lpbnn_resnet#

torch_uncertainty.models.lpbnn_resnet(in_channels, num_classes, arch, num_estimators, dropout_rate=0, conv_bias=True, width_multiplier=1.0, groups=1, style=ResNetStyle.IMAGENET)[source]#

LPBNN version of ResNet.

Parameters:

  • in_channels (int) – Number of input channels.

  • num_classes (int) – Number of classes to predict.

  • arch (int) – The architecture of the ResNet.

  • dropout_rate (float) – Dropout rate. Defaults to 0.

  • conv_bias (bool) – Whether to use bias in convolutions. Defaults to True.

  • num_estimators (int) – Number of estimators in the ensemble.

  • width_multiplier (float) – Width multiplier. Defaults to 1.0.

  • groups (int) – Number of ResNet groups. Defaults to 1.

  • style (ResNetStyle) – Whether to use the ImageNet or CIFAR structure. Defaults to ResNetStyle.IMAGENET.

Returns:

An LPBNN ResNet.

Return type:

_LPBNNResNet