78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
import torch.nn as nn
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import torch
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# official pretrain weights
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model_urls = {
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'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
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'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
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'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
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'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'
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}
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class VGG(nn.Module):
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def __init__(self, features, num_classes=1000, init_weights=False):
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super(VGG, self).__init__()
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self.features = features
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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(p=0.5),
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nn.Linear(4096, 4096),
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nn.ReLU(True),
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nn.Dropout(p=0.5),
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nn.Linear(4096, num_classes)
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)
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if init_weights:
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self._initialize_weights()
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def forward(self, x):
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# N x 3 x 224 x 224
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x = self.features(x)
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# N x 512 x 7 x 7
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x = torch.flatten(x, start_dim=1)
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# N x 512*7*7
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x = self.classifier(x)
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return x
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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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nn.init.xavier_uniform_(m.weight)
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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.Linear):
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nn.init.xavier_uniform_(m.weight)
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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_features(cfg: list):
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layers = []
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in_channels = 3
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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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layers += [conv2d, nn.ReLU(True)]
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in_channels = v
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return nn.Sequential(*layers)
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cfgs = {
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'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
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'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
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}
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def vgg(model_name="vgg16", **kwargs):
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assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name)
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cfg = cfgs[model_name]
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model = VGG(make_features(cfg), **kwargs)
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return model
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