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# Getting Started

- Getting Started

  - Train existing methods

    - Training with CPU

    - Train with single/multiple GPUs

    - Train with multiple machines

    - Launch multiple jobs on a single machine

  - Benchmarks

  - Tools and Tips

    - Count number of parameters

    - Publish a model

    - Use t-SNE

    - Reproducibility

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} ${CONFIG_FILE} ${CKPT_PATH} --device ${DEVICE}

参数:

- `IMG`: an image path used for visualization.

- `CONFIG_FILE`: config file for the pre-trained model.

- `CKPT_PATH`: the path of model’s checkpoint.

- `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 --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.