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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torchvision.models as models
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
}
class VggNet(nn.Module):
def __init__(self, name="vgg16", pretrain=True):
super().__init__()
if name == "vgg16":
base_net = models.vgg16(pretrained=False)
elif name == "vgg16_bn":
base_net = models.vgg16_bn(pretrained=False)
else:
print(" base model is not support !")
if pretrain:
print("load the {} weight from ./cache".format(name))
base_net.load_state_dict(model_zoo.load_url(model_urls[name], model_dir="./cache"))
if name == "vgg16":
self.stage1 = nn.Sequential(*[base_net.features[layer] for layer in range(0, 5)])
self.stage2 = nn.Sequential(*[base_net.features[layer] for layer in range(5, 10)])
self.stage3 = nn.Sequential(*[base_net.features[layer] for layer in range(10, 17)])
self.stage4 = nn.Sequential(*[base_net.features[layer] for layer in range(17, 24)])
self.stage5 = nn.Sequential(*[base_net.features[layer] for layer in range(24, 31)])
elif name == "vgg16_bn":
self.stage1 = nn.Sequential(*[base_net.features[layer] for layer in range(0, 7)])
self.stage2 = nn.Sequential(*[base_net.features[layer] for layer in range(7, 14)])
self.stage3 = nn.Sequential(*[base_net.features[layer] for layer in range(14, 24)])
self.stage4 = nn.Sequential(*[base_net.features[layer] for layer in range(24, 34)])
self.stage5 = nn.Sequential(*[base_net.features[layer] for layer in range(34, 44)])
def forward(self, x):
C1 = self.stage1(x)
C2 = self.stage2(C1)
C3 = self.stage3(C2)
C4 = self.stage4(C3)
C5 = self.stage5(C4)
return C1, C2, C3, C4, C5
if __name__ == '__main__':
import torch
input = torch.randn((4, 3, 512, 512))
net = VggNet()
C1, C2, C3, C4, C5 = net(input)
print(C1.size())
print(C2.size())
print(C3.size())
print(C4.size())
print(C5.size())
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