Tensorflow: restoring a graph and model then running evaluation on a single image

Question

I think it would be immensely helpful to the Tensorflow community if there was a well-documented solution to the crucial task of testing a single new image against the model

Answer 1

Here's how I ran a single image at a time. I'll admit it seems a bit hacky with the reuse of getting the scope.

This is a helper function

def restore_vars(saver, sess, chkpt_dir):
    """ Restore saved net, global score and step, and epsilons OR
    create checkpoint directory for later storage. """
    sess.run(tf.initialize_all_variables())

    checkpoint_dir = chkpt_dir

    if not os.path.exists(checkpoint_dir):
        try:
            os.makedirs(checkpoint_dir)
        except OSError:
            pass

    path = tf.train.get_checkpoint_state(checkpoint_dir)
    #print("path1 = ",path)
    #path = tf.train.latest_checkpoint(checkpoint_dir)
    print(checkpoint_dir,"path = ",path)
    if path is None:
        return False
    else:
        saver.restore(sess, path.model_checkpoint_path)
        return True

Here is the main part of the code that runs a single image at a time within the for loop.

to_restore = True
with tf.Session() as sess:

    for i in test_img_idx_set:

            # Gets the image
            images = get_image(i)
            images = np.asarray(images,dtype=np.float32)
            images = tf.convert_to_tensor(images/255.0)
            # resize image to whatever you're model takes in
            images = tf.image.resize_images(images,256,256)
            images = tf.reshape(images,(1,256,256,3))
            images = tf.cast(images, tf.float32)

            saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=1)

            #print("infer")
            with tf.variable_scope(tf.get_variable_scope()) as scope:
                if to_restore:
                    logits = inference(images)
                else:
                    scope.reuse_variables()
                    logits = inference(images)


            if to_restore:
                restored = restore_vars(saver, sess,FLAGS.train_dir)
                print("restored ",restored)
                to_restore = False

            logit_val = sess.run(logits)
            print(logit_val)

Here is an alternative implementation to the above using place holders it's a bit cleaner in my opinion. but I'll leave the above example for historical reasons.

imgs_place = tf.placeholder(tf.float32, shape=[my_img_shape_put_here])
images = tf.reshape(imgs_place,(1,256,256,3))

saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=1)

#print("infer")
logits = inference(images)

restored = restore_vars(saver, sess,FLAGS.train_dir)
print("restored ",restored)

with tf.Session() as sess:
    for i in test_img_idx_set:
        logit_val = sess.run(logits,feed_dict={imgs_place=i})
        print(logit_val)