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='imagenet', normalization_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)

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, optional) – Whether to use bias in convolutional layers. Defaults to True.

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

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

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

• style (Literal["imagenet", "cifar"], optional) – Style of ResNet. Defaults to "imagenet".

• normalization_layer (nn.Module, optional) – Normalization layer. Defaults to torch.nn.BatchNorm2d.

Returns:

A MIMO-style ResNet.

Return type:

_MIMOResNet