snr.py

# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Callable, Optional

from deprecate import deprecated, void
from torch import Tensor, tensor

from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, snr
from torchmetrics.metric import Metric
from torchmetrics.utilities import _future_warning


class SignalNoiseRatio(Metric):
    r"""Signal-to-noise ratio (SNR_):

    .. math::
        \text{SNR} = \frac{P_{signal}}{P_{noise}}

    where  :math:`P` denotes the power of each signal. The SNR metric compares the level
    of the desired signal to the level of background noise. Therefore, a high value of
    SNR means that the audio is clear.

    Forward accepts

    - ``preds``: ``shape [..., time]``
    - ``target``: ``shape [..., time]``

    Args:
        zero_mean:
            if to zero mean target and preds or not
        compute_on_step:
            Forward only calls ``update()`` and returns None if this is set to False.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step.
        process_group:
            Specify the process group on which synchronization is called.
        dist_sync_fn:
            Callback that performs the allgather operation on the metric state. When `None`, DDP
            will be used to perform the allgather.

    Raises:
        TypeError:
            if target and preds have a different shape

    Returns:
        average snr value

    Example:
        >>> import torch
        >>> from torchmetrics import SignalNoiseRatio
        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
        >>> snr = SignalNoiseRatio()
        >>> snr(preds, target)
        tensor(16.1805)

    References:
        [1] Le Roux, Jonathan, et al. "SDR half-baked or well done." IEEE International Conference on Acoustics, Speech
        and Signal Processing (ICASSP) 2019.

    """
    is_differentiable = True
    higher_is_better = True
    sum_snr: Tensor
    total: Tensor

    def __init__(
        self,
        zero_mean: bool = False,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
    ) -> None:
        super().__init__(
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
            dist_sync_fn=dist_sync_fn,
        )
        self.zero_mean = zero_mean

        self.add_state("sum_snr", default=tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
        """Update state with predictions and targets.

        Args:
            preds: Predictions from model
            target: Ground truth values
        """
        snr_batch = snr(preds=preds, target=target, zero_mean=self.zero_mean)

        self.sum_snr += snr_batch.sum()
        self.total += snr_batch.numel()

    def compute(self) -> Tensor:
        """Computes average SNR."""
        return self.sum_snr / self.total


class SNR(SignalNoiseRatio):
    r"""Signal-to-noise ratio (SNR_):

    .. deprecated:: v0.7
        Use :class:`torchmetrics.SignalNoiseRatio`. Will be removed in v0.8.

    Example:
        >>> import torch
        >>> snr = SNR()
        >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
        tensor(16.1805)

    """

    @deprecated(target=SignalNoiseRatio, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
    def __init__(
        self,
        zero_mean: bool = False,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
    ) -> None:
        void(zero_mean, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)


class ScaleInvariantSignalNoiseRatio(Metric):
    """Scale-invariant signal-to-noise ratio (SI-SNR).

    Forward accepts

    - ``preds``: ``shape [...,time]``
    - ``target``: ``shape [...,time]``

    Args:
        compute_on_step:
            Forward only calls ``update()`` and returns None if this is set to False.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()``
            before returning the value at the step.
        process_group:
            Specify the process group on which synchronization is called.
        dist_sync_fn:
            Callback that performs the allgather operation on the metric state. When `None`, DDP
            will be used to perform the allgather.

    Raises:
        TypeError:
            if target and preds have a different shape

    Returns:
        average si-snr value

    Example:
        >>> import torch
        >>> from torchmetrics import ScaleInvariantSignalNoiseRatio
        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
        >>> si_snr = ScaleInvariantSignalNoiseRatio()
        >>> si_snr(preds, target)
        tensor(15.0918)

    References:
        [1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
        696-700, doi: 10.1109/ICASSP.2018.8462116.
    """

    is_differentiable = True
    sum_si_snr: Tensor
    total: Tensor
    higher_is_better = True

    def __init__(
        self,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
    ) -> None:
        super().__init__(
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
            dist_sync_fn=dist_sync_fn,
        )

        self.add_state("sum_si_snr", default=tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
        """Update state with predictions and targets.

        Args:
            preds: Predictions from model
            target: Ground truth values
        """
        si_snr_batch = scale_invariant_signal_noise_ratio(preds=preds, target=target)

        self.sum_si_snr += si_snr_batch.sum()
        self.total += si_snr_batch.numel()

    def compute(self) -> Tensor:
        """Computes average SI-SNR."""
        return self.sum_si_snr / self.total