Chest X-ray multi-labels binary classifications
- Python 3.6+
- PyTorch 1.1.0+
You need to resize and copy from the extracted original data.
Modify {src,tar}_path at the end of dataset.py properly for your environment.
$ python dataset.py
If no --cuda <comma-separated gpu-ids> given, cpus will be used for training.
If no distributed environment, the first cuda id is used for the training and the others are ignored.
$ python train.py --cuda <comma-separated gpu-ids> --runtime-dir <your-runtime-dir> [--tensorboard] [--slack]
If you want to run in distributed mode, please refer to here.
$ python -m torch.distributed.launch --nproc_per_node=<NUM_GPUS_YOU_HAVE> train.py --cuda <gpu-ids> --runtime-dir <runtime-dir> [any optinal switches]
or using the train.sh with proper modification of node id etc.
if you want to log to slack, add .slack file in yaml format:
token: <your bot-slack-app token>
recipients: <list of log message recipients username>
$ python predict.py <study-dir-to-be-predicted>