This repository is a pytorch version of sd-maskrcnn and maskrcnn. The offical sd-maskrcnn code is written by tensorflow.(refer to https://github.com/BerkeleyAutomation/sd-maskrcnn). And the maskrcnn backbone is mostly adapted from https://github.com/darolt/mask_rcnn. I make some modification as below:
- Reorganized the code struture to more pytorch style.
- I replace some C/CUDA function with pytorch in-build functions(NMS && RoiAlign)
The code can run with multi-gpus and I tested on
- pytorch 1.4.0
- torchvision 0.5.0
- python 3.6
- download dataset(wisdom-sim) from sd-maskrcnn repo(https://github.com/BerkeleyAutomation/sd-maskrcnn)
- make some config change in
./datasets/wisdom/wisdomConfig.yml
(change the dataset path) - run with
CUDA_VISIBLE_DEVICES=X,X python main/trian.py
- all training log will store in
./log
directory. - install
tensorboardX
package and you can monitor the training procedure in browser.
- download the inference data(wisdom-real) from sd-maskrcnn repo.
- change the dataset path in
./datasets/wisdom/widomInference.yml
- run with
CUDA_VISIBLE_DEVICES=0 python main/inference.py