import torch
import torch.nn as nn
class VGG(nn.Module):
def __init__(self, class_num):
super(VGG, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(512*7*7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, class_num),
)
#self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
model = VGG(1000)
print(model)
