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d_lambda.py
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d_lambda.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
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.image.d_lambda import _spectral_distortion_index_compute, _spectral_distortion_index_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
class SpectralDistortionIndex(Metric):
"""Computes Spectral Distortion Index (SpectralDistortionIndex_) also now as D_lambda is used to compare the
spectral distortion between two images.
Args:
p: Large spectral differences
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics import SpectralDistortionIndex
>>> preds = torch.rand([16, 3, 16, 16])
>>> target = torch.rand([16, 3, 16, 16])
>>> sdi = SpectralDistortionIndex()
>>> sdi(preds, target)
tensor(0.0234)
References:
[1] Alparone, Luciano & Aiazzi, Bruno & Baronti, Stefano & Garzelli, Andrea & Nencini,
Filippo & Selva, Massimo. (2008). Multispectral and Panchromatic Data Fusion
Assessment Without Reference. ASPRS Journal of Photogrammetric Engineering
and Remote Sensing. 74. 193-200. 10.14358/PERS.74.2.193.
"""
higher_is_better: bool = True
is_differentiable: bool = True
full_state_update: bool = False
preds: List[Tensor]
target: List[Tensor]
def __init__(
self, p: int = 1, reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean", **kwargs: Any
) -> None:
super().__init__(**kwargs)
rank_zero_warn(
"Metric `SpectralDistortionIndex` will save all targets and"
" predictions in buffer. For large datasets this may lead"
" to large memory footprint."
)
if not isinstance(p, int) or p <= 0:
raise ValueError(f"Expected `p` to be a positive integer. Got p: {p}.")
self.p = p
ALLOWED_REDUCTION = ("elementwise_mean", "sum", "none")
if reduction not in ALLOWED_REDUCTION:
raise ValueError(f"Expected argument `reduction` be one of {ALLOWED_REDUCTION} but got {reduction}")
self.reduction = reduction
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with preds and target.
Args:
preds: Low resolution multispectral image
target: High resolution fused image
"""
preds, target = _spectral_distortion_index_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Computes and returns spectral distortion index."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _spectral_distortion_index_compute(preds, target, self.p, self.reduction)