To train a classifier in CIFAR100, I have already trained a classifier via VGGnets, but how can I convert my VGGnets to FCN?
My VGGnets code like below:
class Unit(nn.Module):
def __init__(self,in_channels,out_channels):
super(Unit,self).__init__()
self.conv = nn.Conv2d(in_channels=in_channels,kernel_size=3,out_channels=out_channels,stride=1,padding=1)
self.bn = nn.BatchNorm2d(num_features=out_channels)
self.relu = nn.ReLU()
def forward(self,input):
output = self.conv(input)
output = self.bn(output)
output = self.relu(output)
return output
class Model(nn.Module):
def __init__(self,num_classes=100):
super(Model,self).__init__()
self.net = nn.Sequential(
Unit(in_channels=3,out_channels=32),
Unit(in_channels=32, out_channels=32),
Unit(in_channels=32, out_channels=32),
nn.MaxPool2d(kernel_size=2),
Unit(in_channels=32, out_channels=64),
Unit(in_channels=64, out_channels=64),
Unit(in_channels=64, out_channels=64),
Unit(in_channels=64, out_channels=64),
nn.MaxPool2d(kernel_size=2),
Unit(in_channels=64, out_channels=128),
Unit(in_channels=128, out_channels=128),
Unit(in_channels=128, out_channels=128),
Unit(in_channels=128, out_channels=128),
nn.MaxPool2d(kernel_size=2),
Unit(in_channels=128, out_channels=128),
Unit(in_channels=128, out_channels=128),
Unit(in_channels=128, out_channels=128),
nn.MaxPool2d(kernel_size=2))
self.dense = nn.Sequential(
nn.Linear(in_features=2*2*128,out_features=num_classes))
def forward(self, input):
output = self.net(input)
output = output.view(-1,2*2*128)
output = self.dense(output)
return output
Sincerely waiting for your help!