venv/lib/python3.11/site-packages/torchmetrics/image/ergas.py

# Copyright The Lightning team.
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# 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.ergas import _ergas_compute, _ergas_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__ = ["ErrorRelativeGlobalDimensionlessSynthesis.plot"]


class ErrorRelativeGlobalDimensionlessSynthesis(Metric):
    r"""Calculate the `Error relative global dimensionless synthesis`_  (ERGAS) metric.

    This metric is used to calculate the accuracy of Pan sharpened image considering normalized average error of each
    band of the result image. It is defined as:

    .. math::
        ERGAS = \frac{100}{r} \cdot \sqrt{\frac{1}{N} \sum_{k=1}^{N} \frac{RMSE(B_k)^2}{\mu_k^2}}

    where :math:`r=h/l` denote the ratio in spatial resolution (pixel size) between the high and low resolution images.
    :math:`N` is the number of spectral bands, :math:`RMSE(B_k)` is the root mean square error of the k-th band between
    low and high resolution images, and :math:`\\mu_k` is the mean value of the k-th band of the reference image.

    As input to ``forward`` and ``update`` the metric accepts the following input

    - ``preds`` (:class:`~torch.Tensor`): Predictions from model
    - ``target`` (:class:`~torch.Tensor`): Ground truth values

    As output of `forward` and `compute` the metric returns the following output

    - ``ergas`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average ERGAS
      value over sample else returns tensor of shape ``(N,)`` with ERGAS values per sample

    Args:
        ratio: ratio of high resolution to low resolution.
        reduction: a method to reduce metric score over labels.

            - ``'elementwise_mean'``: takes the mean (default)
            - ``'sum'``: takes the sum
            - ``'none'`` or ``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 ErrorRelativeGlobalDimensionlessSynthesis
        >>> preds = rand([16, 1, 16, 16])
        >>> target = preds * 0.75
        >>> ergas = ErrorRelativeGlobalDimensionlessSynthesis()
        >>> ergas(preds, target).round()
        tensor(10.)

    """

    higher_is_better: bool = False
    is_differentiable: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0

    preds: List[Tensor]
    target: List[Tensor]

    def __init__(
        self,
        ratio: float = 4,
        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        rank_zero_warn(
            "Metric `UniversalImageQualityIndex` will save all targets and"
            " predictions in buffer. For large datasets this may lead"
            " to large memory footprint."
        )

        self.add_state("preds", default=[], dist_reduce_fx="cat")
        self.add_state("target", default=[], dist_reduce_fx="cat")
        self.ratio = ratio
        self.reduction = reduction

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        preds, target = _ergas_update(preds, target)
        self.preds.append(preds)
        self.target.append(target)

    def compute(self) -> Tensor:
        """Compute explained variance over state."""
        preds = dim_zero_cat(self.preds)
        target = dim_zero_cat(self.target)
        return _ergas_compute(preds, target, self.ratio, 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 ErrorRelativeGlobalDimensionlessSynthesis
            >>> preds = rand([16, 1, 16, 16])
            >>> target = preds * 0.75
            >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
            >>> metric.update(preds, target)
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> from torch import rand
            >>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
            >>> preds = rand([16, 1, 16, 16])
            >>> target = preds * 0.75
            >>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(preds, target))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)