renate.benchmark.models.resnet module#

class renate.benchmark.models.resnet.ResNet(block, layers, num_outputs=10, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, cifar_stem=True, gray_scale=False, prediction_strategy=None, add_icarl_class_means=True)[source]#

Bases: RenateBenchmarkingModule

ResNet model base class.

TODO: Fix citation Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition. CVPR 2016: 770-778

Parameters:

  • block (Type[Union[BasicBlock, Bottleneck]]) – The type of the block to use as the core building block.

  • layers (List[int]) – The number of blocks in the respective parts of ResNet.

  • num_outputs (int) – The number of output units.

  • zero_init_residual (bool) – Whether to set the initial weights of the residual blocks to zero through initializing the Batch Normalization parameters at the end of the block to zero.

  • groups (int) – The number of groups to be used for the group convolution.

  • width_per_group (int) – The width of the group convolution.

  • replace_stride_with_dilation (Optional[List[bool]]) – Whether to replace the stride with a dilation to save memory.

  • norm_layer (Type[Module]) – What kind of normalization layer to use, following convolutions.

  • cifar_stem (bool) – Whether to use a stem for CIFAR-sized images.

  • gray_scale (bool) – Whether input images are gray-scale images, i.e. only 1 color channel.

  • prediction_strategy (Optional[PredictionStrategy]) – Continual learning strategies may alter the prediction at train or test time.

  • add_icarl_class_means (bool) – If True, additional parameters used only by the ICaRLModelUpdater are added. Only required when using that updater.

training: bool#
class renate.benchmark.models.resnet.ResNet18CIFAR(block=<class 'torchvision.models.resnet.BasicBlock'>, layers=[2, 2, 2, 2], cifar_stem=True, **kwargs)[source]#

Bases: ResNet

training: bool#
class renate.benchmark.models.resnet.ResNet34CIFAR(block=<class 'torchvision.models.resnet.BasicBlock'>, layers=[3, 4, 6, 3], cifar_stem=True, **kwargs)[source]#

Bases: ResNet

training: bool#
class renate.benchmark.models.resnet.ResNet50CIFAR(block=<class 'torchvision.models.resnet.Bottleneck'>, layers=[3, 4, 6, 3], cifar_stem=True, **kwargs)[source]#

Bases: ResNet

training: bool#
class renate.benchmark.models.resnet.ResNet18(block=<class 'torchvision.models.resnet.BasicBlock'>, layers=[2, 2, 2, 2], cifar_stem=False, **kwargs)[source]#

Bases: ResNet

training: bool#
class renate.benchmark.models.resnet.ResNet34(block=<class 'torchvision.models.resnet.BasicBlock'>, layers=[3, 4, 6, 3], cifar_stem=False, **kwargs)[source]#

Bases: ResNet

training: bool#
class renate.benchmark.models.resnet.ResNet50(block=<class 'torchvision.models.resnet.Bottleneck'>, layers=[3, 4, 6, 3], cifar_stem=False, **kwargs)[source]#

Bases: ResNet

training: bool#