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Train a model

MMSegmentation 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 some iterations, you can change the evaluation interval by adding the interval argument in the training config.

evaluation = dict(interval=4000)  # This evaluate the model per 4000 iterations.

*Important*: The default learning rate in config files is for 4 GPUs and 2 img/gpu (batch size = 4x2 = 8). Equivalently, you may also use 8 GPUs and 1 imgs/gpu since all models using cross-GPU SyncBN.

To trade speed with GPU memory, you may pass in --cfg-options model.backbone.with_cp=True to enable checkpoint in backbone.

Train with a single GPU

official support:

./tools/dist_train.sh ${CONFIG_FILE} 1 [optional arguments]

experimental support (Convert SyncBN to BN):

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 iterations 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 (to continue the training process).
  • --load-from ${CHECKPOINT_FILE}: Load weights from a checkpoint file (to start finetuning for another task).

Difference between resume-from and load-from:

  • resume-from loads both the model weights and optimizer state including the iteration number.
  • load-from loads only the model weights, starts the training from iteration 0.

Train with multiple machines

If you run MMSegmentation 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 ${WORK_DIR}

Here is an example of using 16 GPUs to train PSPNet on the dev partition.

GPUS=16 ./tools/slurm_train.sh dev pspr50 configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py /nfs/xxxx/psp_r50_512x1024_40ki_cityscapes

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. Otherwise, there will be error message saying RuntimeError: Address already in use.

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

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 slurm_train.sh to launch training jobs, you can set the port in commands with environment variable MASTER_PORT.

MASTER_PORT=29500 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE}
MASTER_PORT=29501 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE}