Error Relative Global Dim. Synthesis (ERGAS)

Module Interface

class torchmetrics.image.ergas.ErrorRelativeGlobalDimensionlessSynthesis(ratio=4, reduction='elementwise_mean', **kwargs)

Calculate Relative dimensionless global error synthesis (ERGAS).

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

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

• preds (Tensor): Predictions from model

• target (Tensor): Ground truth values

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

• ergas (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

Parameters

• ratio (Union[int, float]) – ratio of high resolution to low resolution

• reduction (Literal[‘elementwise_mean’, ‘sum’, ‘none’, None]) –

  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 (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> import torch
>>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ergas = ErrorRelativeGlobalDimensionlessSynthesis()
>>> torch.round(ergas(preds, target))
tensor(154.)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters

• val (Union[Tensor, Sequence[Tensor], None]) – 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 (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type

Tuple[Figure, Union[Axes, ndarray]]

Returns

Figure and Axes object

Raises

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

(Source code, png, hires.png, pdf)

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

(Source code, png, hires.png, pdf)

Functional Interface

torchmetrics.functional.error_relative_global_dimensionless_synthesis(preds, target, ratio=4, reduction='elementwise_mean')

Wrapper for deprecated import.

>>> import torch
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ergds = _error_relative_global_dimensionless_synthesis(preds, target)
>>> torch.round(ergds)
tensor(154.)

Return type

Tensor