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
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Create a conda virtual environment and activate it.
conda create -n open-mmlab python=3.7 -y conda activate open-mmlab
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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.
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Install MIM
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from pypi
python -m pip install mim
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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
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Auto completion (Optional)
In order to activate shell completion, you need to inform your shell that completion is available for your script.
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For Bash, add this to ~/.bashrc:
eval "$(_MIM_COMPLETE=source mim)"
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For Zsh, add this to ~/.zshrc:
eval "$(_MIM_COMPLETE=source_zsh mim)"
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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
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Command
1. install
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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
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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
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command
# uninstall mmcv > mim uninstall mmcv-full # uninstall mmcls > mim uninstall mmcls
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api
from mim import uninstall # uninstall mmcv uninstall('mmcv-full') # uninstall mmcls uninstall('mmcls)
3. list
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command
> mim list > mim list --all
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api
from mim import list_package list_package() list_package(True)
4. search
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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
' > 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
) get_model_info('mmcls', sorted_fields=['bs', 'epoch']) get_model_info('mmcls', shown_fields=['epoch', 'bs', 'weight'])
5. download
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command
> mim download mmcls --config resnet18_b16x8_cifar10 > mim download mmcls --config resnet18_b16x8_cifar10 --dest .
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api
from mim import download download('mmcls', ['resnet18_b16x8_cifar10']) download('mmcls', ['resnet18_b16x8_cifar10'], dest_dir='.')
6. train
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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
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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
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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
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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
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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
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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
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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
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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.
1.Fix the punctuation display problem on Windows, when running the command
2.Fix the path separator problem on Windows, when running the command 