全流程，训练语义分割数据集
- 数据标注
- json转mask
- 运行源码MMSegmentation
- 模型选择运行部分
数据标注
# 安装
pip install labelme
# 启动labelme
labelme
然后 ctrl +N 开启多边形标注即可，命名类为person
之后会保存到同目录下json文件：
json转mask
下载labelme代码里的转换代码：
labels里存储的如下形式
运行指令
python labelme2voc.py ./img output labels.txt
生成如下
运行源码MMSegmentation
mmseg/datasets里生成一个my_data.py文件，这个文件存储的是类别信息和seg颜色
需要多加一个backbone
# Copyright (c) OpenMMLab. All rights reserved.
from mmseg.registry import DATASETS
from .basesegdataset import BaseSegDataset@DATASETS.register_module()
class mydata(BaseSegDataset):"""Cityscapes dataset.The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` isfixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset."""METAINFO = dict(classes=('backbone','person'),palette=[[128, 64, 128], [244, 35, 232]])def __init__(self,img_suffix='.jpg',seg_map_suffix='.png',reduce_zero_label=True,**kwargs) -> None:super().__init__(img_suffix=img_suffix,seg_map_suffix=seg_map_suffix,reduce_zero_label=reduce_zero_label,**kwargs)
mmseg/utils/class_names.py文件里添加：不加backbone也不报错，这里没加，最好加上另外，seg颜色要与上面文件一致
def mydata_classes():"""shengteng class names for external use."""return ['person']def mydata_palette():return [[244, 35, 232]]
mmseg/datasets/init.py中加引入，
from .my_data import mydata
configs/base/datasets文件下新建一个my_data.py文件：
这个就是一个读取数据的文件了，包含数据地址、type和加载增加等方式
# dataset settings
dataset_type = 'mydata' #改
data_root = 'data/my_dataset' #改
crop_size = (512, 512)
train_pipeline = [dict(type='LoadImageFromFile'),dict(type='LoadAnnotations', reduce_zero_label=True),dict(type='RandomResize',scale=(2048, 512),ratio_range=(0.5, 2.0),keep_ratio=True),dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),dict(type='RandomFlip', prob=0.5),dict(type='PhotoMetricDistortion'),dict(type='PackSegInputs')
]
test_pipeline = [dict(type='LoadImageFromFile'),dict(type='Resize', scale=(2048, 512), keep_ratio=True),# add loading annotation after ``Resize`` because ground truth# does not need to do resize data transformdict(type='LoadAnnotations', reduce_zero_label=True),dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [dict(type='LoadImageFromFile', backend_args=None),dict(type='TestTimeAug',transforms=[[dict(type='Resize', scale_factor=r, keep_ratio=True)for r in img_ratios],[dict(type='RandomFlip', prob=0., direction='horizontal'),dict(type='RandomFlip', prob=1., direction='horizontal')], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]])
]
train_dataloader = dict(batch_size=4,num_workers=4,persistent_workers=True,sampler=dict(type='InfiniteSampler', shuffle=True),dataset=dict(type=dataset_type,data_root=data_root,data_prefix=dict(img_path='images/training', seg_map_path='annotations/training'), #改pipeline=train_pipeline))
val_dataloader = dict(batch_size=1,num_workers=4,persistent_workers=True,sampler=dict(type='DefaultSampler', shuffle=False),dataset=dict(type=dataset_type,data_root=data_root,data_prefix=dict(img_path='images/validation', #改seg_map_path='annotations/validation'), #改pipeline=test_pipeline))
test_dataloader = val_dataloaderval_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator
模型选择运行部分
我选择的是configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py，主要是修改继承的数据部分
运行
每次修改配置文件，最好是运行一遍python setup.py install
python setup.py install
python ./tools/train.py ./configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
