packed_resnet

torch_uncertainty.models.packed_resnet(in_channels, num_classes, arch, num_estimators, alpha, gamma, conv_bias=False, width_multiplier=1.0, groups=1, dropout_rate=0, style='imagenet', normalization_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, pretrained=False, linear_implementation='conv1d')

Packed-Ensembles 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.

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

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

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

• alpha (int) – Expansion factor affecting the width of the estimators.

• gamma (int) – Number of groups within each estimator.

• width_multiplier (float) – Width multiplier. Defaults to 1.

• groups (int) – Number of groups within each estimator group.

• style (bool, optional) – Whether to use the ImageNet structure. Defaults to True.

• normalization_layer (nn.Module, optional) – Normalization layer.

• pretrained (bool, optional) – Whether to load pretrained weights. Defaults to False.

• linear_implementation (str, optional) – Implementation of the packed linear layer. Defaults to "conv1d".

Returns:

A Packed-Ensembles ResNet.

Return type:

_PackedResNet