如何使用tf.contrib.graph_editor重新路由训练输入管道以测试tensorflow中的管道？

Question

Suppose now I have a training input pipeline which finally generate train_x and train_y using tf.train.shuffle_batch . I export meta graph and re-import the graph in another code file. Now I want to detach the input pipeline, ie, the train_x and train_y , and connect a new test_x and test_y . How can I make accomplish this using tf.contrib.graph_editor?

EDIT: As suggested by @iga, I change my input directory using input_map

filenames = tf.train.match_filenames_once(FLAGS.data_dir + '*', name='matching_filenames')
if FLAGS.ckpt != '':
    latest = FLAGS.log_dir + FLAGS.ckpt
else:
    latest = tf.train.latest_checkpoint(FLAGS.log_dir)
if not latest or not os.path.exists(latest+'.meta'):
    print("checkpoint " + latest + " does not exist")
    sys.exit(1)
saver = tf.train.import_meta_graph(latest+'.meta', 
                                   input_map={'matching_filenames:0':filenames},
                                   import_scope='import')
g = tf.get_default_graph() 

but I get the following error:

ValueError: graph_def is invalid at node u'matching_filenames/Assign': Input tensor 'matching_filenames:0' Cannot convert a tensor of type string to an input of type string_ref. Are there any elegant way to resolve this?

Answer 1

For this task, you should be able to just use input_map argument to https://www.tensorflow.org/api_docs/python/tf/import_graph_def . If you are using import_meta_graph , you can pass the input_map into its kwargs and it will get passed down to import_graph_def.

RESPONSE TO EDIT : I am assuming that your original graph (the one you are deserializing) had the same matching_filenames variable. Quite confusingly, the tensor name "matching_filenames:0" actually refers to the tensor going from the VariableV2 op to the Assign op. The type of this edge is string_ref and you don't really want to break that edge.

The output from a variable typically goes through an identity op called matching_filenames/read . This is what you want to use as the key in your input_map. For the value, you want the same tensor in your new filenames . So, your call should probably look like:

tf.train.import_meta_graph(latest+'.meta', 
                           input_map={'matching_filenames/read': filenames.read_value()},
                           import_scope='import')

In general, variables are fairly complicated. If this does not work, you can use some placeholder op and feed the names into it manually.