python - 如何修复这个 "attribute error,:load_multiclass_scores"？

Question

    `i have a problem when i try to train the model(train.py)
    INPUT:
    python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config

代码: 导入函数工具 导入 json 导入操作系统 将 tensorflow 导入为 tf 导入系统 sys.path.append("C:\Users\Gilbertchristian\Documents\Anaconda\Object_detection_api\models\research") sys.path.append("C:\Users\Gilbertchristian\Documents\Anaconda\Object_detection_api\models\research\object_detection\utils") sys.path.append("C:\Users\Gilbertchristian\Documents\Anaconda\Object_detection_api\models\research\slim") sys.path.append("C:\Users\Gilbertchristian\Documents\Anaconda\Object_detection_api\models\research\slim\nets")

    from object_detection.builders import dataset_builder
    from object_detection.builders import graph_rewriter_builder
    from object_detection.builders import model_builder
    from object_detection.legacy import trainer
    from object_detection.utils import config_util

    tf.logging.set_verbosity(tf.logging.INFO)

    flags = tf.app.flags
    flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
    flags.DEFINE_integer('task', 0, 'task id')
    flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.')
    flags.DEFINE_boolean('clone_on_cpu', False,
                         'Force clones to be deployed on CPU.  Note that even if '
                         'set to False (allowing ops to run on gpu), some ops may '
                         'still be run on the CPU if they have no GPU kernel.')
    flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer '
                         'replicas.')
    flags.DEFINE_integer('ps_tasks', 0,
                         'Number of parameter server tasks. If None, does not use '
                         'a parameter server.')
    flags.DEFINE_string('train_dir', '',
                        'Directory to save the checkpoints and training summaries.')

    flags.DEFINE_string('pipeline_config_path', '',
                        'Path to a pipeline_pb2.TrainEvalPipelineConfig config '
                        'file. If provided, other configs are ignored')

    flags.DEFINE_string('train_config_path', '',
                        'Path to a train_pb2.TrainConfig config file.')
    flags.DEFINE_string('input_config_path', '',
                        'Path to an input_reader_pb2.InputReader config file.')
    flags.DEFINE_string('model_config_path', '',
                        'Path to a model_pb2.DetectionModel config file.')

    FLAGS = flags.FLAGS


    @tf.contrib.framework.deprecated(None, 'Use object_detection/model_main.py.')
    def main(_):
      assert FLAGS.train_dir, '`train_dir` is missing.'
      if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir)
      if FLAGS.pipeline_config_path:
        configs = config_util.get_configs_from_pipeline_file(
            FLAGS.pipeline_config_path)
        if FLAGS.task == 0:
          tf.gfile.Copy(FLAGS.pipeline_config_path,
                        os.path.join(FLAGS.train_dir, 'pipeline.config'),
                        overwrite=True)
      else:
        configs = config_util.get_configs_from_multiple_files(
            model_config_path=FLAGS.model_config_path,
            train_config_path=FLAGS.train_config_path,
            train_input_config_path=FLAGS.input_config_path)
        if FLAGS.task == 0:
          for name, config in [('model.config', FLAGS.model_config_path),
                               ('train.config', FLAGS.train_config_path),
                               ('input.config', FLAGS.input_config_path)]:
            tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),
                          overwrite=True)

      model_config = configs['model']
      train_config = configs['train_config']
      input_config = configs['train_input_config']

      model_fn = functools.partial(
          model_builder.build,
          model_config=model_config,
          is_training=True)

      def get_next(config):
        return dataset_builder.make_initializable_iterator(
            dataset_builder.build(config)).get_next()

      create_input_dict_fn = functools.partial(get_next, input_config)

      env = json.loads(os.environ.get('TF_CONFIG', '{}'))
      cluster_data = env.get('cluster', None)
      cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None
      task_data = env.get('task', None) or {'type': 'master', 'index': 0}
      task_info = type('TaskSpec', (object,), task_data)

      # Parameters for a single worker.
      ps_tasks = 0
      worker_replicas = 1
      worker_job_name = 'lonely_worker'
      task = 0
      is_chief = True
      master = ''

      if cluster_data and 'worker' in cluster_data:
        # Number of total worker replicas include "worker"s and the "master".
        worker_replicas = len(cluster_data['worker']) + 1
      if cluster_data and 'ps' in cluster_data:
        ps_tasks = len(cluster_data['ps'])
      if worker_replicas > 1 and ps_tasks < 1:
        raise ValueError('At least 1 ps task is needed for distributed training.')
      if worker_replicas >= 1 and ps_tasks > 0:
        # Set up distributed training.
        server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc',
                                 job_name=task_info.type,
                                 task_index=task_info.index)
        if task_info.type == 'ps':
          server.join()
          return

        worker_job_name = '%s/task:%d' % (task_info.type, task_info.index)
        task = task_info.index
        is_chief = (task_info.type == 'master')
        master = server.target

      graph_rewriter_fn = None
      if 'graph_rewriter_config' in configs:
        graph_rewriter_fn = graph_rewriter_builder.build(
            configs['graph_rewriter_config'], is_training=True)

      trainer.train(
          create_input_dict_fn,
          model_fn,
          train_config,
          master,
          task,
          FLAGS.num_clones,
          worker_replicas,
          FLAGS.clone_on_cpu,
          ps_tasks,
          worker_job_name,
          is_chief,
          FLAGS.train_dir,
          graph_hook_fn=graph_rewriter_fn()


    if __name__ == '__main__':
      tf.app.run()

输出: 文件“train.py”，第 191 行，位于 tf.app.run() 文件“C:\Users\Gilbertchristian\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\platform\app.py”，第 125 行，运行中 _sys.exit(主(argv)) 文件“C:\Users\Gilbertchristian\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\util\deprecation.py”，第 324 行，在 new_func 中 返回 func(*args, **kwargs) 文件“train.py”，第 187 行，在 main 中 graph_hook_fn=graph_rewriter_fn) 文件“C:\Users\Gilbertchristian\AppData\Local\Programs\Python\Python35\lib\site-packages\object_detection-0.1-py3.5.egg\object_detection\legacy\trainer.py”，第 280 行，训练中 train_config.prefetch_queue_capacity，data_augmentation_options) 文件“C:\Users\Gilbertchristian\AppData\Local\Programs\Python\Python35\lib\site-packages\object_detection-0.1-py3.5.egg\object_detection\legacy\trainer.py”，第 59 行，在 create_input_queue 中 张量字典 = create_tensor_dict_fn() 文件“train.py”，第 128 行，在 get_next 中 dataset_builder.build(config)).get_next() 文件“C:\Users\Gilbertchristian\AppData\Local\Programs\Python\Python35\lib\site-packages\object_detection-0.1-py3.5.egg\object_detection\builders\dataset_builder.py”，第 120 行，在构建中 load_multiclass_scores=input_reader_config.load_multiclass_scores, 属性错误:load_multiclass_scores

Answer 1

文件/tensorflow/models/research/object_detection/protos/input_reader_pb2.py是否包含name='load_multiclass_scores'如果没有，重新运行可能会有所帮助./bin/protoc object_detection/protos/*.proto --python_out=. (可能有不同的版本)