[Feature] Support eval concate dataset and add tool to show dataset (#833)

* [Feature] Add tool to show origin or augmented train data

* [Feature] Support eval concate dataset

* Add docstring and modify evaluate of concate dataset

Signed-off-by: FreyWang <wangwxyz@qq.com>

* format concat dataset in subfolder of imgfile_prefix

Signed-off-by: FreyWang <wangwxyz@qq.com>

* add unittest of concate dataset

Signed-off-by: FreyWang <wangwxyz@qq.com>

* update unittest for eval dataset with CLASSES is None

Signed-off-by: FreyWang <wangwxyz@qq.com>

* [FIX] bug of generator,  which lead metric to nan when pre_eval=False

Signed-off-by: FreyWang <wangwxyz@qq.com>

* format code

Signed-off-by: FreyWang <wangwxyz@qq.com>

* add more unittest

* add more unittest

* optim concat dataset builder

mmseg/core/evaluation/metrics.py

@@ -112,8 +112,6 @@ def total_intersect_and_union(results,
          ndarray: The prediction histogram on all classes.
          ndarray: The ground truth histogram on all classes.
     """
-    num_imgs = len(results)
-    assert len(list(gt_seg_maps)) == num_imgs
     total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)

mmseg/datasets/builder.py

@@ -30,6 +30,8 @@ def _concat_dataset(cfg, default_args=None):
     img_dir = cfg['img_dir']
     ann_dir = cfg.get('ann_dir', None)
     split = cfg.get('split', None)
+    # pop 'separate_eval' since it is not a valid key for common datasets.
+    separate_eval = cfg.pop('separate_eval', True)
     num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
     if ann_dir is not None:
         num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
@@ -57,7 +59,7 @@ def _concat_dataset(cfg, default_args=None):
             data_cfg['split'] = split[i]
         datasets.append(build_dataset(data_cfg, default_args))
 
-    return ConcatDataset(datasets)
+    return ConcatDataset(datasets, separate_eval)
 
 
 def build_dataset(cfg, default_args=None):

mmseg/datasets/custom.py

@@ -2,7 +2,6 @@
 import os.path as osp
 import warnings
 from collections import OrderedDict
-from functools import reduce
 
 import mmcv
 import numpy as np
@@ -99,6 +98,9 @@ class CustomDataset(Dataset):
         self.label_map = None
         self.CLASSES, self.PALETTE = self.get_classes_and_palette(
             classes, palette)
+        if test_mode:
+            assert self.CLASSES is not None, \
+                '`cls.CLASSES` or `classes` should be specified when testing'
 
         # join paths if data_root is specified
         if self.data_root is not None:
@@ -339,7 +341,12 @@ class CustomDataset(Dataset):
 
         return palette
 
-    def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
+    def evaluate(self,
+                 results,
+                 metric='mIoU',
+                 logger=None,
+                 gt_seg_maps=None,
+                 **kwargs):
         """Evaluate the dataset.
 
         Args:
@@ -350,6 +357,8 @@ class CustomDataset(Dataset):
                 'mDice' and 'mFscore' are supported.
             logger (logging.Logger | None | str): Logger used for printing
                 related information during evaluation. Default: None.
+            gt_seg_maps (generator[ndarray]): Custom gt seg maps as input,
+                used in ConcatDataset
 
         Returns:
             dict[str, float]: Default metrics.
@@ -364,14 +373,9 @@ class CustomDataset(Dataset):
         # test a list of files
         if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
                 results, str):
-            gt_seg_maps = self.get_gt_seg_maps()
-            if self.CLASSES is None:
-                num_classes = len(
-                    reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
-            else:
-                num_classes = len(self.CLASSES)
-            # reset generator
-            gt_seg_maps = self.get_gt_seg_maps()
+            if gt_seg_maps is None:
+                gt_seg_maps = self.get_gt_seg_maps()
+            num_classes = len(self.CLASSES)
             ret_metrics = eval_metrics(
                 results,
                 gt_seg_maps,

mmseg/datasets/dataset_wrappers.py

@@ -1,7 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+from itertools import chain
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
 from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
 
 from .builder import DATASETS
+from .cityscapes import CityscapesDataset
 
 
 @DATASETS.register_module()
@@ -9,16 +16,148 @@ class ConcatDataset(_ConcatDataset):
     """A wrapper of concatenated dataset.
 
     Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
-    concat the group flag for image aspect ratio.
+    support evaluation and formatting results
 
     Args:
         datasets (list[:obj:`Dataset`]): A list of datasets.
+        separate_eval (bool): Whether to evaluate the concatenated
+            dataset results separately, Defaults to True.
     """
 
-    def __init__(self, datasets):
+    def __init__(self, datasets, separate_eval=True):
         super(ConcatDataset, self).__init__(datasets)
         self.CLASSES = datasets[0].CLASSES
         self.PALETTE = datasets[0].PALETTE
+        self.separate_eval = separate_eval
+        assert separate_eval in [True, False], \
+            f'separate_eval can only be True or False,' \
+            f'but get {separate_eval}'
+        if any([isinstance(ds, CityscapesDataset) for ds in datasets]):
+            raise NotImplementedError(
+                'Evaluating ConcatDataset containing CityscapesDataset'
+                'is not supported!')
+
+    def evaluate(self, results, logger=None, **kwargs):
+        """Evaluate the results.
+
+        Args:
+            results (list[tuple[torch.Tensor]] | list[str]]): per image
+                pre_eval results or predict segmentation map for
+                computing evaluation metric.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+
+        Returns:
+            dict[str: float]: evaluate results of the total dataset
+                or each separate
+            dataset if `self.separate_eval=True`.
+        """
+        assert len(results) == self.cumulative_sizes[-1], \
+            ('Dataset and results have different sizes: '
+             f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
+
+        # Check whether all the datasets support evaluation
+        for dataset in self.datasets:
+            assert hasattr(dataset, 'evaluate'), \
+                f'{type(dataset)} does not implement evaluate function'
+
+        if self.separate_eval:
+            dataset_idx = -1
+            total_eval_results = dict()
+            for size, dataset in zip(self.cumulative_sizes, self.datasets):
+                start_idx = 0 if dataset_idx == -1 else \
+                    self.cumulative_sizes[dataset_idx]
+                end_idx = self.cumulative_sizes[dataset_idx + 1]
+
+                results_per_dataset = results[start_idx:end_idx]
+                print_log(
+                    f'\nEvaluateing {dataset.img_dir} with '
+                    f'{len(results_per_dataset)} images now',
+                    logger=logger)
+
+                eval_results_per_dataset = dataset.evaluate(
+                    results_per_dataset, logger=logger, **kwargs)
+                dataset_idx += 1
+                for k, v in eval_results_per_dataset.items():
+                    total_eval_results.update({f'{dataset_idx}_{k}': v})
+
+            return total_eval_results
+
+        if len(set([type(ds) for ds in self.datasets])) != 1:
+            raise NotImplementedError(
+                'All the datasets should have same types when '
+                'self.separate_eval=False')
+        else:
+            if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
+                    results, str):
+                # merge the generators of gt_seg_maps
+                gt_seg_maps = chain(
+                    *[dataset.get_gt_seg_maps() for dataset in self.datasets])
+            else:
+                # if the results are `pre_eval` results,
+                # we do not need gt_seg_maps to evaluate
+                gt_seg_maps = None
+            eval_results = self.datasets[0].evaluate(
+                results, gt_seg_maps=gt_seg_maps, logger=logger, **kwargs)
+            return eval_results
+
+    def get_dataset_idx_and_sample_idx(self, indice):
+        """Return dataset and sample index when given an indice of
+        ConcatDataset.
+
+        Args:
+            indice (int): indice of sample in ConcatDataset
+
+        Returns:
+            int: the index of sub dataset the sample belong to
+            int: the index of sample in its corresponding subset
+        """
+        if indice < 0:
+            if -indice > len(self):
+                raise ValueError(
+                    'absolute value of index should not exceed dataset length')
+            indice = len(self) + indice
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, indice)
+        if dataset_idx == 0:
+            sample_idx = indice
+        else:
+            sample_idx = indice - self.cumulative_sizes[dataset_idx - 1]
+        return dataset_idx, sample_idx
+
+    def format_results(self, results, imgfile_prefix, indices=None, **kwargs):
+        """format result for every sample of ConcatDataset."""
+        if indices is None:
+            indices = list(range(len(self)))
+
+        assert isinstance(results, list), 'results must be a list.'
+        assert isinstance(indices, list), 'indices must be a list.'
+
+        ret_res = []
+        for i, indice in enumerate(indices):
+            dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
+                indice)
+            res = self.datasets[dataset_idx].format_results(
+                [results[i]],
+                imgfile_prefix + f'/{dataset_idx}',
+                indices=[sample_idx],
+                **kwargs)
+            ret_res.append(res)
+        return sum(ret_res, [])
+
+    def pre_eval(self, preds, indices):
+        """do pre eval for every sample of ConcatDataset."""
+        # In order to compat with batch inference
+        if not isinstance(indices, list):
+            indices = [indices]
+        if not isinstance(preds, list):
+            preds = [preds]
+        ret_res = []
+        for i, indice in enumerate(indices):
+            dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
+                indice)
+            res = self.datasets[dataset_idx].pre_eval(preds[i], sample_idx)
+            ret_res.append(res)
+        return sum(ret_res, [])
 
 
 @DATASETS.register_module()

tests/test_data/test_dataset.py

@@ -6,12 +6,13 @@ 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, PascalVOCDataset,
-                            RepeatDataset)
+                            RepeatDataset, build_dataset)
 
 
 def test_classes():
@@ -143,7 +144,8 @@ def test_custom_dataset():
         test_pipeline,
         img_dir=osp.join(osp.dirname(__file__), '../data/pseudo_dataset/imgs'),
         img_suffix='img.jpg',
-        test_mode=True)
+        test_mode=True,
+        classes=('pseudo_class', ))
     assert len(test_dataset) == 5
 
     # training data get
@@ -164,30 +166,21 @@ def test_custom_dataset():
     with pytest.raises(NotImplementedError):
         test_dataset.format_results([], '')
 
-    # 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_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
+    # test past evaluation without CLASSES
+    with pytest.raises(TypeError):
+        eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
 
-    eval_results = train_dataset.evaluate(
-        pseudo_results, metric=['mDice', 'mIoU'])
-    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
+    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)
@@ -221,6 +214,14 @@ def test_custom_dataset():
     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 = []
@@ -258,6 +259,223 @@ def test_custom_dataset():
     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(
@@ -279,6 +497,44 @@ def test_ade():
     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=[],
@@ -311,6 +567,28 @@ def test_cityscapes():
     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)
+
+
 @patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
 @patch('mmseg.datasets.CustomDataset.__getitem__',
        MagicMock(side_effect=lambda idx: idx))
@@ -360,14 +638,23 @@ def test_custom_classes_override_default(dataset, classes):
     assert custom_dataset.CLASSES == [classes[0]]
 
     # Test default behavior
-    custom_dataset = dataset_class(
-        pipeline=[],
-        img_dir=MagicMock(),
-        split=MagicMock(),
-        classes=None,
-        test_mode=True)
+    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
+        assert custom_dataset.CLASSES == original_classes
 
 
 @patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)

tests/test_data/test_dataset_builder.py

@@ -78,7 +78,8 @@ def test_build_dataset():
         pipeline=[],
         data_root=data_root,
         img_dir=[img_dir, img_dir],
-        test_mode=True)
+        test_mode=True,
+        classes=('pseudo_class', ))
     dataset = build_dataset(cfg)
     assert isinstance(dataset, ConcatDataset)
     assert len(dataset) == 10
@@ -90,7 +91,8 @@ def test_build_dataset():
         data_root=data_root,
         img_dir=[img_dir, img_dir],
         split=['splits/val.txt', 'splits/val.txt'],
-        test_mode=True)
+        test_mode=True,
+        classes=('pseudo_class', ))
     dataset = build_dataset(cfg)
     assert isinstance(dataset, ConcatDataset)
     assert len(dataset) == 2

tools/browse_dataset.py

@@ -0,0 +1,167 @@
+import argparse
+import os
+import warnings
+from pathlib import Path
+
+import mmcv
+import numpy as np
+from mmcv import Config
+
+from mmseg.datasets.builder import build_dataset
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Browse a dataset')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--show-origin',
+        default=False,
+        action='store_true',
+        help='if True, omit all augmentation in pipeline,'
+        ' show origin image and seg map')
+    parser.add_argument(
+        '--skip-type',
+        type=str,
+        nargs='+',
+        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
+        help='skip some useless pipeline，if `show-origin` is true, '
+        'all pipeline except `Load` will be skipped')
+    parser.add_argument(
+        '--output-dir',
+        default='./output',
+        type=str,
+        help='If there is no display interface, you can save it')
+    parser.add_argument('--show', default=False, action='store_true')
+    parser.add_argument(
+        '--show-interval',
+        type=int,
+        default=999,
+        help='the interval of show (ms)')
+    parser.add_argument(
+        '--opacity',
+        type=float,
+        default=0.5,
+        help='the opacity of semantic map')
+    args = parser.parse_args()
+    return args
+
+
+def imshow_semantic(img,
+                    seg,
+                    class_names,
+                    palette=None,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None,
+                    opacity=0.5):
+    """Draw `result` over `img`.
+
+    Args:
+        img (str or Tensor): The image to be displayed.
+        seg (Tensor): The semantic segmentation results to draw over
+            `img`.
+        class_names (list[str]): Names of each classes.
+        palette (list[list[int]]] | np.ndarray | None): The palette of
+            segmentation map. If None is given, random palette will be
+            generated. Default: None
+        win_name (str): The window name.
+        wait_time (int): Value of waitKey param.
+            Default: 0.
+        show (bool): Whether to show the image.
+            Default: False.
+        out_file (str or None): The filename to write the image.
+            Default: None.
+        opacity(float): Opacity of painted segmentation map.
+            Default 0.5.
+            Must be in (0, 1] range.
+    Returns:
+        img (Tensor): Only if not `show` or `out_file`
+    """
+    img = mmcv.imread(img)
+    img = img.copy()
+    if palette is None:
+        palette = np.random.randint(0, 255, size=(len(class_names), 3))
+    palette = np.array(palette)
+    assert palette.shape[0] == len(class_names)
+    assert palette.shape[1] == 3
+    assert len(palette.shape) == 2
+    assert 0 < opacity <= 1.0
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    img = img * (1 - opacity) + color_seg * opacity
+    img = img.astype(np.uint8)
+    # if out_file specified, do not show image in window
+    if out_file is not None:
+        show = False
+
+    if show:
+        mmcv.imshow(img, win_name, wait_time)
+    if out_file is not None:
+        mmcv.imwrite(img, out_file)
+
+    if not (show or out_file):
+        warnings.warn('show==False and out_file is not specified, only '
+                      'result image will be returned')
+        return img
+
+
+def _retrieve_data_cfg(_data_cfg, skip_type, show_origin):
+    if show_origin is True:
+        # only keep pipeline of Loading data and ann
+        _data_cfg['pipeline'] = [
+            x for x in _data_cfg.pipeline if 'Load' in x['type']
+        ]
+    else:
+        _data_cfg['pipeline'] = [
+            x for x in _data_cfg.pipeline if x['type'] not in skip_type
+        ]
+
+
+def retrieve_data_cfg(config_path, skip_type, show_origin=False):
+    cfg = Config.fromfile(config_path)
+    train_data_cfg = cfg.data.train
+    if isinstance(train_data_cfg, list):
+        for _data_cfg in train_data_cfg:
+            if 'pipeline' in _data_cfg:
+                _retrieve_data_cfg(_data_cfg, skip_type, show_origin)
+            elif 'dataset' in _data_cfg:
+                _retrieve_data_cfg(_data_cfg['dataset'], skip_type,
+                                   show_origin)
+            else:
+                raise ValueError
+    elif 'dataset' in train_data_cfg:
+        _retrieve_data_cfg(train_data_cfg['dataset'], skip_type, show_origin)
+    else:
+        _retrieve_data_cfg(train_data_cfg, skip_type, show_origin)
+    return cfg
+
+
+def main():
+    args = parse_args()
+    cfg = retrieve_data_cfg(args.config, args.skip_type, args.show_origin)
+    dataset = build_dataset(cfg.data.train)
+    progress_bar = mmcv.ProgressBar(len(dataset))
+    for item in dataset:
+        filename = os.path.join(args.output_dir,
+                                Path(item['filename']).name
+                                ) if args.output_dir is not None else None
+        imshow_semantic(
+            item['img'],
+            item['gt_semantic_seg'],
+            dataset.CLASSES,
+            dataset.PALETTE,
+            show=args.show,
+            wait_time=args.show_interval,
+            out_file=filename,
+            opacity=args.opacity,
+        )
+        progress_bar.update()
+
+
+if __name__ == '__main__':
+    main()

tools/test.py

@@ -215,7 +215,8 @@ def main():
             print(f'\nwriting results to {args.out}')
             mmcv.dump(results, args.out)
         if args.eval:
-            metric = dataset.evaluate(results, args.eval, **eval_kwargs)
+            eval_kwargs.update(metric=args.eval)
+            metric = dataset.evaluate(results, **eval_kwargs)
             metric_dict = dict(config=args.config, metric=metric)
             if args.work_dir is not None and rank == 0:
                 mmcv.dump(metric_dict, json_file, indent=4)