在 Tensorflow 1.15 中導出干涉圖時出錯

Question

我在嘗試導出訓練有素的模型（調整為 1500 步）時遇到以下錯誤：

Traceback (most recent call last):
  File "export_inference_graph.py", line 150, in <module>
    tf.app.run()
  File "C:\Users\USERNAME\anaconda3\envs\test123\lib\site-packages\tensorflow_core\python\platform\app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "C:\Users\USERNAME\anaconda3\envs\test123\lib\site-packages\absl\app.py", line 312, in run
    _run_main(main, args)
  File "C:\Users\USERNAME\anaconda3\envs\test123\lib\site-packages\absl\app.py", line 258, in _run_main
    sys.exit(main(argv))
  File "export_inference_graph.py", line 146, in main
    write_inference_graph=FLAGS.write_inference_graph)
  File "C:\Users\USERNAME\Desktop\DirectML_Tensorflow_Library\Tensorflow\workspace\training_demo\object_detection\exporter.py", line 455, in export_inference_graph
    write_inference_graph=write_inference_graph)
  File "C:\Users\USERNAME\Desktop\DirectML_Tensorflow_Library\Tensorflow\workspace\training_demo\object_detection\exporter.py", line 384, in _export_inference_graph
    trained_checkpoint_prefix=checkpoint_to_use)
  File "C:\Users\USERNAME\Desktop\DirectML_Tensorflow_Library\Tensorflow\workspace\training_demo\object_detection\exporter.py", line 295, in write_graph_and_checkpoint
    saver.restore(sess, trained_checkpoint_prefix)
  File "C:\Users\USERNAME\anaconda3\envs\test123\lib\site-packages\tensorflow_core\python\training\saver.py", line 1282, in restore
    checkpoint_prefix)
    
ValueError: The passed save_path is not a valid checkpoint: C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\my_models\\my_ssd_mobilenet_v1_coco_2018_01_28\\checkpoints\\model.ckpt-1500

這是我設置 model 時的配置路徑：

model {
  ssd {
    num_classes: 1
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    feature_extractor {
      type: "ssd_mobilenet_v1"
      depth_multiplier: 1.0
      min_depth: 16
      conv_hyperparams {
        regularizer {
          l2_regularizer {
            weight: 3.99999989895e-05
          }
        }
        initializer {
          truncated_normal_initializer {
            mean: 0.0
            stddev: 0.0299999993294
          }
        }
        activation: RELU_6
        batch_norm {
          decay: 0.999700009823
          center: true
          scale: true
          epsilon: 0.0010000000475
          train: true
        }
      }
    override_base_feature_extractor_hyperparams: true
      
    }
    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 {
      }
    }
    box_predictor {
      convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l2_regularizer {
              weight: 3.99999989895e-05
            }
          }
          initializer {
            truncated_normal_initializer {
              mean: 0.0
              stddev: 0.0299999993294
            }
          }
          activation: RELU_6
          batch_norm {
            decay: 0.999700009823
            center: true
            scale: true
            epsilon: 0.0010000000475
            train: true
          }
        }
        min_depth: 0
        max_depth: 0
        num_layers_before_predictor: 0
        use_dropout: false
        dropout_keep_probability: 0.800000011921
        kernel_size: 1
        box_code_size: 4
        apply_sigmoid_to_scores: false
      }
    }
    anchor_generator {
      ssd_anchor_generator {
        num_layers: 6
        min_scale: 0.20000000298
        max_scale: 0.949999988079
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 3.0
        aspect_ratios: 0.333299994469
      }
    }
    post_processing {
      batch_non_max_suppression {
        score_threshold: 0.300000011921
        iou_threshold: 0.600000023842
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
    normalize_loss_by_num_matches: true
    loss {
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_loss {
        weighted_sigmoid {
        }
      }
      hard_example_miner {
        num_hard_examples: 3000
        iou_threshold: 0.990000009537
        loss_type: CLASSIFICATION
        max_negatives_per_positive: 3
        min_negatives_per_image: 0
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
  }
}
train_config {
  batch_size: 12
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
  optimizer {
    rms_prop_optimizer {
      learning_rate {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.00400000018999
          decay_steps: 800720
          decay_factor: 0.949999988079
        }
      }
      momentum_optimizer_value: 0.899999976158
      decay: 0.899999976158
      epsilon: 1.0
    }
  }
  fine_tune_checkpoint: "C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\pre-trained-model\\ssd_mobilenet_v1_coco_2018_01_28\\model.ckpt"
  from_detection_checkpoint: true
  num_steps: 1500
}
train_input_reader {
  label_map_path: "C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\annotations\\label_map.pbtxt"
  tf_record_input_reader {
    input_path: "C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\annotations\\train.record"
  }
}
eval_config {
  num_examples: 540
  max_evals: 10
  use_moving_averages: false
}
eval_input_reader {
  label_map_path: "C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\annotations\\label_map.pbtxt"
  shuffle: false
  num_readers: 1
  tf_record_input_reader {
    input_path: "C:\\Users\\USERNAME\\Desktop\\DirectML_Tensorflow_Library\\Tensorflow\\workspace\\training_demo\\annotations\\test.record"
  }
}

我已經用https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md#coco-trained-models中的一些其他模型訓練了相同的數據集，並且再次發生相同的錯誤。

我的檢查點文件夾包含“model.ckpt-1500.data-00000-of-00001”、“model.ckpt-1500.index”、“model.ckpt-1500.meta”和“檢查點”。 在“檢查點”中，model_checkpoint_path：“model.ckpt-1500”。

所以檢查點存在，但是當我嘗試導出它時，它不會被識別為有效的檢查點。

Answer 1

我已經解決了我的問題，我在這里發布我的答案，以防將來可能會幫助其他人在設置 directml 1.15.5 和使用 model 檢測動物園中的預訓練模型時遇到同樣的問題。

Go 進入 anaconda3 --> envs --> [您的 tensorflow 環境的名稱； 在我的情況下 test123] --> Lib --> 站點包 --> tensorflow_estimator --> python --> 估計器 --> run_config.py

將 save_checkpoints_steps = None 更改為 save_checkpoints_steps = 100 （或您選擇的任何數量），然后為了完成這項工作，您還需要將 save_checkpoints_secs = 600 更改為 save_checkpoints_secs = None

所以問題是文件“run_config.py”沒有設置為保存我的檢查點。