tf how to restore two variables from the same variable - tensorflow

Question

I have saved a model and now I am trying to restore it in two branches, like this:
I wrote this code, and it raises ValueError: The same saveable will be restored with two names.
How do I restore two variables from the same variable?
restore_variables = {}
for varr in tf.global_variables()
if varr.op.name in checkpoint_variables:
restore_variables[varr.op.name.split("_red")[0]] = varr
restore_variables[varr.op.name.split("_blue")[0]] = varr
init_saver = tf.train.Saver(restore_variables, max_to_keep=0)

Answer 1

Tested on TF 1.15
Basically the error is saying that it's finding multiple references to the same variable in the restore_variables dict. The fix is simple. Create a copy of your variable using tf.Variable(varr) as follows for one of the references.
I think it's safe to assume that you're not looking for multiple references to the same variable here, rather two separate variables. (I'm assuming this because, if you want to use the same variable multiple times, you can just use the single variable multiple times).
with tf.Session() as sess:
saver.restore(sess, './vars/vars.ckpt-0')
restore_variables = {}
checkpoint_variables=['b']
for varr in tf.global_variables():
if varr.op.name in checkpoint_variables:
restore_variables[varr.op.name.split("_red")[0]] = varr
restore_variables[varr.op.name.split("_blue")[0]] = tf.Variable(varr)
print(restore_variables)
init_saver = tf.train.Saver(restore_variables, max_to_keep=0)
Below you can find the full code to replicate the issue using a toy example. Essentially, we have two variables a and b and out of that, we are creating b_red and b_blue variables.
# Saving the variables
import tensorflow as tf
import numpy as np
a = tf.placeholder(shape=[None, 3], dtype=tf.float64)
w1 = tf.Variable(np.random.normal(size=[3,2]), name='a')
out = tf.matmul(a, w1)
w2 = tf.Variable(np.random.normal(size=[2,3]), name='b')
out = tf.matmul(out, w2)
saver = tf.train.Saver([w1, w2])
with tf.Session() as sess:
tf.global_variables_initializer().run()
saved_path = saver.save(sess, './vars/vars.ckpt', global_step=0)
# Restoring the variables
with tf.Session() as sess:
saver.restore(sess, './vars/vars.ckpt-0')
restore_variables = {}
checkpoint_variables=['b']
for varr in tf.global_variables():
if varr.op.name in checkpoint_variables:
restore_variables[varr.op.name+"_red"] = varr
# Fixing the issue: Instead of varr, do tf.Variable(varr)
restore_variables[varr.op.name+"_blue"] = varr
print(restore_variables)
init_saver = tf.train.Saver(restore_variables, max_to_keep=0)

Answer 2

I may not be understanding the problem correctly, but can't you just make two saver objects? Something like this:
import tensorflow as tf
# Make checkpoint
with tf.Graph().as_default(), tf.Session() as sess:
a = tf.Variable([1., 2.], name='a')
sess.run(a.initializer)
b = tf.Variable([3., 4., 5.], name='b')
sess.run(b.initializer)
saver = tf.train.Saver([a, b])
saver.save(sess, 'tmp/vars.ckpt')
# Restore checkpoint
with tf.Graph().as_default(), tf.Session() as sess:
# Red
a_red = tf.Variable([0., 0.], name='a_red')
b_red = tf.Variable([0., 0., 0.], name='b_red')
saver_red = tf.train.Saver({'a': a_red, 'b': b_red})
saver_red.restore(sess, 'tmp1/vars.ckpt')
print(a_red.eval())
# [1. 2.]
print(b_red.eval())
# [3. 4. 5.]
# Blue
a_blue = tf.Variable([0., 0.], name='a_blue')
b_blue = tf.Variable([0., 0., 0.], name='b_blue')
saver_blue = tf.train.Saver({'a': a_blue, 'b': b_blue})
saver_blue.restore(sess, 'tmp/vars.ckpt')
print(a_blue.eval())
# [1. 2.]
print(b_blue.eval())
# [3. 4. 5.]