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Root Mean Squared Error Using Sliding Window¶

Module Interface¶

class torchmetrics.image.RootMeanSquaredErrorUsingSlidingWindow(window_size=8, **kwargs)[source]

Computes Root Mean Squared Error (RMSE) using sliding window.

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

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

• rmse_sw (Tensor): returns float scalar tensor with average RMSE-SW value over sample

Parameters:

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.

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # 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()

>>> # 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)


Functional Interface¶

torchmetrics.functional.image.root_mean_squared_error_using_sliding_window(preds, target, window_size=8, return_rmse_map=False)[source]

Compute Root Mean Squared Error (RMSE) using sliding window.

Parameters:
Return type:
Returns:

RMSE using sliding window (Optionally) RMSE map

Example

>>> from torchmetrics.functional.image import root_mean_squared_error_using_sliding_window
>>> g = torch.manual_seed(22)
>>> preds = torch.rand(4, 3, 16, 16)
>>> target = torch.rand(4, 3, 16, 16)
>>> root_mean_squared_error_using_sliding_window(preds, target)
tensor(0.3999)

Raises:

ValueError – If window_size is not a positive integer.