fedlab_core.models.resnet

ResNet in PyTorch.

For Pre-activation ResNet, see ‘preact_resnet.py’.

Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

Deep Residual Learning for Image Recognition. arXiv:1512.03385

Module Contents

Functions

ResNet18(use_batchnorm=True)

ResNet34(use_batchnorm=True)

ResNet50(use_batchnorm=True)

ResNet101(use_batchnorm=True)

ResNet152(use_batchnorm=True)

test()

class fedlab_core.models.resnet.BasicBlock(in_planes, planes, stride=1, use_batchnorm=True)

Bases: torch.nn.Module

expansion = 1
forward(self, x)
class fedlab_core.models.resnet.Bottleneck(in_planes, planes, stride=1, use_batchnorm=True)

Bases: torch.nn.Module

expansion = 4
forward(self, x)
class fedlab_core.models.resnet.ResNet(block, num_blocks, num_classes=10, use_batchnorm=True)

Bases: torch.nn.Module

_make_layer(self, block, planes, num_blocks, stride)
forward(self, x)
fedlab_core.models.resnet.ResNet18(use_batchnorm=True)
fedlab_core.models.resnet.ResNet34(use_batchnorm=True)
fedlab_core.models.resnet.ResNet50(use_batchnorm=True)
fedlab_core.models.resnet.ResNet101(use_batchnorm=True)
fedlab_core.models.resnet.ResNet152(use_batchnorm=True)
fedlab_core.models.resnet.test()