import torch.nn as nn import torch # official pretrain weights model_urls = { 'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth', 'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth', 'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth' } class VGG(nn.Module): def __init__(self, features, num_classes=1000, init_weights=False): super(VGG, self).__init__() self.features = features self.classifier = nn.Sequential( nn.Linear(512*7*7, 4096), nn.ReLU(True), nn.Dropout(p=0.5), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(p=0.5), nn.Linear(4096, num_classes) ) if init_weights: self._initialize_weights() def forward(self, x): # N x 3 x 224 x 224 x = self.features(x) # N x 512 x 7 x 7 x = torch.flatten(x, start_dim=1) # N x 512*7*7 x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) # nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def make_features(cfg: list): layers = [] in_channels = 3 for v in cfg: if v == "M": layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) layers += [conv2d, nn.ReLU(True)] in_channels = v return nn.Sequential(*layers) cfgs = { 'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } def vgg(model_name="vgg16", **kwargs): assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name) cfg = cfgs[model_name] model = VGG(make_features(cfg), **kwargs) return model