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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.
import torch
from torch import Tensor
from torchmetrics.functional.image.rmse_sw import _rmse_sw_compute, _rmse_sw_update
from torchmetrics.functional.image.utils import _uniform_filter
def _rase_update(
preds: Tensor, target: Tensor, window_size: int, rmse_map: Tensor, target_sum: Tensor, total_images: Tensor
) -> tuple[Tensor, Tensor, Tensor]:
"""Calculate the sum of RMSE map values for the batch of examples and update intermediate states.
Args:
preds: Deformed image
target: Ground truth image
window_size: Sliding window used for RMSE calculation
rmse_map: Sum of RMSE map values over all examples
target_sum: target...
total_images: Total number of images
Return:
Intermediate state of RMSE map
Updated total number of already processed images
"""
_, rmse_map, total_images = _rmse_sw_update(
preds, target, window_size, rmse_val_sum=None, rmse_map=rmse_map, total_images=total_images
)
target_sum += torch.sum(_uniform_filter(target, window_size) / (window_size**2), dim=0)
return rmse_map, target_sum, total_images
def _rase_compute(rmse_map: Tensor, target_sum: Tensor, total_images: Tensor, window_size: int) -> Tensor:
"""Compute RASE.
Args:
rmse_map: Sum of RMSE map values over all examples
target_sum: target...
total_images: Total number of images.
window_size: Sliding window used for rmse calculation
Return:
Relative Average Spectral Error (RASE)
"""
_, rmse_map = _rmse_sw_compute(rmse_val_sum=None, rmse_map=rmse_map, total_images=total_images)
target_mean = target_sum / total_images
target_mean = target_mean.mean(0) # mean over image channels
rase_map = 100 / target_mean * torch.sqrt(torch.mean(rmse_map**2, 0))
crop_slide = round(window_size / 2)
return torch.mean(rase_map[crop_slide:-crop_slide, crop_slide:-crop_slide])
def relative_average_spectral_error(preds: Tensor, target: Tensor, window_size: int = 8) -> Tensor:
"""Compute Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_).
Args:
preds: Deformed image
target: Ground truth image
window_size: Sliding window used for rmse calculation
Return:
Relative Average Spectral Error (RASE)
Example:
>>> from torch import rand
>>> from torchmetrics.functional.image import relative_average_spectral_error
>>> preds = rand(4, 3, 16, 16)
>>> target = rand(4, 3, 16, 16)
>>> relative_average_spectral_error(preds, target)
tensor(5326.40...)
Raises:
ValueError: If ``window_size`` is not a positive integer.
"""
if not isinstance(window_size, int) or (isinstance(window_size, int) and window_size < 1):
raise ValueError("Argument `window_size` is expected to be a positive integer.")
img_shape = target.shape[1:] # [num_channels, width, height]
rmse_map = torch.zeros(img_shape, dtype=target.dtype, device=target.device)
target_sum = torch.zeros(img_shape, dtype=target.dtype, device=target.device)
total_images = torch.tensor(0.0, device=target.device)
rmse_map, target_sum, total_images = _rase_update(preds, target, window_size, rmse_map, target_sum, total_images)
return _rase_compute(rmse_map, target_sum, total_images, window_size)
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