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import torch.nn as nn
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)
cfgs = {
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512],
}
class VGG(nn.Module):
def __init__(self,features):
super(VGG, self).__init__()
self.features = features
def forward(self, x):
x = self.features(x)
def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
return model
def encoder(pretrained=False, progress=True, **kwargs):
return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs) |