testing.py

import numpy as np
import os
import tensorflow as tf
#import urllib2

#from datasets import imagenet
#from nets import inception
#from preprocessing import inception_preprocessing

from PIL import Image, ImageDraw, ImageFont
import imghdr
import numpy as np

import inception_preprocessing
from inception_resnet_v2 import inception_resnet_v2, inception_resnet_v2_arg_scope

#State your log directory where you can retrieve your model
log_dir = './log'
dataset_dir = './data/test/teeth/1'
#Get the latest checkpoint file
checkpoint_file = tf.train.latest_checkpoint(log_dir)

slim = tf.contrib.slim

batch_size = 3
image_size = 299

checkpoints_filename = checkpoint_file#'inception_resnet_v2_2016_08_30.ckpt'
model_name = 'InceptionResnetV2'
sess = tf.InteractiveSession()
graph = tf.Graph()
graph.as_default()

def main():

    for image_file in os.listdir(dataset_dir):
        try:
            image_type = imghdr.what(os.path.join(dataset_dir, image_file))
            if not image_type:
                continue
        except IsADirectoryError:
            continue
        filepath = os.path.join(dataset_dir, image_file) 
        print(filepath)
        imgPath = filepath#'./data/test/teeth/1/7005.jpg'    
        testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
        testImage = tf.image.decode_jpeg(testImage_string, channels=3)
        processed_image = inception_preprocessing.preprocess_image(testImage, image_size, image_size, is_training=False)
        processed_images = tf.expand_dims(processed_image, 0)

        with slim.arg_scope(inception_resnet_v2_arg_scope()):

            logits, _ = inception_resnet_v2(processed_images, num_classes=16, is_training=False)


            probabilities = tf.nn.softmax(logits)

            init_fn = slim.assign_from_checkpoint_fn(
                checkpoint_file,
                slim.get_model_variables(model_name))

            init_fn(sess)

            np_image, probabilities = sess.run([processed_images, probabilities])
            probabilities = probabilities[0, 0:]
            sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]
            #print(probabilities)
            print(probabilities.argmax(axis=0))

            #names = imagenet.create_readable_names_for_imagenet_labels()
            #for i in range(15):
            #    index = sorted_inds[i]
            #    print((probabilities[index], names[index]))


        
if __name__ == '__main__':
    main()        
        

Hey , were you able to solve the ValueError: When attempting to re-use a scope by suppling adictionary, kwargs must be empty.?
I am stuck at the same error and will really appreciate any help . Thanks!