--- a
+++ b/tests/test_config.py
@@ -0,0 +1,161 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import glob
+import os
+from os.path import dirname, exists, isdir, join, relpath
+
+from mmcv import Config
+from torch import nn
+
+from mmseg.models import build_segmentor
+
+
+def _get_config_directory():
+    """Find the predefined segmentor config directory."""
+    try:
+        # Assume we are running in the source mmsegmentation repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmseg
+        repo_dpath = dirname(dirname(mmseg.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def test_config_build_segmentor():
+    """Test that all segmentation models defined in the configs can be
+    initialized."""
+    config_dpath = _get_config_directory()
+    print('Found config_dpath = {!r}'.format(config_dpath))
+
+    config_fpaths = []
+    # one config each sub folder
+    for sub_folder in os.listdir(config_dpath):
+        if isdir(sub_folder):
+            config_fpaths.append(
+                list(glob.glob(join(config_dpath, sub_folder, '*.py')))[0])
+    config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
+    config_names = [relpath(p, config_dpath) for p in config_fpaths]
+
+    print('Using {} config files'.format(len(config_names)))
+
+    for config_fname in config_names:
+        config_fpath = join(config_dpath, config_fname)
+        config_mod = Config.fromfile(config_fpath)
+
+        config_mod.model
+        print('Building segmentor, config_fpath = {!r}'.format(config_fpath))
+
+        # Remove pretrained keys to allow for testing in an offline environment
+        if 'pretrained' in config_mod.model:
+            config_mod.model['pretrained'] = None
+
+        print('building {}'.format(config_fname))
+        segmentor = build_segmentor(config_mod.model)
+        assert segmentor is not None
+
+        head_config = config_mod.model['decode_head']
+        _check_decode_head(head_config, segmentor.decode_head)
+
+
+def test_config_data_pipeline():
+    """Test whether the data pipeline is valid and can process corner cases.
+
+    CommandLine:
+        xdoctest -m tests/test_config.py test_config_build_data_pipeline
+    """
+    from mmcv import Config
+    from mmseg.datasets.pipelines import Compose
+    import numpy as np
+
+    config_dpath = _get_config_directory()
+    print('Found config_dpath = {!r}'.format(config_dpath))
+
+    import glob
+    config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
+    config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
+    config_names = [relpath(p, config_dpath) for p in config_fpaths]
+
+    print('Using {} config files'.format(len(config_names)))
+
+    for config_fname in config_names:
+        config_fpath = join(config_dpath, config_fname)
+        print(
+            'Building data pipeline, config_fpath = {!r}'.format(config_fpath))
+        config_mod = Config.fromfile(config_fpath)
+
+        # remove loading pipeline
+        load_img_pipeline = config_mod.train_pipeline.pop(0)
+        to_float32 = load_img_pipeline.get('to_float32', False)
+        config_mod.train_pipeline.pop(0)
+        config_mod.test_pipeline.pop(0)
+
+        train_pipeline = Compose(config_mod.train_pipeline)
+        test_pipeline = Compose(config_mod.test_pipeline)
+
+        img = np.random.randint(0, 255, size=(1024, 2048, 3), dtype=np.uint8)
+        if to_float32:
+            img = img.astype(np.float32)
+        seg = np.random.randint(0, 255, size=(1024, 2048, 1), dtype=np.uint8)
+
+        results = dict(
+            filename='test_img.png',
+            ori_filename='test_img.png',
+            img=img,
+            img_shape=img.shape,
+            ori_shape=img.shape,
+            gt_semantic_seg=seg)
+        results['seg_fields'] = ['gt_semantic_seg']
+
+        print('Test training data pipeline: \n{!r}'.format(train_pipeline))
+        output_results = train_pipeline(results)
+        assert output_results is not None
+
+        results = dict(
+            filename='test_img.png',
+            ori_filename='test_img.png',
+            img=img,
+            img_shape=img.shape,
+            ori_shape=img.shape,
+        )
+        print('Test testing data pipeline: \n{!r}'.format(test_pipeline))
+        output_results = test_pipeline(results)
+        assert output_results is not None
+
+
+def _check_decode_head(decode_head_cfg, decode_head):
+    if isinstance(decode_head_cfg, list):
+        assert isinstance(decode_head, nn.ModuleList)
+        assert len(decode_head_cfg) == len(decode_head)
+        num_heads = len(decode_head)
+        for i in range(num_heads):
+            _check_decode_head(decode_head_cfg[i], decode_head[i])
+        return
+    # check consistency between head_config and roi_head
+    assert decode_head_cfg['type'] == decode_head.__class__.__name__
+
+    assert decode_head_cfg['type'] == decode_head.__class__.__name__
+
+    in_channels = decode_head_cfg.in_channels
+    input_transform = decode_head.input_transform
+    assert input_transform in ['resize_concat', 'multiple_select', None]
+    if input_transform is not None:
+        assert isinstance(in_channels, (list, tuple))
+        assert isinstance(decode_head.in_index, (list, tuple))
+        assert len(in_channels) == len(decode_head.in_index)
+    elif input_transform == 'resize_concat':
+        assert sum(in_channels) == decode_head.in_channels
+    else:
+        assert isinstance(in_channels, int)
+        assert in_channels == decode_head.in_channels
+        assert isinstance(decode_head.in_index, int)
+
+    if decode_head_cfg['type'] == 'PointHead':
+        assert decode_head_cfg.channels+decode_head_cfg.num_classes == \
+               decode_head.fc_seg.in_channels
+        assert decode_head.fc_seg.out_channels == decode_head_cfg.num_classes
+    else:
+        assert decode_head_cfg.channels == decode_head.conv_seg.in_channels
+        assert decode_head.conv_seg.out_channels == decode_head_cfg.num_classes