框架是MMSegmentation；
自己的数据集是 voc 格式；
代码：https://github.com/NVlabs/SegFormer
mmlab环境的安装：https://blog.csdn.net/Scenery0519/article/details/129595886?spm=1001.2014.3001.5501
mmseg 教程文档：https://mmsegmentation.readthedocs.io/zh_CN/latest/useful_tools.html#id10
文章目录
一、环境配置
首先需要配置好 mmlab 环境。
参考 mmlab环境的安装：https://blog.csdn.net/Scenery0519/article/details/129595886?spm=1001.2014.3001.5501
安装如下的库，版本按照自己匹配的来
pip install torchvision==0.8.2
pip install timm==0.3.2
pip install mmcv-full==1.2.7
pip install opencv-python==4.5.1.48
cd SegFormer && pip install -e .--user
二、跑训练
# Single-gpu training
python tools/train.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py
报错1：
AssertionError: MMCV==1.7.1 is used but incompatible. Please install mmcv>=[1, 1, 4], <=[1, 7, 0].
修改 /SegFormermaster/mmseg/init.py 文件
使自己的mmcv版本匹配在这个区间里。我使用的是mmcv==1.6.0版本可以正常跑程序。
报错2：
File “/home/8TDisk/wangjl/condaEnv/mmlab/lib/python3.7/site-packages/timm/models/layers/helpers.py”, line 6, in
from torch._six import container_abcs
ImportError: cannot import name ‘container_abcs’ from ‘torch._six’ (/condaEnv/mmlab/lib/python3.7/site-packages/torch/_six.py)
上边的报错内容给出了出错的文件路径，照着路径找到 _six.py 文件修改。
修改 condaEnv/mmlab/lib/python3.7/site-packages/timm/models/layers/helpers.py
修改内容如下所示，将 from torch._six import container_abcs 注释掉，替换下面的代码。
# from torch._six import container_abcsimport torch
TORCH_MAJOR =int(torch.__version__.split('.')[0])
TORCH_MINOR =int(torch.__version__.split('.')[1])if TORCH_MAJOR ==1and TORCH_MINOR <8:from torch._six import container_abcs
else:import collections.abc as container_abcs
报错3：
File “/home/8TDisk/wangjl/condaEnv/mmlab/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py”, line 430, in _get_default_group
"Default process group has not been initialized, "
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
修改 /SegFormermaster/mmseg/apis/train.py 文件如下
代码如下：
if distributed:
print("if")
find_unused_parameters = cfg.get('find_unused_parameters', False)# Sets the `find_unused_parameters` parameter in# torch.nn.parallel.DistributedDataParallel# torch.distributed.init_process_group('nccl',init_method='file:///home/.../my_file',world_size=1,rank=0)
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
print("distributed")else:
print("else")
print("cfg.gpu_ids[0]:{}".format(cfg.gpu_ids[0]))
print("cfg.gpu_ids:{}".format(cfg.gpu_ids))# model = MMDataParallel(# model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
torch.distributed.init_process_group('nccl', init_method='file:///tmp/somefile', rank=0, world_size=1)
model = MMDataParallel(
model, device_ids=cfg.gpu_ids)
print("distributed:false")
如果报这个错：
RuntimeError: open(/tmp/somefile): Permission denied
已放弃 (核心已转储)
说明：‘file:///tmp/somefile’ 这个文件没有访问权限
换一个地址就可以了。
报错4：
File “/condaEnv/mmlab/lib/python3.7/site-packages/mmcv/runner/hooks/logger/text.py”, line "153, in _log_info
log_str += f’time: {log_dict[“time”]:.3f}, ’
KeyError: ‘data_time’
修改：
找到环境目录下
/condaEnv/mmlab/lib/python3.7/site-packages/mmcv/runner/hooks/logger/text.py 下文件，导入 time 库
import time
153行，做如下更改：
# log_str += f'time: {log_dict["time"]:.3f}, ' \# f'data_time: {log_dict["data_time"]:.3f}, '
log_dict["data_time"]= datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
log_str +=f'time: {log_dict["time"]}, ' \
f'data_time: {log_dict["data_time"]}, '
三、构造自己的 VOC 格式数据集
需要更改的文件或创建的文件：
- /SegFormermaster/local_configs/segformer/B0/segformer.b0.512x512.ade.160k.py
- /SegFormermaster/local_configs/base/datasets/pascal_voc12.py
- /SegFormermaster/local_configs/base/models/segformer.py
- /SegFormermaster/mmseg/datasets/voc.py
- /SegFormermaster/mmseg/datasets/init.py
- /SegFormermaster/mmseg/core/evaluation/class_names.py
1、segformer.b0.512x512.ade.160k.py
目录：/SegFormermaster/local_configs/segformer/B0/
参考文件：segformer.b0.512x512.ade.160k.py
新建自己的配置文件：segformer.b0.800x800.self.160k.py
修改：自己数据集的配置文件路径、类别数（num_classes）。
_base_ = ['../../_base_/models/segformer.py','../../_base_/datasets/self_dataset.py',# 改这里，是自己的数据集配置文件路径，也就是下边2、改的文件路径'../../_base_/default_runtime.py','../../_base_/schedules/schedule_160k_adamw.py']# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
find_unused_parameters = True
model = dict(type='EncoderDecoder',
pretrained='pretrained/mit_b0.pth',
backbone=dict(type='mit_b0',
style='pytorch'),
decode_head=dict(type='SegFormerHead',
in_channels=[32, 64, 160, 256],
in_index=[0, 1, 2, 3],
feature_strides=[4, 8, 16, 32],
channels=128,
dropout_ratio=0.1,
num_classes=150,# 改这里，改成自己的数据集的类别数。注意是类别 + 1（包含_background_）
norm_cfg=norm_cfg,
align_corners=False,
decoder_params=dict(embed_dim=256),
loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))# optimizer
optimizer = dict(_delete_=True,type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
paramwise_cfg=dict(custom_keys={'pos_block': dict(decay_mult=0.),'norm': dict(decay_mult=0.),'head': dict(lr_mult=10.)}))
lr_config = dict(_delete_=True, policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-6,
power=1.0, min_lr=0.0, by_epoch=False)data = dict(samples_per_gpu=2)# 每个gpu的迭代书，可改可不改
evaluation = dict(interval=16000, metric='mIoU')
2、pascal_voc12.py
路径：/SegFormermaster/local_configs/base/datasets/
参考文件：pascal_voc12.py
新建文件：self_dataset.py
修改内容： dataset_type、data_root
# dataset settings
dataset_type ='SelfVOCDataset'# 改这里，给自己的数据集type起个名字
data_root ='data/VOCdevkit/VOC2012'# 改这里，是自己的数据集路径
img_norm_cfg =dict(
mean=[123.675,116.28,103.53], std=[58.395,57.12,57.375], to_rgb=True)
crop_size =(512,512)# 裁剪大小# train_pipeline 中的配置参数，随需要更改
train_pipeline =[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations'),dict(type='Resize', img_scale=(2048,512), 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=(2048,512),# 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 =dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(type=dataset_type,
data_root=data_root,
img_dir='JPEGImages',
ann_dir='SegmentationClass',
split='ImageSets/Segmentation/train.txt',
pipeline=train_pipeline),
val=dict(type=dataset_type,
data_root=data_root,
img_dir='JPEGImages',
ann_dir='SegmentationClass',
split='ImageSets/Segmentation/val.txt',
pipeline=test_pipeline),
test=dict(type=dataset_type,
data_root=data_root,
img_dir='JPEGImages',
ann_dir='SegmentationClass',
split='ImageSets/Segmentation/val.txt',
pipeline=test_pipeline))
3、segformer.py
路径：/SegFormermaster/local_configs/base/models/segformer.py
修改内容：num_classes 改成自己数据集的类别数
4、voc.py
路径： /SegFormermaster/mmseg/datasets/
参考文件：voc.py
新建文件：self_voc.py
import os.path as osp
from.builder import DATASETS
from.custom import CustomDataset
@DATASETS.register_module()# 修改这里，给自己的数据集类别起个名字，和 2、self_dataset.py 中的 dataset_type 保持一致classSelfVOCDataset(CustomDataset):"""Pascal VOC dataset.
Args:
split (str): Split txt file for Pascal VOC.
"""# 修改这里，改成自己数据集的类别名称
CLASSES =('background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor')# 修改这里，给自己数据集类别图像上色，类别数量和num_classes保持一致
PALETTE =[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128]]def__init__(self, split,**kwargs):# 修改这里，和类名保持一致super(SelfVOCDataset, self).__init__(
img_suffix='.jpg', seg_map_suffix='.png', split=split,**kwargs)assert osp.exists(self.img_dir)and self.split isnotNone
5、/SegFormermaster/mmseg/datasets/init.py
路径：/SegFormermaster/mmseg/datasets/init.py
from.ade import ADE20KDataset
from.builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from.chase_db1 import ChaseDB1Dataset
from.cityscapes import CityscapesDataset
from.custom import CustomDataset
from.dataset_wrappers import ConcatDataset, RepeatDataset
from.drive import DRIVEDataset
from.hrf import HRFDataset
from.pascal_context import PascalContextDataset
from.stare import STAREDataset
from.voc import PascalVOCDataset
from.mapillary import MapillaryDataset
from.cocostuff import CocoStuff
from.self_voc import SelfVOCDataset # 修改这里，导入自己数据集的类
__all__ =['CustomDataset','build_dataloader','ConcatDataset','RepeatDataset','DATASETS','build_dataset','PIPELINES','CityscapesDataset','PascalVOCDataset','ADE20KDataset','PascalContextDataset','ChaseDB1Dataset','DRIVEDataset','HRFDataset','STAREDataset','MapillaryDataset','CocoStuff','SelfVOCDataset'# 修改这里，在这里添加上自己数据集的类名]
6、class_names.py
路径：/SegFormermaster/mmseg/core/evaluation/class_names.py
在文件中添加两个函数 selfvoc_classes()、selfvoc_palette()。
修改 dataset_aliases
defselfvoc_classes():"""Pascal VOC class names for external use."""return['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']defselfvoc_palette():"""Pascal VOC palette for external use."""return[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128]]
dataset_aliases ={'cityscapes':['cityscapes'],'ade':['ade','ade20k'],'voc':['voc','pascal_voc','voc12','voc12aug'],'selfvoc':['selfvoc']# 数据集type类的小写}
四、单卡跑训练
python tools/train.py local_configs/segformer/B0/segformer.b0_self_160k.py --gpu-ids 0 --work-dir'./work_dir'
–gpu-ids 指定用那块卡
–work-dir 制定跑的日志文件和权重文件保存在哪个路径下
–resume-from 如果训练意外终止，可以通过resume-from加载权重继续训练。注意，配置不能改变。
如果训练出现了卡住的情况，显卡上没有跑起来，可以尝试删除掉上次跑的日志文件，或者制定新的日志文件。因为如果配置参数与日志文件中的配置参数不一致，就会卡住，跑不起来。
其次，不能同时跑两个人任务，多卡分布式跑两个任务，可以通过指定端口来解决，单卡跑的话目前不清楚。
五、多卡跑程序
tools/dist_train.sh local_configs/segformer/B0/segformer.b0_self_160k.py 2
最后的数字是用几张显卡的意思。2，就是用两张显卡。
六、对训练日志 (training logs) 画图
tools/analyze_logs.py 会画出给定的训练日志文件的 loss/mIoU 曲线，首先需要 pip install seaborn 安装依赖包。
pip install seaborn
训练前，对 /SegFormermaster/local_configs/base/default_runtime.py 文件中，dict(type=‘TensorboardLoggerHook’) 取消注释。
对 mIoU, mAcc, aAcc 指标画图
python tools/analyze_logs.py log.json --keys mIoU mAcc aAcc --legend mIoU mAcc aAcc
对 loss 指标画图
python tools/analyze_logs.py log.json --keys loss --legend loss
七、评估
# Single-gpu testing
python tools/test.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file
# Multi-gpu testing./tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>
# Multi-gpu, multi-scale testing
tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM> --aug-test
八、单个图像可视化
python demo/image_demo.py demo/demo.png local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py \
/path/to/checkpoint_file --device cuda:0 --palette cityscapes
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