glasses.models.classification.senet package¶

Module contents¶

class glasses.models.classification.senet.SEResNet(encoder: torch.nn.modules.module.Module = <class 'glasses.models.classification.resnet.ResNetEncoder'>, head: torch.nn.modules.module.Module = <class 'glasses.models.classification.resnet.ResNetHead'>, **kwargs)[source]¶

Bases: glasses.models.classification.resnet.ResNet

Implementation of Squeeze and Excitation ResNet using booth the original spatial se and the channel se proposed in Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks The models with the channel se are labeldb with prefix c

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

classmethod se_resnet101(*args, **kwargs) → glasses.models.classification.senet.SEResNet[source]¶

Original SE resnet101 with Spatial Squeeze and Excitation

Returns: [description]
Return type: SEResNet

classmethod se_resnet152(*args, **kwargs) → glasses.models.classification.senet.SEResNet[source]¶

Original SE resnet152 with Spatial Squeeze and Excitation

Returns: [description]
Return type: SEResNet

classmethod se_resnet18(*args, **kwargs) → glasses.models.classification.senet.SEResNet[source]¶

Original SE resnet18 with Spatial Squeeze and Excitation

Returns: [description]
Return type: SEResNet

classmethod se_resnet34(*args, **kwargs) → glasses.models.classification.senet.SEResNet[source]¶

Original SE resnet34 with Spatial Squeeze and Excitation

Returns: [description]
Return type: SEResNet

classmethod se_resnet50(*args, **kwargs) → glasses.models.classification.senet.SEResNet[source]¶

Original SE resnet50 with Spatial Squeeze and Excitation

Returns: [description]
Return type: SEResNet

training: bool¶