# MIM: MIM Installs OpenMMLab Packages MIM provides a unified interface for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo. ## Major Features - **Package Management** You can use MIM to manage OpenMMLab codebases, install or uninstall them conveniently. - **Model Management** You can use MIM to manage OpenMMLab model zoo, e.g., download checkpoints by name, search checkpoints that meet specific criteria. - **Unified Entrypoint for Scripts** You can execute any script provided by all OpenMMLab codebases with unified commands. Train, test and inference become easier than ever. Besides, you can use `gridsearch` command for vanilla hyper-parameter search. ## License This project is released under the [Apache 2.0 license](LICENSE). ## Changelog v0.1.1 was released in 13/6/2021. ## Customization You can use `.mimrc` for customization. Now we support customize default values of each sub-command. Please refer to [customization.md](docs/en/customization.md) for details. ## Build custom projects with MIM We provide some examples of how to build custom projects based on OpenMMLAB codebases and MIM in [MIM-Example](https://github.com/open-mmlab/mim-example). Without worrying about copying codes and scripts from existing codebases, users can focus on developing new components and MIM helps integrate and run the new project. ## Installation Please refer to [installation.md](docs/en/installation.md) for installation. ## Command
1. install - command ```bash # install latest version of mmcv-full > mim install mmcv-full # wheel # install 1.5.0 > mim install mmcv-full==1.5.0 # install latest version of mmcls > mim install mmcls # install master branch > mim install git+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 git+https://github.com/xxx/mmcls-project.git ``` - api ```python from mim import install # install mmcv install('mmcv-full') # install mmcls will automatically install mmcv if it is not installed install('mmcls') # install extension based on OpenMMLab install('git+https://github.com/xxx/mmcls-project.git') ```
2. uninstall - command ```bash # uninstall mmcv > mim uninstall mmcv-full # uninstall mmcls > mim uninstall mmcls ``` - api ```python from mim import uninstall # uninstall mmcv uninstall('mmcv-full') # uninstall mmcls uninstall('mmcls') ```
3. list - command ```bash > mim list > mim list --all ``` - api ```python from mim import list_package list_package() list_package(True) ```
4. search - command ```bash > mim search mmcls > mim search mmcls==0.23.0 --remote > mim search mmcls --config resnet18_8xb16_cifar10 > mim search mmcls --model resnet > mim search mmcls --dataset cifar-10 > mim search mmcls --valid-field > mim search mmcls --condition 'batch_size>45,epochs>100' > mim search mmcls --condition 'batch_size>45 epochs>100' > mim search mmcls --condition '128 mim search mmcls --sort batch_size epochs > mim search mmcls --field epochs batch_size weight > mim search mmcls --exclude-field weight paper ``` - api ```python from mim import get_model_info get_model_info('mmcls') get_model_info('mmcls==0.23.0', local=False) get_model_info('mmcls', models=['resnet']) get_model_info('mmcls', training_datasets=['cifar-10']) get_model_info('mmcls', filter_conditions='batch_size>45,epochs>100') get_model_info('mmcls', filter_conditions='batch_size>45 epochs>100') get_model_info('mmcls', filter_conditions='128
5. download - command ```bash > mim download mmcls --config resnet18_8xb16_cifar10 > mim download mmcls --config resnet18_8xb16_cifar10 --dest . ``` - api ```python from mim import download download('mmcls', ['resnet18_8xb16_cifar10']) download('mmcls', ['resnet18_8xb16_cifar10'], dest_root='.') ```
6. train - command ```bash # Train models on a single server with CPU by setting `gpus` to 0 and # 'launcher' to 'none' (if applicable). The training script of the # corresponding codebase will fail if it doesn't support CPU training. > mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0 # 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 ```python from mim import train train(repo='mmcls', config='resnet18_8xb16_cifar10.py', gpus=0, other_args=('--work-dir', 'tmp')) train(repo='mmcls', config='resnet18_8xb16_cifar10.py', gpus=1, other_args=('--work-dir', 'tmp')) train(repo='mmcls', config='resnet18_8xb16_cifar10.py', gpus=4, launcher='pytorch', other_args=('--work-dir', 'tmp')) train(repo='mmcls', config='resnet18_8xb16_cifar10.py', gpus=8, launcher='slurm', gpus_per_node=8, partition='partition_name', other_args=('--work-dir', 'tmp')) ```
7. test - command ```bash # 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 ```python 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 - command ```bash # 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 ```python 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 - command ```bash # Parameter search on a single server with CPU by setting `gpus` to 0 and # 'launcher' to 'none' (if applicable). The training script of the # corresponding codebase will fail if it doesn't support CPU training. > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0 \ --search-args '--optimizer.lr 1e-2 1e-3' # 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-jobs 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 ```python from mim import gridsearch gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=0, 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.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')) ```
## Contributing We appreciate all contributions to improve mim. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) for the contributing guideline. ## License This project is released under the [Apache 2.0 license](LICENSE). ## Projects in OpenMMLab - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models. - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries. - [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark. - [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox. - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. - [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark. - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox. - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark. - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark. - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework. - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. - [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.