In this section we demonstrate how to prepare an environment with PyTorch.
MMselfSup works on Linux (Windows and macOS are not officially supported). It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+.
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next section. Otherwise, you can follow these steps for the preparation.
Step 0. Download and install Miniconda from the official website.
Step 1. Create a conda environment and activate it.
conda create --name openmmlab python=3.8 -y
conda activate openmmlabStep 2. Install PyTorch following official instructions, e.g.
On GPU platforms:
conda install pytorch torchvision -c pytorchOn CPU platforms:
conda install pytorch torchvision cpuonly -c pytorchWe recommend that users follow our best practices to install MMSelfSup. However, the whole process is highly customizable. See Customize Installation section for more information.
Step 0. Install MMCV using MIM.
pip install -U openmim
mim install mmcv-fullStep 1. Install MMSelfSup.
Case a: If you develop and run mmselfsup directly, install it from source:
git clone https://github.com/open-mmlab/mmselfsup.git
cd mmselfsup
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.Case b: If you use mmselfsup as a dependency or third-party package, install it with pip:
pip install mmselfsupTo verify whether MMSelfSup is installed correctly, we can run the following sample code to initialize a model and inference a demo image.
import torch
from mmselfsup.models import build_algorithm
model_config = dict(
type='Classification',
backbone=dict(
type='ResNet',
depth=50,
in_channels=3,
num_stages=4,
strides=(1, 2, 2, 2),
dilations=(1, 1, 1, 1),
out_indices=[4], # 0: conv-1, x: stage-x
norm_cfg=dict(type='BN'),
frozen_stages=-1),
head=dict(
type='ClsHead', with_avg_pool=True, in_channels=2048,
num_classes=1000))
model = build_algorithm(model_config).cuda()
image = torch.randn((1, 3, 224, 224)).cuda()
label = torch.tensor([1]).cuda()
loss = model.forward_train(image, label)The above code is supposed to run successfully upon you finish the installation.
The Best Practices is for basic usage, if you need to evaluate your pre-training model with some downstream tasks such as detection or segmentation, please also install MMDetection and MMSegmentation.
If you don't run MMDetection and MMSegmentation benchmark, it is unnecessary to install them.
You can simply install MMDetection and MMSegmentation with the following command:
pip install mmdet mmsegmentationFor more details, you can check the installation page of MMDetection and MMSegmentation.
When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
Please make sure the GPU driver satisfies the minimum version requirements. See this table for more information.
Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.
MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
To install MMCV with pip instead of MIM, please follow MMCV installation guides. This requires manually specifying a find-url based on PyTorch version and its CUDA version.
For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.htmlWe 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 mmselfsup:torch1.10.0-cuda11.3-cudnn8 .Important: Make sure you've installed the nvidia-container-toolkit.
Run the following cmd:
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/workspace/mmselfsup/data mmselfsup:torch1.10.0-cuda11.3-cudnn8 /bin/bash{DATA_DIR} is your local folder containing all these datasets.
Google Colab usually has PyTorch installed, thus we only need to install MMCV and MMSeflSup with the following commands.
Step 0. Install MMCV using MIM.
!pip3 install openmim
!mim install mmcv-fullStep 1. Install MMSelfSup from the source.
!git clone https://github.com/open-mmlab/mmselfsup.git
%cd mmselfsup
!pip install -e .Step 2. Verification.
import mmselfsup
print(mmselfsup.__version__)
# Example output: 0.9.0Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.
If you have some issues during the installation, please first view the FAQ page. You may open an issue on GitHub if no solution is found.
If there are more than one mmselfsup on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.
Another way is to insert the following code to the main scripts (train.py, test.py or any other scripts you run)
import os.path as osp
import sys
sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))Or run the following command in the terminal of corresponding root folder to temporally use the current one.
export PYTHONPATH="$(pwd)":$PYTHONPATH