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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 Optional
import torch
from torch import Tensor, nn
from typing_extensions import Literal
from torchmetrics.functional.image.utils import _gaussian_kernel_2d
from torchmetrics.utilities.checks import _check_same_shape
from torchmetrics.utilities.distributed import reduce
def _uqi_update(preds: Tensor, target: Tensor) -> tuple[Tensor, Tensor]:
"""Update and returns variables required to compute Universal Image Quality Index.
Args:
preds: Predicted tensor
target: Ground truth tensor
"""
if preds.dtype != target.dtype:
raise TypeError(
"Expected `preds` and `target` to have the same data type."
f" Got preds: {preds.dtype} and target: {target.dtype}."
)
_check_same_shape(preds, target)
if len(preds.shape) != 4:
raise ValueError(
f"Expected `preds` and `target` to have BxCxHxW shape. Got preds: {preds.shape} and target: {target.shape}."
)
return preds, target
def _uqi_compute(
preds: Tensor,
target: Tensor,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
) -> Tensor:
"""Compute Universal Image Quality Index.
Args:
preds: estimated image
target: ground truth image
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
Example:
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> preds, target = _uqi_update(preds, target)
>>> _uqi_compute(preds, target)
tensor(0.9216)
"""
if len(kernel_size) != 2 or len(sigma) != 2:
raise ValueError(
"Expected `kernel_size` and `sigma` to have the length of two."
f" Got kernel_size: {len(kernel_size)} and sigma: {len(sigma)}."
)
if any(x % 2 == 0 or x <= 0 for x in kernel_size):
raise ValueError(f"Expected `kernel_size` to have odd positive number. Got {kernel_size}.")
if any(y <= 0 for y in sigma):
raise ValueError(f"Expected `sigma` to have positive number. Got {sigma}.")
device = preds.device
channel = preds.size(1)
dtype = preds.dtype
kernel = _gaussian_kernel_2d(channel, kernel_size, sigma, dtype, device)
pad_h = (kernel_size[0] - 1) // 2
pad_w = (kernel_size[1] - 1) // 2
preds = nn.functional.pad(preds, (pad_h, pad_h, pad_w, pad_w), mode="reflect")
target = nn.functional.pad(target, (pad_h, pad_h, pad_w, pad_w), mode="reflect")
input_list = torch.cat((preds, target, preds * preds, target * target, preds * target)) # (5 * B, C, H, W)
outputs = nn.functional.conv2d(input_list, kernel, groups=channel)
output_list = outputs.split(preds.shape[0])
mu_pred_sq = output_list[0].pow(2)
mu_target_sq = output_list[1].pow(2)
mu_pred_target = output_list[0] * output_list[1]
# Calculate the variance of the predicted and target images, should be non-negative
sigma_pred_sq = torch.clamp(output_list[2] - mu_pred_sq, min=0.0)
sigma_target_sq = torch.clamp(output_list[3] - mu_target_sq, min=0.0)
sigma_pred_target = output_list[4] - mu_pred_target
upper = 2 * sigma_pred_target
lower = sigma_pred_sq + sigma_target_sq
eps = torch.finfo(sigma_pred_sq.dtype).eps
uqi_idx = ((2 * mu_pred_target) * upper) / ((mu_pred_sq + mu_target_sq) * lower + eps)
uqi_idx = uqi_idx[..., pad_h:-pad_h, pad_w:-pad_w]
return reduce(uqi_idx, reduction)
def universal_image_quality_index(
preds: Tensor,
target: Tensor,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
) -> Tensor:
"""Universal Image Quality Index.
Args:
preds: estimated image
target: ground truth image
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
Return:
Tensor with UniversalImageQualityIndex score
Raises:
TypeError:
If ``preds`` and ``target`` don't have the same data type.
ValueError:
If ``preds`` and ``target`` don't have ``BxCxHxW shape``.
ValueError:
If the length of ``kernel_size`` or ``sigma`` is not ``2``.
ValueError:
If one of the elements of ``kernel_size`` is not an ``odd positive number``.
ValueError:
If one of the elements of ``sigma`` is not a ``positive number``.
Example:
>>> from torchmetrics.functional.image import universal_image_quality_index
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> universal_image_quality_index(preds, target)
tensor(0.9216)
References:
[1] Zhou Wang and A. C. Bovik, "A universal image quality index," in IEEE Signal Processing Letters, vol. 9,
no. 3, pp. 81-84, March 2002, doi: 10.1109/97.995823.
[2] Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility
to structural similarity," in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004,
doi: 10.1109/TIP.2003.819861.
"""
preds, target = _uqi_update(preds, target)
return _uqi_compute(preds, target, kernel_size, sigma, reduction)
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