 b/tests/test_data/test_dataset.py
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import shutil
+import tempfile
+from typing import Generator
+from unittest.mock import MagicMock, patch
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+from mmseg.core.evaluation import get_classes, get_palette
+from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
+                            ConcatDataset, CustomDataset, LoveDADataset,
+                            PascalVOCDataset, RepeatDataset, build_dataset)
+def test_classes():
+    assert list(CityscapesDataset.CLASSES) == get_classes('cityscapes')
+    assert list(PascalVOCDataset.CLASSES) == get_classes('voc') == get_classes(
+        'pascal_voc')
+    assert list(
+        ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
+    with pytest.raises(ValueError):
+        get_classes('unsupported')
+def test_classes_file_path():
+    tmp_file = tempfile.NamedTemporaryFile()
+    classes_path = f'{tmp_file.name}.txt'
+    train_pipeline = [dict(type='LoadImageFromFile')]
+    kwargs = dict(pipeline=train_pipeline, img_dir='./', classes=classes_path)
+    # classes.txt with full categories
+    categories = get_classes('cityscapes')
+    with open(classes_path, 'w') as f:
+        f.write('\n'.join(categories))
+    assert list(CityscapesDataset(**kwargs).CLASSES) == categories
+    # classes.txt with sub categories
+    categories = ['road', 'sidewalk', 'building']
+    with open(classes_path, 'w') as f:
+        f.write('\n'.join(categories))
+    assert list(CityscapesDataset(**kwargs).CLASSES) == categories
+    # classes.txt with unknown categories
+    categories = ['road', 'sidewalk', 'unknown']
+    with open(classes_path, 'w') as f:
+        f.write('\n'.join(categories))
+    with pytest.raises(ValueError):
+        CityscapesDataset(**kwargs)
+    tmp_file.close()
+    os.remove(classes_path)
+    assert not osp.exists(classes_path)
+def test_palette():
+    assert CityscapesDataset.PALETTE == get_palette('cityscapes')
+    assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
+        'pascal_voc')
+    assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
+    with pytest.raises(ValueError):
+        get_palette('unsupported')
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+def test_dataset_wrapper():
+    # CustomDataset.load_annotations = MagicMock()
+    # CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
+    dataset_a = CustomDataset(img_dir=MagicMock(), pipeline=[])
+    len_a = 10
+    dataset_a.img_infos = MagicMock()
+    dataset_a.img_infos.__len__.return_value = len_a
+    dataset_b = CustomDataset(img_dir=MagicMock(), pipeline=[])
+    len_b = 20
+    dataset_b.img_infos = MagicMock()
+    dataset_b.img_infos.__len__.return_value = len_b
+    concat_dataset = ConcatDataset([dataset_a, dataset_b])
+    assert concat_dataset[5] == 5
+    assert concat_dataset[25] == 15
+    assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
+    repeat_dataset = RepeatDataset(dataset_a, 10)
+    assert repeat_dataset[5] == 5
+    assert repeat_dataset[15] == 5
+    assert repeat_dataset[27] == 7
+    assert len(repeat_dataset) == 10 * len(dataset_a)
+def test_custom_dataset():
+    img_norm_cfg = dict(
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True)
+    crop_size = (512, 1024)
+    train_pipeline = [
+        dict(type='LoadImageFromFile'),
+        dict(type='LoadAnnotations'),
+        dict(type='Resize', img_scale=(128, 256), ratio_range=(0.5, 2.0)),
+        dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+        dict(type='RandomFlip', prob=0.5),
+        dict(type='PhotoMetricDistortion'),
+        dict(type='Normalize', **img_norm_cfg),
+        dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+        dict(type='DefaultFormatBundle'),
+        dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+    ]
+    test_pipeline = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(128, 256),
+            # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize', **img_norm_cfg),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+    # with img_dir and ann_dir
+    train_dataset = CustomDataset(
+        train_pipeline,
+        data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+        img_dir='imgs/',
+        ann_dir='gts/',
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png')
+    assert len(train_dataset) == 5
+    # with img_dir, ann_dir, split
+    train_dataset = CustomDataset(
+        train_pipeline,
+        data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+        img_dir='imgs/',
+        ann_dir='gts/',
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png',
+        split='splits/train.txt')
+    assert len(train_dataset) == 4
+    # no data_root
+    train_dataset = CustomDataset(
+        train_pipeline,
+        img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
+        ann_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts'),
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png')
+    assert len(train_dataset) == 5
+    # with data_root but img_dir/ann_dir are abs path
+    train_dataset = CustomDataset(
+        train_pipeline,
+        data_root=osp.join(osp.dirname(__file__), '../data/pseudo_dataset'),
+        img_dir=osp.abspath(
+            osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs')),
+        ann_dir=osp.abspath(
+            osp.join(osp.dirname(__file__), '../data/pseudo_dataset/gts')),
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png')
+    assert len(train_dataset) == 5
+    # test_mode=True
+    test_dataset = CustomDataset(
+        test_pipeline,
+        img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
+        img_suffix='img.jpg',
+        test_mode=True,
+        classes=('pseudo_class', ))
+    assert len(test_dataset) == 5
+    # training data get
+    train_data = train_dataset[0]
+    assert isinstance(train_data, dict)
+    # test data get
+    test_data = test_dataset[0]
+    assert isinstance(test_data, dict)
+    # get gt seg map
+    gt_seg_maps = train_dataset.get_gt_seg_maps(efficient_test=True)
+    assert isinstance(gt_seg_maps, Generator)
+    gt_seg_maps = list(gt_seg_maps)
+    assert len(gt_seg_maps) == 5
+    # format_results not implemented
+    with pytest.raises(NotImplementedError):
+        test_dataset.format_results([], '')
+    pseudo_results = []
+    for gt_seg_map in gt_seg_maps:
+        h, w = gt_seg_map.shape
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    # test past evaluation without CLASSES
+    with pytest.raises(TypeError):
+        eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
+    with pytest.raises(TypeError):
+        eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+    with pytest.raises(TypeError):
+        eval_results = train_dataset.evaluate(
+            pseudo_results, metric=['mDice', 'mIoU'])
+    # test past evaluation with CLASSES
+    train_dataset.CLASSES = tuple(['a'] * 7)
+    eval_results = train_dataset.evaluate(pseudo_results, metric='mIoU')
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+    assert isinstance(eval_results, dict)
+    assert 'mDice' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
+    assert isinstance(eval_results, dict)
+    assert 'mRecall' in eval_results
+    assert 'mPrecision' in eval_results
+    assert 'mFscore' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(
+        pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mDice' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    assert 'mFscore' in eval_results
+    assert 'mPrecision' in eval_results
+    assert 'mRecall' in eval_results
+    assert not np.isnan(eval_results['mIoU'])
+    assert not np.isnan(eval_results['mDice'])
+    assert not np.isnan(eval_results['mAcc'])
+    assert not np.isnan(eval_results['aAcc'])
+    assert not np.isnan(eval_results['mFscore'])
+    assert not np.isnan(eval_results['mPrecision'])
+    assert not np.isnan(eval_results['mRecall'])
+    # test evaluation with pre-eval and the dataset.CLASSES is necessary
+    train_dataset.CLASSES = tuple(['a'] * 7)
+    pseudo_results = []
+    for idx in range(len(train_dataset)):
+        h, w = gt_seg_maps[idx].shape
+        pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
+        pseudo_results.extend(train_dataset.pre_eval(pseudo_result, idx))
+    eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(pseudo_results, metric='mDice')
+    assert isinstance(eval_results, dict)
+    assert 'mDice' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
+    assert isinstance(eval_results, dict)
+    assert 'mRecall' in eval_results
+    assert 'mPrecision' in eval_results
+    assert 'mFscore' in eval_results
+    assert 'aAcc' in eval_results
+    eval_results = train_dataset.evaluate(
+        pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+    assert isinstance(eval_results, dict)
+    assert 'mIoU' in eval_results
+    assert 'mDice' in eval_results
+    assert 'mAcc' in eval_results
+    assert 'aAcc' in eval_results
+    assert 'mFscore' in eval_results
+    assert 'mPrecision' in eval_results
+    assert 'mRecall' in eval_results
+    assert not np.isnan(eval_results['mIoU'])
+    assert not np.isnan(eval_results['mDice'])
+    assert not np.isnan(eval_results['mAcc'])
+    assert not np.isnan(eval_results['aAcc'])
+    assert not np.isnan(eval_results['mFscore'])
+    assert not np.isnan(eval_results['mPrecision'])
+    assert not np.isnan(eval_results['mRecall'])
+@pytest.mark.parametrize('separate_eval', [True, False])
+def test_eval_concat_custom_dataset(separate_eval):
+    img_norm_cfg = dict(
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True)
+    test_pipeline = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(128, 256),
+            # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize', **img_norm_cfg),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+    data_root = osp.join(osp.dirname(__file__), '../data/pseudo_dataset')
+    img_dir = 'imgs/'
+    ann_dir = 'gts/'
+    cfg1 = dict(
+        type='CustomDataset',
+        pipeline=test_pipeline,
+        data_root=data_root,
+        img_dir=img_dir,
+        ann_dir=ann_dir,
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png',
+        classes=tuple(['a'] * 7))
+    dataset1 = build_dataset(cfg1)
+    assert len(dataset1) == 5
+    # get gt seg map
+    gt_seg_maps = dataset1.get_gt_seg_maps(efficient_test=True)
+    assert isinstance(gt_seg_maps, Generator)
+    gt_seg_maps = list(gt_seg_maps)
+    assert len(gt_seg_maps) == 5
+    # test past evaluation
+    pseudo_results = []
+    for gt_seg_map in gt_seg_maps:
+        h, w = gt_seg_map.shape
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    eval_results1 = dataset1.evaluate(
+        pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
+    # We use same dir twice for simplicity
+    # with ann_dir
+    cfg2 = dict(
+        type='CustomDataset',
+        pipeline=test_pipeline,
+        data_root=data_root,
+        img_dir=[img_dir, img_dir],
+        ann_dir=[ann_dir, ann_dir],
+        img_suffix='img.jpg',
+        seg_map_suffix='gt.png',
+        classes=tuple(['a'] * 7),
+        separate_eval=separate_eval)
+    dataset2 = build_dataset(cfg2)
+    assert isinstance(dataset2, ConcatDataset)
+    assert len(dataset2) == 10
+    eval_results2 = dataset2.evaluate(
+        pseudo_results * 2, metric=['mIoU', 'mDice', 'mFscore'])
+    if separate_eval:
+        assert eval_results1['mIoU'] == eval_results2[
+            '0_mIoU'] == eval_results2['1_mIoU']
+        assert eval_results1['mDice'] == eval_results2[
+            '0_mDice'] == eval_results2['1_mDice']
+        assert eval_results1['mAcc'] == eval_results2[
+            '0_mAcc'] == eval_results2['1_mAcc']
+        assert eval_results1['aAcc'] == eval_results2[
+            '0_aAcc'] == eval_results2['1_aAcc']
+        assert eval_results1['mFscore'] == eval_results2[
+            '0_mFscore'] == eval_results2['1_mFscore']
+        assert eval_results1['mPrecision'] == eval_results2[
+            '0_mPrecision'] == eval_results2['1_mPrecision']
+        assert eval_results1['mRecall'] == eval_results2[
+            '0_mRecall'] == eval_results2['1_mRecall']
+    else:
+        assert eval_results1['mIoU'] == eval_results2['mIoU']
+        assert eval_results1['mDice'] == eval_results2['mDice']
+        assert eval_results1['mAcc'] == eval_results2['mAcc']
+        assert eval_results1['aAcc'] == eval_results2['aAcc']
+        assert eval_results1['mFscore'] == eval_results2['mFscore']
+        assert eval_results1['mPrecision'] == eval_results2['mPrecision']
+        assert eval_results1['mRecall'] == eval_results2['mRecall']
+    # test get dataset_idx and sample_idx from ConcateDataset
+    dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(3)
+    assert dataset_idx == 0
+    assert sample_idx == 3
+    dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(7)
+    assert dataset_idx == 1
+    assert sample_idx == 2
+    dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-7)
+    assert dataset_idx == 0
+    assert sample_idx == 3
+    # test negative indice exceed length of dataset
+    with pytest.raises(ValueError):
+        dataset_idx, sample_idx = dataset2.get_dataset_idx_and_sample_idx(-11)
+    # test negative indice value
+    indice = -6
+    dataset_idx1, sample_idx1 = dataset2.get_dataset_idx_and_sample_idx(indice)
+    dataset_idx2, sample_idx2 = dataset2.get_dataset_idx_and_sample_idx(
+        len(dataset2) + indice)
+    assert dataset_idx1 == dataset_idx2
+    assert sample_idx1 == sample_idx2
+    # test evaluation with pre-eval and the dataset.CLASSES is necessary
+    pseudo_results = []
+    eval_results1 = []
+    for idx in range(len(dataset1)):
+        h, w = gt_seg_maps[idx].shape
+        pseudo_result = np.random.randint(low=0, high=7, size=(h, w))
+        pseudo_results.append(pseudo_result)
+        eval_results1.extend(dataset1.pre_eval(pseudo_result, idx))
+    assert len(eval_results1) == len(dataset1)
+    assert isinstance(eval_results1[0], tuple)
+    assert len(eval_results1[0]) == 4
+    assert isinstance(eval_results1[0][0], torch.Tensor)
+    eval_results1 = dataset1.evaluate(
+        eval_results1, metric=['mIoU', 'mDice', 'mFscore'])
+    pseudo_results = pseudo_results * 2
+    eval_results2 = []
+    for idx in range(len(dataset2)):
+        eval_results2.extend(dataset2.pre_eval(pseudo_results[idx], idx))
+    assert len(eval_results2) == len(dataset2)
+    assert isinstance(eval_results2[0], tuple)
+    assert len(eval_results2[0]) == 4
+    assert isinstance(eval_results2[0][0], torch.Tensor)
+    eval_results2 = dataset2.evaluate(
+        eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
+    if separate_eval:
+        assert eval_results1['mIoU'] == eval_results2[
+            '0_mIoU'] == eval_results2['1_mIoU']
+        assert eval_results1['mDice'] == eval_results2[
+            '0_mDice'] == eval_results2['1_mDice']
+        assert eval_results1['mAcc'] == eval_results2[
+            '0_mAcc'] == eval_results2['1_mAcc']
+        assert eval_results1['aAcc'] == eval_results2[
+            '0_aAcc'] == eval_results2['1_aAcc']
+        assert eval_results1['mFscore'] == eval_results2[
+            '0_mFscore'] == eval_results2['1_mFscore']
+        assert eval_results1['mPrecision'] == eval_results2[
+            '0_mPrecision'] == eval_results2['1_mPrecision']
+        assert eval_results1['mRecall'] == eval_results2[
+            '0_mRecall'] == eval_results2['1_mRecall']
+    else:
+        assert eval_results1['mIoU'] == eval_results2['mIoU']
+        assert eval_results1['mDice'] == eval_results2['mDice']
+        assert eval_results1['mAcc'] == eval_results2['mAcc']
+        assert eval_results1['aAcc'] == eval_results2['aAcc']
+        assert eval_results1['mFscore'] == eval_results2['mFscore']
+        assert eval_results1['mPrecision'] == eval_results2['mPrecision']
+        assert eval_results1['mRecall'] == eval_results2['mRecall']
+    # test batch_indices for pre eval
+    eval_results2 = dataset2.pre_eval(pseudo_results,
+                                      list(range(len(pseudo_results))))
+    assert len(eval_results2) == len(dataset2)
+    assert isinstance(eval_results2[0], tuple)
+    assert len(eval_results2[0]) == 4
+    assert isinstance(eval_results2[0][0], torch.Tensor)
+    eval_results2 = dataset2.evaluate(
+        eval_results2, metric=['mIoU', 'mDice', 'mFscore'])
+    if separate_eval:
+        assert eval_results1['mIoU'] == eval_results2[
+            '0_mIoU'] == eval_results2['1_mIoU']
+        assert eval_results1['mDice'] == eval_results2[
+            '0_mDice'] == eval_results2['1_mDice']
+        assert eval_results1['mAcc'] == eval_results2[
+            '0_mAcc'] == eval_results2['1_mAcc']
+        assert eval_results1['aAcc'] == eval_results2[
+            '0_aAcc'] == eval_results2['1_aAcc']
+        assert eval_results1['mFscore'] == eval_results2[
+            '0_mFscore'] == eval_results2['1_mFscore']
+        assert eval_results1['mPrecision'] == eval_results2[
+            '0_mPrecision'] == eval_results2['1_mPrecision']
+        assert eval_results1['mRecall'] == eval_results2[
+            '0_mRecall'] == eval_results2['1_mRecall']
+    else:
+        assert eval_results1['mIoU'] == eval_results2['mIoU']
+        assert eval_results1['mDice'] == eval_results2['mDice']
+        assert eval_results1['mAcc'] == eval_results2['mAcc']
+        assert eval_results1['aAcc'] == eval_results2['aAcc']
+        assert eval_results1['mFscore'] == eval_results2['mFscore']
+        assert eval_results1['mPrecision'] == eval_results2['mPrecision']
+        assert eval_results1['mRecall'] == eval_results2['mRecall']
+def test_ade():
+    test_dataset = ADE20KDataset(
+        pipeline=[],
+        img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+    assert len(test_dataset) == 5
+    # Test format_results
+    pseudo_results = []
+    for _ in range(len(test_dataset)):
+        h, w = (2, 2)
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    file_paths = test_dataset.format_results(pseudo_results, '.format_ade')
+    assert len(file_paths) == len(test_dataset)
+    temp = np.array(Image.open(file_paths[0]))
+    assert np.allclose(temp, pseudo_results[0] + 1)
+    shutil.rmtree('.format_ade')
+@pytest.mark.parametrize('separate_eval', [True, False])
+def test_concat_ade(separate_eval):
+    test_dataset = ADE20KDataset(
+        pipeline=[],
+        img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+    assert len(test_dataset) == 5
+    concat_dataset = ConcatDataset([test_dataset, test_dataset],
+                                   separate_eval=separate_eval)
+    assert len(concat_dataset) == 10
+    # Test format_results
+    pseudo_results = []
+    for _ in range(len(concat_dataset)):
+        h, w = (2, 2)
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    # test format per image
+    file_paths = []
+    for i in range(len(pseudo_results)):
+        file_paths.extend(
+            concat_dataset.format_results([pseudo_results[i]],
+                                          '.format_ade',
+                                          indices=[i]))
+    assert len(file_paths) == len(concat_dataset)
+    temp = np.array(Image.open(file_paths[0]))
+    assert np.allclose(temp, pseudo_results[0] + 1)
+    shutil.rmtree('.format_ade')
+    # test default argument
+    file_paths = concat_dataset.format_results(pseudo_results, '.format_ade')
+    assert len(file_paths) == len(concat_dataset)
+    temp = np.array(Image.open(file_paths[0]))
+    assert np.allclose(temp, pseudo_results[0] + 1)
+    shutil.rmtree('.format_ade')
+def test_cityscapes():
+    test_dataset = CityscapesDataset(
+        pipeline=[],
+        img_dir=osp.join(
+            osp.dirname(__file__),
+            '../data/pseudo_cityscapes_dataset/leftImg8bit'),
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
+    assert len(test_dataset) == 1
+    gt_seg_maps = list(test_dataset.get_gt_seg_maps())
+    # Test format_results
+    pseudo_results = []
+    for idx in range(len(test_dataset)):
+        h, w = gt_seg_maps[idx].shape
+        pseudo_results.append(np.random.randint(low=0, high=19, size=(h, w)))
+    file_paths = test_dataset.format_results(pseudo_results, '.format_city')
+    assert len(file_paths) == len(test_dataset)
+    temp = np.array(Image.open(file_paths[0]))
+    assert np.allclose(temp,
+                       test_dataset._convert_to_label_id(pseudo_results[0]))
+    # Test cityscapes evaluate
+    test_dataset.evaluate(
+        pseudo_results, metric='cityscapes', imgfile_prefix='.format_city')
+    shutil.rmtree('.format_city')
+@pytest.mark.parametrize('separate_eval', [True, False])
+def test_concat_cityscapes(separate_eval):
+    cityscape_dataset = CityscapesDataset(
+        pipeline=[],
+        img_dir=osp.join(
+            osp.dirname(__file__),
+            '../data/pseudo_cityscapes_dataset/leftImg8bit'),
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_cityscapes_dataset/gtFine'))
+    assert len(cityscape_dataset) == 1
+    with pytest.raises(NotImplementedError):
+        _ = ConcatDataset([cityscape_dataset, cityscape_dataset],
+                          separate_eval=separate_eval)
+    ade_dataset = ADE20KDataset(
+        pipeline=[],
+        img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'))
+    assert len(ade_dataset) == 5
+    with pytest.raises(NotImplementedError):
+        _ = ConcatDataset([cityscape_dataset, ade_dataset],
+                          separate_eval=separate_eval)
+def test_loveda():
+    test_dataset = LoveDADataset(
+        pipeline=[],
+        img_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_loveda_dataset/img_dir'),
+        ann_dir=osp.join(
+            osp.dirname(__file__), '../data/pseudo_loveda_dataset/ann_dir'))
+    assert len(test_dataset) == 3
+    gt_seg_maps = list(test_dataset.get_gt_seg_maps())
+    # Test format_results
+    pseudo_results = []
+    for idx in range(len(test_dataset)):
+        h, w = gt_seg_maps[idx].shape
+        pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
+    file_paths = test_dataset.format_results(pseudo_results, '.format_loveda')
+    assert len(file_paths) == len(test_dataset)
+    # Test loveda evaluate
+    test_dataset.evaluate(
+        pseudo_results, metric='mIoU', imgfile_prefix='.format_loveda')
+    shutil.rmtree('.format_loveda')
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+@pytest.mark.parametrize('dataset, classes', [
+    ('ADE20KDataset', ('wall', 'building')),
+    ('CityscapesDataset', ('road', 'sidewalk')),
+    ('CustomDataset', ('bus', 'car')),
+    ('PascalVOCDataset', ('aeroplane', 'bicycle')),
+])
+def test_custom_classes_override_default(dataset, classes):
+    dataset_class = DATASETS.get(dataset)
+    original_classes = dataset_class.CLASSES
+    # Test setting classes as a tuple
+    custom_dataset = dataset_class(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=classes,
+        test_mode=True)
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == classes
+    # Test setting classes as a list
+    custom_dataset = dataset_class(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=list(classes),
+        test_mode=True)
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == list(classes)
+    # Test overriding not a subset
+    custom_dataset = dataset_class(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=[classes[0]],
+        test_mode=True)
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == [classes[0]]
+    # Test default behavior
+    if dataset_class is CustomDataset:
+        with pytest.raises(AssertionError):
+            custom_dataset = dataset_class(
+                pipeline=[],
+                img_dir=MagicMock(),
+                split=MagicMock(),
+                classes=None,
+                test_mode=True)
+    else:
+        custom_dataset = dataset_class(
+            pipeline=[],
+            img_dir=MagicMock(),
+            split=MagicMock(),
+            classes=None,
+            test_mode=True)
+        assert custom_dataset.CLASSES == original_classes
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+def test_custom_dataset_random_palette_is_generated():
+    dataset = CustomDataset(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=('bus', 'car'),
+        test_mode=True)
+    assert len(dataset.PALETTE) == 2
+    for class_color in dataset.PALETTE:
+        assert len(class_color) == 3
+        assert all(x >= 0 and x <= 255 for x in class_color)
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+def test_custom_dataset_custom_palette():
+    dataset = CustomDataset(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=('bus', 'car'),
+        palette=[[100, 100, 100], [200, 200, 200]],
+        test_mode=True)
+    assert tuple(dataset.PALETTE) == tuple([[100, 100, 100], [200, 200, 200]])