Tensorflow object_detection评估错误

Question

我已经在自己的数据集中训练了一个模型。 现在我正在尝试使用eval.py评估模型并得到以下错误

  tf.app.run()
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
Traceback (most recent call last):

  File "<ipython-input-5-44cda3e31e6a>", line 1, in <module>
    tf.app.run()

  File "/Users/amit.sood/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))

  File "<ipython-input-4-8980f3a486a4>", line 48, in main
    FLAGS.checkpoint_dir, FLAGS.eval_dir)

  File "/Users/amit.sood/Documents/Analytics/github/models-master/research/object_detection/evaluator.py", line 210, in evaluate
    save_graph_dir=(eval_dir if eval_config.save_graph else ''))

  File "/Users/amit.sood/Documents/Analytics/github/models-master/research/object_detection/eval_util.py", line 393, in repeated_checkpoint_run
    return metrics

UnboundLocalError: local variable 'metrics' referenced before assignment

我的配置文件看起来像这样

# SSD with Mobilenet v1, configured for Oxford-IIIT Pets 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: 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 {
          anchorwise_output: true
        }
      }
      localization_loss {
        weighted_smooth_l1 {
          anchorwise_output: true
        }
      }
      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: ""
  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: 2500
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "/Users/amit.sood/Documents/Analytics/github/Deep-Learning-master/amit/items_train_new.record"
  }
  label_map_path: "/Users/amit.sood/Documents/Analytics/github/Deep-Learning-master/amit/toy_label_map.pbtxt"
}

eval_config: {
  num_examples: 2000
  # 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: "/Users/amit.sood/Documents/Analytics/github/Deep-Learning-master/amit/items_val_new.record"
  }
  label_map_path: "/Users/amit.sood/Documents/Analytics/github/Deep-Learning-master/amit/toy_label_map.pbtxt"
  shuffle: false
  num_readers: 1
}

请告诉我我在这里想念的是什么

Answer 1

在较新版本的Object Detection API中，可能由于无法加载任何检查点来执行评估而导致'metrics' referenced before assignment错误'metrics' referenced before assignment此'metrics' referenced before assignment （请参见此处的线程）。 该--checkpoint_dir参数eval.py必须在你的检查点文件所在的目录 ， 和里面 ，你需要它的内容是检查点的文本文件

model_checkpoint_path：“ name_of_checkpoint.ckpt”

all_model_checkpoint_paths：“名称_检查点.ckpt”

并且它必须准确地命名为“检查点”（目前已进行硬编码）！ “ name_of_checkpoint.ckpt”是检查点文件的“ .meta”和“ .index”之前的所有内容。

通过将from tensorflow.python.platform import tf_logging as logging import logging替换from tensorflow.python.platform import tf_logging as logging object_detection中的eval_util.py中的from tensorflow.python.platform import tf_logging as logging ，您可以阅读日志记录消息以找出问题eval_util.py 。