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nets/vggnet.py

+# -*- coding: utf-8 -*-
+# @Time    : 2021/7/6 15:51
+# @Author  : Mat
+# @File    : vggnet.py
+# @Software: PyCharm
+import torch.nn as nn
+import math
+
+
+# **************************VGGNet********************
+class VGG(nn.Module):
+
+    def __init__(self, features, num_classes=10, init_weights=True):
+        super(VGG, self).__init__()
+        self.features = features
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * 7 * 7, 4096),
+            nn.ReLU(True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(True),
+            nn.Dropout(),
+            nn.Linear(4096, num_classes),
+        )
+        if init_weights:
+            self._initialize_weights()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+def make_layers(cfg, batch_norm=False):
+    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)
+            if batch_norm:
+                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+            else:
+                layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+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'],
+}
+
+
+def vgg11(**kwargs):
+    model = VGG(make_layers(cfg['A']), **kwargs)
+    return model
+
+
+def vgg11_bn(**kwargs):
+    model = VGG(make_layers(cfg['A'], batch_norm=True), **kwargs)
+    return model
+
+
+def vgg13(**kwargs):
+    model = VGG(make_layers(cfg['B']), **kwargs)
+    return model
+
+
+def vgg13_bn(**kwargs):
+    model = VGG(make_layers(cfg['B'], batch_norm=True), **kwargs)
+    return model
+
+
+def vgg16(**kwargs):
+    model = VGG(make_layers(cfg['D']), **kwargs)
+    return model
+
+
+def vgg16_bn(**kwargs):
+    model = VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
+    return model
+
+
+def vgg19(**kwargs):
+    model = VGG(make_layers(cfg['E']), **kwargs)
+    return model
+
+
+def vgg19_bn(**kwargs):
+    model = VGG(make_layers(cfg['E'], batch_norm=True), **kwargs)
+    return model
+
+
+if __name__ == '__main__':
+    # 'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19_bn', 'vgg19'
+    # Example
+    net = vgg11()
+    print(net)
+