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1: Inference and train with existing models and standard datasets

Inference with existing models

Here we provide testing scripts to evaluate a whole dataset (SUNRGBD, ScanNet, KITTI, etc.).

For high-level apis easier to integrated into other projects and basic demos, please refer to Verification/Demo under Get Started.

Test existing models on standard datasets

  • single GPU
  • single node multiple GPU
  • multiple node

You can use the following commands to test a dataset.

# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]

Optional arguments:

  • RESULT_FILE: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
  • EVAL_METRICS: Items to be evaluated on the results. Allowed values depend on the dataset. Typically we default to use official metrics for evaluation on different datasets, so it can be simply set to mAP as a placeholder, which applies to nuScenes, Lyft, ScanNet and SUNRGBD. For KITTI, if we only want to evaluate the 2D detection performance, we can simply set the metric to img_bbox (unstable, stay tuned). For Waymo, we provide both KITTI-style evaluation (unstable) and Waymo-style official protocol, corresponding to metric kitti and waymo respectively. We recommend to use the default official metric for stable performance and fair comparison with other methods.
  • --show: If specified, detection results will be plotted in the silient mode. It is only applicable to single GPU testing and used for debugging and visualization. This should be used with --show-dir.
  • --show-dir: If specified, detection results will be plotted on the ***_points.obj and ***_pred.ply files in the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option.

Examples:

Assume that you have already downloaded the checkpoints to the directory checkpoints/.

  1. Test votenet on ScanNet and save the points and prediction visualization results.

    python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
    checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
    --show --show-dir ./data/scannet/show_results

  2. Test votenet on ScanNet, save the points, prediction, groundtruth visualization results, and evaluate the mAP.

    python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
    checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
    --eval mAP
    --options 'show=True' 'out_dir=./data/scannet/show_results'

  3. Test votenet on ScanNet (without saving the test results) and evaluate the mAP.

    python tools/test.py configs/votenet/votenet_8x8_scannet-3d-18class.py \
    checkpoints/votenet_8x8_scannet-3d-18class_20200620_230238-2cea9c3a.pth \
    --eval mAP

  4. Test SECOND with 8 GPUs, and evaluate the mAP.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
    checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth \
    --out results.pkl --eval mAP

  5. Test PointPillars on nuscenes with 8 GPUs, and generate the json file to be submit to the official evaluation server.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py \
    checkpoints/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth \
    --format-only --options 'jsonfile_prefix=./pointpillars_nuscenes_results'

    The generated results be under ./pointpillars_nuscenes_results directory.

  6. Test SECOND on KITTI with 8 GPUs, and generate the pkl files and submission datas to be submit to the official evaluation server.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
    checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth \
    --format-only --options 'pklfile_prefix=./second_kitti_results' 'submission_prefix=./second_kitti_results'

    The generated results be under ./second_kitti_results directory.

  7. Test PointPillars on Lyft with 8 GPUs, generate the pkl files and make a submission to the leaderboard.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_fpn_sbn-2x8_2x_lyft-3d.py \
    checkpoints/hv_pointpillars_fpn_sbn-2x8_2x_lyft-3d_latest.pth --out results/pp_lyft/results_challenge.pkl \
    --format-only --options 'jsonfile_prefix=results/pp_lyft/results_challenge' \
    'csv_path=results/pp_lyft/results_challenge.csv'

    Notice: To generate submissions on Lyft, csv_path must be given in the options. After generating the csv file, you can make a submission with kaggle commands given on the website.

  8. Test PointPillars on waymo with 8 GPUs, and evaluate the mAP with waymo metrics.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
    checkpoints/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car_latest.pth --out results/waymo-car/results_eval.pkl \
    --eval waymo --options 'pklfile_prefix=results/waymo-car/kitti_results' \
    'submission_prefix=results/waymo-car/kitti_results'

    Notice: For evaluation on waymo, please follow the instruction to build the binary file compute_detection_metrics_main for metrics computation and put it into mmdet3d/core/evaluation/waymo_utils/.(Sometimes when using bazel to build compute_detection_metrics_main, an error 'round' is not a member of 'std' may appear. We just need to remove the std:: before round in that file.) pklfile_prefix should be given in the options for the bin file generation. For metrics, waymo is the recommended official evaluation prototype. Currently, evaluating with choice kitti is adapted from KITTI and the results for each difficulty are not exactly the same as the definition of KITTI. Instead, most of objects are marked with difficulty 0 currently, which will be fixed in the future. The reasons of its instability include the large computation for evalution, the lack of occlusion and truncation in the converted data, different definition of difficulty and different methods of computing average precision.

  9. Test PointPillars on waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.

    ./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} configs/pointpillars/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car.py \
    checkpoints/hv_pointpillars_secfpn_sbn-2x16_2x_waymo-3d-car_latest.pth --out results/waymo-car/results_eval.pkl \
    --format-only --options 'pklfile_prefix=results/waymo-car/kitti_results' \
    'submission_prefix=results/waymo-car/kitti_results'

    Notice: After generating the bin file, you can simply build the binary file create_submission and use them to create a submission file by following the instruction. For evaluation on the validation set with the eval server, you can also use the same way to generate a submission.

Train predefined models on standard datasets

MMDetection 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.

By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by adding the interval argument in the training config.

evaluation = dict(interval=12)  # This evaluate the model per 12 epoch.

Important: 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. '2x8' means 2 samples per GPU using 8 GPUs. According to the Linear Scaling Rule, you 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.

Train with a single GPU

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

If you want to specify the working directory in the command, you can add an argument --work_dir ${YOUR_WORK_DIR}.

Train with multiple GPUs

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

Optional arguments are:

  • --no-validate (not suggested): By default, the codebase will perform evaluation at every k (default value is 1, which can be modified like this) epochs during the training. To disable this behavior, use --no-validate.
  • --work-dir ${WORK_DIR}: Override the working directory specified in the config file.
  • --resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.
  • --options 'Key=value': Overide some settings in the used config.

Difference between resume-from and load-from: resume-from loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. load-from only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.

Train with multiple machines

If you run MMDetection on a cluster managed with slurm, you can use the script slurm_train.sh. (This script also supports single machine training.)

[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}

Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition.

GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x

You can check slurm_train.sh for full arguments and environment variables.

If you have just multiple machines connected with ethernet, you can refer to PyTorch launch utility. Usually it is slow if you do not have high speed networking like InfiniBand.

Launch multiple jobs on a single machine

If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, you need to specify different ports (29500 by default) for each job to avoid communication conflict.

If you use dist_train.sh to launch training jobs, you can set the port in commands.

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4

If you use launch training jobs with Slurm, there are two ways to specify the ports.

  1. Set the port through --options. This is more recommended since it does not change the original configs.

    CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'

  2. Modify the config files (usually the 6th line from the bottom in config files) to set different communication ports.

    In config1.py,

    dist_params = dict(backend='nccl', port=29500)

    In config2.py,

    dist_params = dict(backend='nccl', port=29501)

    Then you can launch two jobs with config1.py ang config2.py.

    CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}