VGG网络的Pytorch实现

  1 import torch.nn as nn
  2 try:
  3     from torch.hub import load_state_dict_from_url
  4 except ImportError:
  5     from torch.utils.model_zoo import load_url as load_state_dict_from_url
  6 
  7 __all__ = [
  8     ‘VGG‘, ‘vgg11‘, ‘vgg11_bn‘, ‘vgg13‘, ‘vgg13_bn‘, ‘vgg16‘, ‘vgg16_bn‘,
  9     ‘vgg19_bn‘, ‘vgg19‘,
 10 ]
 11 
 12 
 13 model_urls = {
 14     ‘vgg11‘: ‘https://download.pytorch.org/models/vgg11-bbd30ac9.pth‘,
 15     ‘vgg13‘: ‘https://download.pytorch.org/models/vgg13-c768596a.pth‘,
 16     ‘vgg16‘: ‘https://download.pytorch.org/models/vgg16-397923af.pth‘,
 17     ‘vgg19‘: ‘https://download.pytorch.org/models/vgg19-dcbb9e9d.pth‘,
 18     ‘vgg11_bn‘: ‘https://download.pytorch.org/models/vgg11_bn-6002323d.pth‘,
 19     ‘vgg13_bn‘: ‘https://download.pytorch.org/models/vgg13_bn-abd245e5.pth‘,
 20     ‘vgg16_bn‘: ‘https://download.pytorch.org/models/vgg16_bn-6c64b313.pth‘,
 21     ‘vgg19_bn‘: ‘https://download.pytorch.org/models/vgg19_bn-c79401a0.pth‘,
 22 }
 23 
 24 
 25 class VGG(nn.Module):
 26 
 27     def __init__(self, features, num_classes=1000, init_weights=True):
 28         super(VGG, self).__init__()
 29         self.features = features
 30         self.avgpool = nn.AdaptiveAvgPool2d((7, 7))　　#固定全连接层的输入
 31         self.classifier = nn.Sequential(
 32             nn.Linear(512 * 7 * 7, 4096),
 33             nn.ReLU(True),
 34             nn.Dropout(),
 35             nn.Linear(4096, 4096),
 36             nn.ReLU(True),
 37             nn.Dropout(),
 38             nn.Linear(4096, num_classes),
 39         )
 40         if init_weights:
 41             self._initialize_weights()
 42 
 43     def forward(self, x):
 44         x = self.features(x)
 45         x = self.avgpool(x)
 46         x = x.view(x.size(0), -1)
 47         x = self.classifier(x)
 48         return x
 49 
 50     def _initialize_weights(self):
 51         for m in self.modules():
 52             if isinstance(m, nn.Conv2d):
 53                 nn.init.kaiming_normal_(m.weight, mode=‘fan_out‘, nonlinearity=‘relu‘)
 54                 if m.bias is not None:
 55                     nn.init.constant_(m.bias, 0)
 56             elif isinstance(m, nn.BatchNorm2d):
 57                 nn.init.constant_(m.weight, 1)
 58                 nn.init.constant_(m.bias, 0)
 59             elif isinstance(m, nn.Linear):
 60                 nn.init.normal_(m.weight, 0, 0.01)
 61                 nn.init.constant_(m.bias, 0)
 62 
 63 
 64 def make_layers(cfg, batch_norm=False):
 65     layers = []
 66     in_channels = 3
 67     for v in cfg:
 68         if v == ‘M‘:
 69             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
 70         else:
 71             conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
 72             if batch_norm:
 73                 layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
 74             else:
 75                 layers += [conv2d, nn.ReLU(inplace=True)]
 76             in_channels = v
 77     return nn.Sequential(*layers)
 78 
 79 
 80 cfgs = {
 81     ‘A‘: [64, ‘M‘, 128, ‘M‘, 256, 256, ‘M‘, 512, 512, ‘M‘, 512, 512, ‘M‘],
 82     ‘B‘: [64, 64, ‘M‘, 128, 128, ‘M‘, 256, 256, ‘M‘, 512, 512, ‘M‘, 512, 512, ‘M‘],
 83     ‘D‘: [64, 64, ‘M‘, 128, 128, ‘M‘, 256, 256, 256, ‘M‘, 512, 512, 512, ‘M‘, 512, 512, 512, ‘M‘],
 84     ‘E‘: [64, 64, ‘M‘, 128, 128, ‘M‘, 256, 256, 256, 256, ‘M‘, 512, 512, 512, 512, ‘M‘, 512, 512, 512, 512, ‘M‘],
 85 }
 86 
 87 
 88 def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
 89     if pretrained:
 90         kwargs[‘init_weights‘] = False
 91     model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
 92     if pretrained:
 93         state_dict = load_state_dict_from_url(model_urls[arch],
 94                                               progress=progress)
 95         model.load_state_dict(state_dict)
 96     return model
 97 
 98 
 99 def vgg11(pretrained=False, progress=True, **kwargs):
100     """VGG 11-layer model (configuration "A")
101     Args:
102         pretrained (bool): If True, returns a model pre-trained on ImageNet
103         progress (bool): If True, displays a progress bar of the download to stderr
104     """
105     return _vgg(‘vgg11‘, ‘A‘, False, pretrained, progress, **kwargs)
106 
107 
108 def vgg11_bn(pretrained=False, progress=True, **kwargs):
109     """VGG 11-layer model (configuration "A") with batch normalization
110     Args:
111         pretrained (bool): If True, returns a model pre-trained on ImageNet
112         progress (bool): If True, displays a progress bar of the download to stderr
113     """
114     return _vgg(‘vgg11_bn‘, ‘A‘, True, pretrained, progress, **kwargs)
115 
116 
117 def vgg13(pretrained=False, progress=True, **kwargs):
118     """VGG 13-layer model (configuration "B")
119     Args:
120         pretrained (bool): If True, returns a model pre-trained on ImageNet
121         progress (bool): If True, displays a progress bar of the download to stderr
122     """
123     return _vgg(‘vgg13‘, ‘B‘, False, pretrained, progress, **kwargs)
124 
125 
126 def vgg13_bn(pretrained=False, progress=True, **kwargs):
127     """VGG 13-layer model (configuration "B") with batch normalization
128     Args:
129         pretrained (bool): If True, returns a model pre-trained on ImageNet
130         progress (bool): If True, displays a progress bar of the download to stderr
131     """
132     return _vgg(‘vgg13_bn‘, ‘B‘, True, pretrained, progress, **kwargs)
133 
134 
135 def vgg16(pretrained=False, progress=True, **kwargs):
136     """VGG 16-layer model (configuration "D")
137     Args:
138         pretrained (bool): If True, returns a model pre-trained on ImageNet
139         progress (bool): If True, displays a progress bar of the download to stderr
140     """
141     return _vgg(‘vgg16‘, ‘D‘, False, pretrained, progress, **kwargs)
142 
143 
144 def vgg16_bn(pretrained=False, progress=True, **kwargs):
145     """VGG 16-layer model (configuration "D") with batch normalization
146     Args:
147         pretrained (bool): If True, returns a model pre-trained on ImageNet
148         progress (bool): If True, displays a progress bar of the download to stderr
149     """
150     return _vgg(‘vgg16_bn‘, ‘D‘, True, pretrained, progress, **kwargs)
151 
152 
153 def vgg19(pretrained=False, progress=True, **kwargs):
154     """VGG 19-layer model (configuration "E")
155     Args:
156         pretrained (bool): If True, returns a model pre-trained on ImageNet
157         progress (bool): If True, displays a progress bar of the download to stderr
158     """
159     return _vgg(‘vgg19‘, ‘E‘, False, pretrained, progress, **kwargs)
160 
161 
162 def vgg19_bn(pretrained=False, progress=True, **kwargs):
163     """VGG 19-layer model (configuration ‘E‘) with batch normalization
164     Args:
165         pretrained (bool): If True, returns a model pre-trained on ImageNet
166         progress (bool): If True, displays a progress bar of the download to stderr
167     """
168     return _vgg(‘vgg19_bn‘, ‘E‘, True, pretrained, progress, **kwargs)