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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.

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.

Install MMEngine without MIM

To install MMEngine with pip instead of MIM, please follow [MMEngine installation guides](https://mmengine.readthedocs.io/en/latest/get_started/installation.html).

For example, you can install MMEngine by the following command.

pip install "mmengine>=0.6.0"

Install MMCV without MIM

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 the PyTorch version and its CUDA version.

For example, the following command installs MMCV built for PyTorch 1.12.x and CUDA 11.6.

pip install "mmcv>=2.0.0rc4" -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.12.0/index.html

Install on CPU-only platforms

MMDetection can be built for the CPU-only environment. In CPU mode you can train (requires MMCV version >= 2.0.0rc1), test, or infer a model.

However, some functionalities are gone in this mode:

  • Deformable Convolution
  • Modulated Deformable Convolution
  • ROI pooling
  • Deformable ROI pooling
  • CARAFE
  • SyncBatchNorm
  • CrissCrossAttention
  • MaskedConv2d
  • Temporal Interlace Shift
  • nms_cuda
  • sigmoid_focal_loss_cuda
  • bbox_overlaps

If you try to train/test/infer a model containing the above ops, an error will be raised. The following table lists affected algorithms.

Operator Model
Deformable Convolution/Modulated Deformable Convolution DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS
MaskedConv2d Guided Anchoring
CARAFE CARAFE
SyncBatchNorm ResNeSt

Install on Google Colab

Google Colab usually has PyTorch installed, thus we only need to install MMEngine, MMCV, MMDetection, and MMYOLO with the following commands.

Step 1. Install MMEngine and MMCV using MIM.

!pip3 install openmim
!mim install "mmengine>=0.6.0"
!mim install "mmcv>=2.0.0rc4,<2.1.0"
!mim install "mmdet>=3.0.0,<4.0.0"

Step 2. Install MMYOLO from the source.

!git clone https://github.com/open-mmlab/mmyolo.git
%cd mmyolo
!pip install -e .

Step 3. Verification.

import mmyolo
print(mmyolo.__version__)
# Example output: 0.1.0, or an another version.
Within 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.

Develop using multiple MMYOLO versions

The training and testing scripts have been modified in PYTHONPATH to ensure that the scripts use MMYOLO in the current directory.

To have the default MMYOLO installed in your environment instead of what is currently in use, you can remove the code that appears in the relevant script:

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