如何在Tensorflow 2.x中打印准确性和其他指标？

我正在使用自定义数据集进行对象检测项目。我的问题是，很难理解我应该如何以及在何处进行更改以评估我的训练集（准确性，mAP指标）。我使用colab和我现在只得到了，因为它是在下面的图片中显示的损耗值的tensorflow 2.3.0： 。

另外，这是我的张量板的图片：。

要训​​练模型，我使用model_main_tf2.py，

!python /content/gdrive/My\ Drive/models/research/object_detection/model_main_tf2.py \
    --pipeline_config_path={pipeline_file} \
    --model_dir={model_dir} \
    --alsologtostderr \
    --num_train_steps={num_steps} \
    --sample_1_of_n_eval_examples=10 \
    --eval_training_data=True \
    --sample_1_of_n_eval_on_train_examples=10 \
    --num_eval_steps={num_eval_steps} 

在配置文件中，我有： eval_config { metrics_set: "coco_detection_metrics" use_moving_averages: false }

我已经尝试了各种方法，就像使用eval.py（我在tensorflow 1.x上阅读该方法）一样，但是我遇到了很多错误，或者就像来自github的对象检测存储库object_detection（repository）中的其他脚本一样。

目前最重要的是准确性。我发现损失可能是在845-858行的model_lib_v2.py中定义的：

 eval_metrics = {}

  for evaluator in evaluators:
    eval_metrics.update(evaluator.evaluate())
  for loss_key in loss_metrics:
    eval_metrics[loss_key] = loss_metrics[loss_key].result()

  eval_metrics = {str(k): v for k, v in eval_metrics.items()}
  tf.logging.info('Eval metrics at step %d', global_step)
  for k in eval_metrics:
    tf.compat.v2.summary.scalar(k, eval_metrics[k], step=global_step)
    tf.logging.info('\t+ %s: %f', k, eval_metrics[k])

  return eval_metrics

但我不知道如何更改代码以增加准确性。

如果有帮助，我可以使用ssd_mobilenet_v2_fpnlite_640x640模型和gdrive加载数据并运行脚本。

更新：我使用的配置文件如下：

model {
  ssd {
    num_classes: 18
    image_resizer {
      fixed_shape_resizer {
        height: 640
        width: 640
      }
    }
    feature_extractor {
      type: "ssd_mobilenet_v2_fpn_keras"
      depth_multiplier: 1.0
      min_depth: 16
      conv_hyperparams {
        regularizer {
          l2_regularizer {
            weight: 3.9999998989515007e-05
          }
        }
        initializer {
          random_normal_initializer {
            mean: 0.0
            stddev: 0.009999999776482582
          }
        }
        activation: RELU_6
        batch_norm {
          decay: 0.996999979019165
          scale: true
          epsilon: 0.0010000000474974513
        }
      }
      use_depthwise: true
      override_base_feature_extractor_hyperparams: true
      fpn {
        min_level: 3
        max_level: 7
        additional_layer_depth: 128
      }
    }
    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: 0.75
        unmatched_threshold: 0.25
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l2_regularizer {
              weight: 3.9999998989515007e-05
            }
          }
          initializer {
            random_normal_initializer {
              mean: 0.0
              stddev: 0.009999999776482582
            }
          }
          activation: RELU_6
          batch_norm {
            decay: 0.996999979019165
            scale: true
            epsilon: 0.0010000000474974513
          }
        }
        depth: 128
        num_layers_before_predictor: 4
        kernel_size: 3
        class_prediction_bias_init: -4.599999904632568
        share_prediction_tower: true
        use_depthwise: true
      }
    }
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        scales_per_octave: 2
      }
    }
    post_processing {
      batch_non_max_suppression {
        score_threshold: 9.99999993922529e-09
        iou_threshold: 0.6000000238418579
        max_detections_per_class: 100
        max_total_detections: 100
        use_static_shapes: false
      }
      score_converter: SIGMOID
    }
    normalize_loss_by_num_matches: true
    loss {
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_loss {
        weighted_sigmoid_focal {
          gamma: 2.0
          alpha: 0.25
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    encode_background_as_zeros: true
    normalize_loc_loss_by_codesize: true
    inplace_batchnorm_update: true
    freeze_batchnorm: false
  }
}
train_config {
  batch_size: 16
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_object_covered: 0.0
      min_aspect_ratio: 0.75
      max_aspect_ratio: 3.0
      min_area: 0.75
      max_area: 1.0
      overlap_thresh: 0.0
    }
  }
  sync_replicas: true
  optimizer {
    momentum_optimizer {
      learning_rate {
        cosine_decay_learning_rate {
          learning_rate_base: 0.07999999821186066
          total_steps: 50000
          warmup_learning_rate: 0.026666000485420227
          warmup_steps: 1000
        }
      }
      momentum_optimizer_value: 0.8999999761581421
    }
    use_moving_average: false
  }
  fine_tune_checkpoint: "/content/gdrive/My Drive/models/research/deploy/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0"
  num_steps: 20000
  startup_delay_steps: 0.0
  replicas_to_aggregate: 8
  max_number_of_boxes: 1
  unpad_groundtruth_tensors: false
  fine_tune_checkpoint_type: "detection"
  fine_tune_checkpoint_version: V2
}
train_input_reader {
  label_map_path: "/content/gdrive/My Drive/models/research/deploy/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "/content/gdrive/My Drive/models/research/object_detection/data/train.record"
  }
}
eval_config {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
}
eval_input_reader {
  label_map_path: "/content/gdrive/My Drive/models/research/deploy/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "/content/gdrive/My Drive/models/research/object_detection/data/test.record"
  }
}