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psnr.py
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psnr.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 Optional, Tuple, Union
import torch
from torch import Tensor, tensor
from typing_extensions import Literal
from torchmetrics.utilities import rank_zero_warn, reduce
def _psnr_compute(
sum_squared_error: Tensor,
n_obs: Tensor,
data_range: Tensor,
base: float = 10.0,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
) -> Tensor:
"""Computes peak signal-to-noise ratio.
Args:
sum_squared_error: Sum of square of errors over all observations
n_obs: Number of predictions or observations
data_range: the range of the data. If None, it is determined from the data (max - min).
``data_range`` must be given when ``dim`` is not None.
base: a base of a logarithm to use
reduction: 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
Example:
>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> data_range = target.max() - target.min()
>>> sum_squared_error, n_obs = _psnr_update(preds, target)
>>> _psnr_compute(sum_squared_error, n_obs, data_range)
tensor(2.5527)
"""
psnr_base_e = 2 * torch.log(data_range) - torch.log(sum_squared_error / n_obs)
psnr_vals = psnr_base_e * (10 / torch.log(tensor(base)))
return reduce(psnr_vals, reduction=reduction)
def _psnr_update(
preds: Tensor,
target: Tensor,
dim: Optional[Union[int, Tuple[int, ...]]] = None,
) -> Tuple[Tensor, Tensor]:
"""Updates and returns variables required to compute peak signal-to-noise ratio.
Args:
preds: Predicted tensor
target: Ground truth tensor
dim: 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.
"""
if dim is None:
sum_squared_error = torch.sum(torch.pow(preds - target, 2))
n_obs = tensor(target.numel(), device=target.device)
return sum_squared_error, n_obs
diff = preds - target
sum_squared_error = torch.sum(diff * diff, dim=dim)
if isinstance(dim, int):
dim_list = [dim]
else:
dim_list = list(dim)
if not dim_list:
n_obs = tensor(target.numel(), device=target.device)
else:
n_obs = tensor(target.size(), device=target.device)[dim_list].prod()
n_obs = n_obs.expand_as(sum_squared_error)
return sum_squared_error, n_obs
def peak_signal_noise_ratio(
preds: Tensor,
target: Tensor,
data_range: Optional[float] = None,
base: float = 10.0,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
dim: Optional[Union[int, Tuple[int, ...]]] = None,
) -> 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).
``data_range`` must be given when ``dim`` is not None.
base: a base of a logarithm to use
reduction: 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:
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:
Tensor with PSNR score
Raises:
ValueError:
If ``dim`` is not ``None`` and ``data_range`` is not provided.
Example:
>>> from torchmetrics.functional 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
"""
if dim is None and reduction != "elementwise_mean":
rank_zero_warn(f"The `reduction={reduction}` will not have any effect when `dim` is None.")
if data_range is None:
if dim is not None:
# Maybe we could use `torch.amax(target, dim=dim) - torch.amin(target, dim=dim)` in PyTorch 1.7 to calculate
# `data_range` in the future.
raise ValueError("The `data_range` must be given when `dim` is not None.")
data_range = target.max() - target.min()
else:
data_range = tensor(float(data_range))
sum_squared_error, n_obs = _psnr_update(preds, target, dim=dim)
return _psnr_compute(sum_squared_error, n_obs, data_range, base=base, reduction=reduction)