resnet

torch_uncertainty.models.resnet(in_channels, num_classes, arch, conv_bias=False, dropout_rate=0.0, width_multiplier=1.0, groups=1, style='imagenet', activation_fn=<function relu>, normalization_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)

ResNet model.

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 False.

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

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

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

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

• activation_fn (Callable, optional) – Activation function. Defaults to torch.nn.functional.relu.

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

Returns:

The ResNet model.

Return type:

_ResNet