mimo_resnet#

torch_uncertainty.models.mimo_resnet(in_channels, num_classes, arch, num_estimators, conv_bias=True, dropout_rate=0.0, width_multiplier=1.0, groups=1, style=ResNetStyle.IMAGENET, normalization_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]#

MIMO ResNet.

Parameters:

  • in_channels (int) – Number of input channels.

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

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

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

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

  • dropout_rate (float) – Dropout rate. Defaults to 0.0.

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

  • groups (int) – Number of groups for grouped convolution. Defaults to 1.

  • style (Union[ResNetStyle, Literal['imagenet', 'cifar']]) – Whether to use the ImageNet or CIFAR structure. Defaults to ResNetStyle.IMAGENET.

  • normalization_layer (type[Module]) – Normalization layer. Defaults to torch.nn.BatchNorm2d.

Returns:

A MIMO-style ResNet.

Return type:

_MIMOResNet