Learn to train and test¶

Train¶

This section will show how to train existing models on supported datasets. The following training environments are supported:

CPU
single GPU
single node multiple GPUs
multiple nodes

You can also manage jobs with Slurm.

Important:

You can change the evaluation interval during training by modifying the train_cfg as train_cfg = dict(val_interval=10). That means evaluating the model every 10 epochs.
The default learning rate in all config files is for 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 8 GPUs * 1 img/gpu and lr=0.04 for 16 GPUs * 2 imgs/gpu.
During training, log files and checkpoints will be saved to the working directory, which is specified by CLI argument --work-dir. It uses ./work_dirs/CONFIG_NAME as default.
If you want the mixed precision training, simply specify CLI argument --amp.

1. Train on CPU¶

The model is default put on cuda device. Only if there are no cuda devices, the model will be put on cpu. So if you want to train the model on CPU, you need to export CUDA_VISIBLE_DEVICES=-1 to disable GPU visibility first. More details in MMEngine.

CUDA_VISIBLE_DEVICES=-1 python tools/train.py ${CONFIG_FILE} [optional arguments]

An example of training the MOT model QDTrack on CPU:

CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py

2. Train on single GPU¶

If you want to train the model on single GPU, you can directly use the tools/train.py as follows.

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

You can use export CUDA_VISIBLE_DEVICES=$GPU_ID to select the GPU.

An example of training the MOT model QDTrack on single GPU:

CUDA_VISIBLE_DEVICES=2 python tools/train.py configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py

3. Train on single node multiple GPUs¶

We provide tools/dist_train.sh to launch training on multiple GPUs. The basic usage is as follows.

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

If you would like to 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.

For example, you can set the port in commands as follows.

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

An example of training the MOT model QDTrack on single node multiple GPUs:

bash ./tools/dist_train.sh configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py 8

4. Train on multiple nodes¶

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

On the first machine:

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

On the second machine:

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

Usually it is slow if you do not have high speed networking like InfiniBand.

5. Train with Slurm¶

Slurm is a good job scheduling system for computing clusters. On a cluster managed by Slurm, you can use slurm_train.sh to spawn training jobs. It supports both single-node and multi-node training.

The basic usage is as follows.

bash ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} ${GPUS}

An example of training the MOT model QDTrack with Slurm:

PORT=29501 \
GPUS_PER_NODE=8 \
SRUN_ARGS="--quotatype=reserved" \
bash ./tools/slurm_train.sh \
mypartition \
mottrack
configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py
./work_dirs/QDTrack \
8

Test¶

This section will show how to test existing models on supported datasets. The following testing environments are supported:

CPU
single GPU
single node multiple GPUs
multiple nodes

You can also manage jobs with Slurm.

Important:

In MOT, some algorithms like DeepSORT, SORT, StrongSORT need load the weight of the reid and the weight of the detector separately. Other algorithms such as ByteTrack, OCSORT and QDTrack don’t need. So we provide --checkpoint, --detector and --reid to load weights.
We provide two ways to evaluate and test models, video_basede test and image_based test. some algorithms like StrongSORT, Mask2former only support video_based test. if your GPU memory can’t fit the entire video, you can switch test way by set sampler type. For example: video_based test: sampler=dict(type='DefaultSampler', shuffle=False, round_up=False) image_based test: sampler=dict(type='TrackImgSampler')
You can set the results saving path by modifying the key outfile_prefix in evaluator. For example, val_evaluator = dict(outfile_prefix='results/sort_mot17'). Otherwise, a temporal file will be created and will be removed after evaluation.
If you just want the formatted results without evaluation, you can set format_only=True. For example, test_evaluator = dict(type='MOTChallengeMetric', metric=['HOTA', 'CLEAR', 'Identity'], outfile_prefix='sort_mot17_results', format_only=True)

1. Test on CPU¶

The model is default put on cuda device. Only if there are no cuda devices, the model will be put on cpu. So if you want to test the model on CPU, you need to export CUDA_VISIBLE_DEVICES=-1 to disable GPU visibility first. More details in MMEngine.

CUDA_VISIBLE_DEVICES=-1 python tools/test_tracking.py ${CONFIG_FILE} [optional arguments]

An example of testing the MOT model SORT on CPU:

CUDA_VISIBLE_DEVICES=-1 python tools/test_tracking.py configs/sort/sort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py --detector ${CHECKPOINT_FILE}

2. Test on single GPU¶

If you want to test the model on single GPU, you can directly use the tools/test_tracking.py as follows.

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

You can use export CUDA_VISIBLE_DEVICES=$GPU_ID to select the GPU.

An example of testing the MOT model QDTrack on single GPU:

CUDA_VISIBLE_DEVICES=2 python tools/test_tracking.py configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py --detector ${CHECKPOINT_FILE}

3. Test on single node multiple GPUs¶

We provide tools/dist_test_tracking.sh to launch testing on multiple GPUs. The basic usage is as follows.

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

An example of testing the MOT model DeepSort on single node multiple GPUs:

bash ./tools/dist_test_tracking.sh configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py 8 --detector ${CHECKPOINT_FILE} --reid ${CHECKPOINT_FILE}

4. Test on multiple nodes¶

You can test on multiple nodes, which is similar with “Train on multiple nodes”.

5. Test with Slurm¶

On a cluster managed by Slurm, you can use slurm_test_tracking.sh to spawn testing jobs. It supports both single-node and multi-node testing.

The basic usage is as follows.

[GPUS=${GPUS}] bash tools/slurm_test_tracking.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} [optional arguments]

An example of testing the VIS model Mask2former with Slurm:

GPUS=8
bash tools/slurm_test_tracking.sh \
mypartition \
vis \
configs/mask2former_vis/mask2former_r50_8xb2-8e_youtubevis2021.py \
--checkpoint ${CHECKPOINT_FILE}