"""
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.
"""
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
__all__ = [
"ResNet",
"resnet8",
"resnet14",
"resnet20",
"resnet32",
"resnet44",
"resnet56",
"resnet110",
"resnet1202",
]
def _weights_init(module):
if isinstance(module, (nn.Linear, nn.Conv2d)):
init.kaiming_normal_(module.weight)
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, features):
return self.lambd(features)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, option="A"):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
if option == "A":
# For CIFAR10 ResNet paper uses option A.
self.shortcut = LambdaLayer(
lambda x: F.pad(
x[:, :, ::2, ::2],
(0, 0, 0, 0, planes // 4, planes // 4),
"constant",
0,
)
)
elif option == "B":
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, features):
out = F.relu(self.bn1(self.conv1(features)))
out = self.bn2(self.conv2(out))
out += self.shortcut(features)
out = F.relu(out)
return out
[docs]class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
self.linear = nn.Linear(64, num_classes)
self.apply(_weights_init)
[docs] def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
[docs] def forward(self, features):
out = F.relu(self.bn1(self.conv1(features)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = F.avg_pool2d(out, out.size()[3])
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
[docs]def resnet8():
return ResNet(BasicBlock, [1, 1, 1])
[docs]def resnet14():
return ResNet(BasicBlock, [2, 2, 2])
[docs]def resnet20():
return ResNet(BasicBlock, [3, 3, 3])
[docs]def resnet32():
return ResNet(BasicBlock, [5, 5, 5])
[docs]def resnet44():
return ResNet(BasicBlock, [7, 7, 7])
[docs]def resnet56():
return ResNet(BasicBlock, [9, 9, 9])
[docs]def resnet110():
return ResNet(BasicBlock, [18, 18, 18])
[docs]def resnet1202():
return ResNet(BasicBlock, [200, 200, 200])
def test(net):
import numpy as np
total_params = 0
for x in filter(lambda p: p.requires_grad, net.parameters()):
total_params += np.prod(x.data.numpy().shape)
print("Total number of params", total_params)
print(
"Total layers",
len(
list(
filter(
lambda p: p.requires_grad and len(p.data.size()) > 1,
net.parameters(),
)
)
),
)
if __name__ == "__main__":
for net_name in __all__:
if net_name.startswith("resnet"):
print(net_name)
test(globals()[net_name]())
print()