rDrErFrGrrHrrIrrrrrJrrr)r5r9rrr rrArLr'r'r,r.rPs. )   2rPN)collections.abcrtypingrrrtorchrr!torchmetrics.functional.audio.sdrrr r torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_requires____doctest_skip__rrMrPr'r'r'r.s    m