import tensorflow as tf

    img = np.expand_dims(np.asarray(img), axis = 0)
   
    # Create a placeholder for the input image
    input_node = tf.placeholder(tf.float32, shape=(None, height, width, channels))
    
    # Construct the network
    net = models.ResNet50UpProj({'data': input_node}, batch_size)
        
    with tf.Session() as sess:

        # Load the converted parameters
        print('Loading the model')
        net.load(model_data_path, sess)      
        
        uninitialized_vars = []
        for var in tf.global_variables():
