agnapprox.nets.base.resnet

Properly implemented ResNet-s for CIFAR10 as described in paper [1].

The implementation and structure of this file is hugely influenced by [2] which is implemented for ImageNet and doesn’t have option A for identity. Moreover, most of the implementations on the web is copy-paste from torchvision’s resnet and has wrong number of params.

Proper ResNet-s for CIFAR10 (for fair comparision and etc.) has following number of layers and parameters:

name | layers | params ResNet20 | 20 | 0.27M ResNet32 | 32 | 0.46M ResNet44 | 44 | 0.66M ResNet56 | 56 | 0.85M ResNet110 | 110 | 1.7M ResNet1202| 1202 | 19.4m

which this implementation indeed has.

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

Deep Residual Learning for Image Recognition. arXiv:1512.03385

[2] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

If you use this implementation in you work, please don’t forget to mention the author, Yerlan Idelbayev.

Module Contents

Classes

ResNet

Base class for all neural network modules.

Functions

resnet8()
resnet14()
resnet20()
resnet32()
resnet44()
resnet56()
resnet110()
resnet1202()

class agnapprox.nets.base.resnet.ResNet(block, num_blocks, num_classes=10)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

_make_layer(block, planes, num_blocks, stride)

forward(features)

agnapprox.nets.base.resnet.resnet8()

agnapprox.nets.base.resnet.resnet14()

agnapprox.nets.base.resnet.resnet20()

agnapprox.nets.base.resnet.resnet32()

agnapprox.nets.base.resnet.resnet44()

agnapprox.nets.base.resnet.resnet56()

agnapprox.nets.base.resnet.resnet110()

agnapprox.nets.base.resnet.resnet1202()