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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_s import _spatial_distortion_index_compute, _spatial_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, _TORCHVISION_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SpatialDistortionIndex.plot"]
if not _TORCHVISION_AVAILABLE:
__doctest_skip__ = ["SpatialDistortionIndex", "SpatialDistortionIndex.plot"]
class SpatialDistortionIndex(Metric):
r"""Compute Spatial Distortion Index (SpatialDistortionIndex_) also now as D_s.
The metric is used to compare the spatial distortion between two images. A value of 0 indicates no distortion
(optimal value) and corresponds to the case where the high resolution panchromatic image is equal to the low
resolution panchromatic image. The metric is defined as:
.. math::
D_s = \\sqrt[q]{\frac{1}{L}\\sum_{l=1}^L|Q(\\hat{G_l}, P) - Q(\tilde{G}, \tilde{P})|^q}
where :math:`Q` is the universal image quality index (see this
:class:`~torchmetrics.image.UniversalImageQualityIndex` for more info), :math:`\\hat{G_l}` is the l-th band of the
high resolution multispectral image, :math:`\tilde{G}` is the high resolution panchromatic image, :math:`P` is the
high resolution panchromatic image, :math:`\tilde{P}` is the low resolution panchromatic image, :math:`L` is the
number of bands and :math:`q` is the order of the norm applied on the difference.
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): High resolution multispectral image of shape ``(N,C,H,W)``.
- ``target`` (:class:`~Dict`): A dictionary containing the following keys:
- ``ms`` (:class:`~torch.Tensor`): Low resolution multispectral image of shape ``(N,C,H',W')``.
- ``pan`` (:class:`~torch.Tensor`): High resolution panchromatic image of shape ``(N,C,H,W)``.
- ``pan_lr`` (:class:`~torch.Tensor`): Low resolution panchromatic image of shape ``(N,C,H',W')``.
where H and W must be multiple of H' and 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:
norm_order: Order of the norm applied on the difference.
window_size: Window size of the filter applied to degrade the high resolution panchromatic image.
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 SpatialDistortionIndex
>>> preds = rand([16, 3, 32, 32])
>>> target = {
... 'ms': rand([16, 3, 16, 16]),
... 'pan': rand([16, 3, 32, 32]),
... }
>>> sdi = SpatialDistortionIndex()
>>> sdi(preds, target)
tensor(0.0090)
"""
higher_is_better: bool = False
is_differentiable: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
ms: List[Tensor]
pan: List[Tensor]
pan_lr: List[Tensor]
def __init__(
self,
norm_order: int = 1,
window_size: int = 7,
reduction: Literal["elementwise_mean", "sum", "none"] = "elementwise_mean",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
rank_zero_warn(
"Metric `SpatialDistortionIndex` will save all targets and"
" predictions in buffer. For large datasets this may lead"
" to large memory footprint."
)
if not isinstance(norm_order, int) or norm_order <= 0:
raise ValueError(f"Expected `norm_order` to be a positive integer. Got norm_order: {norm_order}.")
self.norm_order = norm_order
if not isinstance(window_size, int) or window_size <= 0:
raise ValueError(f"Expected `window_size` to be a positive integer. Got window_size: {window_size}.")
self.window_size = window_size
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("ms", default=[], dist_reduce_fx="cat")
self.add_state("pan", default=[], dist_reduce_fx="cat")
self.add_state("pan_lr", default=[], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: dict[str, Tensor]) -> None:
"""Update state with preds and target.
Args:
preds: High resolution multispectral image.
target: A dictionary containing the following keys:
- ``'ms'``: low resolution multispectral image.
- ``'pan'``: high resolution panchromatic image.
- ``'pan_lr'``: (optional) low resolution panchromatic image.
Raises:
ValueError:
If ``target`` doesn't have ``ms`` and ``pan``.
"""
if "ms" not in target:
raise ValueError(f"Expected `target` to have key `ms`. Got target: {target.keys()}.")
if "pan" not in target:
raise ValueError(f"Expected `target` to have key `pan`. Got target: {target.keys()}.")
ms = target["ms"]
pan = target["pan"]
pan_lr = target.get("pan_lr")
preds, ms, pan, pan_lr = _spatial_distortion_index_update(preds, ms, pan, pan_lr)
self.preds.append(preds)
self.ms.append(target["ms"])
self.pan.append(target["pan"])
if "pan_lr" in target:
self.pan_lr.append(target["pan_lr"])
def compute(self) -> Tensor:
"""Compute and returns spatial distortion index."""
preds = dim_zero_cat(self.preds)
ms = dim_zero_cat(self.ms)
pan = dim_zero_cat(self.pan)
pan_lr = dim_zero_cat(self.pan_lr) if len(self.pan_lr) > 0 else None
target = {"ms": ms, "pan": pan}
target.update({"pan_lr": pan_lr} if pan_lr is not None else {})
return _spatial_distortion_index_compute(
preds, ms, pan, pan_lr, self.norm_order, self.window_size, 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 SpatialDistortionIndex
>>> preds = rand([16, 3, 32, 32])
>>> target = {
... 'ms': rand([16, 3, 16, 16]),
... 'pan': rand([16, 3, 32, 32]),
... }
>>> metric = SpatialDistortionIndex()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import rand
>>> from torchmetrics.image import SpatialDistortionIndex
>>> preds = rand([16, 3, 32, 32])
>>> target = {
... 'ms': rand([16, 3, 16, 16]),
... 'pan': rand([16, 3, 32, 32]),
... }
>>> metric = SpatialDistortionIndex()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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