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Installation

Dependency

  • mindspore >= 2.3
  • numpy >= 1.17.0
  • pyyaml >= 5.3
  • openmpi 4.0.3 (for distributed mode)

To install the dependency, please run

pip install -r requirements.txt

MindSpore can be easily installed by following the official instructions where you can select your hardware platform for the best fit. To run in distributed mode, openmpi is required to install.

⚠️ The current version only supports the Ascend platform, and the GPU platform will be supported later.

Install with PyPI

MindYOLO is published as a Python package and can be installed with pip, ideally by using a virtual environment. Open up a terminal and install MindYOLO with:

pip install mindyolo

Install from Source (Bleeding Edge Version)

from VCS

pip install git+https://github.com/mindspore-lab/mindyolo.git

from local src

As this project is in active development, if you are a developer or contributor, please prefer this installation!

MindYOLO can be directly used from GitHub by cloning the repository into a local folder which might be useful if you want to use the very latest version:

git clone https://github.com/mindspore-lab/mindyolo.git

After cloning from git, it is recommended that you install using "editable" mode, which can help resolve potential module import issues:

cd mindyolo
pip install -e .

In addition, we provide an optional fast coco api to improve eval speed. The code is provided in C++, and you can try compiling with the following commands (This operation is optional) :

cd mindyolo/csrc
sh build.sh

We also provide fused GPU operators which are built upon MindSpore ops.Custom API. The fused GPU operators are able to improve train speed. The source code is provided in C++ and CUDA and is in the folder examples/custom_gpu_op/. To enable this feature in the GPU training process, you shall modify the method bbox_iou in mindyolo/models/losses/iou_loss.py by referring to the demo script examples/custom_gpu_op/iou_loss_fused.py. Before runing iou_loss_fused.py, you shall compile the C++ and CUDA source code to dynamic link libraries with the following commands (This operation is optional) :

bash examples/custom_gpu_op/fused_op/build.sh