mimo_wideresnet28x10#
- torch_uncertainty.models.mimo_wideresnet28x10(in_channels, num_classes, num_estimators, conv_bias=True, dropout_rate=0.3, groups=1, style=ResNetStyle.IMAGENET, activation_fn=<function relu>, normalization_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>)[source]#
MIMO of Wide-ResNet-28x10.
- Parameters:
in_channels (int) – Number of input channels.
num_classes (int) – Number of classes to predict.
num_estimators (int) – Number of estimators in the ensemble.
groups (int) – Number of subgroups in the convolutions.
conv_bias (bool) – Whether to use bias in convolutions. Defaults to
True.dropout_rate (float, optional) – Dropout rate. Defaults to
0.3.style (ResNetStyle | Literal["imagenet", "cifar"]) – Whether to use the ImageNet or CIFAR structure. Defaults to
ResNetStyle.IMAGENET.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:
A MIMO Wide-ResNet-28x10.
- Return type:
_MIMOWideResNet