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# Copyright The 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 collections.abc import Sequence
from functools import partial
from typing import Any, Optional, Union
import torch
from torch import Tensor, tensor
from typing_extensions import Literal
from torchmetrics.functional.image.psnr import _psnr_compute, _psnr_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["PeakSignalNoiseRatio.plot"]
class PeakSignalNoiseRatio(Metric):
r"""`Compute Peak Signal-to-Noise Ratio`_ (PSNR).
.. math:: \text{PSNR}(I, J) = 10 * \log_{10} \left(\frac{\max(I)^2}{\text{MSE}(I, J)}\right)
Where :math:`\text{MSE}` denotes the `mean-squared-error`_ function.
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)``
- ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)``
As output of `forward` and `compute` the metric returns the following output
- ``psnr`` (:class:`~torch.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
Args:
data_range:
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: 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 and all batches.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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)
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
min_target: Tensor
max_target: Tensor
def __init__(
self,
data_range: Optional[Union[float, tuple[float, float]]] = None,
base: float = 10.0,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
dim: Optional[Union[int, tuple[int, ...]]] = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
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 dim is None:
self.add_state("sum_squared_error", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
else:
self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat")
self.add_state("total", default=[], dist_reduce_fx="cat")
self.clamping_fn = 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.")
self.data_range = None
self.add_state("min_target", default=tensor(0.0), dist_reduce_fx=torch.min)
self.add_state("max_target", default=tensor(0.0), dist_reduce_fx=torch.max)
elif isinstance(data_range, tuple):
self.add_state("data_range", default=tensor(data_range[1] - data_range[0]), dist_reduce_fx="mean")
self.clamping_fn = partial(torch.clamp, min=data_range[0], max=data_range[1])
else:
self.add_state("data_range", default=tensor(float(data_range)), dist_reduce_fx="mean")
self.base = base
self.reduction = reduction
self.dim = tuple(dim) if isinstance(dim, Sequence) else dim
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
if self.clamping_fn is not None:
preds = self.clamping_fn(preds)
target = self.clamping_fn(target)
sum_squared_error, num_obs = _psnr_update(preds, target, dim=self.dim)
if self.dim is None:
if self.data_range is None:
# keep track of min and max target values
self.min_target = torch.minimum(target.min(), self.min_target)
self.max_target = torch.maximum(target.max(), self.max_target)
if not isinstance(self.sum_squared_error, Tensor):
raise TypeError(
f"Expected `self.sum_squared_error` to be a Tensor, but got {type(self.sum_squared_error)}"
)
if not isinstance(self.total, Tensor):
raise TypeError(f"Expected `self.total` to be a Tensor, but got {type(self.total)}")
self.sum_squared_error += sum_squared_error
self.total += num_obs
else:
if not isinstance(self.sum_squared_error, list):
raise TypeError(
f"Expected `self.sum_squared_error` to be a list, but got {type(self.sum_squared_error)}"
)
if not isinstance(self.total, list):
raise TypeError(f"Expected `self.total` to be a list, but got {type(self.total)}")
self.sum_squared_error.append(sum_squared_error)
self.total.append(num_obs)
def compute(self) -> Tensor:
"""Compute peak signal-to-noise ratio over state."""
data_range = self.data_range if self.data_range is not None else self.max_target - self.min_target
if isinstance(self.sum_squared_error, torch.Tensor):
sum_squared_error = self.sum_squared_error
elif isinstance(self.sum_squared_error, list):
sum_squared_error = torch.cat([value.flatten() for value in self.sum_squared_error])
else:
raise TypeError("Expected sum_squared_error to be a Tensor or a list of Tensors")
if isinstance(self.total, torch.Tensor):
total = self.total
elif isinstance(self.total, list):
total = torch.cat([value.flatten() for value in self.total])
else:
raise TypeError("Expected total to be a Tensor or a list of Tensors")
return _psnr_compute(sum_squared_error, total, data_range, base=self.base, reduction=self.reduction)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: 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: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # 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()
.. plot::
:scale: 75
>>> # 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)
"""
return self._plot(val, ax)