tensorflow.python.framework.errors_impl.NotFoundError：ディレクトリの作成に失敗しました：training / export \ Servo \ temp-b'1576742954 '

Question

私はanaconda（python3.6）、tensorflow 1.12.0を使用して、Windows 10で オブジェクト検出 を学習します。

このコマンドを使用してトレーニングしました。

cd E：\ test\models-master\research\object_detection

python model_main.py --pipeline_config_path = training/ssd_mobilenet_v1_coco.config --model_dir = training/--num_train_steps = 10000

モデルがトレーニングを終了するとき、そのような間違いがありました：

......
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.024
Traceback (most recent call last):
File "model_main.py", line 109, in 
tf.app.run()
File "E:\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "model_main.py", line 105, in main
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 471, in train_and_evaluate
return executor.run()
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 610, in run
return self.run_local()
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 711, in run_local
saving_listeners=saving_listeners)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 354, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1207, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1241, in _train_model_default
saving_listeners)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1471, in _train_with_estimator_spec
_, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
File "E:\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 783, in exit
self._close_internal(exception_type)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 816, in _close_internal
h.end(self._coordinated_creator.tf_sess)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\training\basic_session_run_hooks.py", line 590, in end
l.end(session, last_step)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 531, in end
self._evaluate(global_step_value)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 537, in _evaluate
self._evaluator.evaluate_and_export())
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 924, in evaluate_and_export
is_the_final_export)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\training.py", line 957, in _export_eval_result
is_the_final_export=is_the_final_export))
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\exporter.py", line 418, in export
is_the_final_export)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\exporter.py", line 126, in export
strip_default_attrs=self._strip_default_attrs)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 663, in export_savedmodel
mode=model_fn_lib.ModeKeys.PREDICT)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 789, in _export_saved_model_for_mode
strip_default_attrs=strip_default_attrs)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\estimator\estimator.py", line 883, in _export_all_saved_models
builder = saved_model_builder.SavedModelBuilder(temp_export_dir)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\saved_model\builder_impl.py", line 97, in init
file_io.recursive_create_dir(self._export_dir)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 379, in recursive_create_dir
pywrap_tensorflow.RecursivelyCreateDir(compat.as_bytes(dirname), status)
File "E:\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.NotFoundError: Failed to create a directory: training/export\Servo\temp-b'1576742954'; No such file or directory

Ssd_mobilenet_v1_coco.configのコンテンツ：

# SSD with Mobilenet v1 configuration for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    num_classes: 2
    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: 10
  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: "PATH_TO_BE_CONFIGURED/model.ckpt"
  #from_detection_checkpoint: 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: 1000
  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/side_vehicle.pbtxt'
}

eval_config: {
  num_examples: 8000
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: 'data/test.record'
  }
  label_map_path: 'data/side_vehicle.pbtxt'
  shuffle: false
  num_readers: 1
}

トレーニングディレクトリに生成されたckptファイルは次のようになります：

何も問題はありませんでしたが、どこが問題ですか？

Answer 1

エラーにより、トレーニングフォルダ内にディレクトリを作成できません。

トレーニングフォルダ（training/export/Servo）内にエクスポートフォルダとサーボフォルダを作成して、以下のようにmodel_dirパラメータを指定してください。

model_dir=/training/export/Servo/