vgg.py 文件源码

def __init__(self):
        super(VGG, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(3, 64, 3, stride=1, pad=1)
            self.conv1_2 = L.Convolution2D(64, 64, 3, stride=1, pad=1)
            self.conv2_1 = L.Convolution2D(64, 128, 3, stride=1, pad=1)
            self.conv2_2 = L.Convolution2D(128, 128, 3, stride=1, pad=1)
            self.conv3_1 = L.Convolution2D(128, 256, 3, stride=1, pad=1)
            self.conv3_2 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv3_3 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv4_1 = L.Convolution2D(256, 512, 3, stride=1, pad=1)
            self.conv4_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv4_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_1 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.fc6 = L.Linear(25088, 4096)
            self.fc7 = L.Linear(4096, 4096)
            self.fc8 = L.Linear(4096, 1000)

        # Keep track of the pooling indices inside each function instance
        self.conv_blocks = [
            [self.conv1_1, self.conv1_2],
            [self.conv2_1, self.conv2_2],
            [self.conv3_1, self.conv3_2, self.conv3_3],
            [self.conv4_1, self.conv4_2, self.conv4_3],
            [self.conv5_1, self.conv5_2, self.conv5_3]
        ]
        self.deconv_blocks = []
        self.mps = [F.MaxPooling2D(2, 2) for _ in self.conv_blocks]