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rmse_sw.py
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rmse_sw.py
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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 typing import Any, Dict, Optional, Sequence, Union
import torch
from torch import Tensor
from torchmetrics.functional.image.rmse_sw import _rmse_sw_compute, _rmse_sw_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["RootMeanSquaredErrorUsingSlidingWindow.plot"]
class RootMeanSquaredErrorUsingSlidingWindow(Metric):
"""Computes Root Mean Squared Error (RMSE) using sliding window.
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
- ``rmse_sw`` (:class:`~torch.Tensor`): returns float scalar tensor with average RMSE-SW value over sample
Args:
window_size: Sliding window used for rmse calculation
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics.image import RootMeanSquaredErrorUsingSlidingWindow
>>> g = torch.manual_seed(22)
>>> preds = torch.rand(4, 3, 16, 16)
>>> target = torch.rand(4, 3, 16, 16)
>>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
>>> rmse_sw(preds, target)
tensor(0.3999)
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
rmse_val_sum: Tensor
rmse_map: Optional[Tensor] = None
total_images: 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("Argument `window_size` is expected to be a positive integer.")
self.window_size = window_size
self.add_state("rmse_val_sum", default=torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("total_images", default=torch.tensor(0.0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
if self.rmse_map is None:
_img_shape = target.shape[1:] # channels, width, height
self.rmse_map = torch.zeros(_img_shape, dtype=target.dtype, device=target.device)
self.rmse_val_sum, self.rmse_map, self.total_images = _rmse_sw_update(
preds, target, self.window_size, self.rmse_val_sum, self.rmse_map, self.total_images
)
def compute(self) -> Optional[Tensor]:
"""Compute Root Mean Squared Error (using sliding window) and potentially return RMSE map."""
assert self.rmse_map is not None # noqa: S101 # needed for mypy
rmse, _ = _rmse_sw_compute(self.rmse_val_sum, self.rmse_map, self.total_images)
return rmse
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 RootMeanSquaredErrorUsingSlidingWindow
>>> metric = RootMeanSquaredErrorUsingSlidingWindow()
>>> 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
>>> import torch
>>> from torchmetrics.image import RootMeanSquaredErrorUsingSlidingWindow
>>> metric = RootMeanSquaredErrorUsingSlidingWindow()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)