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MIM: MIM Installs OpenMMLab Packages

MIM provides a unified API for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo.

Installation

  1. Create a conda virtual environment and activate it.

    conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

  2. Install PyTorch and torchvision following the official instructions, e.g.,

    conda install pytorch torchvision -c pytorch

    Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

  3. Install MIM

    • from pypi

      python -m pip install mim

    • from source

      git clone https://github.com/open-mmlab/mim.git
cd mim
pip install -e .
# python setup.py develop or python setup.py install

  4. Auto completion (Optional)

    In order to activate shell completion, you need to inform your shell that completion is available for your script.

    • For Bash, add this to ~/.bashrc:

      eval "$(_MIM_COMPLETE=source mim)"

    • For Zsh, add this to ~/.zshrc:

      eval "$(_MIM_COMPLETE=source_zsh mim)"

    • For Fish, add this to ~/.config/fish/completions/mim.fish:

      eval (env _MIM_COMPLETE=source_fish mim)

    Open a new shell to enable completion. Or run the eval command directly in your current shell to enable it temporarily.

    The above eval command will invoke your application every time a shell is started. This may slow down shell startup time significantly.

    Alternatively, you can activate the script. Please refer to activation-script

Command

1. install

asciicast

  • command

    # install latest version of mmcv-full
> mim install mmcv-full  # wheel
# install 1.3.1
> mim install mmcv-full==1.3.1
# install master branch
> mim install mmcv-full -f https://github.com/open-mmlab/mmcv.git

# install latest version of mmcls
> mim install mmcls
# install 0.11.0
> mim install mmcls==0.11.0  # v0.11.0
# install master branch
> mim install mmcls -f https://github.com/open-mmlab/mmclassification.git
# install local repo
> git clone https://github.com/open-mmlab/mmclassification.git
> cd mmclassification
> mim install .

# install extension based on OpenMMLab
mim install mmcls-project -f https://github.com/xxx/mmcls-project.git

  • api

    from mim import install

# install mmcv
install('mmcv-full')

# install mmcls
# install mmcls will automatically install mmcv if it is not installed
install('mmcv-full', find_url='https://github.com/open-mmlab/mmcv.git')
install('mmcv-full==1.3.1', find_url='https://github.com/open-mmlab/mmcv.git')

# install extension based on OpenMMLab
install('mmcls-project', find_url='https://github.com/xxx/mmcls-project.git')
2. uninstall

asciicast

  • command

    # uninstall mmcv
> mim uninstall mmcv-full

# uninstall mmcls
> mim uninstall mmcls

  • api

    from mim import uninstall

# uninstall mmcv
uninstall('mmcv-full')

# uninstall mmcls
uninstall('mmcls)
3. list

asciicast

  • command

    > mim list
> mim list --all

  • api

    from mim import list_package

list_package()
list_package(True)
4. search

asciicast

  • command

    > mim search mmcls
> mim search mmcls==0.11.0 --remote
> mim search mmcls --config resnet18_b16x8_cifar10
> mim search mmcls --model resnet
> mim search mmcls --dataset cifar-10
> mim search mmcls --valid-field
> mim search mmcls --condition 'bs>45,epoch>100'
> mim search mmcls --condition 'bs>45 epoch>100'
> mim search mmcls --condition '128<bs<=256'
> mim search mmcls --sort bs epoch
> mim search mmcls --field epoch bs weight
> mim search mmcls --exclude-field weight paper

  • api

    from mim import get_model_info

get_model_info('mmcls')
get_model_info('mmcls==0.11.0', local=False)
get_model_info('mmcls', models=['resnet'])
get_model_info('mmcls', training_datasets=['cifar-10'])
get_model_info('mmcls', filter_conditions='bs>45,epoch>100')
get_model_info('mmcls', filter_conditions='bs>45 epoch>100')
get_model_info('mmcls', filter_conditions='128<bs<=256')
get_model_info('mmcls', sorted_fields=['bs', 'epoch'])
get_model_info('mmcls', shown_fields=['epoch', 'bs', 'weight'])
5. download

asciicast

  • command

    > mim download mmcls --config resnet18_b16x8_cifar10
> mim download mmcls --config resnet18_b16x8_cifar10 --dest .

  • api

    from mim import download

download('mmcls', ['resnet18_b16x8_cifar10'])
download('mmcls', ['resnet18_b16x8_cifar10'], dest_dir='.')
6. train

asciicast

  • command

    # Train models on a single server with one GPU
> mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1
# Train models on a single server with 4 GPUs and pytorch distributed
> mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 4 \
    --launcher pytorch
# Train models on a slurm HPC with one 8-GPU node
> mim train mmcls resnet101_b16x8_cifar10.py --launcher slurm --gpus 8 \
    --gpus-per-node 8 --partition partition_name --work-dir tmp
# Print help messages of sub-command train
> mim train -h
# Print help messages of sub-command train and the training script of mmcls
> mim train mmcls -h

  • api

    from mim import train

train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=1,
      other_args='--work-dir tmp')
train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=4,
      launcher='pytorch', other_args='--work-dir tmp')
train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=8,
      launcher='slurm', gpus_per_node=8, partition='partition_name',
      other_args='--work-dir tmp')
7. test

asciicast

  • command

    # Test models on a single server with 1 GPU, report accuracy
> mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
    tmp/epoch_3.pth --gpus 1 --metrics accuracy
# Test models on a single server with 1 GPU, save predictions
> mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
    tmp/epoch_3.pth --gpus 1 --out tmp.pkl
# Test models on a single server with 4 GPUs, pytorch distributed,
# report accuracy
> mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
    tmp/epoch_3.pth --gpus 4 --launcher pytorch --metrics accuracy
# Test models on a slurm HPC with one 8-GPU node, report accuracy
> mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
    tmp/epoch_3.pth --gpus 8 --metrics accuracy --partition \
    partition_name --gpus-per-node 8 --launcher slurm
# Print help messages of sub-command test
> mim test -h
# Print help messages of sub-command test and the testing script of mmcls
> mim test mmcls -h

  • api

    from mim import test
test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=1, other_args='--metrics accuracy')
test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=1, other_args='--out tmp.pkl')
test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=4, launcher='pytorch',
     other_args='--metrics accuracy')
test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=8, partition='partition_name',
     launcher='slurm', gpus_per_node=8, other_args='--metrics accuracy')
8. run

asciicast

  • command

    # Get the Flops of a model
> mim run mmcls get_flops resnet101_b16x8_cifar10.py
# Publish a model
> mim run mmcls publish_model input.pth output.pth
# Train models on a slurm HPC with one GPU
> srun -p partition --gres=gpu:1 mim run mmcls train \
    resnet101_b16x8_cifar10.py --work-dir tmp
# Test models on a slurm HPC with one GPU, report accuracy
> srun -p partition --gres=gpu:1 mim run mmcls test \
    resnet101_b16x8_cifar10.py tmp/epoch_3.pth --metrics accuracy
# Print help messages of sub-command run
> mim run -h
# Print help messages of sub-command run, list all available scripts in
# codebase mmcls
> mim run mmcls -h
# Print help messages of sub-command run, print the help message of
# training script in mmcls
> mim run mmcls train -h

  • api

    from mim import run

run(repo='mmcls', command='get_flops',
    other_args='resnet101_b16x8_cifar10.py')
run(repo='mmcls', command='publish_model',
    other_args='input.pth output.pth')
run(repo='mmcls', command='train',
    other_args='resnet101_b16x8_cifar10.py --work-dir tmp')
run(repo='mmcls', command='test',
    other_args='resnet101_b16x8_cifar10.py tmp/epoch_3.pth --metrics accuracy')
9. gridsearch

asciicast

  • command

    # Parameter search with on a single server with one GPU, search learning
# rate
> mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
    --search-args '--optimizer.lr 1e-2 1e-3'
# Parameter search with on a single server with one GPU, search
# weight_decay
> mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
    --search-args '--optimizer.weight_decay 1e-3 1e-4'
# Parameter search with on a single server with one GPU, search learning
# rate and weight_decay
> mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
    --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \
    1e-4'
# Parameter search on a slurm HPC with one 8-GPU node, search learning
# rate and weight_decay
> mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \
    --partition partition_name --gpus-per-node 8 --launcher slurm \
    --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \
    1e-4'
# Parameter search on a slurm HPC with one 8-GPU node, search learning
# rate and weight_decay, max parallel jobs is 2
> mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \
    --partition partition_name --gpus-per-node 8 --launcher slurm \
    --max-workers 2 --search-args '--optimizer.lr 1e-2 1e-3 \
    --optimizer.weight_decay 1e-3 1e-4'
# Print the help message of sub-command search
> mim gridsearch -h
# Print the help message of sub-command search and the help message of the
# training script of codebase mmcls
> mim gridsearch mmcls -h

  • api

    from mim import gridsearch

gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
           search_args='--optimizer.lr 1e-2 1e-3',
           other_args='--work-dir tmp')
gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
           search_args='--optimizer.weight_decay 1e-3 1e-4',
           other_args='--work-dir tmp')
gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
           search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                       '1e-3 1e-4',
           other_args='--work-dir tmp')
gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=8,
           partition='partition_name', gpus_per_node=8, launcher='slurm',
           search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                       ' 1e-3 1e-4',
           other_args='--work-dir tmp')
gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=8,
           partition='partition_name', gpus_per_node=8, launcher='slurm',
           max_workers=2,
           search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                       ' 1e-3 1e-4',
           other_args='--work-dir tmp')

Build custom projects with MIM

We provide some examples about how to build custom projects based on OpenMMLAB codebases and MIM in MIM-Example. In mmcls_custom_backbone, we define a custom backbone and a classification config file that uses the backbone. To train this model, you can use the command:

# The working directory is `mim-example/mmcls_custom_backbone`
PYTHONPATH=$PWD:$PYTHONPATH mim train mmcls custom_net_config.py --work-dir tmp --gpus 1

Contributing

We appreciate all contributions to improve mim. Please refer to CONTRIBUTING.md for the contributing guideline.

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