Add models.vgg

models/vgg.py

@@ -0,0 +1,110 @@
+'''
+Modified from https://github.com/pytorch/vision.git
+'''
+import math
+
+import torch.nn as nn
+from .base import PyTorchModel
+
+__all__ = [
+    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
+    'vgg19_bn', 'vgg19',
+]
+
+
+class VGG(nn.Module, PyTorchModel):
+    '''
+    VGG model
+    '''
+
+    def __init__(self, features):
+        super(VGG, self).__init__()
+        self.features = features
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(True),
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(True),
+            nn.Linear(512, 10),
+        )
+        # Initialize weights
+        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))
+                m.bias.data.zero_()
+
+        self.criterion = nn.CrossEntropyLoss()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+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():
+    """VGG 11-layer model (configuration "A")"""
+    return VGG(make_layers(cfg['A']))
+
+
+def vgg11_bn():
+    """VGG 11-layer model (configuration "A") with batch normalization"""
+    return VGG(make_layers(cfg['A'], batch_norm=True))
+
+
+def vgg13():
+    """VGG 13-layer model (configuration "B")"""
+    return VGG(make_layers(cfg['B']))
+
+
+def vgg13_bn():
+    """VGG 13-layer model (configuration "B") with batch normalization"""
+    return VGG(make_layers(cfg['B'], batch_norm=True))
+
+
+def vgg16():
+    """VGG 16-layer model (configuration "D")"""
+    return VGG(make_layers(cfg['D']))
+
+
+def vgg16_bn():
+    """VGG 16-layer model (configuration "D") with batch normalization"""
+    return VGG(make_layers(cfg['D'], batch_norm=True))
+
+
+def vgg19():
+    """VGG 19-layer model (configuration "E")"""
+    return VGG(make_layers(cfg['E']))
+
+
+def vgg19_bn():
+    """VGG 19-layer model (configuration 'E') with batch normalization"""
+    return VGG(make_layers(cfg['E'], batch_norm=True))