title: Tensorflow Object Detection Api TypeError: Expected binary or unicode string, got None
body: <p>Then i try to train model with </p>
  
  <pre><code>  python3 model_main.py —logtostderr —train_dir=training/ —pipelnie_config_path=training/ssd_mobilenet_v1_pets.config
  </code></pre>
  
  <p>i got following error. All configurations are setted up. Firstly i tried it on Mac and it's worked. But the training process took so long on cpu i decided to go on cloud computing with GPU(paperspace). I did everything exact the same and got this error. All files are presented. What could i did wrong? It's seems something wrong with configuration file</p>
  
  <pre><code>Traceback (most recent call last):
  File "model_main.py", line 109, in &lt;module&gt;
  tf.app.run()
  File "/home/paperspace/.local/lib/python3.6/site- 
  packages/tensorflow/python/platform/app.py", line 125, in run
  _sys.exit(main(argv))
  File "model_main.py", line 71, in main
  FLAGS.sample_1_of_n_eval_on_train_examples))
  File "/home/paperspace/Desktop/models/research/object_detection/model_lib.py", line 589, in create_estimator_and_inputs
  pipeline_config_path, config_override=config_override)
  File "/home/paperspace/Desktop/models/research/object_detection/utils/config_util.py", line 97, in get_configs_from_pipeline_file
  proto_str = f.read()
  File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py", line 125, in read
  self._preread_check()
  File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/lib/io/file_io.py", line 85, in _preread_check
  compat.as_bytes(self.__name), 1024 * 512, status)
  File "/home/paperspace/.local/lib/python3.6/site-packages/tensorflow/python/util/compat.py", line 61, in as_bytes
  (bytes_or_text,))
  TypeError: Expected binary or unicode string, got None
  </code></pre>
  
  <p>Config file:</p>
  
  <pre><code>model {
    ssd {
      num_classes: 1
      box_coder {
        faster_rcnn_box_coder {
          y_scale: 10.0
          x_scale: 10.0
          height_scale: 5.0
          width_scale: 5.0
        }
      }
      matcher {
        argmax_matcher {
          matched_threshold: 0.5
          unmatched_threshold: 0.5
          ignore_thresholds: false
          negatives_lower_than_unmatched: true
          force_match_for_each_row: true
        }
      }
      similarity_calculator {
        iou_similarity {
        }
      }
      anchor_generator {
        ssd_anchor_generator {
          num_layers: 6
          min_scale: 0.2
          max_scale: 0.95
          aspect_ratios: 1.0
          aspect_ratios: 2.0
          aspect_ratios: 0.5
          aspect_ratios: 3.0
          aspect_ratios: 0.3333
        }
      }
      image_resizer {
        fixed_shape_resizer {
          height: 300
          width: 300
        }
      }
      box_predictor {
        convolutional_box_predictor {
          min_depth: 0
          max_depth: 0
          num_layers_before_predictor: 0
          use_dropout: false
          dropout_keep_probability: 0.8
          kernel_size: 1
          box_code_size: 4
          apply_sigmoid_to_scores: false
          conv_hyperparams {
            activation: RELU_6,
            regularizer {
              l2_regularizer {
                weight: 0.00004
              }
            }
            initializer {
              truncated_normal_initializer {
                stddev: 0.03
                mean: 0.0
              }
            }
            batch_norm {
              train: true,
              scale: true,
              center: true,
              decay: 0.9997,
              epsilon: 0.001,
            }
          }
        }
      }
      feature_extractor {
        type: 'ssd_mobilenet_v1'
        min_depth: 16
        depth_multiplier: 1.0
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            truncated_normal_initializer {
              stddev: 0.03
              mean: 0.0
            }
          }
          batch_norm {
            train: true,
            scale: true,
            center: true,
            decay: 0.9997,
            epsilon: 0.001,
          }
        }
      }
      loss {
        classification_loss {
          weighted_sigmoid {
          }
        }
        localization_loss {
          weighted_smooth_l1 {
          }
        }
        hard_example_miner {
          num_hard_examples: 3000
          iou_threshold: 0.99
          loss_type: CLASSIFICATION
          max_negatives_per_positive: 3
          min_negatives_per_image: 0
        }
        classification_weight: 1.0
        localization_weight: 1.0
      }
      normalize_loss_by_num_matches: true
      post_processing {
        batch_non_max_suppression {
          score_threshold: 1e-8
          iou_threshold: 0.6
          max_detections_per_class: 100
          max_total_detections: 100
        }
        score_converter: SIGMOID
      }
    }
  }
  
  train_config: {
    batch_size: 24
    optimizer {
      rms_prop_optimizer: {
        learning_rate: {
          exponential_decay_learning_rate {
            initial_learning_rate: 0.004
            decay_steps: 800720
            decay_factor: 0.95
          }
        }
        momentum_optimizer_value: 0.9
        decay: 0.9
        epsilon: 1.0
      }
    }
    fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
    from_detection_checkpoint: true
    load_all_detection_checkpoint_vars: true
    # Note: The below line limits the training process to 200K steps, which we
    # empirically found to be sufficient enough to train the pets dataset. This
    # effectively bypasses the learning rate schedule (the learning rate will
    # never decay). Remove the below line to train indefinitely.
    num_steps: 200000
    data_augmentation_options {
      random_horizontal_flip {
      }
    }
    data_augmentation_options {
      ssd_random_crop {
      }
    }
  }
  
  train_input_reader: {
    tf_record_input_reader {
      input_path: "data/train.record"
    }
    label_map_path: "data/label_map.pbtxt"
  }
  
  eval_config: {
    metrics_set: "coco_detection_metrics"
    num_examples: 1100
  }
  
  eval_input_reader: {
    tf_record_input_reader {
      input_path: "data/test.record"
    }
    label_map_path: "data/label_map.pbtxt"
    shuffle: false
    num_readers: 1
  }
  </code></pre>