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Example Project

This is an example README for community projects/. You can write your README in your own project. Here are some recommended parts of a README for others to understand and use your project, you can copy or modify them according to your project.

Usage

Setup Environment

Please refer to Get Started to install MMPreTrain.

At first, add the current folder to PYTHONPATH, so that Python can find your code. Run command in the current directory to add it.

Please run it every time after you opened a new shell.

export PYTHONPATH=`pwd`:$PYTHONPATH

Data Preparation

Prepare the ImageNet-2012 dataset according to the instruction.

Training commands

To train with single GPU:

mim train mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py

To train with multiple GPUs:

mim train mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py --launcher pytorch --gpus 8

To train with multiple GPUs by slurm:

mim train mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py --launcher slurm \
    --gpus 16 --gpus-per-node 8 --partition $PARTITION

Testing commands

To test with single GPU:

mim test mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py --checkpoint $CHECKPOINT

To test with multiple GPUs:

mim test mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py --checkpoint $CHECKPOINT --launcher pytorch --gpus 8

To test with multiple GPUs by slurm:

mim test mmpretrain configs/starnet/startnet_s1_32xb32_in1k.py --checkpoint $CHECKPOINT --launcher slurm \
    --gpus 16 --gpus-per-node 8 --partition $PARTITION

Citation

@misc{2023mmpretrain,
    title={OpenMMLab's Pre-training Toolbox and Benchmark},
    author={MMPreTrain Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmpretrain}},
    year={2023}
}

Checklist

Here is a checklist of this project's progress. And you can ignore this part if you don't plan to contribute to MMPreTrain projects.

Milestone 1: PR-ready, and acceptable to be one of the projects/.
- Finish the code
- Basic docstrings & proper citation
- Converted checkpoint and results (Only for reproduction)
Milestone 2: Indicates a successful model implementation.
- Training results
Milestone 3: Good to be a part of our core package!
- Unit tests
- Code style
- metafile.yml and README.md

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