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# Peak Signal-to-Noise Ratio (PSNR)¶

## Module Interface¶

class torchmetrics.image.PeakSignalNoiseRatio(data_range=None, base=10.0, reduction='elementwise_mean', dim=None, **kwargs)[source]
$\text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)$

Where $$\text{MSE}$$ denotes the mean-squared-error function.

As input to forward and update the metric accepts the following input

• preds (Tensor): Predictions from model of shape (N,C,H,W)

• target (Tensor): Ground truth values of shape (N,C,H,W)

As output of forward and compute the metric returns the following output

• psnr (Tensor): if reduction!='none' returns float scalar tensor with average PSNR value over sample else returns tensor of shape (N,) with PSNR values per sample

Parameters:
• data_range (Union[float, Tuple[float, float], None]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. The data_range must be given when dim is not None.

• base (float) – a base of a logarithm to use.

• reduction (Literal['elementwise_mean', 'sum', 'none', None]) –

a method to reduce metric score over labels.

• 'elementwise_mean': takes the mean (default)

• 'sum': takes the sum

• 'none' or None: no reduction will be applied

• dim (Union[int, Tuple[int, ...], None]) – Dimensions to reduce PSNR scores over, provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions and all batches.

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises:

ValueError – If dim is not None and data_range is not given.

Example

>>> from torchmetrics.image import PeakSignalNoiseRatio
>>> psnr = PeakSignalNoiseRatio()
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> psnr(preds, target)
tensor(2.5527)
plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
• val (Union[Tensor, Sequence[Tensor], None]) – 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 (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import PeakSignalNoiseRatio
>>> metric = PeakSignalNoiseRatio()
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import PeakSignalNoiseRatio
>>> metric = PeakSignalNoiseRatio()
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

## Functional Interface¶

torchmetrics.functional.image.peak_signal_noise_ratio(preds, target, data_range=None, base=10.0, reduction='elementwise_mean', dim=None)[source]

Compute the peak signal-to-noise ratio.

Parameters:
• preds (Tensor) – estimated signal

• target (Tensor) – groun truth signal

• data_range (Union[float, Tuple[float, float], None]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. The data_range must be given when dim is not None.

• base (float) – a base of a logarithm to use

• reduction (Literal['elementwise_mean', 'sum', 'none', None]) –

a method to reduce metric score over labels.

• 'elementwise_mean': takes the mean (default)

• 'sum': takes the sum

• 'none' or None: no reduction will be applied

• dim (Union[int, Tuple[int, ...], None]) – Dimensions to reduce PSNR scores over provided as either an integer or a list of integers. Default is None meaning scores will be reduced across all dimensions.

Return type:

Tensor

Returns:

Tensor with PSNR score

Raises:

ValueError – If dim is not None and data_range is not provided.

Example

>>> from torchmetrics.functional.image import peak_signal_noise_ratio
>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> peak_signal_noise_ratio(pred, target)
tensor(2.5527)

Note

Half precision is only support on GPU for this metric