# This implementation is adapted from https://github.com/akamaster/pytorch_resnet_cifar10/tree/master.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
__all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
def _weights_init(m):
classname = m.__class__.__name__
#print(classname)
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
#init.kaiming_normal_(m.weight)
init.xavier_normal_(m.weight)
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
from torch.nn import Parameter
class NormedLinear(nn.Module):
def __init__(self, in_features, out_features):
super(NormedLinear, self).__init__()
self.weight = Parameter(torch.Tensor(in_features, out_features))
self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
def forward(self, x):
out = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0))
return out
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, x):
out = activation_func(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = activation_func(out)
return out
[docs]
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=1, last_activation='sigmoid', pretrained=False):
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)
self.last_activation = last_activation
if self.last_activation is not None:
self.sigmoid = nn.Sigmoid()
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)
def forward(self, x):
out = activation_func(self.bn1(self.conv1(x)))
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)
if self.last_activation == 'sigmoid':
out = self.sigmoid(out)
elif self.last_activation == 'none' or self.last_activation==None:
out = out
elif self.last_activation == 'l2':
out= F.normalize(out,dim=0,p=2)
else:
out = self.sigmoid(out)
return out
[docs]
def resnet20(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [3, 3, 3], last_activation=last_activation, **kwargs)
[docs]
def resnet32(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [5, 5, 5], last_activation=last_activation, **kwargs)
[docs]
def resnet44(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [7, 7, 7], last_activation=last_activation, **kwargs)
[docs]
def resnet56(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [9, 9, 9], last_activation=last_activation, **kwargs)
[docs]
def resnet110(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [18, 18, 18], last_activation=last_activation, **kwargs)
[docs]
def resnet1202(pretrained=False, activations='relu', last_activation=None, **kwargs):
global activation_func
activation_func = F.relu if activations=='relu' else F.elu
# print (activation_func)
return ResNet(BasicBlock, [200, 200, 200], last_activation=last_activation, **kwargs)
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()))))
# alias
ResNet20 = resnet20
ResNet32 = resnet32
ResNet44 = resnet44
ResNet56 = resnet56
ResNet110 = resnet110
ResNet1202 = resnet1202
if __name__ == "__main__":
for net_name in __all__:
if net_name.startswith('resnet'):
print(net_name)
test(globals()[net_name]())
print()