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Versions : I am using tensorflow (version : v1.1.0-13-g8ddd727 1.1.0) in python3 (Python 3.4.3 (default, Nov 17 2016, 01:08:31) [GCC 4.8.4] on linux), it is installed from source and GPU-based (name: GeForce GTX TITAN X major: 5 minor: 2 memoryClockRate (GHz) 1.076).

Context : Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative network. When the high-dimensional distribution describes images of a particular data set, the network should learn to generate visually similar image samples for latent variables that are close to each other in the latent space. For tasks such as image retrieval and image classification, it may be useful to exploit the arrangement of the latent space by projecting images into it, and using this as a representation for discriminative tasks.

Context Problem : I am trying to invert a generator (compute L2 norm between an input image in cifar10 and a image g(z) of the generator, where z is a parameter to be trained with stochastic gradient descent in order to minimize this norm and find an approximation of the preimage of the input image).

Technical Issue : Therefore, I am building a new graph in a new session in tensorflow but I need to use a trained gan that was trained in another session, which I cannot import because the two graphs are not the same. That is to say, when I use sess.run(), the variables are not found and therefore there is a Error Message.

The code is 

import tensorflow as tf
from data import cifar10, utilities
from . import dcgan
import logging

logger = logging.getLogger("gan.test")

BATCH_SIZE = 1


random_z = tf.get_variable(name='z_to_invert', shape=[BATCH_SIZE, 100], initializer=tf.random_normal_initializer())
#random_z = tf.random_normal([BATCH_SIZE, 100], mean=0.0, stddev=1.0, name='random_z')

# Generate images with generator
generator = dcgan.generator(random_z, is_training=True, name='generator')


# Add summaries to visualise output images 
generator_visualisation = tf.cast(((generator / 2.0) + 0.5) * 255.0, tf.uint8)
summary_generator = tf.summary.\
                    image('summary/generator', generator_visualisation,
                     max_outputs=8)


#Create one image to test inverting
test_image = map((lambda inp: (inp[0]*2. -1., inp[1])), 
                     utilities.infinite_generator(cifar10.get_train(), BATCH_SIZE))
inp, _ = next(test_image)
summary_inp = tf.summary.image('input_image', inp)

img_summary = tf.summary.merge([summary_generator, summary_inp])

with tf.name_scope('error'):
    error = inp - generator #generator = g(z)
    # We set axis = None because norm(tensor, ord=ord) is equivalent to norm(reshape(tensor, [-1]), ord=ord)
    error_norm = tf.norm(error, ord=2, axis=None, keep_dims=False, name='L2Norm')
    summary_error = tf.summary.scalar('error_norm', error_norm)

with tf.name_scope('Optimizing'):
    optimizer = tf.train.AdamOptimizer(0.001).minimize(error_norm, var_list=z)

sv = tf.train.Supervisor(logdir="gan/invert_logs/", save_summaries_secs=None, save_model_secs=None)

batch = 0
with sv.managed_session() as sess:
    logwriter = tf.summary.FileWriter("gan/invert_logs/", sess.graph)
    while not sv.should_stop():
        if batch > 0 and batch % 100 == 0:
            logger.debug('Step {} '.format(batch))
        (_, s) = sess.run((optimizer, summary_error))
        logwriter.add_summary(s, batch)
        print('step %d: Patiente un peu poto!' % batch)
        img = sess.run(img_summary)
        logwriter.add_summary(img, batch)
        batch += 1
        print(batch)

I understood what is the problem, it is actually that I am trying to run a session which is saved in gan/train_logs but the graph does not have those variables I am trying to run.

Therefore, I tried to implement this instead : 

graph = tf.Graph()
tf.reset_default_graph()
with tf.Session(graph=graph) as sess:

    ckpt = tf.train.get_checkpoint_state('gan/train_logs/')
    saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta', clear_devices=True)
    saver.restore(sess, ckpt.model_checkpoint_path)
    logwriter = tf.summary.FileWriter("gan/invert_logs/", sess.graph)
    #inp, _ = next(test_image)

    BATCH_SIZE = 1

    #Create one image to test inverting
    test_image = map((lambda inp: (inp[0]*2. -1., inp[1])), 
                     utilities.infinite_generator(cifar10.get_train(), BATCH_SIZE))
    inp, _ = next(test_image)
    #M_placeholder = tf.placeholder(tf.float32, shape=cifar10.get_shape_input(), name='M_input')
    M_placeholder = inp
    zmar = tf.summary.image('input_image', inp)
    #Create sample noise from random normal distribution
    z = tf.get_variable(name='z', shape=[BATCH_SIZE, 100], initializer=tf.random_normal_initializer())

    # Function g(z) zhere z is randomly generated
    g_z = dcgan.generator(z, is_training=True, name='generator')
    generator_visualisation = tf.cast(((g_z / 2.0) + 0.5) * 255.0, tf.uint8)
    sum_generator = tf.summary.image('summary/generator', generator_visualisation)

    img_summary = tf.summary.merge([sum_generator, zmar])
    with tf.name_scope('error'):
        error = M_placeholder - g_z
        # We set axis = None because norm(tensor, ord=ord) is equivalent to norm(reshape(tensor, [-1]), ord=ord)
        error_norm = tf.norm(error, ord=2, axis=None, keep_dims=False, name='L2Norm')
        summary_error = tf.summary.scalar('error_norm', error_norm)

    with tf.name_scope('Optimizing'):
        optimizer = tf.train.AdamOptimizer(0.001).minimize(error_norm, var_list=z)

    sess.run(tf.global_variables_initializer())
    for i in range(10000):
        (_, s) = sess.run((optimizer, summary_error))
        logwriter.add_summary(s, i)
        print('step %d: Patiente un peu poto!' % i)
        img = sess.run(img_summary)
        logwriter.add_summary(img, i)

    print('Done Training')

This script runs, but I have checked on tensorboard, the generator that is used here does not have the trained weights and it only produces noise.

I think I am trying to run a session in a graph that uses another graph and its trained session. I have read thoroughly the Graphs and Session documentation on tensorflow website https://www.tensorflow.org/versions/r1.3/programmers_guide/graphs, I have found an interesting tf.import_graph_def function :

  1. You can rebind tensors in the imported graph to tf.Tensor objects in the default graph by passing the optional input_map argument. For example, input_map enables you to take import a graph fragment defined in a tf.GraphDef, and statically connect tensors in the graph you are building to tf.placeholder tensors in that fragment.

    1. You can return tf.Tensor or tf.Operation objects from the imported graph by passing their names in the return_elements list.

But I don't know how to use this function, no example is given, and also I only found those two links that may help me : https://github.com/tensorflow/tensorflow/issues/7508 Tensorflow: How to use a trained model in a application?

It would be really nice to have your help on this topic. This should be straightforward for someone who has already used the tf.import_graph_def function... What I really need is to get the trained generator to apply it to a new variable z which is to be trained in another session.

Thanks

houssamzenati
  • 35
  • 6

0 Answers0