docstr: Evaluate object detection model on a test dataset in COCO format.
  
  Parameters
  ----------
  anno_file_or_df
      The annotation file in COCO format
  metrics
      Metrics used for evaluation.
  return_pred
      Whether to return the prediction result of each row.
  eval_tool
      The eval_tool for object detection. Could be "pycocotools" or "torchmetrics".
docstr_tok: ["Evaluate","object","detection","model","on","a","test","dataset","in","COCO","format",".","Parameters","--","--","--","--","--","anno_file_or_df","The","annotation","file","in","COCO","format","metrics","Metrics","used","for","evaluation",".","return_pred","Whether","to","return","the","prediction","result","of","each","row",".","eval_tool","The","eval_tool","for","object","detection",".","Could","be","``","pycocotools","''","or","``","torchmetrics","''","."]
code: def evaluate_coco(
      self,
      anno_file_or_df: str,
      metrics: str,
      return_pred: Optional[bool] = False,
      eval_tool: Optional[str] = None,
  ):
      """
      Evaluate object detection model on a test dataset in COCO format.
  
      Parameters
      ----------
      anno_file_or_df
          The annotation file in COCO format
      metrics
          Metrics used for evaluation.
      return_pred
          Whether to return the prediction result of each row.
      eval_tool
          The eval_tool for object detection. Could be "pycocotools" or "torchmetrics".
      """
      if isinstance(anno_file_or_df, str):
          anno_file = anno_file_or_df
          data = from_coco_or_voc(
              anno_file, "test"
          )  # TODO: maybe remove default splits hardcoding (only used in VOC)
          if os.path.isdir(anno_file):
              eval_tool = "torchmetrics"  # we can only use torchmetrics for VOC format evaluation.
      else:
          # during validation, it will call evaluate with df as input
          anno_file = self._detection_anno_train
          data = anno_file_or_df
  
      outputs = self.predict_per_run(
          data=data,
          realtime=False,
          requires_label=True,
      )  # outputs shape: num_batch, 1(["bbox"]), batch_size, 2(if using mask_rcnn)/na, 80, n, 5
  
      # Cache prediction results as COCO format # TODO: refactor this
      self._save_path = setup_save_path(
          old_save_path=self._save_path,
          warn_if_exist=False,
      )
      cocoeval_cache_path = os.path.join(
          self._save_path, "object_detection_result_cache.json"
      )
      eval_results = cocoeval(
          outputs=outputs,
          data=data,
          anno_file=anno_file,
          cache_path=cocoeval_cache_path,
          metrics=metrics,
          tool=eval_tool,
      )
      if return_pred:
          return eval_results, outputs
      else:
          return eval_results
  
imports: import json
  import logging
  import os
  from datetime import timedelta
  from typing import Dict, List, Optional, Union
  import pandas
  from omegaconf import DictConfig, OmegaConf
  from torch import nn
  from ..constants import BBOX, DDP, MAP, MULTI_IMAGE_MIX_DATASET, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, OVD_RET, XYWH
  from ..data import BaseDataModule, MultiImageMixDataset, MultiModalFeaturePreprocessor, infer_rois_column_type
  from ..optimization import LitModule, MMDetLitModule
  from ..utils import check_if_packages_installed, cocoeval, convert_pred_to_xywh, create_fusion_model, extract_from_output, from_coco_or_voc, get_detection_classes, object_detection_data_to_df, save_ovd_result_df, save_result_df, setup_save_path, split_train_tuning_data
  from .base import BaseLearner
label_desc: Use image node_id 16 for calling the ObjectDetectionLearner obj's underlying member method code with example usage: obj.evaluate_coco(anno_file_or_df, metrics, return_pred, eval_tool) and returns: eval_results,  outputs, eval_results