Installation

Prerequisites

Linux | Windows | macOS
Python 3.7
PyTorch 1.6 or higher
torchvision 0.7.0
CUDA 10.1
NCCL 2
GCC 5.4.0 or higher
MMCV
MMDetection

MMOCR has different version requirements on MMCV and MMDetection at each release to guarantee the implementation correctness. Please refer to the table below and ensure the package versions fit the requirement.

MMOCR	MMCV	MMDetection
master	1.3.8 <= mmcv <= 1.5.0	2.14.0 <= mmdet <= 3.0.0
0.4.0, 0.4.1	1.3.8 <= mmcv <= 1.5.0	2.14.0 <= mmdet <= 2.20.0
0.3.0	1.3.8 <= mmcv <= 1.4.0	2.14.0 <= mmdet <= 2.20.0
0.2.1	1.3.8 <= mmcv <= 1.4.0	2.13.0 <= mmdet <= 2.20.0
0.2.0	1.3.4 <= mmcv <= 1.4.0	2.11.0 <= mmdet <= 2.13.0
0.1.0	1.2.6 <= mmcv <= 1.3.4	2.9.0 <= mmdet <= 2.11.0

We have tested the following versions of OS and software:

OS: Ubuntu 16.04
CUDA: 10.1
GCC(G++): 5.4.0
MMCV 1.3.8
MMDetection 2.14.0
PyTorch 1.6.0
torchvision 0.7.0

MMOCR depends on PyTorch and mmdetection.

Step-by-Step Installation Instructions

a. Create a Conda virtual environment and activate it.

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

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

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

:::{note} Make sure that your compilation CUDA version and runtime CUDA version matches. You can check the supported CUDA version for precompiled packages on the PyTorch website. :::

c. Install mmcv, we recommend you to install the pre-build mmcv as below.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html

Please replace {cu_version} and {torch_version} in the url with your desired one. For example, to install the latest mmcv-full with CUDA 11 and PyTorch 1.7.0, use the following command:

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html

:::{note} mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.

# We can ignore the micro version of PyTorch
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7/index.html

::: :::{note}

If it compiles during installation, then please check that the CUDA version and PyTorch version exactly matches the version in the mmcv-full installation command.

See official installation guide for different versions of MMCV compatible to different PyTorch and CUDA versions. :::

:::{warning} You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError. :::

d. Install mmdet, we recommend you to install the latest mmdet with pip. See here for different versions of mmdet.

pip install mmdet

Optionally you can choose to install mmdet following the official installation guide.

e. Clone the MMOCR repository.

git clone https://github.com/open-mmlab/mmocr.git
cd mmocr

f. Install build requirements and then install MMOCR.

pip install -r requirements.txt
pip install -v -e . # or "python setup.py develop"
export PYTHONPATH=$(pwd):$PYTHONPATH

Full Set-up Script

Here is the full script for setting up MMOCR with Conda.

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

# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

# install the latest mmcv-full
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

# install mmdetection
pip install mmdet

# install mmocr
git clone https://github.com/open-mmlab/mmocr.git
cd mmocr

pip install -r requirements.txt
pip install -v -e .  # or "python setup.py develop"
export PYTHONPATH=$(pwd):$PYTHONPATH

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmocr docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmocr/data mmocr

Prepare Datasets

It is recommended to symlink the dataset root to mmocr/data. Please refer to datasets.md to prepare your datasets. If your folder structure is different, you may need to change the corresponding paths in config files.

The mmocr folder is organized as follows:

├── configs/
├── demo/
├── docker/
├── docs/
├── LICENSE
├── mmocr/
├── README.md
├── requirements/
├── requirements.txt
├── resources/
├── setup.cfg
├── setup.py
├── tests/
├── tools/