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| 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) |
|
|
