code: def test_evaluate(self):
      # create dummy data
      fake_json_file = osp.join(self.tmp_dir.name, "fake_data.json")
      self._create_dummy_coco_json(fake_json_file)
      dummy_pred = self._create_dummy_results()
  
      # test single coco dataset evaluation
      coco_metric = CocoMetric(
          ann_file=fake_json_file,
          classwise=False,
          outfile_prefix=f"{self.tmp_dir.name}/test",
      )
      coco_metric.dataset_meta = dict(classes=["car", "bicycle"])
      coco_metric.process(
          {},
          [
              dict(
                  pred_instances=dummy_pred,
                  img_id=0,
                  ori_shape=(640, 640),
              )
          ],
      )
      eval_results = coco_metric.evaluate(size=1)
      target = {
          "coco/bbox_mAP": 1.0,
          "coco/bbox_mAP_50": 1.0,
          "coco/bbox_mAP_75": 1.0,
          "coco/bbox_mAP_s": 1.0,
          "coco/bbox_mAP_m": 1.0,
          "coco/bbox_mAP_l": 1.0,
      }
      self.assertDictEqual(eval_results, target)
      self.assertTrue(
          osp.isfile(osp.join(self.tmp_dir.name, "test.bbox.json"))
      )
  
      # test box and segm coco dataset evaluation
      coco_metric = CocoMetric(
          ann_file=fake_json_file,
          metric=["bbox", "segm"],
          classwise=False,
          outfile_prefix=f"{self.tmp_dir.name}/test",
      )
      coco_metric.dataset_meta = dict(classes=["car", "bicycle"])
      coco_metric.process(
          {},
          [
              dict(
                  pred_instances=dummy_pred,
                  img_id=0,
                  ori_shape=(640, 640),
              )
          ],
      )
      eval_results = coco_metric.evaluate(size=1)
      target = {
          "coco/bbox_mAP": 1.0,
          "coco/bbox_mAP_50": 1.0,
          "coco/bbox_mAP_75": 1.0,
          "coco/bbox_mAP_s": 1.0,
          "coco/bbox_mAP_m": 1.0,
          "coco/bbox_mAP_l": 1.0,
          "coco/segm_mAP": 1.0,
          "coco/segm_mAP_50": 1.0,
          "coco/segm_mAP_75": 1.0,
          "coco/segm_mAP_s": 1.0,
          "coco/segm_mAP_m": 1.0,
          "coco/segm_mAP_l": 1.0,
      }
      self.assertDictEqual(eval_results, target)
      self.assertTrue(
          osp.isfile(osp.join(self.tmp_dir.name, "test.bbox.json"))
      )
      self.assertTrue(
          osp.isfile(osp.join(self.tmp_dir.name, "test.segm.json"))
      )
  
      # test invalid custom metric_items
      with self.assertRaisesRegex(
          KeyError, 'metric item "invalid" is not supported'
      ):
          coco_metric = CocoMetric(
              ann_file=fake_json_file, metric_items=["invalid"]
          )
          coco_metric.dataset_meta = dict(classes=["car", "bicycle"])
          coco_metric.process(
              {},
              [
                  dict(
                      pred_instances=dummy_pred,
                      img_id=0,
                      ori_shape=(640, 640),
                  )
              ],
          )
          coco_metric.evaluate(size=1)
  
      # test custom metric_items
      coco_metric = CocoMetric(
          ann_file=fake_json_file, metric_items=["mAP_m"]
      )
      coco_metric.dataset_meta = dict(classes=["car", "bicycle"])
      coco_metric.process(
          {},
          [
              dict(
                  pred_instances=dummy_pred,
                  img_id=0,
                  ori_shape=(640, 640),
              )
          ],
      )
      eval_results = coco_metric.evaluate(size=1)
      target = {
          "coco/bbox_mAP_m": 1.0,
      }
      self.assertDictEqual(eval_results, target)
  
imports: import os.path
  import tempfile
  from unittest import TestCase
  import numpy
  import pycocotools.mask
  import torch
  from mmengine.fileio import dump
  from mmdet.evaluation import CocoMetric
label_desc: Use image node_id 6 for calling the TestCocoMetric obj's underlying member method code with example usage: obj.test_evaluate() without return types