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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
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
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SpectralDistortionIndex.plot"]
class SpectralDistortionIndex(Metric):
"""Compute Spectral Distortion Index (SpectralDistortionIndex_) also now as D_lambda.
The metric is used to compare the spectral distortion between two images.
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H,W)``
- ``target``(:class:`~torch.Tensor`): High resolution fused image of shape ``(N,C,H,W)``
As output of `forward` and `compute` the metric returns the following output
- ``sdi`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SDI value
over sample else returns tensor of shape ``(N,)`` with SDI values per sample
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:
>>> from torch import rand
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = rand([16, 3, 16, 16])
>>> target = rand([16, 3, 16, 16])
>>> sdi = SpectralDistortionIndex()
>>> sdi(preds, target)
tensor(0.0234)
"""
higher_is_better: bool = True
is_differentiable: 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]
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_reductions = ("elementwise_mean", "sum", "none")
if reduction not in allowed_reductions:
raise ValueError(f"Expected argument `reduction` be one of {allowed_reductions} 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:
"""Update state with preds and target."""
preds, target = _spectral_distortion_index_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Compute 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)
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
>>> from torch import rand
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = rand([16, 3, 16, 16])
>>> target = rand([16, 3, 16, 16])
>>> metric = SpectralDistortionIndex()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import rand
>>> from torchmetrics.image import SpectralDistortionIndex
>>> preds = rand([16, 3, 16, 16])
>>> target = rand([16, 3, 16, 16])
>>> metric = SpectralDistortionIndex()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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