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vgg.py
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vgg.py
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# imports
import torch
import torch.nn as nn
import torch.optim as optim
import torch.functional as F
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
VGG_types = {
'VGG11' : [64, 'M', 128 ,'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13' : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16' : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'VGG19' : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M']
}
# then flatten 4096,4096
class VGG_net(nn.Module):
def __init__(self,in_channels = 3,num_classes =1000):
super(VGG_net,self).__init__()
self.in_channels = in_channels
self.conv_layers = self.create_conv_layers(VGG_types['VGG11'])
self.fcs = nn.Sequential(
nn.Linear(512*7*7, 4096),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096,4096),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096,num_classes)
)
def forward(self,x):
x = self.conv_layers(x)
x = x.reshape(x.shape[0],-1)
x = self.fcs(x)
return x
def create_conv_layers(self,architecture):
in_channels = self.in_channels
layers = []
for x in architecture:
if type(x) == int:
out_channels = x
layers +=[nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=(3,3),padding=(1,1),stride=(1,1)),
nn.BatchNorm2d(x),
nn.ReLU()]
in_channels = x
elif x == 'M':
layers += [nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))]
return nn.Sequential(*layers)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = VGG_net(in_channels= 3 , num_classes=1000).to(device)
x = torch.randn(1,3,224,224).to(device)
print(model(x).shape)