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vgg.py
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vgg.py
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import math
import torch.nn as nn
import torch.nn.init as init
__all__ = ['VGG', 'vgg11', 'vgg11_bw']
class VGG(nn.Module):
def __init__(self, features):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Linear(512, 10, bias=False),
)
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))
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, bw=False):
layers = []
if bw:
in_channels = 1
else:
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, bias=False)
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
cfgs = {
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'F': [64, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'] # added for mnist
}
def vgg11():
return VGG(make_layers(cfgs['A']))
def vgg11_bw():
return VGG(make_layers(cfgs['F'], bw=True))