在Tensorlfow对象检测API中导出推理图时出错

时间:2019-12-10 13:25:00

标签: python tensorflow computer-vision object-detection object-detection-api

嗨,我在做tensorflow object detection api。我已经按照仓库中的所有主要说明进行了操作,直到现在为止一切正常,但是突然出现了一些奇怪的错误。我之前使用的是fast rcnn,现在切换到ssd mobile v2 coco

使用命令生成推理图时

python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/faster_rcnn_inception_v2_pets.config --trained_checkpoint_prefix training/model.ckpt-10250 --output_directory inference_graph

我得到以下错误:

  

回溯(最近通话最近):文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,行1356,在_do_call中       在_run_fn中的第1341行中返回fn(* args)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”       选项,feed_dict,fetch_list,target_list,run_metadata)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,行1429,在_call_tf_sessionrun中       run_metadata)tensorflow.python.framework.errors_impl.NotFoundError:关键转换/偏见   在检查点[[{{node save / RestoreV2}}]]中找不到

     

在处理上述异常期间,发生了另一个异常:

     

回溯(最近通话最近):文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py”,第1286行,在还原中       {self.saver_def.filename_tensor_name:save_path})文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,第950行,正在运行       run_metadata_ptr)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,行_run       feed_dict_tensor,选项,run_metadata)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,行1350,在_do_run中       run_metadata)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/client/session.py”,行1370,在_do_call中       提高类型(e)(node_def,op,消息)tensorflow.python.framework.errors_impl.NotFoundError:关键转换/偏见   在检查点[[node save / RestoreV2(在   /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py:331)   ]]

     

“ save / RestoreV2”的原始堆栈跟踪:文件   第162行中的“ export_inference_graph.py”       tf.app.run()文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/platform/app.py”,   40行       _run(main = main,argv = argv,flags_parser = _parse_flags_tolerate_undef)文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   299行       _run_main(main,args)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   _run_main中的第250行       sys.exit(main(argv))文件“ export_inference_graph.py”,第158行,位于主目录中       write_inference_graph = FLAGS.write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   第497行,在export_inference_graph中       write_inference_graph = write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   _export_inference_graph中的第426行       (文件为“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”),   在write_graph_and_checkpoint中的第331行       tf.import_graph_def(inference_graph_def,name =“'')文件” /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py“,   第507行,在new_func中       返回func(* args,** kwargs)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/importer.py”,   import_graph_def中的第443行       _ProcessNewOps(graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/importer.py”,   _ProcessNewOps中的第236行       用于graph._add_new_tf_operations(compute_devices = False)中的new_op:#pylint:disable = protected-access File“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops .py”,   第375行,在_add_new_tf_operations中       对于c_api_util.new_tf_operations(self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”中的c_op,   行3751,在       对于c_api_util.new_tf_operations(self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”中的c_op,   第3641行,在_create_op_from_tf_operation中       ret = Operation(c_op,self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”,   第2005行, init       self._traceback = tf_stack.extract_stack()

     

在处理上述异常期间,发生了另一个异常:

     

回溯(最近通话最近):文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py”,行1296,在还原中       names_to_keys = object_graph_key_mapping(save_path)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py”,行1614,在object_graph_key_mapping中       object_graph_string = reader.get_tensor(trackable.OBJECT_GRAPH_PROTO_KEY)文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py”,   第678行,位于get_tensor中       返回CheckpointReader_GetTensor(自身,compat.as_bytes(tensor_str))   tensorflow.python.framework.errors_impl.NotFoundError:键   在检查点中找不到_CHECKPOINTABLE_OBJECT_GRAPH

     

在处理上述异常期间,发生了另一个异常:

     

回溯(最近一次通话最近):文件“ export_inference_graph.py”,   162行,在       tf.app.run()文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/platform/app.py”,   40行       _run(main = main,argv = argv,flags_parser = _parse_flags_tolerate_undef)文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   299行       _run_main(main,args)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   _run_main中的第250行       sys.exit(main(argv))文件“ export_inference_graph.py”,第158行,位于主目录中       write_inference_graph = FLAGS.write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   第497行,在export_inference_graph中       write_inference_graph = write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   _export_inference_graph中的第426行       (文件为“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”),   第335行,在write_graph_and_checkpoint中       saver.restore(sess,trained_checkpoint_prefix)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/training/saver.py”,第1302行,在还原中       err,“缺少变量名或其他图形键”)tensorflow.python.framework.errors_impl.NotFoundError:从中还原   检查点失败。这很可能是由于变量名或其他   检查点缺少的图形键。请确保您   尚未更改基于检查点的预期图形。原版的   错误:

     

在检查点[[节点保存/还原V2中找不到密钥转换/偏向   (定义为   /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py:331)   ]]

     

“ save / RestoreV2”的原始堆栈跟踪:文件   第162行中的“ export_inference_graph.py”       tf.app.run()文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/platform/app.py”,   40行       _run(main = main,argv = argv,flags_parser = _parse_flags_tolerate_undef)文件   “ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   299行       _run_main(main,args)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/absl/app.py”,   _run_main中的第250行       sys.exit(main(argv))文件“ export_inference_graph.py”,第158行,位于主目录中       write_inference_graph = FLAGS.write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   第497行,在export_inference_graph中       write_inference_graph = write_inference_graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”,   _export_inference_graph中的第426行       (文件为“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/object_detection/exporter.py”),   在write_graph_and_checkpoint中的第331行       tf.import_graph_def(inference_graph_def,name =“'')文件” /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py“,   第507行,在new_func中       返回func(* args,** kwargs)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/importer.py”,   import_graph_def中的第443行       _ProcessNewOps(graph)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/importer.py”,   _ProcessNewOps中的第236行       用于graph._add_new_tf_operations(compute_devices = False)中的new_op:#pylint:disable = protected-access File“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops .py”,   第375行,在_add_new_tf_operations中       对于c_api_util.new_tf_operations(self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”中的c_op,   行3751,在       对于c_api_util.new_tf_operations(self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”中的c_op,   第3641行,在_create_op_from_tf_operation中       ret = Operation(c_op,self)文件“ /home/user/anaconda3/envs/my_env/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”,   第2005行, init       self._traceback = tf_stack.extract_stack()

实际上,它一直在正确地工作,无法弄清楚现在发生了什么。我也尝试过使用快速rcnn(它之前已经工作过),但是它也开始失败了

这是配置文件。我目前正在为2个班级做

# Faster R-CNN with Inception v2, 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 {
  faster_rcnn {
    num_classes: 2
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_inception_v2'
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 16
        width_stride: 16
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 14
    maxpool_kernel_size: 2
    maxpool_stride: 2
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 300
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

train_config: {
  batch_size: 1
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0002
          schedule {
            step: 1
            learning_rate: .0002
          }
          schedule {
            step: 900000
            learning_rate: .00002
          }
          schedule {
            step: 1200000
            learning_rate: .000002
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  fine_tune_checkpoint: "/home/user/Downloads/Data_Science/Git/models/research/object_detection/faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt"
  from_detection_checkpoint: true
  load_all_detection_checkpoint_vars: 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: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}


train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/user/Downloads/Data_Science/Git/models/research/object_detection/train.record"
  }
  label_map_path: "/home/user/Downloads/Data_Science/Git/models/research/object_detection/training/labelmap.pbtxt"
}

eval_config: {
  num_examples: 67
  # 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: "C:/tensorflow1/models/research/object_detection/test.record"
  }
  label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
  shuffle: false
  num_readers: 1
}

在github中发现了onetwo类似的错误。但这没有用。任何帮助将不胜感激。如果您需要更多信息,请发表评论。谢谢!

1 个答案:

答案 0 :(得分:0)

您确定您的模型training/model.ckpt-10250faster_rcnn_inception_v2_pets模型吗?错误NotFoundError: Key Conv/biases not found in checkpoint [[{{node save/RestoreV2}}]]表示它无法从检查点恢复Conv/biases

或者确保您使用的是对象检测框架支持的TF版本。您可以找到所有版本here