CTPN代码中使用的backbone是VGG16。介绍VGG16的文章实在是太多了，我就不写了。
直接上代码吧。
def vgg_16(inputs, scope='vgg_16'):
with tf.variable_scope(scope, 'vgg_16', [inputs])as sc:
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d]):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
return net
这是tensorflow1中slim库的实现。这和一般实现不一样的地方就是，slim中用了slim.repeat()，使得代码更加简洁，可读性也不低。
