Getting Started

• Train Predefined Models on Standard Datasets
  • Train with a single GPU
  • Train with multiple GPUs
  • Train with multiple machines
• Test Existing Models on Standard Datasets

Train Predefined Models on Standard Datasets

• This codebase implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel, respectively.

• All outputs (log files and checkpoints) will be saved to the working directory, which is specified by work_dir in the config file.

• :warning: Important: By default, we evaluate the model on the validation set after each epoch, without using extra tricks including model ensemble and test-time-augmentation (TTA). To ensure fair comparisons, we advise you to follow the same configuration strictly.

• You can change the evaluation interval by adding the interval argument in the training config.

  train_cfg = dict(type='EpochBasedTrainLoop', val_interval=1)
  

• Note1: We used 4 GPUs for all experiments.

• Note2: The default learning rate in config files is for 8 GPUs and the exact batch size is marked by the config’s file name, e.g., ‘2xb8’ means 2 samples per GPU using 8 GPUs. According to the Linear Scaling Rule, you might need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu. However, since most of the models in this repo use Adam rather than SGD for optimization, the rule may not hold and users need to tune the learning rate by themselves.

:hourglass: Train with a single GPU

• The default command is as follows:

  python tools/train.py ${CONFIG_FILE} [optional arguments]
  

• :memo: For example, to train LaserMix with the Cylinder3D backbone on SemanticKITTI under a 10% annotation budget using a single GPU, run the following command:

  python tools/train.py configs/lidarweather_minkunet/sj+lpd+minkunet_semantickitti.py
  

• Note: If you want to specify the working directory in the command, you can add an argument --work-dir ${YOUR_WORK_DIR}.

:hourglass: Train with multiple GPUs

• The default command is as follows:

  ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
  

• :memo: For example, to train LaserMix with the Cylinder3D backbone on SemanticKITTI under a 10% annotation budget using four GPUs, run the following command:

  ./tools/dist_train.sh configs/lidarweather_minkunet/sj+lpd+minkunet_semantickitti.py 4
  

  ./tools/dist_train.sh projects/CENet/lidarweather_cenet/sj+lpd+cenet_semantickitti.py 4
  

• Note: Optional argument: --cfg-options 'Key=value', which overrides some settings in the used config.

Test Existing Models on Standard Datasets

• The default command is as follows:

  python tools/test.py ${CONFIG_FILE} ${CHECKPOINT}
  

• :memo: For example, to train LaserMix with the Cylinder3D backbone on SemanticKITTI under a 10% annotation budget using a single GPU, run the following command:

  python tools/test.py configs/lidarweather_minkunet/sj+lpd+minkunet_semantickitti.py work_dirs/sj+lpd+minkunet_semantickitti/epoch_15.pth
  

• Note: If you want to specify the working directory in the command, you can add an argument --work-dir ${YOUR_WORK_DIR}.