2

我正在根据本指南运行 tensorflow 对象检测 api https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#configuring-a-training-job但是稍微有点修改了用于制作记录文件的代码并使用以下系统：

系统信息：

  • 操作系统平台和发行版：Ubuntu 20.04.1 LTS
  • 蟒蛇版本：
  • TensorFlow 版本：2.4.0
  • CUDA/cuDNN 版本：11.0/8.0.5
  • GPU 型号和内存：GeForce RTX 3090、24268 MiB

我想将模型 CenterNet MobileNetV2 FPN 512x512 用于来自 TensorFlow2 检测模型动物园（https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md）的框。

我根据然后运行的指南设置了培训工作

python model_main_tf2.py --model_dir=models/my_centernet_mn_fpn --pipeline_config_path=models/my_centernet_mn_fpn/pipeline.config

这样做时我收到以下错误

Traceback (most recent call last):
  File "model_main_tf2.py", line 115, in <module>
    tf.compat.v1.app.run()
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/absl/app.py", line 300, in run
    _run_main(main, args)
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "model_main_tf2.py", line 106, in main
    model_lib_v2.train_loop(
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/object_detection/model_lib_v2.py", line 636, in train_loop
    loss = _dist_train_step(train_input_iter)
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 828, in __call__
    result = self._call(*args, **kwds)
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 888, in _call
    return self._stateless_fn(*args, **kwds)
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2942, in __call__
    return graph_function._call_flat(
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1918, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 555, in call
    outputs = execute.execute(
  File "/opt/anaconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  indices[0] = 0 is not in [0, 0)
     [[{{node GatherV2_7}}]]
     [[MultiDeviceIteratorGetNextFromShard]]
     [[RemoteCall]]
     [[IteratorGetNext]]
  (1) Invalid argument:  indices[0] = 0 is not in [0, 0)
     [[{{node GatherV2_7}}]]
     [[MultiDeviceIteratorGetNextFromShard]]
     [[RemoteCall]]
     [[IteratorGetNext]]
     [[ToAbsoluteCoordinates_42/Assert/AssertGuard/branch_executed/_386/_1231]]
0 successful operations.
0 derived errors ignored. [Op:__inference__dist_train_step_54439]

Function call stack:
_dist_train_step -> _dist_train_step

当谷歌搜索此错误时，会出现一些答案，即错误是在创建 TFRecord 文件时，您需要include_masks在创建它们时添加。但是，当从模型动物园运行其他 CenterNet 模型时，我没有收到此错误，所以这似乎很奇怪，这将是错误。

任何想法，如果它可以是别的什么？

4

1 回答 1

2

感谢亚历山德拉。从配置文件中删除关键点关联。

原代码（删除标有“//delete”的部分）：

model {
  center_net {
    num_classes: 90
    feature_extractor {
      type: "mobilenet_v2_fpn_sep_conv"
    }
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 512
        max_dimension: 512
        pad_to_max_dimension: true
      }
    }
    use_depthwise: true
    object_detection_task {
      task_loss_weight: 1.0
      offset_loss_weight: 1.0
      scale_loss_weight: 0.1
      localization_loss {
        l1_localization_loss {
        }
      }
    }
    object_center_params {
      object_center_loss_weight: 1.0
      classification_loss {
        penalty_reduced_logistic_focal_loss {
          alpha: 2.0
          beta: 4.0
        }
      }
      min_box_overlap_iou: 0.7
      max_box_predictions: 20
    }
  }
}
train_config {
  batch_size: 512
  data_augmentation_options {        // delete
    random_horizontal_flip {           
      keypoint_flip_permutation: 0
      keypoint_flip_permutation: 2
      keypoint_flip_permutation: 1
      keypoint_flip_permutation: 4
      keypoint_flip_permutation: 3
      keypoint_flip_permutation: 6
      keypoint_flip_permutation: 5
      keypoint_flip_permutation: 8
      keypoint_flip_permutation: 7
      keypoint_flip_permutation: 10
      keypoint_flip_permutation: 9
      keypoint_flip_permutation: 12
      keypoint_flip_permutation: 11
      keypoint_flip_permutation: 14
      keypoint_flip_permutation: 13
      keypoint_flip_permutation: 16
      keypoint_flip_permutation: 15
    }
  }                                     // delete
  data_augmentation_options {
    random_patch_gaussian {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_aspect_ratio: 0.5
      max_aspect_ratio: 1.7
      random_coef: 0.25
    }
  }
  data_augmentation_options {
    random_adjust_hue {
    }
  }
  data_augmentation_options {
    random_adjust_contrast {
    }
  }
  data_augmentation_options {
    random_adjust_saturation {
    }
  }
  data_augmentation_options {
    random_adjust_brightness {
    }
  }
  data_augmentation_options {
    random_absolute_pad_image {
      max_height_padding: 200
      max_width_padding: 200
      pad_color: 0.0
      pad_color: 0.0
      pad_color: 0.0
    }
  }
  optimizer {
    adam_optimizer {
      learning_rate {
        cosine_decay_learning_rate {
          learning_rate_base: 5e-3
          total_steps: 300000
          warmup_learning_rate: 1e-4
          warmup_steps: 5000
        }
      }
    }
    use_moving_average: false
  }
  num_steps: 300000
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
  fine_tune_checkpoint_type: ""
}
train_input_reader {
  label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
  tf_record_input_reader {
    input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
  }
  filenames_shuffle_buffer_size: 256
  num_keypoints: 17  // delete
}
eval_config {
  num_visualizations: 10
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
  min_score_threshold: 0.20000000298023224
  max_num_boxes_to_visualize: 20
  batch_size: 1
}
eval_input_reader {
  label_map_path: "PATH_TO_BE_CONFIGURED/label_map.txt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
  }
  num_keypoints: 17    // delete
}

下面的代码是我的工作方式（修改）：

model {
  center_net {
    num_classes: 4
    feature_extractor {
      type: "mobilenet_v2_fpn_sep_conv"
    }
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 512
        max_dimension: 512
        pad_to_max_dimension: true
      }
    }
    use_depthwise: true
    object_detection_task {
      task_loss_weight: 1.0
      offset_loss_weight: 1.0
      scale_loss_weight: 0.1
      localization_loss {
        l1_localization_loss {
        }
      }
    }
    object_center_params {
      object_center_loss_weight: 1.0
      classification_loss {
        penalty_reduced_logistic_focal_loss {
          alpha: 2.0
          beta: 4.0
        }
      }
      min_box_overlap_iou: 0.7
      max_box_predictions: 20
    }
  }
}
train_config {
  batch_size: 8
  data_augmentation_options {
    random_patch_gaussian {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_aspect_ratio: 0.5
      max_aspect_ratio: 1.7
      random_coef: 0.25
    }
  }
  data_augmentation_options {
    random_adjust_hue {
    }
  }
  data_augmentation_options {
    random_adjust_contrast {
    }
  }
  data_augmentation_options {
    random_adjust_saturation {
    }
  }
  data_augmentation_options {
    random_adjust_brightness {
    }
  }
  data_augmentation_options {
    random_absolute_pad_image {
      max_height_padding: 200
      max_width_padding: 200
      pad_color: 0.0
      pad_color: 0.0
      pad_color: 0.0
    }
  }
  optimizer {
    adam_optimizer {
      learning_rate {
        cosine_decay_learning_rate {
          learning_rate_base: 5e-3
          total_steps: 50000
          warmup_learning_rate: 1e-4
          warmup_steps: 2000
        }
      }
    }
    use_moving_average: false
  }
  num_steps: 6000
  max_number_of_boxes: 4
  unpad_groundtruth_tensors: false
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "pre-trained-models/centernet_mobilenetv2_fpn_od/checkpoint/ckpt-301"
  fine_tune_checkpoint_type: "detection"
}
train_input_reader {
  label_map_path: "annotations/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "annotations/train_c.record"
  }
  filenames_shuffle_buffer_size: 256
}
eval_config {
  num_visualizations: 10
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
  min_score_threshold: 0.20000000298023224
  max_num_boxes_to_visualize: 20
  batch_size: 1
}
eval_input_reader {
  label_map_path: "annotations/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "annotations/test_c.record"
  }
}
于 2021-05-26T13:30:21.360 回答