PyTorch实现的VGGNet
PyTorch: https://github.com/shanglianlm0525/ClassicNetwork
PyTorch代码
import torch
import torch.nn as nn
import torchvision
def Conv3x3BNReLU(in_channels,out_channels):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=1,padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True)
)
class VGGNet(nn.Module):
def __init__(self, block_nums,num_classes=1000):
super(VGGNet, self).__init__()
self.stage1 = self._make_layers(in_channels=3, out_channels=64, block_num=block_nums[0])
self.stage2 = self._make_layers(in_channels=64, out_channels=128, block_num=block_nums[1])
self.stage3 = self._make_layers(in_channels=128, out_channels=256, block_num=block_nums[2])
self.stage4 = self._make_layers(in_channels=256, out_channels=512, block_num=block_nums[3])
self.stage5 = self._make_layers(in_channels=512, out_channels=512, block_num=block_nums[4])
self.classifier = nn.Sequential(
nn.Linear(in_features=512*7*7,out_features=4096),
nn.ReLU6(inplace=True),
nn.Dropout(p=0.2),
nn.Linear(in_features=4096, out_features=4096),
nn.ReLU6(inplace=True),
nn.Dropout(p=0.2),
nn.Linear(in_features=4096, out_features=num_classes)
)
def _make_layers(self, in_channels, out_channels, block_num):
layers = []
layers.append(Conv3x3BNReLU(in_channels,out_channels))
for i in range(1,block_num):
layers.append(Conv3x3BNReLU(out_channels,out_channels))
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
return nn.Sequential(*layers)
def forward(self, x):
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.stage5(x)
x = x.view(x.size(0),-1)
out = self.classifier(x)
return out
def VGG16():
block_nums = [2, 2, 3, 3, 3]
model = VGGNet(block_nums)
return model
def VGG19():
block_nums = [2, 2, 4, 4, 4]
model = VGGNet(block_nums)
return model
if __name__ == '__main__':
model = VGG19()
print(model)
input = torch.randn(1,3,224,224)
out = model(input)
print(out.shape)