No variables to save deconv_visualization

Hey,

First of all, great job on this library. I tried to implement this myself, but this repository saved me a lot of time.

I am trying to use all visualizations functions, but I seem to only get the activation_visualization working to work. The code i use for my activation visualization (which works properly):

with detection_graph.as_default():
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        image = Image.open("0064.jpg")
        image_np = load_image_into_numpy_array(image)
        image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
        tf_cnnvis.activation_visualization(sess_graph_path = None, value_feed_dict={image_tensor:np.expand_dims(image_np, 0)} , layers=layers , path_logdir='LogDiff', path_outdir='OutputDiff')
        sess.close()

Now when using this same code for deconv_visualization:

     with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        image = Image.open("0064.jpg")
        image_np = load_image_into_numpy_array(image)
        image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
        tf_cnnvis.deconv_visualization(sess_graph_path = None, value_feed_dict={image_tensor:np.expand_dims(image_np, 0)} , layers=layers , path_logdir='LogDeconv', path_outdir='OutputDeconv')
        sess.close()

I receive the following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-ac0615204cc9> in <module>()
      5         image_np = load_image_into_numpy_array(image)
      6         image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
----> 7         tf_cnnvis.deconv_visualization(sess_graph_path = None, value_feed_dict={image_tensor:np.expand_dims(image_np, 0)} , layers=layers , path_logdir='LogDeconv', path_outdir='OutputDeconv')
      8         sess.close()

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in deconv_visualization(sess_graph_path, value_feed_dict, input_tensor, layers, path_logdir, path_outdir)
    408 def deconv_visualization(sess_graph_path, value_feed_dict, input_tensor = None,  layers = 'r', path_logdir = './Log', path_outdir = "./Output"):
    409     is_success = _get_visualization(sess_graph_path, value_feed_dict, input_tensor = input_tensor, layers = layers, method = "deconv",
--> 410         path_logdir = path_logdir, path_outdir = path_outdir)
    411     return is_success
    412 

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _get_visualization(sess_graph_path, value_feed_dict, input_tensor, layers, path_logdir, path_outdir, method)
    137         # None input defaults to the default session if available, to the default graoh otherwise.
    138         if isinstance(tf.get_default_session(), tf.Session):
--> 139             PATH = _save_model(tf.get_default_session())
    140         else:
    141             PATH = _save_model(tf.get_default_graph())

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _save_model(graph_or_sess)
     75     PATH = os.path.join("model", "tmp-model")
     76     make_dir(path = os.path.dirname(PATH))
---> 77     saver = tf.train.Saver()
     78     #i should deal with the case in which sess is closed.
     79     saver.save(sess, PATH)

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\saver.py in __init__(self, var_list, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, saver_def, builder, defer_build, allow_empty, write_version, pad_step_number, save_relative_paths, filename)
   1237     self._filename = filename
   1238     if not defer_build and context.in_graph_mode():
-> 1239       self.build()
   1240     if self.saver_def:
   1241       self._check_saver_def()

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\saver.py in build(self)
   1246     if context.in_eager_mode():
   1247       raise RuntimeError("Use save/restore instead of build in eager mode.")
-> 1248     self._build(self._filename, build_save=True, build_restore=True)
   1249 
   1250   def _build_eager(self, checkpoint_path, build_save, build_restore):

c:\users\berger\appdata\local\continuum\anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\saver.py in _build(self, checkpoint_path, build_save, build_restore)
   1270           return
   1271         else:
-> 1272           raise ValueError("No variables to save")
   1273       self._is_empty = False
   1274 

ValueError: No variables to save

In your example you pass both functions the same arguments, so is this the right approach or should I change some of my argument in the deconv_visualization? More general, can you help me fixing this error?

Thanks in advance!

Kind regards,

Roy

infocusp / tf_cnnvis

No variables to save deconv_visualization #75