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) 版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
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