Skip to content

Latest commit

 

History

History
94 lines (66 loc) · 2.56 KB

INSTALL.md

File metadata and controls

94 lines (66 loc) · 2.56 KB
Raw

Installation

Requirements

  • Linux (Windows is not officially supported)
  • Python 3.5+ (Python 2 is not supported)
  • PyTorch 1.1 or higher
  • CUDA 9.0 or higher
  • NCCL 2
  • GCC 4.9 or higher
  • mmcv with version number 0.2.13 or 0.2.14

We have tested the following versions of OS and CUDA:

  • OS: Ubuntu 16.04/18.04
  • CUDA: 10.0

Install mmdetection

a. Create a conda virtual environment and activate it.

conda create -n ppdet python=3.7 -y
conda activate ppdet

b. Install PyTorch stable or nightly and torchvision following the official instructions, e.g.,

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

c. Clone the PPDet repository.

git clone https://github.com/nerminsamet/ppdet.git
cd ppdet

d. Install mmcv

pip install mmcv==0.2.13

e. Install PPDet (other dependencies will be installed automatically).

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

Note:

  1. The git commit id will be written to the version number with step e, e.g. 1.0.rc. The version will also be saved in trained models. It is recommended that you run step e each time you pull some updates from github. If C/CUDA codes are modified, then step e is compulsory.

  2. Following the above instructions, PPDet is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).

Prepare datasets

It is recommended to symlink the dataset root to $PPDet/data. If your folder structure is different, you may need to change the corresponding paths in config files.

COCO_DIR
├── annotations
├── images
│    ├── train2017
│    ├── val2017
│    ├── test2017

Scripts

Here is a script for setting up mmdetection with conda.

Multiple versions

If there are more than one mmdetection 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 folder.

export PYTHONPATH=`pwd`:$PYTHONPATH