# 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](https://mmdetection3d.readthedocs.io/en/latest/getting_started.html).

### Test existing models on standard datasets

- single GPU
- CPU
- single node multiple GPU
- multiple node

You can use the following commands to test a dataset.

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

# CPU: disable GPUs and run single-gpu testing script (experimental)
export CUDA_VISIBLE_DEVICES=-1
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show] [--show-dir ${SHOW_DIR}]

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

**Note**:

For now, CPU testing is only supported for SMOKE.

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 for detection tasks, 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. Similarly, the metric can be set to `mIoU` for segmentation tasks, which applies to S3DIS and ScanNet.
- `--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.obj` 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.

   ```shell
   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.

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

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

   ```shell
   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 on KITTI with 8 GPUs, and evaluate the mAP.

   ```shell
   ./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.

   ```shell
   ./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 --eval-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 data to be submit to the official evaluation server.

   ```shell
   ./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 --eval-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.

   ```shell
   ./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 --eval-options 'jsonfile_prefix=results/pp_lyft/results_challenge' \
       'csv_savepath=results/pp_lyft/results_challenge.csv'
   ```

   **Notice**: To generate submissions on Lyft, `csv_savepath` must be given in the `--eval-options`. After generating the csv file, you can make a submission with kaggle commands given on the [website](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/submit).

   Note that in the [config of Lyft dataset](../../configs/_base_/datasets/lyft-3d.py), the value of `ann_file` keyword in `test` is `data_root + 'lyft_infos_test.pkl'`, which is the official test set of Lyft without annotation. To test on the validation set, please change this to `data_root + 'lyft_infos_val.pkl'`.

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

   ```shell
   ./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 --eval-options 'pklfile_prefix=results/waymo-car/kitti_results' \
       'submission_prefix=results/waymo-car/kitti_results'
   ```

   **Notice**: For evaluation on waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) 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 `--eval-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 evaluation, 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.

   ```shell
   ./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 --eval-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](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/). 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

MMDetection3D 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.

```python
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](https://arxiv.org/abs/1706.02677), 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

```shell
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}`.

### Training with CPU (experimental)

The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.

```shell
export CUDA_VISIBLE_DEVICES=-1
```

And then run the script of train with a single GPU.

**Note**:

For now, most of the point cloud related algorithms rely on 3D CUDA op, which can not be trained on CPU. Some monocular 3D object detection algorithms, like FCOS3D and SMOKE can be trained on CPU. We do not recommend users to use CPU for training because it is too slow. We support this feature to allow users to debug certain models on machines without GPU for convenience.

### Train with multiple GPUs

```shell
./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](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py#L75)) 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'`: Override 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 MMDetection3D on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)

```shell
[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.

```shell
GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py /nfs/xxxx/pp_kitti_3class
```

You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables.

If you launch with multiple machines simply connected with ethernet, you can simply run following commands:

On the first machine:

```shell
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
```

On the second machine:

```shell
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
```

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.

```shell
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.

   ```shell
   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`,

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

   In `config2.py`,

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

   Then you can launch two jobs with `config1.py` and `config2.py`.

   ```shell
   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}
   ```
