inference.py 文件源码

def inference(reader, checkpoint_file, train_dir, data_pattern, out_file_location, batch_size, top_k):
  with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess, gfile.Open(out_file_location, "w+") as out_file:
    video_id_batch, video_batch, num_frames_batch = get_input_data_tensors(reader, data_pattern, batch_size)
    if checkpoint_file:
      if not gfile.Exists(checkpoint_file + ".meta"):
        logging.fatal("Unable to find checkpoint file at provided location '%s'" % checkpoint_file)
      latest_checkpoint = checkpoint_file
    else:
      latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    if latest_checkpoint is None:
      raise Exception("unable to find a checkpoint at location: %s" % train_dir)
    else:
      meta_graph_location = latest_checkpoint + ".meta"
      logging.info("loading meta-graph: " + meta_graph_location)
    saver = tf.train.import_meta_graph(meta_graph_location, clear_devices=True)
    logging.info("restoring variables from " + latest_checkpoint)
    saver.restore(sess, latest_checkpoint)
    input_tensor = tf.get_collection("input_batch_raw")[0]
    num_frames_tensor = tf.get_collection("num_frames")[0]
    predictions_tensor = tf.get_collection("predictions")[0]

    # Workaround for num_epochs issue.
    def set_up_init_ops(variables):
      init_op_list = []
      for variable in list(variables):
        if "train_input" in variable.name:
          init_op_list.append(tf.assign(variable, 1))
          variables.remove(variable)
      init_op_list.append(tf.variables_initializer(variables))
      return init_op_list

    sess.run(set_up_init_ops(tf.get_collection_ref(
        tf.GraphKeys.LOCAL_VARIABLES)))

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    num_examples_processed = 0
    start_time = time.time()
    out_file.write("VideoId,LabelConfidencePairs\n")

    try:
      while not coord.should_stop():
          video_id_batch_val, video_batch_val,num_frames_batch_val = sess.run([video_id_batch, video_batch, num_frames_batch])
          predictions_val, = sess.run([predictions_tensor], feed_dict={input_tensor: video_batch_val, num_frames_tensor: num_frames_batch_val})
          now = time.time()
          num_examples_processed += len(video_batch_val)
          num_classes = predictions_val.shape[1]
          logging.info("num examples processed: " + str(num_examples_processed) + " elapsed seconds: " + "{0:.2f}".format(now-start_time))
          for line in format_lines(video_id_batch_val, predictions_val, top_k):
            out_file.write(line)
          out_file.flush()


    except tf.errors.OutOfRangeError:
        logging.info('Done with inference. The output file was written to ' + out_file_location)
    finally:
        coord.request_stop()

    coord.join(threads)
    sess.close()