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Prerequisites

  • Linux
  • Python 3.6+
  • PyTorch 1.5+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • MMCV (MMCV-FULL)

The compatible MMGeneration and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.

MMGeneration version MMCV version
master mmcv-full>=1.3.0

Note: 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.

Installation

  1. Create a conda virtual environment and activate it. (Here, we assume the new environment is called open-mmlab)

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

  2. 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, you need to install the prebuilt PyTorch with CUDA 10.1.

    conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

    E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.5.1., you need to install the prebuilt PyTorch with CUDA 9.2.

    conda install pytorch=1.5.1 cudatoolkit=9.2 torchvision=0.6.1 -c pytorch

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

  3. Install mmcv-full, we recommend you to install the pre-build package as below.

    pip install mmcv-full={mmcv_version} -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 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

    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 will be installed after this step
cd ..

    Or directly run

    pip install mmcv-full

  4. Clone the MMGeneration repository.

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

  5. Install build requirements and then install MMGeneration.

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

Note:

a. Following the above instructions, MMGeneration is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it.

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

Install with CPU only

The code can be built for CPU only environment (where CUDA isn't available).

A from-scratch setup script

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

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

conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.1 -c pytorch -y

# install the latest mmcv
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html

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

Developing with multiple MMGeneration versions

The train and test scripts already modify the PYTHONPATH to ensure the script uses the MMGeneration in the current directory.

To use the default MMGeneration installed in the environment rather than that you are working with, you can remove the following line in those scripts

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH

Verification

To verify whether MMGeneration and the required environment are installed correctly, we can run sample Python code to initialize an unconditional model and use it to generate random samples:

from mmgen.apis import init_model, sample_uncoditional_model

config_file = 'configs/styleganv2/stylegan2_c2_lsun-church_256_b4x8_800k.py'
# you can download this checkpoint in advance and use a local file path.
checkpoint_file = 'https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth'
device = 'cuda:0'
# init a generatvie
model = init_model(config_file, checkpoint_file, device=device)
# sample images
fake_imgs = sample_uncoditional_model(model, 4)

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