Note
You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.
Getting Started¶
This page provides basic tutorials about the usage of MMSelfSup. For installation instructions, please see install.md.
Train existing methods¶
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/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¶
bash tools/dist_train.sh ${CONFIG_FILE} ${GPUS} --work-dir ${YOUR_WORK_DIR} [optional arguments]
Optional arguments are:
--resume-from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.--deterministic: Switch on “deterministic” mode which slows down training but the results are reproducible.
An example:
# checkpoints and logs saved in WORK_DIR=work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
bash tools/dist_train.sh configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py 8 --work-dir work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
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} ${MMSELFSUP}/work_dirs
Alternatively, if you run MMSelfSup on a cluster managed with slurm:
GPUS_PER_NODE=${GPUS_PER_NODE} GPUS=${GPUS} SRUN_ARGS=${SRUN_ARGS} bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${YOUR_WORK_DIR} [optional arguments]
An example:
GPUS_PER_NODE=8 GPUS=8 bash tools/slurm_train.sh Dummy Test_job configs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k.py work_dirs/selfsup/odc/odc_resnet50_8xb64-steplr-440e_in1k/
Train with multiple machines¶
If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
On the first machine:
NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR 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.
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/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/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 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 bash tools/slurm_train.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 bash tools/slurm_train.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 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py tmp_work_dir_2 --cfg-options dist_params.port=29501
Benchmarks¶
We also provide commands to evaluate your pre-trained model on several downstream task, and you can refer to Benchmarks for the details.
Tools and Tips¶
Count number of parameters¶
python tools/analysis_tools/count_parameters.py ${CONFIG_FILE}
Publish a model¶
Before you publish a model, you may want to
Convert model weights to CPU tensors.
Delete the optimizer states.
Compute the hash of the checkpoint file and append the hash id to the filename.
python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
Use t-SNE¶
We provide an off-the-shelf tool to visualize the quality of image representations by t-SNE.
python tools/analysis_tools/visualize_tsne.py ${CONFIG_FILE} --checkpoint ${CKPT_PATH} --work-dir ${WORK_DIR} [optional arguments]
Arguments:
CONFIG_FILE: config file for the pre-trained model.CKPT_PATH: the path of model’s checkpoint.WORK_DIR: the directory to save the results of visualization.[optional arguments]: for optional arguments, you can refer to visualize_tsne.py
MAE Visualization¶
We provide a tool to visualize the mask and reconstruction image of MAE model.
python tools/misc/mae_visualization.py ${IMG_PATH} ${CONFIG_FILE} ${CKPT_PATH} ${OUT_FILE} --device ${DEVICE}
Arguments:
IMG_PATH: an image path used for visualization.CONFIG_FILE: config file for the pre-trained model.CKPT_PATH: the path of model’s checkpoint.OUT_FILE: the image path used for visualization results.DEVICE: device used for inference.
An example:
python tools/misc/mae_visualization.py tests/data/color.jpg configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py mae_epoch_400.pth results.jpg --device 'cuda:0'
Reproducibility¶
If you want to make your performance exactly reproducible, please switch on --deterministic to train the final model to be published. Note that this flag will switch off torch.backends.cudnn.benchmark and slow down the training speed.