fcn.py 文件源码

def fcn_net(self, x, train=True):
        conv1 = conv2d(x, [3, 3, 3, 32], 'conv1')
        maxp1 = maxpooling(conv1)

        conv2 = conv2d(maxp1, [3, 3, 32, 32], 'conv2')
        maxp2 = maxpooling(conv2)

        conv3 = conv2d(maxp2, [3, 3, 32, 64], 'conv3')
        maxp3 = maxpooling(conv3)

        conv4 = conv2d(maxp3, [3, 3, 64, 64], 'conv4')
        maxp4 = maxpooling(conv4)

        conv5 = conv2d(maxp4, [3, 3, 64, 128], 'conv5')
        maxp5 = maxpooling(conv5)

        conv6 = conv2d(maxp5, [3, 3, 128, 128], 'conv6')
        maxp6 = maxpooling(conv6)

        conv7 = conv2d(maxp6, [3, 3, 128, 256], 'conv7')
        maxp7 = maxpooling(conv7)

        conv8 = conv2d(maxp7, [3, 3, 256, 256], 'conv8')
        maxp8 = maxpooling(conv8)

        conv9 = conv2d(maxp8, [3, 3, 256, 512], 'conv9')
        maxp9 = maxpooling(conv9)

        drop = tf.nn.dropout(maxp9, self.dropout)

        # 1x1 convolution + sigmoid activation
        net = conv2d(drop, [1, 1, 512, self.input_size*self.input_size], 'conv10', activation='no')

        # squeeze the last two dimension in train
        if train:
            net = tf.squeeze(net, [1, 2], name="squeezed")

        return net