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# Copyright The PyTorch 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 torchmetrics.functional.image.rase import relative_average_spectral_error
from torchmetrics.metric import Metric
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__ = ["RelativeAverageSpectralError.plot"]
class RelativeAverageSpectralError(Metric):
"""Computes Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_).
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)``
- ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)``
As output of `forward` and `compute` the metric returns the following output
- ``rase`` (:class:`~torch.Tensor`): returns float scalar tensor with average RASE value over sample
Args:
window_size: Sliding window used for rmse calculation
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Return:
Relative Average Spectral Error (RASE)
Example:
>>> from torch import rand
>>> preds = rand(4, 3, 16, 16)
>>> target = rand(4, 3, 16, 16)
>>> rase = RelativeAverageSpectralError()
>>> rase(preds, target)
tensor(5326.40...)
Raises:
ValueError: If ``window_size`` is not a positive integer.
"""
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,
window_size: int = 8,
**kwargs: dict[str, Any],
) -> None:
super().__init__(**kwargs)
if not isinstance(window_size, int) or (isinstance(window_size, int) and window_size < 1):
raise ValueError(f"Argument `window_size` is expected to be a positive integer, but got {window_size}")
self.window_size = window_size
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 predictions and targets."""
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Compute Relative Average Spectral Error (RASE)."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return relative_average_spectral_error(preds, target, self.window_size)
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
>>> import torch
>>> from torchmetrics.image import RelativeAverageSpectralError
>>> metric = RelativeAverageSpectralError()
>>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import rand
>>> from torchmetrics.image import RelativeAverageSpectralError
>>> metric = RelativeAverageSpectralError()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(rand(4, 3, 16, 16), rand(4, 3, 16, 16)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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