Styjun

I'm using tf.contrib.copy_graph.copy_op_to_graph() to copy an operation from g1 to g2.

Edited code:

BATCH_SIZE = 1, TIME_STEP = 2
def noise_rnn(self, BATCH_SIZE, TIME_STEP):
 with tf.variable_scope("noise_rnn", reuse=tf.AUTO_REUSE, initializer=tf.orthogonal_initializer()):
 gaussianNoiseRnnInputList=[]
 for batch in range(BATCH_SIZE):
 gaussianNoiseInputList=[]
 for i in range(TIME_STEP):
 gaussianNoiseInput = tf.truncated_normal(shape=[1, 1, 10], mean=0, stddev=tf.sqrt(0.6))
 gaussianNoiseInputList.append(gaussianNoiseInput)
 gaussianNoiseInput = tf.concat(gaussianNoiseInputList, axis=1, name='gaussianNoiseInput_concat')
 gaussianNoiseRnnInputList.append(gaussianNoiseInput)
 gaussianNoiseRnnInput = tf.concat(gaussianNoiseRnnInputList, axis=0, name='gaussianNoiseRnnInput_concat')
 cell = tf.nn.rnn_cell.GRUCell(10)
 hiddens, states = tf.nn.dynamic_rnn(cell=cell, inputs=gaussianNoiseRnnInput, dtype=tf.float32)
 return hiddens

with noiseGraph.as_default():
 gaussianRnnOutput = speech2vid.noise_rnn(BATCH_SIZE, TIME_STEP)
 BATCH_SIZE_copy = tf.contrib.copy_graph.copy_variable_to_graph(BATCH_SIZE, g2)
 TIME_STEP_copy = tf.contrib.copy_graph.copy_variable_to_graph(TIME_STEP, g2)
 gaussianRnnOutput_copy = tf.contrib.copy_graph.copy_op_to_graph(gaussianRnnOutput, g2, [])

New error when copy_variable_to_graph(BATCH_SIZE, g2):

TypeError: 1(BATCH_SIZE) is not a Variable.

If I comment the following two lines:

BATCH_SIZE_copy = tf.contrib.copy_graph.copy_variable_to_graph(BATCH_SIZE, g2)
TIME_STEP_copy = tf.contrib.copy_graph.copy_variable_to_graph(TIME_STEP, g2)

I get another error:

......
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 172, in copy_op_to_graph
 new_op = copy_op_to_graph(op, to_graph, variables, scope)
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 172, in copy_op_to_graph
 new_op = copy_op_to_graph(op, to_graph, variables, scope)
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 172, in copy_op_to_graph
 new_op = copy_op_to_graph(op, to_graph, variables, scope)
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 172, in copy_op_to_graph
 new_op = copy_op_to_graph(op, to_graph, variables, scope)
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 172, in copy_op_to_graph
 new_op = copy_op_to_graph(op, to_graph, variables, scope)
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/contrib/copy_graph/python/util/copy_elements.py", line 200, in copy_op_to_graph
 for x in op.inputs]
 File "/media/data2/liuhan/envs/tf/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1879, in inputs
 return Operation._InputList(self)
RuntimeError: maximum recursion depth exceeded

Actually, I do not know how to use this function. Some one can explain the third param [] in the function for me? And how to solve this error?

Thank you!

I saw an example here. But I do not know the meaning of []?

EDIT: Edit code and errors.

By looking at the source code, it seems that the variables argument is used to retrieve already copied variables:

#Extract names of variables
copied_variables = dict((x.name, x) for x in variables)

#If a variable by the new name already exists, return the
#correspondng tensor that will act as an input
if new_name in copied_variables:
 return to_graph.get_tensor_by_name(copied_variables[new_name].name)

However the following lines retrieve copied variables (or ops/tensors) as well:

try:
 already_present = to_graph.as_graph_element(
 new_name, allow_tensor=True, allow_operation=True)
 return already_present
except:
 pass

So my guess is that you can pass an empty list as third argument.

The simplified code you presented is not enough to understand what's your problem is, but in general, copy_op_to_graph() takes operation or tensor (if it is a tensor, then it is an output of some unerlying operation) as input and copies it to a new graph. If it has variables as inputs you should copy those variables using copy_variable_to_graph() before you attempt to copy an op. Take a look at this example of copying two variables and addition operation to a new graph:

import tensorflow as tf

var1 = tf.Variable(2*tf.ones([2, 2]), name='var1')
var2 = tf.Variable(tf.ones([2, 2]), name='var2')
add_tensor = tf.add(var1, var2)

to_graph = tf.Graph() # graph where everything above will be copied to

var1_copied = tf.contrib.copy_graph.copy_variable_to_graph(var1, to_graph)
var2_copied = tf.contrib.copy_graph.copy_variable_to_graph(var2, to_graph)

add_tensor_copied = tf.contrib.copy_graph.copy_op_to_graph(add_tensor, to_graph, [])

with tf.Session(graph=tf.get_default_graph()) as sess:
 sess.run(tf.global_variables_initializer())
 print(add_tensor.eval())
 # [[3. 3.]
 # [3. 3.]]

with tf.Session(graph=to_graph) as sess:
 sess.run(tf.global_variables_initializer())
 print(add_tensor_copied.eval())
 # [[3. 3.]
 # [3. 3.]]