mmsegmentation/tests/test_config.py

# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
from os.path import dirname, exists, isdir, join, relpath

import numpy as np
from mmengine import Config
from mmengine.dataset import Compose
from torch import nn

from mmseg.models import build_segmentor
from mmseg.utils import register_all_modules


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(f'Found config_dpath = {config_dpath!r}')

    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(f'Using {len(config_names)} config files')

    for config_fname in config_names:
        config_fpath = join(config_dpath, config_fname)
        config_mod = Config.fromfile(config_fpath)

        config_mod.model
        print(f'Building segmentor, config_fpath = {config_fpath!r}')

        # Remove pretrained keys to allow for testing in an offline environment
        if 'pretrained' in config_mod.model:
            config_mod.model['pretrained'] = None

        print(f'building {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
    """

    register_all_modules()
    config_dpath = _get_config_directory()
    print(f'Found config_dpath = {config_dpath!r}')

    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(f'Using {len(config_names)} config files')

    for config_fname in config_names:
        config_fpath = join(config_dpath, config_fname)
        print(f'Building data pipeline, config_fpath = {config_fpath!r}')
        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)
        del config_mod.train_pipeline[0]
        del config_mod.test_pipeline[0]
        # remove loading annotation in test pipeline
        del config_mod.test_pipeline[-2]

        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_seg_map=seg)
        results['seg_fields'] = ['gt_seg_map']

        print(f'Test training data pipeline: \n{train_pipeline!r}')
        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(f'Test testing data pipeline: \n{test_pipeline!r}')
        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