Shortcuts

Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch.

MMClassification works on Linux, Windows and macOS. It requires Python 3.7+, CUDA 9.2+ and PyTorch 1.6+.

Note

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 1. Download and install Miniconda from the official website.

Step 2. Create a conda environment and activate it.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab

Step 3. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch torchvision -c pytorch

Warning

This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they match your environment.

On CPU platforms:

conda install pytorch torchvision cpuonly -c pytorch

Installation

Best Practices

According to your needs, we support two install modes:

  • Install from source (Recommended): You want to develop your own image classification task or new features based on MMClassification framework. For example, adding new dataset or new models. And you can use all tools we provided.

  • Install as a Python package: You just want to call MMClassification’s APIs or import MMClassification’s modules in your project.

Install from source

In this case, install mmcls from source:

git clone -b 1.x https://github.com/open-mmlab/mmclassification.git
cd mmclassification
pip install -U openmim && mim install -e .

Note

"-e" means installing a project in editable mode, thus any local modifications made to the code will take effect without reinstallation.

Install as a Python package

Just install with mim.

pip install -U openmim && mim install "mmcls>=1.0.0rc0"

Note

mim is a light-weight command-line tool to setup appropriate environment for OpenMMLab repositories according to PyTorch and CUDA version. It also has some useful functions for deep-learning experiments.

Verify the installation

To verify whether MMClassification is installed correctly, we provide some sample codes to run an inference demo.

Step 1. We need to download config and checkpoint files.

mim download mmcls --config resnet50_8xb32_in1k --dest .

Step 2. Verify the inference demo.

Option (a). If you install mmcls from the source, just run the following command:

python demo/image_demo.py demo/demo.JPEG resnet50_8xb32_in1k.py resnet50_8xb32_in1k_20210831-ea4938fc.pth --device cpu

You will see the output result dict including pred_label, pred_score and pred_class in your terminal.

Option (b). If you install mmcls as a python package, open your python interpreter and copy&paste the following codes.

from mmcls import get_model, inference_model

model = get_model('resnet18_8xb32_in1k', device='cpu')  # or device='cuda:0'
inference_model(model, 'demo/demo.JPEG')

You will see a dict printed, including the predicted label, score and category name.

Customize Installation

CUDA versions

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.

Note

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, and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in conda install command.

Install on CPU-only platforms

MMClassification can be built for CPU only environment. In CPU mode you can train, test or inference a model.

Install on Google Colab

See the Colab tutorial.

Using MMClassification with Docker

We provide a Dockerfile to build an image. Ensure that your docker version >=19.03.

# build an image with PyTorch 1.8.1, CUDA 10.2
# If you prefer other versions, just modified the Dockerfile
docker build -t mmclassification docker/

Run it with

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

Trouble shooting

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.

Read the Docs v: dev-1.x
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.