警告：tensorflow：忽略圖像 ID 為 1016176252 的檢測，因為它之前已添加

Question

嗨，我使用 fast_rcnn_resnet101_v1_1024x1024_coco17_tpu-8 預訓練 model。 我在評估 model 時遇到問題。 培訓順利進行。 我使用以下命令開始評估 model：

python model_main_tf2.py --pipeline_config_path=./training_outlook_action_ctx/training_1/pipeline.config --model_dir=./training_outlook_action_ctx/training_1 --checkpoint_dir=./training_outlook_action_ctx/training_1

在第一個加載的 cuDNN 版本 8400 之后，它開始向我拋出以下錯誤，該錯誤會不斷重復，直到中斷

WARNING:tensorflow:Ignoring ground truth with image id 1016176252 since it was previously added
W0810 10:17:12.131517 140545620840832 coco_evaluation.py:113] Ignoring ground truth with image id 1016176252 since it was previously added
WARNING:tensorflow:Ignoring detection with image id 1016176252 since it was previously added
W0810 10:17:12.131881 140545620840832 coco_evaluation.py:196] Ignoring detection with image id 1016176252 since it was previously added
WARNING:tensorflow:Ignoring ground truth with image id 1016176252 since it was previously added
W0810 10:17:12.652873 140545620840832 coco_evaluation.py:113] Ignoring ground truth with image id 1016176252 since it was previously added
WARNING:tensorflow:Ignoring detection with image id 1016176252 since it was previously added
W0810 10:17:12.653055 140545620840832 coco_evaluation.py:196] Ignoring detection with image id 1016176252 since it was previously added
WARNING:tensorflow:Ignoring ground truth with image id 1016176252 since it was previously added

這是我的 pipeline.config 文件

# Faster R-CNN with Resnet-50 (v1)
# Trained on COCO, initialized from Imagenet classification checkpoint

# This config is TPU compatible.

model {
  faster_rcnn {
    num_classes: 7
    image_resizer {
      fixed_shape_resizer {
        width: 1024
        height: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_resnet101_keras'
      batch_norm_trainable: true
    }
    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
            }
          }
        }
        share_box_across_classes: true
      }
    }
    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
    use_static_shapes: true
    use_matmul_crop_and_resize: true
    clip_anchors_to_image: true
    use_static_balanced_label_sampler: true
    use_matmul_gather_in_matcher: true
  }
}

train_config: {
  batch_size: 2
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 8
  num_steps: 200000
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .04
          total_steps: 100000
          warmup_learning_rate: .013333
          warmup_steps: 2000
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "/pretrained_models/faster_rcnn_resnet101_v1_1024x1024_coco17_tpu-8/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  data_augmentation_options {
    random_horizontal_flip {
    }
  }

  data_augmentation_options {
    random_adjust_hue {
    }
  }

  data_augmentation_options {
    random_adjust_contrast {
    }
  }

  data_augmentation_options {
    random_adjust_saturation {
    }
  }

  data_augmentation_options {
     random_square_crop_by_scale {
      scale_min: 0.6
      scale_max: 1.3
    }
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
  use_bfloat16: true  # works only on TPUs
}
train_input_reader: {
  label_map_path: "./training_outlook_action_ctx/data/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "./training_outlook_action_ctx/data/train.records"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
  batch_size: 2
}

eval_input_reader: {
  label_map_path: "./training_outlook_action_ctx/data/label_map.pbtxt"
  shuffle: false
  tf_record_input_reader {
    input_path: "./training_outlook_action_ctx/data/train.records"
  }
}

操作系統：Debian GNU/Linux 11（靶心）

Python：3.9.12

Tensorflow：2.9.1

我嘗試添加num_examples和max_evals但失敗了。 無論我如何調整它們，它仍然會拋出相同的錯誤

我必須提到第二個數據集的評估對我來說正常工作

提前感謝埃迪

Answer 1

伙計們。 我找到了如何使用腳本創建圖像和注釋的解決方案。 更准確地說，我使用了一個腳本來裁剪我的一級注釋並為裁剪的圖像創建新的 XML 文件。 XML 文件中的文件名和路徑對我不利（編寫腳本時路徑錯誤）。

更改后，評估碼錯誤消失了。

對於所有問題，我都有空。