41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
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import torch
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from torch.nn import functional as F
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from pytorch_lightning.metrics.functional.reduction import reduce
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def psnr(
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pred: torch.Tensor,
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target: torch.Tensor,
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data_range: float = None,
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base: float = 10.0,
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reduction: str = 'elementwise_mean'
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) -> torch.Tensor:
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"""
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Computes the peak signal-to-noise ratio metric
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Args:
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pred: estimated signal
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target: groun truth signal
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data_range: the range of the data. If None, it is determined from the data (max - min).
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base: a base of a logarithm to use (default: 10)
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reduction: method for reducing psnr (default: takes the mean)
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Example:
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>>> from pytorch_lightning.metrics.regression import PSNR
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>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
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>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
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>>> metric = PSNR()
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>>> metric(pred, target)
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tensor(2.5527)
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"""
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if data_range is None:
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data_range = max(target.max() - target.min(), pred.max() - pred.min())
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else:
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data_range = torch.tensor(float(data_range))
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mse = F.mse_loss(pred.view(-1), target.view(-1), reduction=reduction)
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psnr_base_e = 2 * torch.log(data_range) - torch.log(mse)
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return psnr_base_e * (10 / torch.log(torch.tensor(base)))
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