mmsegmentation实战一：ISBI2012数据实战

---

一、数据格式转换

在tools/convert_datasets路径下新建ISBI2012.py，使得原本的tif格式数据能被程序读取。

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import os
import os.path as osp
import tempfile
import zipfile
from skimage import io


import mmcv



def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert ISBI2012 dataset to mmsegmentation format')

    # No "--" means use default paraments

    parser.add_argument('--dataset_path', default="openmmlab/dataset/ISBI2012/", help='path of ISBI2012 DATASET')
    parser.add_argument('--tmp_dir', help='path of the temporary directory')
    parser.add_argument('-o', '--out_dir', help='output path')
    args = parser.parse_args()
    return args


def main():
    ISBI2012_TRAIN = 20
    args = parse_args()

    dataset_path = args.dataset_path
    if args.out_dir is None:
        out_dir = osp.join('data', 'ISBI2012')
    else:
        out_dir = args.out_dir

    print('Making directories...')
    mmcv.mkdir_or_exist(out_dir)
    mmcv.mkdir_or_exist(osp.join(out_dir, 'images'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'images', 'test'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
    mmcv.mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))


    train_volume = io.imread(osp.join(dataset_path, "train-volume.tif"))
    train_labels = io.imread(osp.join(dataset_path, "train-labels.tif"))
    train_labels[train_labels == 255] = 1
    train_labels = 1 - train_labels
    test_volume = io.imread(osp.join(dataset_path, "test-volume.tif"))

    print('Generating training dataset...')
    for raw_index in range(0, ISBI2012_TRAIN):
        cv2.imwrite(osp.join(out_dir, 'images', 'training',
                           "%05d"%raw_index + '.png'), train_volume[raw_index, :, :], [cv2.IMWRITE_PNG_COMPRESSION, 0])
    
    print('Generating training label...')
    for label_index in range(0, ISBI2012_TRAIN):
        cv2.imwrite(osp.join(out_dir, 'annotations', 'training',
                           "%05d"%label_index + '.png'), train_labels[label_index, :, :], [cv2.IMWRITE_PNG_COMPRESSION, 0])



    print('Generating validation dataset...')
    for raw_index in range(ISBI2012_TRAIN, train_volume.shape[0]):
        cv2.imwrite(osp.join(out_dir, 'images', 'validation',
                           "%05d"%raw_index + '.png'), train_volume[raw_index, :, :], [cv2.IMWRITE_PNG_COMPRESSION, 0])


    print('Generating validation label...')
    for label_index in range(ISBI2012_TRAIN, train_labels.shape[0]):
        cv2.imwrite(osp.join(out_dir, 'annotations', 'validation',
                           "%05d"%label_index + '.png'), train_labels[label_index, :, :], [cv2.IMWRITE_PNG_COMPRESSION, 0])

    print('Generating test dataset...')
    for raw_index in range(test_volume.shape[0]):
        cv2.imwrite(osp.join(out_dir, 'images', 'test',
                           "%05d"%raw_index + '.png'), test_volume[raw_index, :, :], [cv2.IMWRITE_PNG_COMPRESSION, 0])

    print('Done!')


if __name__ == '__main__':
    main()
    print("ok")

运行完成后，得到的效果如图所示：

由于ISBI2012数据集是灰度图，但mmsegmentation整个环境是基于彩色图编写，mmcv库默认读入进行转化，这里直接保存为灰度即可

https://github.com/open-mmlab/mmdetection/issues/178

二、dataset class文件配置；

1.设置数据集文件

在mmseg/datasets/目录下新建ISBI2012.py文件

# Copyright (c) OpenMMLab. All rights reserved.

from .builder import DATASETS
from .custom import CustomDataset


@DATASETS.register_module()
class ISBI2012Dataset(CustomDataset):
    """Isbi2012 dataset.

    In segmentation map annotation for Isbi2012, 0 stands for background,
    which is included in 2 categories. ``reduce_zero_label`` is fixed to False.
    The ``img_suffix`` is fixed to '.png' and ``seg_map_suffix`` is fixed to
    '.png'.
    """

    CLASSES = ('background', 'membrane')

    PALETTE = [[120, 120, 120], [6, 230, 230]]

    def __init__(self, **kwargs):
        super(ISBI2012Dataset, self).__init__(
            img_suffix='.png',
            seg_map_suffix='.png',
            reduce_zero_label=False,
            **kwargs)
        assert self.file_client.exists(self.img_dir)

其中
ISBI2012Dataset：表示自定义的数据集
CLASSES：表示你数据集的背景+类别
PALETTE：训练完后进行测试后的结果保存，mmseg默认为把分割出来的mask结果直接画在原图上进行显示，PALETTE好像指定了画上原图的mask蒙版的颜色。
img_suffix：原图图像后缀
seg_map_suffix：mask图像后缀

注意：mmseg要求mask的像素在[0，num_classes-1]范围内，比如我是2分类，背景像素值为0，那么目标像素值应该为1。如果你也是二分类，mask为单通道（8 bit）二值化的0（背景）/255（目标）图像的话,先去把图像改为0（背景）/1（目标）图像，否则能跑起来，但是指标异常，几乎全是0。（这是个大坑！！！一定要注意）

2.修改mmseg/datasets/目录下的_init_.py

末尾加入Isbi2012Dataset数据

# Copyright (c) OpenMMLab. All rights reserved.
from .ade import ADE20KDataset
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .chase_db1 import ChaseDB1Dataset
from .cityscapes import CityscapesDataset
from .coco_stuff import COCOStuffDataset
from .custom import CustomDataset
from .dark_zurich import DarkZurichDataset
from .dataset_wrappers import (ConcatDataset, MultiImageMixDataset,
                               RepeatDataset)
from .drive import DRIVEDataset
from .hrf import HRFDataset
from .isaid import iSAIDDataset
from .isprs import ISPRSDataset
from .loveda import LoveDADataset
from .night_driving import NightDrivingDataset
from .pascal_context import PascalContextDataset, PascalContextDataset59
from .potsdam import PotsdamDataset
from .stare import STAREDataset
from .voc import PascalVOCDataset

from .ISBI2012 import ISBI2012Dataset # 注意添加！

__all__ = [
    'CustomDataset', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
    'DATASETS', 'build_dataset', 'PIPELINES', 'CityscapesDataset',
    'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
    'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
    'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
    'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
    'iSAIDDataset', 'ISPRSDataset', 'PotsdamDataset', 'ISBI2012Dataset'
]

三、在configs/_ base _/datasets下面对数据加载进行定义

在configs/__ base __/datasets/新建ISBI2012.py。

# dataset settings
dataset_type = 'ISBI2012Dataset' # 定义的数据集的名字
data_root = 'data/ISBI2012' # 数据集存储路径
img_norm_cfg = dict(
    mean=[127, 127, 127], std=[57, 57, 57], to_rgb=True) # 数据集的均值和标准差，空引用默认的，也可以网上搜代码计算
img_scale = (512, 512) # 原图数据大小
crop_size = (256, 256) # 数据增强时裁剪的大小
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=img_scale, 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=img_scale, # 测试集img_scale图像尺寸
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
        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 = dict(
    samples_per_gpu=4, # batch_size
    workers_per_gpu=4, # nums gpu
    train=dict(
        type='RepeatDataset',
        times=40000,
        dataset=dict(
            type=dataset_type,
            data_root=data_root,
            img_dir='images/training', # 训练图像路径
            ann_dir='annotations/training', # 训练mask路径
            pipeline=train_pipeline)),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='images/validation', # 验证图像路径
        ann_dir='annotations/validation', # 验证mask路径
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='images/test', # 测试图像路径
        # ann_dir='annotations/validation', # 无测试mask路径
        pipeline=test_pipeline))

四、在configs/下面选择你需要的模型参数进行修改

以U-Net为例，在configs/unet下新建fcn_unet_s5-d16_256x256_40k_isbi2012.py

_base_ = [
    '../_base_/models/fcn_unet_s5-d16_isbi2012.py',  # 这个是网络的骨架，使用单卡记得去骨架模型里将SyncBN改成BN
    '../_base_/datasets/ISBI2012.py', # 换成自己定义的数据集
    '../_base_/default_runtime.py', # 运行信息配置（加载预训练模型和断点训练）
    '../_base_/schedules/schedule_40k.py' # 运行信息配置（模型训练的最大次数、训练每个几次保留一个checkpoints、间隔多少次进行模型训练，模型训练评估的指标为、保留最好的模型）
]
model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170))) # 测试阶段剪切大小与滑动步长
evaluation = dict(metric='mDice')

新建对应的模型_base_/models/fcn_unet_s5-d16_isbi2012.py，是否有需要特别改动之处：

# model settings
norm_cfg = dict(type='BN', requires_grad=True) # SyncBN代表分布式训练，单卡训练将其改成BN
# norm_cfg = dict(type='SyncBN', requires_grad=True) # SyncBN代表分布式训练，单卡训练将其改成BN
model = dict(
    type='EncoderDecoder',
    pretrained=None,
    backbone=dict(
        type='UNet',
        in_channels=3, # 输入的通道数，ISBI2012是灰度图，自动转换为rgb，这里选择3即可
        base_channels=64,
        num_stages=5,
        strides=(1, 1, 1, 1, 1),
        enc_num_convs=(2, 2, 2, 2, 2),
        dec_num_convs=(2, 2, 2, 2),
        downsamples=(True, True, True, True),
        enc_dilations=(1, 1, 1, 1, 1),
        dec_dilations=(1, 1, 1, 1),
        with_cp=False,
        conv_cfg=None,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='ReLU'),
        upsample_cfg=dict(type='InterpConv'),
        norm_eval=False),
    decode_head=dict(
        type='FCNHead',
        in_channels=64,
        in_index=4,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), # type处可以指定loss类型
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=128,
        in_index=3,
        channels=64,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=2,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),# type处可以指定loss类型
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='slide', crop_size=256, stride=170))

五、返回tools/train.py进行训练

训练命令：

python tools/train.py /path/to/fcn_unet_s5-d16_256x256_40k_isbi2012.py --work-dir ${YOUR_WORK_DIR}

/path/to/fcn_unet_s5-d16_256x256_40k_isbi2012.py：表示配置文件的位置，也就是上一步的fcn_unet_s5-d16_256x256_40k_isbi2012.py所在位置
work-dir：是用来存储模型和日志的地方，你可以自己指定，不指定的话会自己创建
也可以在tools/train.py里面进行指定，随后直接运行train.py就行

在train.py中你需要修改的只有以下几个内容:
1、–config指定网络文件加载配置文件的位置
2、–work-dir是你结果存储路径
3、–gpu-id是你指定哪个GPU进行训练，–gpus与–gpu-id作者在API文档中已经介绍这两弃用了，至于为啥不删，估计是在其他地方还在加载。

开始训练，并得到最终结果。

六、推理

在tools/test.py中进行如下设置

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import shutil
import time
import warnings

import mmcv
import torch
from mmcv.cnn.utils import revert_sync_batchnorm
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                         wrap_fp16_model)
from mmcv.utils import DictAction

from mmseg import digit_version
from mmseg.apis import multi_gpu_test, single_gpu_test
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.models import build_segmentor
from mmseg.utils import build_ddp, build_dp, get_device, setup_multi_processes


def parse_args():
    parser = argparse.ArgumentParser(
        description='mmseg test (and eval) a model')
    # parser.add_argument('config', help='test config file path')
    # parser.add_argument('checkpoint', help='checkpoint file')
    parser.add_argument('--config',
                        default='configs/unet/fcn_unet_s5-d16_256x256_40k_isbi2012.py',
                        help='train config file path')
    parser.add_argument('--checkpoint',
                        default='output/ISBI2012/unet/latest.pth', 
                        help='checkpoint file')
    parser.add_argument(
        '--work-dir',
        help=('if specified, the evaluation metric results will be dumped'
              'into the directory as json'))
    parser.add_argument(
        '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
    # parser.add_argument('--out', help='output result file in pickle format')
    parser.add_argument('--out', help='output result file in pickle format')
    parser.add_argument(
        '--format-only',
        action='store_true',
        help='Format the output results without perform evaluation. It is'
        'useful when you want to format the result to a specific format and '
        'submit it to the test server')
    # parser.add_argument(
    #     '--eval',
    #     type=str,
    #     nargs='+',
    #     help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
    #     ' for generic datasets, and "cityscapes" for Cityscapes')
    parser.add_argument(
        '--eval',
        type=str,
        nargs='+',
        default='mDice',
        help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
        ' for generic datasets, and "cityscapes" for Cityscapes')
    # parser.add_argument('--show', action='store_true', help='show results')
    parser.add_argument('--show', 
        default=True,
        action='store_true', help='show results')
    # parser.add_argument(
    #     '--show-dir', help='directory where painted images will be saved')
    parser.add_argument(
        '--show-dir', 
        default='output/ISBI2012/unet/show_dir',
        help='directory where painted images will be saved')
    parser.add_argument(
        '--gpu-collect',
        action='store_true',
        help='whether to use gpu to collect results.')
    parser.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
        '(only applicable to non-distributed testing)')
    parser.add_argument(
        '--tmpdir',
        help='tmp directory used for collecting results from multiple '
        'workers, available when gpu_collect is not specified')
    parser.add_argument(
        '--options',
        nargs='+',
        action=DictAction,
        help="--options is deprecated in favor of --cfg_options' and it will "
        'not be supported in version v0.22.0. Override some settings in the '
        'used config, the key-value pair in xxx=yyy format will be merged '
        'into config file. If the value to be overwritten is a list, it '
        'should be like key="[a,b]" or key=a,b It also allows nested '
        'list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation '
        'marks are necessary and that no white space is allowed.')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--eval-options',
        nargs='+',
        action=DictAction,
        help='custom options for evaluation')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument(
        '--opacity',
        type=float,
        default=0.5,
        help='Opacity of painted segmentation map. In (0, 1] range.')
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    if 'LOCAL_RANK' not in os.environ:
        os.environ['LOCAL_RANK'] = str(args.local_rank)

    if args.options and args.cfg_options:
        raise ValueError(
            '--options and --cfg-options cannot be both '
            'specified, --options is deprecated in favor of --cfg-options. '
            '--options will not be supported in version v0.22.0.')
    if args.options:
        warnings.warn('--options is deprecated in favor of --cfg-options. '
                      '--options will not be supported in version v0.22.0.')
        args.cfg_options = args.options

    return args


def main():
    args = parse_args()
    assert args.out or args.eval or args.format_only or args.show \
        or args.show_dir, \
        ('Please specify at least one operation (save/eval/format/show the '
         'results / save the results) with the argument "--out", "--eval"'
         ', "--format-only", "--show" or "--show-dir"')

    if args.eval and args.format_only:
        raise ValueError('--eval and --format_only cannot be both specified')

    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
        raise ValueError('The output file must be a pkl file.')

    cfg = mmcv.Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    # set multi-process settings
    setup_multi_processes(cfg)

    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    if args.aug_test:
        # hard code index
        cfg.data.test.pipeline[1].img_ratios = [
            0.5, 0.75, 1.0, 1.25, 1.5, 1.75
        ]
        cfg.data.test.pipeline[1].flip = True
    cfg.model.pretrained = None
    cfg.data.test.test_mode = True

    if args.gpu_id is not None:
        cfg.gpu_ids = [args.gpu_id]

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        cfg.gpu_ids = [args.gpu_id]
        distributed = False
        if len(cfg.gpu_ids) > 1:
            warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
                          f'{cfg.gpu_ids[0:1]} to avoid potential error in '
                          'non-distribute testing time.')
            cfg.gpu_ids = cfg.gpu_ids[0:1]
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    rank, _ = get_dist_info()
    # allows not to create
    if args.work_dir is not None and rank == 0:
        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
        if args.aug_test:
            json_file = osp.join(args.work_dir,
                                 f'eval_multi_scale_{timestamp}.json')
        else:
            json_file = osp.join(args.work_dir,
                                 f'eval_single_scale_{timestamp}.json')
    elif rank == 0:
        work_dir = osp.join('./work_dirs',
                            osp.splitext(osp.basename(args.config))[0])
        mmcv.mkdir_or_exist(osp.abspath(work_dir))
        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
        if args.aug_test:
            json_file = osp.join(work_dir,
                                 f'eval_multi_scale_{timestamp}.json')
        else:
            json_file = osp.join(work_dir,
                                 f'eval_single_scale_{timestamp}.json')

    # build the dataloader
    # TODO: support multiple images per gpu (only minor changes are needed)
    dataset = build_dataset(cfg.data.test)
    # The default loader config
    loader_cfg = dict(
        # cfg.gpus will be ignored if distributed
        num_gpus=len(cfg.gpu_ids),
        dist=distributed,
        shuffle=False)
    # The overall dataloader settings
    loader_cfg.update({
        k: v
        for k, v in cfg.data.items() if k not in [
            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
            'test_dataloader'
        ]
    })
    test_loader_cfg = {
        **loader_cfg,
        'samples_per_gpu': 1,
        'shuffle': False,  # Not shuffle by default
        **cfg.data.get('test_dataloader', {})
    }
    # build the dataloader
    data_loader = build_dataloader(dataset, **test_loader_cfg)

    # build the model and load checkpoint
    cfg.model.train_cfg = None
    model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
    if 'CLASSES' in checkpoint.get('meta', {}):
        model.CLASSES = checkpoint['meta']['CLASSES']
    else:
        print('"CLASSES" not found in meta, use dataset.CLASSES instead')
        model.CLASSES = dataset.CLASSES
    if 'PALETTE' in checkpoint.get('meta', {}):
        model.PALETTE = checkpoint['meta']['PALETTE']
    else:
        print('"PALETTE" not found in meta, use dataset.PALETTE instead')
        model.PALETTE = dataset.PALETTE

    # clean gpu memory when starting a new evaluation.
    torch.cuda.empty_cache()
    eval_kwargs = {} if args.eval_options is None else args.eval_options

    # Deprecated
    efficient_test = eval_kwargs.get('efficient_test', False)
    if efficient_test:
        warnings.warn(
            '``efficient_test=True`` does not have effect in tools/test.py, '
            'the evaluation and format results are CPU memory efficient by '
            'default')

    eval_on_format_results = (
        args.eval is not None and 'cityscapes' in args.eval)
    if eval_on_format_results:
        assert len(args.eval) == 1, 'eval on format results is not ' \
                                    'applicable for metrics other than ' \
                                    'cityscapes'
    if args.format_only or eval_on_format_results:
        if 'imgfile_prefix' in eval_kwargs:
            tmpdir = eval_kwargs['imgfile_prefix']
        else:
            tmpdir = '.format_cityscapes'
            eval_kwargs.setdefault('imgfile_prefix', tmpdir)
        mmcv.mkdir_or_exist(tmpdir)
    else:
        tmpdir = None

    cfg.device = get_device()
    if not distributed:
        warnings.warn(
            'SyncBN is only supported with DDP. To be compatible with DP, '
            'we convert SyncBN to BN. Please use dist_train.sh which can '
            'avoid this error.')
        if not torch.cuda.is_available():
            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
                'Please use MMCV >= 1.4.4 for CPU training!'
        model = revert_sync_batchnorm(model)
        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
        results = single_gpu_test(
            model,
            data_loader,
            args.show,
            args.show_dir,
            False,
            args.opacity,
            pre_eval=args.eval is not None and not eval_on_format_results,
            format_only=args.format_only or eval_on_format_results,
            format_args=eval_kwargs)
    else:
        model = build_ddp(
            model,
            cfg.device,
            device_ids=[int(os.environ['LOCAL_RANK'])],
            broadcast_buffers=False)
        results = multi_gpu_test(
            model,
            data_loader,
            args.tmpdir,
            args.gpu_collect,
            False,
            pre_eval=args.eval is not None and not eval_on_format_results,
            format_only=args.format_only or eval_on_format_results,
            format_args=eval_kwargs)

    rank, _ = get_dist_info()
    if rank == 0:
        if args.out:
            warnings.warn(
                'The behavior of ``args.out`` has been changed since MMSeg '
                'v0.16, the pickled outputs could be seg map as type of '
                'np.array, pre-eval results or file paths for '
                '``dataset.format_results()``.')
            print(f'\nwriting results to {args.out}')
            mmcv.dump(results, args.out)
        if args.eval:
            eval_kwargs.update(metric=args.eval)
            metric = dataset.evaluate(results, **eval_kwargs)
            metric_dict = dict(config=args.config, metric=metric)
            mmcv.dump(metric_dict, json_file, indent=4)
            if tmpdir is not None and eval_on_format_results:
                # remove tmp dir when cityscapes evaluation
                shutil.rmtree(tmpdir)


if __name__ == '__main__':
    main()
    print('ok')

如果没有标签的测试集中，在–eval参数中设置为None即可。

    parser.add_argument(
        '--eval',
        type=str,
        nargs='+',
        default=None, # change this parament
        help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
        ' for generic datasets, and "cityscapes" for Cityscapes')

如果想要输出最终的图像为Mask掩膜，不将原图数据作为背景，可以设置opacity参数为1

    parser.add_argument(
        '--opacity',
        type=float,
        default=1,
        help='Opacity of painted segmentation map. In (0, 1] range.')

最后结果：

warnings.warn(
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 30/30, 3.9 task/s, elapsed: 8s, ETA:     0