Python-DeeplabV3和PSPNet的PyTorch实现

# Pytorch-segmentation-toolbox [DOC](https://weiyc.github.io/assets/pdf/toolbox.pdf) Pytorch code for semantic segmentation. This is a minimal code to run PSPnet and Deeplabv3 on Cityscape dataset. Shortly afterwards, the code will be reviewed and reorganized for convenience. **The new version toolbox is released on branch [Pytorch-1.1](https://github.com/speedinghzl/pytorch-segmentation-toolbox/tree/pytorch-1.1) which supports Pytorch 1.0 or later and distributed multiprocessing training and testing** ### Highlights of Our Implementations - Synchronous BN - Fewness of Training Time - Better Reproduced Performance ### Requirements To install PyTorch==0.4.0, please refer to https://github.com/pytorch/pytorch#installation. 4 x 12g GPUs (e.g. TITAN XP) Python 3.6 ### Compiling Some parts of InPlace-ABN have a native CUDA implementation, which must be compiled with the following commands: ```bash cd libs sh build.sh python build.py ``` The `build.sh` script assumes that the `nvcc` compiler is available in the current system search path. The CUDA kernels are compiled for `sm_50`, `sm_52` and `sm_61` by default. To change this (_e.g._ if you are using a Kepler GPU), please edit the `CUDA_GENCODE` variable in `build.sh`. ### Dataset and pretrained model Plesae download cityscapes dataset and unzip the dataset into `YOUR_CS_PATH`. Please download MIT imagenet pretrained [resnet101-imagenet.pth](http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth), and put it into `dataset` folder. ### Training and Evaluation ```bash ./run_local.sh YOUR_CS_PATH ``` ### Benefits Some recent projects have already benefited from our implementations. For example, [CCNet: Criss-Cross Attention for semantic segmentation](https://github.com/speedinghzl/CCNet) and [Object Context Network(OCNet)](https://github.com/PkuRainBow/OCNet) currently achieve the state-of-the-art resultson Cityscapes and ADE20K. In addition, Our code also make great contributions to [Context Embedding with EdgePerceiving (CE2P)](https://github.com/liutinglt/CE2P), which won the 1st places in all human parsing tracks in the 2nd LIP Challange. ### Citing If you find this code useful in your research, please consider citing: @misc{huang2018torchseg, author = {Huang, Zilong and Wei, Yunchao and Wang, Xinggang, and Liu, Wenyu}, title = {A PyTorch Semantic Segmentation Toolbox}, howpublished = {\url{https://github.com/speedinghzl/pytorch-segmentation-toolbox}}, year = {2018} } ### Thanks to the Third Party Libs [inplace_abn](https://github.com/mapillary/inplace_abn) - [Pytorch-Deeplab](https://github.com/speedinghzl/Pytorch-Deeplab) - [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)