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snr.py
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snr.py
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# Copyright The PyTorch 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 typing import Any, Callable, Optional
from torch import Tensor, tensor
from torchmetrics.functional.audio.snr import snr
from torchmetrics.metric import Metric
class SNR(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. default: True
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. default: None (which selects the entire world)
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 SNR
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> snr = SNR()
>>> snr_val = snr(preds, target)
>>> snr_val
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.
"""
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
@property
def is_differentiable(self) -> bool:
return True