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torcheval.metrics.StructuralSimilarity

class torcheval.metrics.StructuralSimilarity(device: Optional[device] = None)[source]

Compute the structural similarity index (SSIM) between two sets of images.

Args: device (torch.device): The device where the computations will be performed.

If None, the default device will be used.
__init__(device: Optional[device] = None) None[source]

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, or a dictionary with torch.Tensor as values

Methods

__init__([device]) Initialize a metric object and its internal states.
compute() Compute the mean of the mssim across all comparisons.
load_state_dict(state_dict[, strict]) Loads metric state variables from state_dict.
merge_state(metrics) Merge the metric state with its counterparts from other metric instances.
reset() Reset the metric state variables to their default value.
state_dict() Save metric state variables in state_dict.
to(device, *args, **kwargs) Move tensors in metric state variables to device.
update(images_1, images_2) Update the metric state with new input.

Attributes

device The last input device of Metric.to().

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