66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
from typing import Optional, Tuple
|
|
|
|
import torch
|
|
|
|
from pytorch_lightning.utilities import rank_zero_warn
|
|
|
|
|
|
def _psnr_compute(
|
|
sum_squared_error: torch.Tensor,
|
|
n_obs: int,
|
|
data_range: float,
|
|
base: float = 10.0,
|
|
reduction: str = 'elementwise_mean',
|
|
) -> torch.Tensor:
|
|
if reduction != 'elementwise_mean':
|
|
rank_zero_warn(f'The `reduction={reduction}` parameter is unused and will not have any effect.')
|
|
psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / n_obs)
|
|
psnr = psnr_base_e * (10 / torch.log(torch.tensor(base)))
|
|
return psnr
|
|
|
|
|
|
def _psnr_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:
|
|
sum_squared_error = torch.sum(torch.pow(preds - target, 2))
|
|
n_obs = target.numel()
|
|
return sum_squared_error, n_obs
|
|
|
|
|
|
def psnr(
|
|
preds: torch.Tensor,
|
|
target: torch.Tensor,
|
|
data_range: Optional[float] = None,
|
|
base: float = 10.0,
|
|
reduction: str = 'elementwise_mean',
|
|
) -> torch.Tensor:
|
|
"""
|
|
Computes the peak signal-to-noise ratio
|
|
|
|
Args:
|
|
preds: estimated signal
|
|
target: groun truth signal
|
|
data_range: the range of the data. If None, it is determined from the data (max - min)
|
|
base: a base of a logarithm to use (default: 10)
|
|
reduction: a method to reduce metric score over labels.
|
|
|
|
- ``'elementwise_mean'``: takes the mean (default)
|
|
- ``'sum'``: takes the sum
|
|
- ``'none'``: no reduction will be applied
|
|
|
|
Return:
|
|
Tensor with PSNR score
|
|
|
|
Example:
|
|
|
|
>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
|
|
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
|
|
>>> psnr(pred, target)
|
|
tensor(2.5527)
|
|
|
|
"""
|
|
if data_range is None:
|
|
data_range = target.max() - target.min()
|
|
else:
|
|
data_range = torch.tensor(float(data_range))
|
|
sum_squared_error, n_obs = _psnr_update(preds, target)
|
|
return _psnr_compute(sum_squared_error, n_obs, data_range, base, reduction)
|