:mod:`fedlab_core.models.resnet`
================================

.. py:module:: fedlab_core.models.resnet

.. autoapi-nested-parse::

   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
---------------

Classes
~~~~~~~

.. autoapisummary::

   fedlab_core.models.resnet.BasicBlock
   fedlab_core.models.resnet.Bottleneck
   fedlab_core.models.resnet.ResNet



Functions
~~~~~~~~~

.. autoapisummary::

   fedlab_core.models.resnet.ResNet18
   fedlab_core.models.resnet.ResNet34
   fedlab_core.models.resnet.ResNet50
   fedlab_core.models.resnet.ResNet101
   fedlab_core.models.resnet.ResNet152
   fedlab_core.models.resnet.test


.. class:: BasicBlock(in_planes, planes, stride=1, use_batchnorm=True)


   Bases: :class:`torch.nn.Module`

   .. attribute:: expansion
      :annotation: = 1

      

   .. method:: forward(self, x)



.. class:: Bottleneck(in_planes, planes, stride=1, use_batchnorm=True)


   Bases: :class:`torch.nn.Module`

   .. attribute:: expansion
      :annotation: = 4

      

   .. method:: forward(self, x)



.. class:: ResNet(block, num_blocks, num_classes=10, use_batchnorm=True)


   Bases: :class:`torch.nn.Module`

   .. method:: _make_layer(self, block, planes, num_blocks, stride)


   .. method:: forward(self, x)



.. function:: ResNet18(use_batchnorm=True)


.. function:: ResNet34(use_batchnorm=True)


.. function:: ResNet50(use_batchnorm=True)


.. function:: ResNet101(use_batchnorm=True)


.. function:: ResNet152(use_batchnorm=True)


.. function:: test()