norse.torch.models.vgg module

class norse.torch.models.vgg.VGG(features, num_classes=1000, init_weights=True)

Bases: torch.nn.modules.module.Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

norse.torch.models.vgg.vgg11(pretrained=False, progress=True, **kwargs)

VGG 11-layer model (configuration “A”) from “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg11_bn(pretrained=False, progress=True, **kwargs)

VGG 11-layer model (configuration “A”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg13(pretrained=False, progress=True, **kwargs)

VGG 13-layer model (configuration “B”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg13_bn(pretrained=False, progress=True, **kwargs)

VGG 13-layer model (configuration “B”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg16(pretrained=False, progress=True, **kwargs)

VGG 16-layer model (configuration “D”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg16_bn(pretrained=False, progress=True, **kwargs)

VGG 16-layer model (configuration “D”) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg19(pretrained=False, progress=True, **kwargs)

VGG 19-layer model (configuration “E”) “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool

norse.torch.models.vgg.vgg19_bn(pretrained=False, progress=True, **kwargs)

VGG 19-layer model (configuration ‘E’) with batch normalization “Very Deep Convolutional Networks For Large-Scale Image Recognition” :param pretrained: If True, returns a model pre-trained on ImageNet :type pretrained: bool :param progress: If True, displays a progress bar of the download to stderr :type progress: bool