vgg16.py 文件源码

def __init__(self, bn=False):
        super(VGG16, self).__init__()

        self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
                                   Conv2d(64, 64, 3, same_padding=True, bn=bn),
                                   nn.MaxPool2d(2))
        self.conv2 = nn.Sequential(Conv2d(64, 128, 3, same_padding=True, bn=bn),
                                   Conv2d(128, 128, 3, same_padding=True, bn=bn),
                                   nn.MaxPool2d(2))
        network.set_trainable(self.conv1, requires_grad=False)
        network.set_trainable(self.conv2, requires_grad=False)

        self.conv3 = nn.Sequential(Conv2d(128, 256, 3, same_padding=True, bn=bn),
                                   Conv2d(256, 256, 3, same_padding=True, bn=bn),
                                   Conv2d(256, 256, 3, same_padding=True, bn=bn),
                                   nn.MaxPool2d(2))
        self.conv4 = nn.Sequential(Conv2d(256, 512, 3, same_padding=True, bn=bn),
                                   Conv2d(512, 512, 3, same_padding=True, bn=bn),
                                   Conv2d(512, 512, 3, same_padding=True, bn=bn),
                                   nn.MaxPool2d(2))
        self.conv5 = nn.Sequential(Conv2d(512, 512, 3, same_padding=True, bn=bn),
                                   Conv2d(512, 512, 3, same_padding=True, bn=bn),
                                   Conv2d(512, 512, 3, same_padding=True, bn=bn))