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 which supports Pytorch 1.0 or later and distributed multiprocessing training and testing
Some parts of InPlace-ABN have a native CUDA implementation, which must be compiled with the following commands:
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, and put it into dataset folder.