VoxelNet (50%)
1. ๊ฐ์
2. ์ค์น (Docker ๊ธฐ๋ฐ)
๋ฐ์ดํฐ ์ค๋น
โโโ DATA_DIR
โโโ training <-- training data
| โโโ image_2
| โโโ label_2
| โโโ velodyne
โโโ validation <--- evaluation data
| โโโ image_2
| โโโ label_2
| โโโ velodyne
๋์ปค pull & ์คํ
$ docker pull adioshun/voxelnet
$ docker run --runtime=nvidia -it --privileged --network=host -v /tmp/.X11-unix:/tmp/.X11-unix --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -e DISPLAY -v /media/{DATA_DIR}/datasets:/dataset --name 'voxelnet' adioshun/voxelnet /bin/bash
3. ์คํ
์ค์ ์์ (In the docker )
config.py
# for dataset dir
__C.DATA_DIR = '/voxelnet/data/dataset'
__C.CALIB_DIR = '/voxelnet/data/dataset/training/calib'
# for gpu allocation
__C.GPU_AVAILABLE = '0,1'
__C.GPU_USE_COUNT = len(__C.GPU_AVAILABLE.split(','))
__C.GPU_MEMORY_FRACTION = 1
kitti_eval/launch_test.sh
train
python3 train.py --vis true
log ์ ์ฅ ์์น :
log/default
#Tensorboard ์ง์validation results :
predictions/{epoch number}/data
validation results(์ด๋ฏธ์ง) :
predictions/{epoch number}/vis
# --vis true ์ฌ์ฉ์ (๊ธฐ๋ณธ false)model ์ ์ฅ ์์น :
save_model/default
ํ์ต๋ model ์ ์ฅ ์์น :
save_model/pre_trained_car
Nvidia 1080 Ti GPUs๋ก ์ฝ 3์ผ์ด ์์ ๋๋ฏ๋ก ํ์ต๋ ๋ชจ๋ธ ์ฌ์ฉ์ ๊ถ์ฅ ํฉ๋๋ค.
ํ์ต ์๋ฃ ํ Learning Curve ํ์ธ
python3 parse_log.py predictions
# predictions.jpg ์์ฑ
Evaluate
$ python3 test.py -n default --vis True#ํ์ต๋ ๊ฒฐ๊ณผ๋ฌผ ํ์ฉ
$ python3 test.py -n pre_trained_car --vis True#์ฌ์ ํ์ต๋ ๊ฒฐ๊ณผ๋ฌผ ํ์ฉ `save_model/pre_trained_car`
๊ฒฐ๊ณผ ์ ์ฅ ํด๋ :
predictions/data
๊ฒฐ๊ณผ ์ ์ฅ ํด๋(์ด๋ฏธ์ง) :
predictions/vis
# --vis true ์ฌ์ฉ์ (๊ธฐ๋ณธ false)
๊ฒฐ๊ณผ ํ์ธ
./kitti_eval/evaluate_object_3d_offline ./../data/dataset/validation/label_2/ ./prediction

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