| """vgg in pytorch | |
| [1] Karen Simonyan, Andrew Zisserman | |
| Very Deep Convolutional Networks for Large-Scale Image Recognition. | |
| https://arxiv.org/abs/1409.1556v6 | |
| """ | |
| '''VGG11/13/16/19 in Pytorch.''' | |
| import torch | |
| import torch.nn as nn | |
| cfg = { | |
| 'A' : [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], | |
| 'B' : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], | |
| 'D' : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], | |
| 'E' : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'] | |
| } | |
| class VGG(nn.Module): | |
| def __init__(self, features, num_class=100): | |
| super().__init__() | |
| self.features = features | |
| self.classifier = nn.Sequential( | |
| nn.Linear(512, 4096), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(), | |
| nn.Linear(4096, 4096), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(), | |
| nn.Linear(4096, num_class) | |
| ) | |
| def forward(self, x): | |
| output = self.features(x) | |
| output = output.view(output.size()[0], -1) | |
| output = self.classifier(output) | |
| return output | |
| def make_layers(cfg, batch_norm=False): | |
| layers = [] | |
| input_channel = 3 | |
| for l in cfg: | |
| if l == 'M': | |
| layers += [nn.MaxPool2d(kernel_size=2, stride=2)] | |
| continue | |
| layers += [nn.Conv2d(input_channel, l, kernel_size=3, padding=1)] | |
| if batch_norm: | |
| layers += [nn.BatchNorm2d(l)] | |
| layers += [nn.ReLU(inplace=True)] | |
| input_channel = l | |
| return nn.Sequential(*layers) | |
| def vgg11_bn(): | |
| return VGG(make_layers(cfg['A'], batch_norm=True)) | |
| def vgg13_bn(): | |
| return VGG(make_layers(cfg['B'], batch_norm=True)) | |
| def vgg16_bn(): | |
| return VGG(make_layers(cfg['D'], batch_norm=True)) | |
| def vgg19_bn(): | |
| return VGG(make_layers(cfg['E'], batch_norm=True)) | |