76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
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import torch.nn as nn
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from torch.hub import load_state_dict_from_url
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class VGG(nn.Module):
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def __init__(self, features, num_classes=1000):
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super(VGG, self).__init__()
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self.features = features
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self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
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self.classifier = nn.Sequential(
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nn.Linear(512 * 7 * 7, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(True),
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nn.Dropout(),
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nn.Linear(4096, num_classes),
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)
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self._initialize_weights()
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def forward(self, x):
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# x = self.features(x)
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# x = self.avgpool(x)
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# x = torch.flatten(x, 1)
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# x = self.classifier(x)
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feat1 = self.features[ :4 ](x)
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feat2 = self.features[4 :9 ](feat1)
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feat3 = self.features[9 :16](feat2)
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feat4 = self.features[16:23](feat3)
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feat5 = self.features[23:-1](feat4)
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return [feat1, feat2, feat3, feat4, feat5]
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def _initialize_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.constant_(m.bias, 0)
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def make_layers(cfg, batch_norm=False, in_channels = 3):
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layers = []
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for v in cfg:
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if v == 'M':
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layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
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else:
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conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
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else:
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layers += [conv2d, nn.ReLU(inplace=True)]
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in_channels = v
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return nn.Sequential(*layers)
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# 512,512,3 -> 512,512,64 -> 256,256,64 -> 256,256,128 -> 128,128,128 -> 128,128,256 -> 64,64,256
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# 64,64,512 -> 32,32,512 -> 32,32,512
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cfgs = {
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'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
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}
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def VGG16(pretrained, in_channels = 3, **kwargs):
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model = VGG(make_layers(cfgs["D"], batch_norm = False, in_channels = in_channels), **kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url("https://download.pytorch.org/models/vgg16-397923af.pth", model_dir="./model_data")
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model.load_state_dict(state_dict)
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del model.avgpool
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del model.classifier
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return model
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