code: def test_evaluate(self):
      dummy_mask1 = np.zeros((1, 20, 20), dtype=np.uint8)
      dummy_mask1[:, :10, :10] = 1
      dummy_mask2 = np.zeros((1, 20, 20), dtype=np.uint8)
      dummy_mask2[:, :10, :10] = 1
  
      self.outfile_prefix = osp.join(self.tmp_dir.name, "test")
      self.seg_prefix = osp.join(
          self.tmp_dir.name, "cityscapes/gtFine/val"
      )
      city = "lindau"
      sequenceNb = "000000"
      frameNb = "000019"
      img_name1 = (
          f"{city}_{sequenceNb}_{frameNb}_gtFine_instanceIds.png"
      )
      img_path1 = osp.join(self.seg_prefix, city, img_name1)
  
      frameNb = "000020"
      img_name2 = (
          f"{city}_{sequenceNb}_{frameNb}_gtFine_instanceIds.png"
      )
      img_path2 = osp.join(self.seg_prefix, city, img_name2)
      os.makedirs(osp.join(self.seg_prefix, city))
  
      masks1 = np.zeros((20, 20), dtype=np.int32)
      masks1[:10, :10] = 24 * 1000
      Image.fromarray(masks1).save(img_path1)
  
      masks2 = np.zeros((20, 20), dtype=np.int32)
      masks2[:10, :10] = 24 * 1000 + 1
      Image.fromarray(masks2).save(img_path2)
  
      data_samples = [
          {
              "img_path": img_path1,
              "pred_instances": {
                  "scores": torch.from_numpy(np.array([1.0])),
                  "labels": torch.from_numpy(np.array([0])),
                  "masks": torch.from_numpy(dummy_mask1),
              },
          },
          {
              "img_path": img_path2,
              "pred_instances": {
                  "scores": torch.from_numpy(np.array([0.98])),
                  "labels": torch.from_numpy(np.array([1])),
                  "masks": torch.from_numpy(dummy_mask2),
              },
          },
      ]
  
      target = {"cityscapes/mAP": 0.5, "cityscapes/AP@50": 0.5}
      metric = CityScapesMetric(
          seg_prefix=self.seg_prefix,
          format_only=False,
          outfile_prefix=self.outfile_prefix,
      )
      metric.dataset_meta = dict(
          classes=(
              "person",
              "rider",
              "car",
              "truck",
              "bus",
              "train",
              "motorcycle",
              "bicycle",
          )
      )
      metric.process({}, data_samples)
      results = metric.evaluate(size=2)
      self.assertDictEqual(results, target)
      del metric
      self.assertTrue(not osp.exists("{self.outfile_prefix}.results"))
  
      # test format_only
      metric = CityScapesMetric(
          seg_prefix=self.seg_prefix,
          format_only=True,
          outfile_prefix=self.outfile_prefix,
      )
      metric.dataset_meta = dict(
          classes=(
              "person",
              "rider",
              "car",
              "truck",
              "bus",
              "train",
              "motorcycle",
              "bicycle",
          )
      )
      metric.process({}, data_samples)
      results = metric.evaluate(size=2)
      self.assertDictEqual(results, dict())
  
file_path: mmdetection/tests/test_evaluation/test_metrics/test_cityscapes_metric.py
imports: import os
  import os.path
  import tempfile
  import unittest
  import numpy
  import torch
  from PIL import Image
  from mmdet.evaluation import CityScapesMetric
label_desc: Use image node_id 4 for calling the TestCityScapesMetric obj's underlying member method code with example usage: obj.test_evaluate() without return types