1.安装需求
Linux or macOS (Windows is in experimental support)Python 3.6+PyTorch 1.3+CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)GCC 5+MMCV
windows不推荐安装和使用
兼容的 MMSegmentation and MMCV 版本如下所示. 需要安装对应版本以避免不必要问题。
MMSegmentation version MMCV version
master mmcv-full>=1.3.13, <1.4.00.17.0 mmcv-full>=1.3.7, <1.4.00.16.0 mmcv-full>=1.3.7, <1.4.00.15.0 mmcv-full>=1.3.7, <1.4.00.14.1 mmcv-full>=1.3.7, <1.4.00.14.0 mmcv-full>=1.3.1, <1.3.20.13.0 mmcv-full>=1.3.1, <1.3.20.12.0 mmcv-full>=1.1.4, <1.3.20.11.0 mmcv-full>=1.1.4, <1.3.00.10.0 mmcv-full>=1.1.4, <1.3.00.9.0 mmcv-full>=1.1.4, <1.3.00.8.0 mmcv-full>=1.1.4, <1.2.00.7.0 mmcv-full>=1.1.2, <1.2.00.6.0 mmcv-full>=1.1.2, <1.2.0
注意：
pip install同时安装了mmcv ， mmcv-full会有问题，需要卸载其中之一。
s1: 新建环境
conda create -n open-mmlab python=3.7 -yconda activate open-mmlab
s2: 安装pytorch
conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
注意：安装相应版本和方法，自己按照需求安装， 建议1.8.0
s3: 安装mmcv-full(推荐安装full版本)
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
注意：对应的cu101, torch1.6.0， 请根据自己安装cuda, torch版本对应更换
s4: 安装MMSegmentation
pip install mmsegmentation # install the latest release
or
pip install git+https://github.com/open-mmlab/mmsegmentation.git # install the master branch
s5： 验证脚本，确认正常安装：
a .在mmseg根目录新建·checkpoints文件夹，下载pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth 权重连接
b 在根目录新建新建如下脚本 test.py, 在安装虚拟环境中执行测试
python test.py
from mmseg.apis import inference_segmentor, init_segmentorimport mmcvconfig_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'# build the model from a config file and a checkpoint filemodel = init_segmentor(config_file, checkpoint_file, device='cuda:0')# test a single image and show the resultsimg = 'demo/demo.png' # or img = mmcv.imread(img), which will only load it onceresult = inference_segmentor(model, img)# visualize the results in a new windowmodel.show_result(img, result, show=True)# or save the visualization results to image files# you can change the opacity of the painted segmentation map in (0, 1].model.show_result(img, result, out_file='result.jpg', opacity=0.5)# test a video and show the results#video = mmcv.VideoReader('video.mp4')#for frame in video:# result = inference_segmentor(model, frame)# model.show_result(frame, result, wait_time=1) c 显示结果如下，表示正常安装
1）.主要文档总结：【DL】如何使用MMSegmentation训练数据集
2） github下载地址
3） 官方文档
3） 安装说明
4）相关模型权重下载
