Object detection net(eg:faster_rcnn_resnet101) not worked with deconv_visualization

Object detection net(eg:faster_rcnn_resnet101) not worked with deconv_visualization. With activation_visualization works well. Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-9aba9f0fced7> in <module>()
      3                                      layers=layers,
      4                                      path_logdir=os.path.join("Log","Inception5"),
----> 5                                      path_outdir=os.path.join("Output","Inception5"))

/notebooks/workspace/github/tf_cnnvis/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 

/notebooks/workspace/github/tf_cnnvis/tf_cnnvis/tf_cnnvis.py in _get_visualization(sess_graph_path, value_feed_dict, input_tensor, layers, path_logdir, path_outdir, method)
    167                 elif layer != None and layer.lower() in dict_layer.keys():
    168                     layer_type = dict_layer[layer.lower()]
--> 169                     is_success = _visualization_by_layer_type(g, value_feed_dict, input_tensor, layer_type, method, path_logdir, path_outdir)
    170                 else:
    171                     print("Skipping %s . %s is not valid layer name or layer type" % (layer, layer))

/notebooks/workspace/github/tf_cnnvis/tf_cnnvis/tf_cnnvis.py in _visualization_by_layer_type(graph, value_feed_dict, input_tensor, layer_type, method, path_logdir, path_outdir)
    225 
    226     for layer in layers:
--> 227         is_success = _visualization_by_layer_name(graph, value_feed_dict, input_tensor, layer, method, path_logdir, path_outdir)
    228     return is_success
    229 

/notebooks/workspace/github/tf_cnnvis/tf_cnnvis/tf_cnnvis.py in _visualization_by_layer_name(graph, value_feed_dict, input_tensor, layer_name, method, path_logdir, path_outdir)
    289         elif method == "deconv":
    290             # deconvolution
--> 291             results = _deconvolution(graph, sess, op_tensor, X, feed_dict)
    292         elif method == "deepdream":
    293             # deepdream

/notebooks/workspace/github/tf_cnnvis/tf_cnnvis/tf_cnnvis.py in _deconvolution(graph, sess, op_tensor, X, feed_dict)
    335                         c += 1
    336                 if c > 0:
--> 337                     out.extend(sess.run(reconstruct[:c], feed_dict = feed_dict))
    338     return out
    339 def _deepdream(graph, sess, op_tensor, X, feed_dict, layer, path_outdir, path_logdir):

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    898     try:
    899       result = self._run(None, fetches, feed_dict, options_ptr,
--> 900                          run_metadata_ptr)
    901       if run_metadata:
    902         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1118     # Create a fetch handler to take care of the structure of fetches.
   1119     fetch_handler = _FetchHandler(
-> 1120         self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
   1121 
   1122     # Run request and get response.

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in __init__(self, graph, fetches, feeds, feed_handles)
    425     """
    426     with graph.as_default():
--> 427       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    428     self._fetches = []
    429     self._targets = []

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in for_fetch(fetch)
    243     elif isinstance(fetch, (list, tuple)):
    244       # NOTE(touts): This is also the code path for namedtuples.
--> 245       return _ListFetchMapper(fetch)
    246     elif isinstance(fetch, dict):
    247       return _DictFetchMapper(fetch)

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in __init__(self, fetches)
    350     """
    351     self._fetch_type = type(fetches)
--> 352     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
    353     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
    354 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in <listcomp>(.0)
    350     """
    351     self._fetch_type = type(fetches)
--> 352     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
    353     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
    354 

/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in for_fetch(fetch)
    240     if fetch is None:
    241       raise TypeError('Fetch argument %r has invalid type %r' % (fetch,
--> 242                                                                  type(fetch)))
    243     elif isinstance(fetch, (list, tuple)):
    244       # NOTE(touts): This is also the code path for namedtuples.

TypeError: Fetch argument None has invalid type <class 'NoneType'>

Addition:

Variable reconstruct (defined at line 327 in tf_cnnvis.py ) is [None, None, None, None, None, None, None, None]. TensorFlow version is 1.9. When I use TensorFlow 1.4, I got same Error as #26

[wildpig22]

@aggpankaj2 which model are you using, can you provide a link?

Also, what is the error?

[aggpankaj2]

@wildpig22 thanks for your code, but not working.