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Installation

Prerequisites

  • Linux
  • Python 3.6+
  • PyTorch 1.5 or higher
  • CUDA 9.0 or higher
  • NCCL 2
  • GCC 5.4 or higher
  • mmcv 1.3.15 or higher

Prepare environment

a. Create a conda virtual environment and activate it.

conda create -n openmmlab python=3.7 -y
conda activate openmmlab

b. Install PyTorch and torchvision following the official instructions

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

E.g.1 If you have CUDA 10.2 installed under /usr/local/cuda and would like to install the latest PyTorch, you can run this command.

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

E.g.2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.7.0., you can run this command.

conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=9.2 -c pytorch

If you build PyTorch from source instead of installing the pre-built package, you can use more CUDA versions such as 9.0.

c. Install MMCV, we recommend you to install the pre-built 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 to your desired one. For example, to install the latest mmcv-full with CUDA 10.2 and PyTorch 1.10.0, use the following command:

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html

See here for different versions of MMCV compatible to different PyTorch and CUDA versions.

Optionally you can choose to compile mmcv from source by the following command

git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full, which contains cuda ops, will be installed after this step
# OR pip install -e .  # package mmcv, which contains no cuda ops, will be installed after this step
cd ..

Important: 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.

Install MMFlow

a. Clone the MMFlow repository.

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

b. Install build requirements and then install mmflow.

pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

Note:

  1. The git commit id will be written to the version number, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.

  2. Following the above instructions, MMFlow 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).

  3. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up mmflow with conda.

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

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

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

# install mmflow
git clone https://github.com/open-mmlab/mmflow.git
cd mmflow
pip install -r requirements/build.txt
pip install -v -e .

Verification

To verify whether MMFlow is installed correctly, we can run the following sample code to initialize a model and inference a demo image.

from mmflow.apis import inference_model, init_model

config_file = 'configs/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
# url: https://download.openmmlab.com/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_final_384x768.pth
checkpoint_file = 'checkpoints/pwcnet_ft_4x1_300k_sintel_final_384x768.pth'
device = 'cuda:0'
# init a model
model = init_model(config_file, checkpoint_file, device=device)
# inference the demo image
inference_model(model, 'demo/frame_0001.png', 'demo/frame_0002.png')

The above code is supposed to run successfully upon you finish the installation.