python - tensorflow -对象检测.pbtxt；没有这样的文件或目录

Question

我的问题很基本，但我不明白为什么会遇到这个问题，因此我不知道如何解决。

我正在尝试按照 this tutorial 的步骤重新训练预训练模型或 the corresponding youtube video .

这是我的目录结构的相关部分:

~/Desktop/models/research/object_detection$ ls
anchor_generators  evaluator.py               __init__.py         model_main.py                     test_ckpt
box_coders         eval_util.py               inputs.py           models                            test_data
builders           eval_util_test.py          inputs_test.py      model_tpu_main.py                 test_images
CONTRIBUTING.md    exporter.py                matchers            object_detection_tutorial.ipynb   trainer.py
core               exporter_test.py           meta_architectures  protos                            trainer_test.py
data               export_inference_graph.py  metrics             __pycache__                       training
data_decoders      g3doc                      model_hparams.py    README.md                         train.py
dataset_tools      images                     model_lib.py        samples                           utils
eval.py            inference                  model_lib_test.py   ssd_mobilenet_v1_coco_11_06_2017

data
├── ava_label_map_v2.1.pbtxt
├── kitti_label_map.pbtxt
├── mscoco_label_map.pbtxt
├── oid_bbox_trainable_label_map.pbtxt
├── oid_object_detection_challenge_500_label_map.pbtxt
├── pascal_label_map.pbtxt
├── pet_label_map.pbtxt
├── test_labels.csv
├── test.record
├── train_labels.csv
└── train.record
images
├── test
    ├── image1.jpg
    ├── ...
└── train
    ├── imageA.jpg
    ├── ...

models
├── embedded_ssd_mobilenet_v1_feature_extractor.py
├── embedded_ssd_mobilenet_v1_feature_extractor_test.py
├── ...
├── __init__.py
├── __pycache__
│   ├── ...
│   ├── ssd_mobilenet_v1_feature_extractor.cpython-35.pyc
│   ├── ssd_mobilenet_v2_feature_extractor.cpython-35.pyc
│   └── ssd_resnet_v1_fpn_feature_extractor.cpython-35.pyc
├── ssd_feature_extractor_test.py
├── ...
└── ssd_resnet_v1_fpn_feature_extractor_test.py

ssd_mobilenet_v1_coco_11_06_2017/
├── frozen_inference_graph.pb
├── graph.pbtxt
├── model.ckpt.data-00000-of-00001
├── model.ckpt.index
└── model.ckpt.meta

train.py

现在，当我尝试按如下方式运行 train.py 时:python3 train.py --logtostderr --train_dir=training/--pipeline_config_path=training/ssd_mobilenet_v1_pets.config

我得到 /home/John/Desktop/models/research/object_detection/data/object-detection.pbtxt；没有这样的文件或目录，而这实际上是 AFAIK 应该生成的输出文件!

我使用的train.py脚本可以找到here这是我的 ssd_mobilenet_v1_pets.config 的内容:

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: 10
  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: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
  from_detection_checkpoint: true
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "data/train.record"
  }
  label_map_path: "/home/John/Desktop/models/research/object_detection/data/object-detection.pbtxt"
}

eval_config: {
  num_examples: 40
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "data/test.record"
  }
  label_map_path: "/home/John/Desktop/models/research/object_detection/data/hand-detection.pbtxt"
  shuffle: false
  num_readers: 1
}

谁能告诉我我做错了什么？

Answer 1

我遇到了同样的问题。我的解决方法是在 .config 文件中使用 label_map 的相对路径。实际上，我遇到了同样的问题，但使用了 eval.py 脚本。