Tensorflow: How To Swap Variables Between Scopes And Set Variables In Scope From Another

I have different scopes and they have variables with same names but with different values. I want to swap values of these variables between scopes. Example: with tf.variable_scope

Solution 1:

I want to swap relatively small scopes, so it's not a problem have temporary scope for swapping. I made working prototype. It doesn't look cool and actually ugly but works.

defswap_tf_collections(col1, col2, tmp_col):
    col2_dict = {}
    for i in xrange(len(col1)):
        curr_var_name = col2[i].name.split('/')[-1]
        col2_dict[curr_var_name] = col2[i]

    col1_dict = {}
    for i in xrange(len(col1)):
        curr_var_name = col1[i].name.split('/')[-1]
        col1_dict[curr_var_name] = col1[i]

    # set values from second scope to tmp_dict 
    tmp_dict = {}
    assigns_arr = []
    for i in xrange(len(tmp_col)):
        curr_var_name = tmp_col[i].name.split('/')[-1]
        tmp_dict[curr_var_name] = tmp_col[i]
        assign0 = tmp_dict[curr_var_name].assign(col2_dict[curr_var_name])
        assigns_arr.append(assign0)

    for i in xrange(len(col2)):
        curr_var_name = col2[i].name.split('/')[-1]
        curr_col1_var = col1_dict[curr_var_name]
        tmp_t = tmp_dict[curr_var_name]
        with tf.control_dependencies(assigns_arr):
            assign1 = col2[i].assign(curr_col1_var)
            assigns_arr.append(assign1)
            with tf.control_dependencies(assigns_arr):
                assign2 = curr_col1_var.assign(tmp_t)
                assigns_arr.append(assign2)
    return assigns_arr



# first scopewith tf.variable_scope('sc1'):
    a1 = tf.get_variable(name='test_var1', initializer=0.)
    b1 = tf.Variable(0, name='test_var2')
# second scope    with tf.variable_scope('sc2'):
    a2 = tf.get_variable(name='test_var1', initializer=1.)
    b2 = tf.Variable(1, name='test_var2')

# getting them as collections
col1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='sc1')
col2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='sc2')

# creating temporary scope. It MUST have same variables names as our scopes but it doesn't have to have same data as second scopewith tf.variable_scope('tmp_scope_for_scopes_swap'):
    for i in xrange(len(col2)):
        col2_var = col2[i]
        col2_var_name = col2[i].name.split('/')[-1].split(':')[0]
        var = tf.Variable(col2_var.initialized_value(), name=col2_var_name)
        tmp_col.append(var)

# exec
sess = tf.Session()
with sess.as_default():
    sess.run(tf.initialize_all_variables())
    tf_ops_t = swap_tf_collections(col1, col2, tmp_col) 
    sess.run(tf_ops_t)  # swap will not work without this line

col1_dict = {i.name:i for i in col1}
col2_dict = {i.name:i for i in col2}
print sess.run(col1_dict)
print sess.run(col2_dict)

Note that I use dependency control! Without it function results will be undefined.

Also despite that function name is swap_tf_collections I think that it will not work for arbitrary collections (to be fair I doubt even about scopes).

Solution 2:

import tensorflow as tf
import numpy as np

with tf.variable_scope('sc1'):
    a1 = tf.get_variable(name='test_var1', initializer=0.)
    b1 = tf.Variable(0, name='test_var2')

with tf.variable_scope('sc2'):
    a2 = tf.get_variable(name='test_var1', initializer=1.)
    b2 = tf.Variable(1, name='test_var2')


defswap_tf_scopes(col1, col2):
    col1_dict = {}
    for curr_var in col1:
        curr_var_name = curr_var.name.split('/')[-1]
        col1_dict[curr_var_name] = curr_var
    for curr_var in col2:
        curr_var_name = curr_var.name.split('/')[-1]
        curr_col1_var = col1_dict[curr_var_name]
        tmp_t =tf.Variable(curr_col1_var.initialized_value()) 
        sess.run(tmp_t.initializer)
        sess.run(tf.assign(curr_col1_var,curr_var))
        sess.run(tf.assign(curr_var,tmp_t))

col1 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='sc1')
col2 = tf.get_collection(tf.GraphKeys.VARIABLES, scope='sc2')
sess = tf.Session()
sess.run(tf.initialize_all_variables())
swap_tf_scopes(col1, col2)
print(sess.run(col1)) 
print(sess.run(col2))

Hello！Try this one. I guess it will work.