[Object Detection] TypeError: `name` must be string, given: 0

[nikitaverbis]

System information

• What is the top-level directory of the model you are using: ~/tf_1_10_src/tensorflow/tensorflow/models
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Ubuntu 16.04.3 LTS
• TensorFlow installed from (source or binary): source
• TensorFlow version (use command below): 1.10.1
• Bazel version (if compiling from source): 0.15.2
• CUDA/cuDNN version: CUDA 9.0/cuDNN 7.3
• GPU model and memory: GTX TitanXp
• Exact command to reproduce: cd ~/tf_1_10_src/tensorflow/tensorflow/models/research; ~/train_object_detection_v1.sh

Describe the problem

Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.

Source code / logs

Script train_object_detection_v1.sh source:

!/bin/bash
echo "Object detection script v.1"
echo "Check execution path"
if [[ "$PWD" = "$TFMODELPATH/research" ]]
then
  echo "Current working directory is correct."
  
  PROJECT_DIR=/media/nikita/LinuxBD4Tb/SSD_PROJECT
  PIPELINE_CONFIG_PATH=$PROJECT_DIR/ssd_inception_v2_coco_nik.config
  MODEL_DIR=$PROJECT_DIR/models/model
  NUM_TRAIN_STEPS=4000000
  SAMPLE_1_OF_N_EVAL_EXAMPLES=1
  
  echo "PIPELINE_CONFIG_PATH=$PIPELINE_CONFIG_PATH"
  echo "MODEL_DIR=$MODEL_DIR"
  echo "NUM_TRAIN_STEPS=$NUM_TRAIN_STEPS"
  echo "SAMPLE_1_OF_N_EVAL_EXAMPLES=$SAMPLE_1_OF_N_EVAL_EXAMPLES"
  echo "-----------------"
  echo "Start object_detection/model_main.py"
  python object_detection/model_main.py \
    --pipeline_config_path=${PIPELINE_CONFIG_PATH} \
    --model_dir=${MODEL_DIR} \
    --num_train_steps=${NUM_TRAIN_STEPS} \
    --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES \
    --alsologtostderr 
else
  echo "Current working directory must be 'tensorflow/models/research/'."
  echo "Correct path is '$TFMODELPATH/research'"
  echo "Exit."  
fi

Dir struct:

./SSD_PROJECT/
├── data
│   ├── mscoco_label_map.pbtxt
│   ├── mscoco_train.record
│   └── mscoco_val.record
├── models
│   └── model
│       ├── eval
│       ├── pipeline.config
│       └── train
└── ssd_inception_v2_coco_nik.config

File ssd_inception_v2_coco_nik.config:

model {
  ssd {
    num_classes: 90
    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
        reduce_boxes_in_lowest_layer: true
      }
    }
    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: 3
        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
            }
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_inception_v2'
      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,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }
    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: 32
  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: "/home/nikita/tf_1_10_src/pretrained_InceptionV2_ImageNet_CLS2012/inception_v2.ckpt"
  from_detection_checkpoint: false
  # 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: 300000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "/media/nikita/LinuxBD4Tb/SSD_PROJECT/data/mscoco_train.record"
  }
  label_map_path: "/media/nikita/LinuxBD4Tb/SSD_PROJECT/data/mscoco_label_map.pbtxt"
}

eval_config: {
  num_examples: 5000
  # 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: "/media/nikita/LinuxBD4Tb/SSD_PROJECT/data/mscoco_val.record"
  }
  label_map_path: "/media/nikita/LinuxBD4Tb/SSD_PROJECT/data/mscoco_label_map.pbtxt"
  shuffle: false
  num_readers: 1
}

Error log:

nikita@ubuntulinux:~/tf_1_10_src/tensorflow/tensorflow/models/research$ ~/train_object_detection_v1.sh 
Object detection script v.1
Check execution path
Current working directory is correct.
PIPELINE_CONFIG_PATH=/media/nikita/LinuxBD4Tb/SSD_PROJECT/ssd_inception_v2_coco_nik.config
MODEL_DIR=/media/nikita/LinuxBD4Tb/SSD_PROJECT/models/model
NUM_TRAIN_STEPS=4000000
SAMPLE_1_OF_N_EVAL_EXAMPLES=1
-----------------
Start object_detection/model_main.py
/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/utils/visualization_utils.py:27: UserWarning: 
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

The backend was *originally* set to u'TkAgg' by the following code:
  File "object_detection/model_main.py", line 26, in <module>
    from object_detection import model_lib
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/model_lib.py", line 27, in <module>
    from object_detection import eval_util
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/eval_util.py", line 27, in <module>
    from object_detection.metrics import coco_evaluation
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/metrics/coco_evaluation.py", line 20, in <module>
    from object_detection.metrics import coco_tools
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/metrics/coco_tools.py", line 47, in <module>
    from pycocotools import coco
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/pycocotools/coco.py", line 49, in <module>
    import matplotlib.pyplot as plt
  File "/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.py", line 69, in <module>
    from matplotlib.backends import pylab_setup
  File "/usr/local/lib/python2.7/dist-packages/matplotlib/backends/__init__.py", line 14, in <module>
    line for line in traceback.format_stack()


  import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements
WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
W0924 15:58:51.491380 140112981780224 tf_logging.py:125] Forced number of epochs for all eval validations to be 1.
WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
W0924 15:58:51.491605 140112981780224 tf_logging.py:125] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
WARNING:tensorflow:Estimator's model_fn (<function model_fn at 0x7f6e49985c80>) includes params argument, but params are not passed to Estimator.
W0924 15:58:51.491858 140112981780224 tf_logging.py:125] Estimator's model_fn (<function model_fn at 0x7f6e49985c80>) includes params argument, but params are not passed to Estimator.
1) exporter_name=Servo_0; eval_spec_name=0(type <type 'int'>)
Traceback (most recent call last):
  File "object_detection/model_main.py", line 109, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "object_detection/model_main.py", line 102, in main
    eval_on_train_data=False)
  File "/home/nikita/tf_1_10_src/tensorflow/tensorflow/models/research/object_detection/model_lib.py", line 659, in create_train_and_eval_specs
    exporters=exporter))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 237, in __new__
    raise TypeError('`name` must be string, given: {}'.format(name))
TypeError: `name` must be string, given: 0

Debug of file object_detection/model_lib.py shows:
File modifications:

eval_specs = [] #line 646
  cntr = 0 # add counter
  for eval_spec_name, eval_input_fn in zip(eval_spec_names, eval_input_fns):
    cntr += 1 # add counter inc
    exporter_name = '{}_{}'.format(final_exporter_name, eval_spec_name)
    print("{}) exporter_name={}; eval_spec_name={}(type {})".format(cntr, exporter_name, eval_spec_name, type(eval_spec_name))) # add debuging variables
    exporter = tf.estimator.FinalExporter(
        name=exporter_name, serving_input_receiver_fn=predict_input_fn)
    eval_specs.append(
        tf.estimator.EvalSpec(
            name=eval_spec_name),
            input_fn=eval_input_fn,
            steps=None,
            exporters=exporter))

Result:

...
1) exporter_name=Servo_0; eval_spec_name=0(type <type 'int'>)
...

Possible solution (IMHO):
Change in object_detection/model_lib.py:

eval_specs.append(
        tf.estimator.EvalSpec(
            name=eval_spec_name),
            input_fn=eval_input_fn,
            steps=None,
            exporters=exporter))

to:

eval_specs.append(
        tf.estimator.EvalSpec(
            name=str(eval_spec_name),
            input_fn=eval_input_fn,
            steps=None,
            exporters=exporter))

P.S. Can't understand why in file object_detection/model_lib.py at line 644 list of integers generated:

if eval_spec_names is None: # line 643
  eval_spec_names = range(len(eval_input_fns)) # creates integers. Line 644

[zishanahmed08]

@pkulzc - I face the same issue with tf1_11 and python 3.6 . Could you please help us out

[feynmanliang]

Give this line of change a shot (wrapping eval_spec_name with str):
https://github.com/tensorflow/models/pull/5372/files#diff-76df8ca264a059e8a3003851fe4d7849R653

[feynmanliang]

Upstream sent #5354 to fix this

[Kevze]

Give this line of change a shot (wrapping eval_spec_name with str):
https://github.com/tensorflow/models/pull/5372/files#diff-76df8ca264a059e8a3003851fe4d7849R653

This seems to work for me, thanks!

[tensorflowbutler]

Hi There,
We are checking to see if you still need help on this, as this seems to be considerably old issue. Please update this issue with the latest information, code snippet to reproduce your issue and error you are seeing.
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