# 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)