Installation¶
Requirements¶
Python 3.6+
PyTorch 1.5+
The compatible MMClassification and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.
| MMClassification version | MMCV version |
|---|---|
| master | mmcv>=1.3.16, <=1.5.0 |
| 0.18.0 | mmcv>=1.3.16, <=1.5.0 |
| 0.17.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.16.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.15.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.15.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.14.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.13.0 | mmcv>=1.3.8, <=1.5.0 |
| 0.12.0 | mmcv>=1.3.1, <=1.5.0 |
| 0.11.1 | mmcv>=1.3.1, <=1.5.0 |
| 0.11.0 | mmcv>=1.3.0 |
| 0.10.0 | mmcv>=1.3.0 |
| 0.9.0 | mmcv>=1.1.4 |
| 0.8.0 | mmcv>=1.1.4 |
| 0.7.0 | mmcv>=1.1.4 |
| 0.6.0 | mmcv>=1.1.4 |
Note
Since the master branch is under frequent development, the mmcv
version dependency may be inaccurate. If you encounter problems when using
the master branch, please try to update mmcv to the latest version.
Install MMClassification¶
a. Create a conda virtual environment and activate it.
conda create -n open-mmlab python=3.8 -y
conda activate open-mmlab
b. 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.
E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install
PyTorch 1.5.1, you need to install the prebuilt PyTorch with CUDA 10.1.
conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch
E.g.2 If you have CUDA 11.3 installed under /usr/local/cuda and would like to install
PyTorch 1.10.0., you need to install the prebuilt PyTorch with CUDA 11.3.
conda install pytorch==1.10.0 torchvision==0.11.1 cudatoolkit=11.3 -c pytorch
If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.
c. Install MMClassification repository.
Release version¶
We recommend you to install MMClassification with MIM.
pip install git+https://github.com/open-mmlab/mim.git
mim install mmcls
MIM can automatically install OpenMMLab projects and their requirements, and it can also help us to train, parameter search and pretrain model download.
Or, you can install MMClassification with pip:
pip install mmcls
Develop version¶
First, clone the MMClassification repository.
git clone https://github.com/open-mmlab/mmclassification.git
cd mmclassification
And then, install build requirements and install MMClassification.
pip install -e . # or "python setup.py develop"
Note
Following above instructions, MMClassification is installed on dev mode,
any local modifications made to the code will take effect without the need to
reinstall it (unless you submit some commits and want to update the version
number).
Another option: Docker Image¶
We provide a Dockerfile to build an image.
# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7.
docker build -f ./docker/Dockerfile --rm -t mmcls:torch1.6.0-cuda10.1-cudnn7 .
Important
Make sure you’ve installed the nvidia-container-toolkit.
Run a container built from mmcls image with command:
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/workspace/mmclassification/data mmcls:torch1.6.0-cuda10.1-cudnn7 /bin/bash
Using multiple MMClassification versions¶
The train and test scripts already modify the PYTHONPATH to ensure the script use the MMClassification in the current directory.
To use the default MMClassification installed in the environment rather than that you are working with, you can remove the following line in those scripts
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH