Metrics Documentation

# Classes

Metric

The basic metric class

• extends: torch.nn.Module

• Could be use as a decorator of a function

• Metric tensor is released from memory as soon as the result returned

• [Deprecation Warning]: Method call is deprecated from v1.0.0 and will be removed from v1.1.0, override the forward method instead."

• Properties:
  • result: The torch.Tensor of average metric results

• Constructor

  • Parameters:
    • metric_fn: An optional Callable metrics function that accepts Any kind of prediction input and target and returns a metric torch.Tensor. A call method must be overriden if this parameter is set as None.
    • target: A str of target name in input and target during direct calling

• forward

  • Forward the current result method
  • Parameters:
    • input: The prediction, or y_pred, in Any kind
    • target: The label, or y_true, in Any kind
  • Returns: The metric in torch.Tensor

• reset

  • Reset the current results list

Accuracy

The traditional accuracy metric to compare two torch.Tensor

• extends: Metric

SparseCategoricalAccuracy

The accuracy metric for normal integer labels

• extends: Accuracy

• Properties: - dim: An int of the probability dim index for the input

• Constructor

    - Parameters:
    - dim: An `int` of the classification dimension
    - target: A `str` of target name in `input` and `target` during direct calling
  

CategoricalAccuracy

The accuracy metric for categorical labels

• extends: SparseCategoricalAccuracy

MAE

The Mean Absolute Error metric

• extends: Metric

ConfusionMetrics

The metric that calculates confusion metrics

• extends: Metric

• Constructor - Parameters: - num_classes: An int of the total number of classes - target: A str of target name in input and target during direct calling

InstanceIoU

The iIoU metric for segmentation

• extends: ConfusionMetrics

MeanIoU

The mIoU metric for segmentation

• extends: Metric

• The old MIoU metric in v1.0.3 calculates iIoU and has been renamed to InstanceIoU

• Constructor - Parameters: - dim: An int of class dimension - smooth: A float of smooth value to avoid zero devision