Linux系统搭建Swin-Transformer环境_linux部署swin transformer semantic segmentation dock

• 1.mmcv-full安装

同MMdetection环境配置一样，可参考我上一篇文章

• 2.下载仓库

下载仓库时，将git clone的地址换成Swin-Transformer的仓库地址

git clone https://github.com/SwinTransformer/Swin-Transformer-Object-Detection.git
cd Swin-Transformer-Object-Detection

• 3.重新安装mmcv-full

git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full will be installed after this step
cd ..
注意-e 后面一点不要漏了

• 4.安装依赖

pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

• 5.apex(混合精度训练）optional

如果安装，输入

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

则需要在配置文件末尾，将下述代码注释

# do not use mmdet version fp16
fp16 = None
optimizer_config = dict(
    type="DistOptimizerHook",
    update_interval=1,
    grad_clip=None,
    coalesce=True,
    bucket_size_mb=-1,
    use_fp16=True,
)

并将

runner = dict(type='EpochBasedRunnerAmp',max_epochs=36)
改成
runner = dict(type='EpochBasedRunner',max_epochs=36)

• 可行搭配

• python = 3.7 cuda = 10.2 pytorch = 1.8.0
  python = 3.7 cuda = 11.1 pytorch = 1.7.0