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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor, tensor
from torchmetrics.functional.audio.snr import (
complex_scale_invariant_signal_noise_ratio,
scale_invariant_signal_noise_ratio,
signal_noise_ratio,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = [
"SignalNoiseRatio.plot",
"ScaleInvariantSignalNoiseRatio.plot",
"ComplexScaleInvariantSignalNoiseRatio.plot",
]
class SignalNoiseRatio(Metric):
r"""Calculate `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio.
.. 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.
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
- ``snr`` (:class:`~torch.Tensor`): float scalar tensor with average SNR 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 SignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> snr = SignalNoiseRatio()
>>> snr(preds, target)
tensor(16.1805)
"""
full_state_update: bool = False
is_differentiable: bool = True
higher_is_better: bool = True
sum_snr: Tensor
total: Tensor
plot_lower_bound: Optional[float] = None
plot_upper_bound: Optional[float] = None
def __init__(
self,
zero_mean: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
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:
"""Update state with predictions and targets."""
snr_batch = signal_noise_ratio(preds=preds, target=target, zero_mean=self.zero_mean)
self.sum_snr += snr_batch.sum()
self.total += snr_batch.numel()
def compute(self) -> Tensor:
"""Compute metric."""
return self.sum_snr / self.total
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot 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 SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(4), torch.rand(4)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class ScaleInvariantSignalNoiseRatio(Metric):
"""Calculate `Scale-invariant signal-to-noise ratio`_ (SI-SNR) metric for evaluating quality of audio.
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_snr`` (:class:`~torch.Tensor`): float scalar tensor with average SI-SNR value over samples
Args:
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 torch import tensor
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> si_snr = ScaleInvariantSignalNoiseRatio()
>>> si_snr(preds, target)
tensor(15.0918)
"""
is_differentiable = True
sum_si_snr: Tensor
total: Tensor
higher_is_better = True
plot_lower_bound: Optional[float] = None
plot_upper_bound: Optional[float] = None
def __init__(
self,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
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:
"""Update state with predictions and targets."""
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:
"""Compute metric."""
return self.sum_si_snr / self.total
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
"""Plot 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 ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
>>> metric = ScaleInvariantSignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(4), torch.rand(4)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class ComplexScaleInvariantSignalNoiseRatio(Metric):
"""Calculate `Complex scale-invariant signal-to-noise ratio`_ (C-SI-SNR) metric for evaluating quality of audio.
As input to `forward` and `update` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
tensor with shape ``(..., frequency,time)``
- ``target`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
tensor with shape ``(..., frequency,time)``
As output of `forward` and `compute` the metric returns the following output
- ``c_si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average C-SI-SNR 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:
ValueError:
If ``zero_mean`` is not an bool
TypeError:
If ``preds`` is not the shape (..., frequency, time, 2) (after being converted to real if it is complex).
If ``preds`` and ``target`` does not have the same shape.
Example:
>>> from torch import randn
>>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
>>> preds = randn((1,257,100,2))
>>> target = randn((1,257,100,2))
>>> c_si_snr = ComplexScaleInvariantSignalNoiseRatio()
>>> c_si_snr(preds, target)
tensor(-38.8832)
"""
is_differentiable = True
ci_snr_sum: Tensor
num: Tensor
higher_is_better = True
plot_lower_bound: Optional[float] = None
plot_upper_bound: Optional[float] = None
def __init__(
self,
zero_mean: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not isinstance(zero_mean, bool):
raise ValueError(f"Expected argument `zero_mean` to be an bool, but got {zero_mean}")
self.zero_mean = zero_mean
self.add_state("ci_snr_sum", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("num", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
v = complex_scale_invariant_signal_noise_ratio(preds=preds, target=target, zero_mean=self.zero_mean)
self.ci_snr_sum += v.sum()
self.num += v.numel()
def compute(self) -> Tensor:
"""Compute metric."""
return self.ci_snr_sum / self.num
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
"""Plot 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 ComplexScaleInvariantSignalNoiseRatio
>>> metric = ComplexScaleInvariantSignalNoiseRatio()
>>> metric.update(torch.rand(1,257,100,2), torch.rand(1,257,100,2))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
>>> metric = ComplexScaleInvariantSignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(1,257,100,2), torch.rand(1,257,100,2)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)