Train different type algorithms¶
Currently our algorithms support mmclassification, mmdetection and mmsegmentation. Before running our algorithms, you may need to prepare the datasets according to the instructions in the corresponding document.
Note:
Since our algorithms have the same interface for all three tasks, in the following introduction, we use
${task}to represent one ofmmcls、mmdetandmmseg.We dynamically pass arguments
cfg-options(e.g.,mutable_cfgin nas algorithm orchannel_cfgin pruning algorithm) to avoid the need for a config for each subnet or checkpoint. If you want to specify different subnets for retraining or testing, you just need to change this arguments.
NAS¶
There are three steps to start neural network search(NAS), including supernet pre-training, search for subnet on the trained supernet and subnet retraining.
Supernet Pre-training¶
python tools/${task}/train_${task}.py ${CONFIG_FILE} [optional arguments]
The usage of optional arguments are the same as corresponding tasks like mmclassification, mmdetection and mmsegmentation.
For example,
python ./tools/mmcls/train_mmcls.py \ configs/nas/spos/spos_supernet_shufflenetv2_8xb128_in1k.py \ --work-dir $WORK_DIR
Search for Subnet on The Trained Supernet¶
python tools/${task}/search_${task}.py ${CONFIG_FILE} ${CHECKPOINT_PATH} [optional arguments]
For example,
python ./tools/mmcls/search_mmcls.py \ configs/nas/spos/spos_evolution_search_shufflenetv2_8xb2048_in1k.py \ $STEP1_CKPT \ --work-dir $WORK_DIR
Subnet Retraining¶
python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.mutable_cfg=${MUTABLE_CFG_PATH} [optional arguments]
MUTABLE_CFG_PATH: Path ofmutable_cfg.mutable_cfgrepresents config for mutable of the subnet searched out, used to specify different subnets for retraining. An example formutable_cfgcan be found here, and the usage can be found here.
For example,
python ./tools/mmcls/train_mmcls.py \ configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py \ --work-dir $WORK_DIR \ --cfg-options algorithm.mutable_cfg=configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml
We note that instead of using --cfg-options, you can also directly modify configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py like this:
mutable_cfg = 'configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml' algorithm = dict(..., mutable_cfg=mutable_cfg)
Pruning¶
Pruning has three steps, including supernet pre-training, search for subnet on the trained supernet and subnet retraining. The commands of the first two steps are similar to NAS, except that we need to use CONFIG_FILE of Pruning here. The commands of the subnet retraining are as follows.
Subnet Retraining¶
python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.channel_cfg=${CHANNEL_CFG_PATH} [optional arguments]
Different from NAS, the argument that needs to be specified here is channel_cfg instead of mutable_cfg.
CHANNEL_CFG_PATH: Path ofchannel_cfg.channel_cfgrepresents config for channel of the subnet searched out, used to specify different subnets for testing. An example forchannel_cfgcan be found here, and the usage can be found here.
For example,
python ./tools/mmcls/train_mmcls.py \ configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py \ --work-dir your_work_dir \ --cfg-options algorithm.channel_cfg=configs/pruning/autoslim/AUTOSLIM_MBV2_530M_OFFICIAL.yaml,configs/pruning/autoslim/AUTOSLIM_MBV2_320M_OFFICIAL.yaml,configs/pruning/autoslim/AUTOSLIM_MBV2_220M_OFFICIAL.yaml
Distillation¶
There is only one step to start knowledge distillation.
python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.distiller.teacher.init_cfg.type=Pretrained algorithm.distiller.teacher.init_cfg.checkpoint=${TEACHER_CHECKPOINT_PATH} [optional arguments]
TEACHER_CHECKPOINT_PATH: Path ofteacher_checkpoint.teacher_checkpointrepresents checkpoint of teacher model, used to specify different checkpoints for distillation.
For example,
python ./tools/mmseg/train_mmseg.py \ configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py \ --work-dir your_work_dir \ --cfg-options algorithm.distiller.teacher.init_cfg.type=Pretrained algorithm.distiller.teacher.init_cfg.checkpoint=https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
Train with different devices¶
Note: The default learning rate in config files is for 8 GPUs. If using different number GPUs, the total batch size will change in proportion, you have to scale the learning rate following new_lr = old_lr * new_ngpus / old_ngpus. We recommend to use tools/xxx/dist_train.sh even with 1 gpu, since some methods do not support non-distributed training.
Training with CPU¶
export CUDA_VISIBLE_DEVICES=-1
python tools/train.py ${CONFIG_FILE}
Note: We do not recommend users to use CPU for training because it is too slow and some algorithms are using SyncBN which is based on distributed training. We support this feature to allow users to debug on machines without GPU for convenience.
Train with single/multiple GPUs¶
sh tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work_dir ${YOUR_WORK_DIR} [optional arguments]
Note: During training, checkpoints and logs are saved in the same folder structure as the config file under work_dirs/. Custom work directory is not recommended since evaluation scripts infer work directories from the config file name. If you want to save your weights somewhere else, please use symlink, for example:
ln -s ${YOUR_WORK_DIRS} ${MMRAZOR}/work_dirs
Alternatively, if you run MMRazor on a cluster managed with slurm:
GPUS_PER_NODE=${GPUS_PER_NODE} GPUS=${GPUS} SRUN_ARGS=${SRUN_ARGS} sh tools/xxx/slurm_train_xxx.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${YOUR_WORK_DIR} [optional arguments]
Train with multiple machines¶
If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
On the first machine:
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/xxx/dist_train.sh $CONFIG $GPUS
On the second machine:
NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/xxx/dist_train.sh $CONFIG $GPUS
Usually it is slow if you do not have high speed networking like InfiniBand.
If you launch with slurm, the command is the same as that on single machine described above, but you need refer to slurm_train.sh to set appropriate parameters and environment variables.
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:
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 sh tools/xxx/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/xxx/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_2
If you use launch training jobs with slurm, you have two options to set different communication ports:
Option 1:
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 and config2.py.
CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 sh tools/xxx/slurm_train_xxx.sh ${PARTITION} ${JOB_NAME} config1.py tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 sh tools/xxx/slurm_train_xxx.sh ${PARTITION} ${JOB_NAME} config2.py tmp_work_dir_2
Option 2:
You can set different communication ports without the need to modify the configuration file, but have to set the cfg-options to overwrite the default port in configuration file.
CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 sh tools/xxx/slurm_train_xxx.sh ${PARTITION} ${JOB_NAME} config1.py tmp_work_dir_1 --cfg-options dist_params.port=29500
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 sh tools/xxx/slurm_train_xxx.sh ${PARTITION} ${JOB_NAME} config2.py tmp_work_dir_2 --cfg-options dist_params.port=29501