我要投稿

使用Docker安装detectron2的配置方法

admin Linux服务器

2024-04-16 0 1,008

拉取cuda116镜像

docker pull nvcr.io/nvidia/cuda:11.6.1-cudnn8-devel-ubuntu20.04

创建容器

docker run –gpus=all -it –name ernerf -v D:\\Projects\\ER-NeRF:/ernerf nvcr.io/nvidia/cuda:11.6.1-cudnn8-devel-ubuntu20.04

安装依赖环境

apt-get update -yq –fix-missing \\
&& DEBIAN_FRONTEND=noninteractive apt-get install -yq –no-install-recommends \\
pkg-config \\
wget \\
cmake \\
curl \\
git \\
vim

安装Miniconda3

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
sh Miniconda3-latest-Linux-x86_64.sh -b -u -p ~/miniconda3
~/miniconda3/bin/conda init
source ~/.bashrc

创建环境

conda create -n detectron python=3.10
conda activate detectron

安装依赖库

pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 –extra-index-url https://download.pytorch.org/whl/cu113
pip install cython opencv-python opencv-python-headless

安装detectron2

git clone https://github.com/facebookresearch/detectron2.git
pip install -e detectron2

调用示例

from detectron2.config import get_cfg
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
import cv2
# 加载配置文件
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(\”COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml\”))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # 设置阈值
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\”COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml\”)
# 创建预测器
predictor = DefaultPredictor(cfg)
# 加载图像
im = cv2.imread(\”./image.jpg\”)
# 进行预测
outputs = predictor(im)
# 可视化预测结果
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
v = v.draw_instance_predictions(outputs[\”instances\”].to(\”cpu\”))
cv2.imwrite(\’./output.jpg\’, v.get_image()[:, :, ::-1])