Creating a Slim classifier using pretrained ResNet V2 model - tensorflow

Question

I am trying to create an image classifier that utilizes the pre-trained ResNet V2 model provided in the slim documentation.
Here is the code so far:
import tensorflow as tf
slim = tf.contrib.slim
from PIL import Image
from inception_resnet_v2 import *
import numpy as np
checkpoint_file = 'inception_resnet_v2_2016_08_30.ckpt'
sample_images = ['carrot.jpg']
input_tensor = tf.placeholder(tf.float32, shape=(None,299,299,3), name='input_image')
scaled_input_tensor = tf.scalar_mul((1.0/255), input_tensor)
scaled_input_tensor = tf.subtract(scaled_input_tensor, 0.5)
scaled_input_tensor = tf.multiply(scaled_input_tensor, 2.0)
variables_to_restore = slim.get_model_variables()
print(variables_to_restore)
init_fn = slim.assign_from_checkpoint_fn(
checkpoint_file,
slim.get_model_variables('InceptionResnetV2'))
sess = tf.Session()
init_fn(sess)
arg_scope = inception_resnet_v2_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
for image in sample_images:
im = Image.open(image).resize((299,299))
im = np.array(im)
im = im.reshape(-1,299,299,3)
predict_values, logit_values = sess.run([end_points['Predictions'], logits], feed_dict={input_tensor: im})
print (np.max(predict_values), np.max(logit_values))
print (np.argmax(predict_values), np.argmax(logit_values))
The problem is I keep getting this error:
Traceback (most recent call last):
File "./classify.py", line 21, in <module>
slim.get_model_variables('InceptionResnetV2'))
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 584, in assign_from_checkpoint_fn
saver = tf_saver.Saver(var_list, reshape=reshape_variables)
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1040, in __init__
self.build()
File "/home/ubuntu/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1061, in build
raise ValueError("No variables to save")
ValueError: No variables to save
So it seems TF/Slim is unable to find any variables and this is made clear when I call:
variables_to_restore = slim.get_model_variables()
print(variables_to_restore)
As it outputs an empty array.
How can I go about using the pre-trained model?

Answer 1

This happens because you haven't constructed the model in your graph yet to have any variables starting with the name "InceptionResnetV2" to be captured and restored by the saver.
I believe you should put the model construction before using slim.get_variables_to_restore().
For instance:
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
variables_to_restore = slim.get_model_variables()
This way, the Tensor variables will be constructed and you should see variables_to_restore is no longer empty.

Answer 2

You need to manually add the model variables.
Try this
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(scaled_input_tensor, is_training=False)
# Add model variables
for var in tf.global_variables(scope='inception_resnet_v2'):
slim.add_model_variable(var)