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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 typing import Any, List, Optional, Union
from torch import Tensor, tensor
from typing_extensions import Literal
from torchmetrics.functional.image.uqi import _uqi_compute, _uqi_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["UniversalImageQualityIndex.plot"]
class UniversalImageQualityIndex(Metric):
"""Compute Universal Image Quality Index (UniversalImageQualityIndex_).
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
- ``uiqi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average UIQI value
over sample else returns tensor of shape ``(N,)`` with UIQI values per sample
Args:
kernel_size: size of the gaussian kernel
sigma: Standard deviation of the gaussian kernel
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
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Return:
Tensor with UniversalImageQualityIndex score
Example:
>>> import torch
>>> from torchmetrics.image import UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> uqi = UniversalImageQualityIndex()
>>> uqi(preds, target)
tensor(0.9216)
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
target: List[Tensor]
sum_uqi: Tensor
numel: Tensor
def __init__(
self,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if reduction not in ("elementwise_mean", "sum", "none", None):
raise ValueError(
f"The `reduction` {reduction} is not valid. Valid options are `elementwise_mean`, `sum`, `none`, None."
)
if reduction is None or reduction == "none":
rank_zero_warn(
"Metric `UniversalImageQualityIndex` will save all targets and predictions in the buffer when using"
"`reduction=None` or `reduction='none'. For large datasets, this may lead to a large memory footprint."
)
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
else:
self.add_state("sum_uqi", tensor(0.0), dist_reduce_fx="sum")
self.add_state("numel", tensor(0), dist_reduce_fx="sum")
self.kernel_size = kernel_size
self.sigma = sigma
self.reduction = reduction
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
preds, target = _uqi_update(preds, target)
if self.reduction is None or self.reduction == "none":
self.preds.append(preds)
self.target.append(target)
else:
uqi_score = _uqi_compute(preds, target, self.kernel_size, self.sigma, reduction="sum")
self.sum_uqi += uqi_score
ps = preds.shape
self.numel += ps[0] * ps[1] * (ps[2] - self.kernel_size[0] + 1) * (ps[3] - self.kernel_size[1] + 1)
def compute(self) -> Tensor:
"""Compute explained variance over state."""
if self.reduction == "none" or self.reduction is None:
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _uqi_compute(preds, target, self.kernel_size, self.sigma, self.reduction)
return self.sum_uqi / self.numel if self.reduction == "elementwise_mean" else self.sum_uqi
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 UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> metric = UniversalImageQualityIndex()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import UniversalImageQualityIndex
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> metric = UniversalImageQualityIndex()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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