Tensorflow: Download and run pretrained VGG or ResNet model

Question

Let's start at the beginning. So far I have created and trained small networks in Tensorflow myself. During the training I save my model and get the following files in my directory:

model.ckpt.meta
model.ckpt.index
model.ckpt.data-00000-of-00001

Later, I load the model saved in network_dir to do some classifications and extract the trainable variables of my model.

saver = tf.train.import_meta_graph(network_dir + ".meta")
variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="NETWORK")

Now I want to work with larger pretrained models like the VGG16 or ResNet and want to use my code to do that. I want to load pretrained models like my own networks as shown above.

On this site, I found many pretrained models:

https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models

I downloaded the VGG16 checkpoint and realized that these are only the trained parameters.

I would like to know how or where I can get the saved model or graph structure of these pretrained network? How do I use, for example, the VGG16 checkpoint without model.ckpt.meta, model.ckpt.index and the model.ckpt.data-00000-of-00001 files?

Answer 1

Next to the weights link, there is link to the code that defines the model. For instance, for VGG16: Code. Create the model using the code and restore variables from the checkpoint:

import tensorflow as tf

slim = tf.contrib.slim

image = ...  # Define your input somehow, e.g with placeholder
logits, _ = vgg.vgg_16(image)
predictions = tf.argmax(logits, 1)
variables_to_restore = slim.get_variables_to_restore()

saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
    saver.restore(sess, "/path/to/model.ckpt")

So, the code contained in vgg.py will create all the variables for you. Using the tf-slim helper, you can get the list. Then, just follow the usual procedure. There was a similar question on this.