train.py 文件中没有名为“tensorflow.contrib”的模块

Question

出于 object 检测的目的运行 train.py 文件时出现错误。 错误是：

错误

Traceback (most recent call last):
  File "train.py", line 48, in <module>
    from tensorflow.contrib import framework as contrib_framework
ModuleNotFoundError: No module named 'tensorflow.contrib'

笔记

我搜索了错误，发现 Tensorflow version-2 doesn't support 'tensorflow.contrib' (This is only supported in version-1)。 我试图通过安装版本 1 来解决问题，但它根本不起作用。 没有合适的解决办法。 任何人都可以建议逐步解决方案，例如代码中要更改的位置和内容吗？

火车.py

 # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    
    r"""Training executable for detection models.
    
    This executable is used to train DetectionModels. There are two ways of
    configuring the training job:
    
    1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file
    can be specified by --pipeline_config_path.
    
    Example usage:
        ./train \
            --logtostderr \
            --train_dir=path/to/train_dir \
            --pipeline_config_path=pipeline_config.pbtxt
    
    2) Three configuration files can be provided: a model_pb2.DetectionModel
    configuration file to define what type of DetectionModel is being trained, an
    input_reader_pb2.InputReader file to specify what training data will be used and
    a train_pb2.TrainConfig file to configure training parameters.
    
    Example usage:
        ./train \
            --logtostderr \
            --train_dir=path/to/train_dir \
            --model_config_path=model_config.pbtxt \
            --train_config_path=train_config.pbtxt \
            --input_config_path=train_input_config.pbtxt
    """
    
    import functools
    import json
    import os
    import tensorflow as tf
    from tensorflow.contrib import framework as contrib_framework
    
    
    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
    
    
    @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()

Answer 1

抱歉， TensorFlow.contrib在tensorflow 2.x中不存在。您必须将 go 返回到 TensorFlow 1.15.0 或寻找非 contrib 替代方案来为您完成这项工作。

您还可以使用以下命令禁用tensorflow 2.x中的版本 2 行为：

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

最后，您可以 go 到 tensorflow 存储库中的插件来查找一些贡献模块