Tensorflow inception_v2_resnet inference

Question

With reference to this post:

Using pre-trained inception_resnet_v2 with Tensorflow

i am trying to use the inception_resnet_v2 model to get predictions of images also. Therefore i looked at the snippet and tried to get it running, but it says "input_tensor" is not defined. Is there anything missing in the code mentioned or can anyone get me some hint to get it running / how to define the input_tensor variable?

Here is the snippet again:

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 = ['dog.jpg', 'panda.jpg']
#Load the model
sess = tf.Session()
arg_scope = inception_resnet_v2_arg_scope()
with slim.arg_scope(arg_scope):
  logits, end_points = inception_resnet_v2(input_tensor, is_training=False)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
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))

Thanks

Answer 1

The code snippet appears to lack any definition for input_tensor. Looking at the definition of the inception_resnet_v2() function, the fact that the tensor is used in a feed_dict, and the fact that the size of your image is 299 x 299, you could define input_tensor as follows:

input_tensor = tf.placeholder(tf.float32, [None, 299, 299, 3])