o yi0@sjddlmZmZddlmZmZddlmZmZddl m Z ddgiZ Gddde Z Gdd d e Z d S) )AnyOptional)Tensortensor)'scale_invariant_signal_distortion_ratiosignal_distortion_ratio)MetricSignalDistortionRatio fast_bss_evalc seZdZUdZeed<eed<dZeed<dZeed<dZ eed<  dd e e d e d ede e de dd f fdd Zdededd fddZdefddZZS)r a Calculates 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 torchmetrics.audio import SignalDistortionRatio >>> import torch >>> g = torch.manual_seed(1) >>> preds = torch.randn(8000) >>> target = torch.randn(8000) >>> sdr = SignalDistortionRatio() >>> sdr(preds, target) tensor(-12.0589) >>> # use with pit >>> from torchmetrics.audio import PermutationInvariantTraining >>> from torchmetrics.functional.audio import signal_distortion_ratio >>> preds = torch.randn(4, 2, 8000) # [batch, spk, time] >>> target = torch.randn(4, 2, 8000) >>> pit = PermutationInvariantTraining(signal_distortion_ratio, 'max') >>> pit(preds, target) tensor(-11.6051) sum_sdrtotalFfull_state_updateTis_differentiablehigher_is_betterN use_cg_iter filter_length zero_mean load_diagkwargsreturnc sVtjdi|||_||_||_||_|jdtddd|jdtddddS)Nr sumdefaultdist_reduce_fxr r)super__init__rrrr add_stater)selfrrrrr __class__rJ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/audio/sdr.pyrSszSignalDistortionRatio.__init__predstargetcCsBt|||j|j|j|j}|j|7_|j|7_dS)*Update state with predictions and targets.N) rrrrrr rr numel)r r$r% sdr_batchrrr#updatees zSignalDistortionRatio.updatecC |j|jSzComputes metric.)r r r rrr#computen zSignalDistortionRatio.compute)NrFN)__name__ __module__ __qualname____doc__r__annotations__r boolrrrintfloatrrr)r- __classcell__rrr!r#r s4 4    cspeZdZUdZdZdZeed<eed< ddede dd ffd d Z d ed edd fddZ defddZ Z S)#ScaleInvariantSignalDistortionRatioa`Scale-invariant signal-to-distortion ratio`_ (SI-SDR). The SI-SDR value is in general considered an overall measure of how good a source sound. As input to `forward` and `update` the metric accepts the following input - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)`` - ``target`` (: :class:`~torch.Tensor`): float tensor with shape ``(...,time)`` As output of `forward` and `compute` the metric returns the following output - ``si_sdr`` (: :class:`~torch.Tensor`): float scalar tensor with average SI-SDR value over samples Args: zero_mean: if to zero mean target and preds or not kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: TypeError: if target and preds have a different shape Example: >>> import torch >>> from torchmetrics import ScaleInvariantSignalDistortionRatio >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0]) >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0]) >>> si_sdr = ScaleInvariantSignalDistortionRatio() >>> si_sdr(preds, target) tensor(18.4030) T sum_si_sdrr FrrrNc sDtjdi|||_|jdtddd|jdtddddS)Nr9rrrr rr)rrrrr)r rrr!rr#rsz,ScaleInvariantSignalDistortionRatio.__init__r$r%cCs8t|||jd}|j|7_|j|7_dS)r&)r$r%rN)rrr9rr r')r r$r% si_sdr_batchrrr#r)sz*ScaleInvariantSignalDistortionRatio.updatecCr*r+)r9r r,rrr#r-r.z+ScaleInvariantSignalDistortionRatio.compute)F)r/r0r1r2rrrr3r4rrr)r-r7rrr!r#r8ss  r8N)typingrrtorchrr!torchmetrics.functional.audio.sdrrrtorchmetrics.metricr__doctest_requires__r r8rrrr#s   [