VGG网络的Pytorch实现

1.文章原文地址

Very Deep Convolutional Networks for Large-Scale Image Recognition

2.文章摘要

在这项工作中，我们研究了在大规模的图像识别数据集上卷积神经网络的深度对准确率的影响。我们主要贡献是使用非常小（3×3）卷积核的架构对深度增加的网络进行全面的评估，其结果表明将深度增大到16-19层时网络的性能会显著提升。这些发现是基于我们在ImageNet Challenge 2014的目标检测和分类任务分别获得了第一名和第二名的成绩而得出的。另外该网络也可以很好的推广到其他数据集上，在这些数据集上获得了当前最好结果。我们已经公开了性能最佳的ConvNet模型，为了促进在计算机视觉中使用深度视觉表征的进一步研究。

3.网络结构

4.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)

 参考

https://github.com/pytorch/vision/tree/master/torchvision/models

posted @ 2019-05-08 23:09  ysyouaremyall  阅读(7538)  评论(2)  编辑  收藏  举报