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uqi.py
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uqi.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 Any, List, Optional, Sequence
from torch import 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
class UniversalImageQualityIndex(Metric):
"""Computes Universal Image Quality Index (UniversalImageQualityIndex_).
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
data_range: Range of the image. If ``None``, it is determined from the image (max - min)
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Return:
Tensor with UniversalImageQualityIndex score
Example:
>>> import torch
>>> from torchmetrics 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
preds: List[Tensor]
target: List[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",
data_range: Optional[float] = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
rank_zero_warn(
"Metric `UniversalImageQualityIndex` will save all targets and"
" predictions in buffer. For large datasets this may lead"
" to large memory footprint."
)
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
self.kernel_size = kernel_size
self.sigma = sigma
self.data_range = data_range
self.reduction = reduction
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _uqi_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Computes explained variance over state."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _uqi_compute(preds, target, self.kernel_size, self.sigma, self.reduction, self.data_range)