Introduction

Hello!

Below you can find a outline of how to reproduce my solution for the UW-Madison GI Tract Image Segmentation | Kaggle

If you run into any trouble with the setup/code or have any questions please contact me at 273806108@qq.com

Contents

sh preprocess.py: data preprocessing codes inference.py: inferencing codes other: necessary codes for `mmsegmentation` and `monai` toolboxes

Hardware

sh Ubuntu 16.04 LTS (512 GB boot disk) 48 x Intel(R) Xeon(R) Gold 5118 CPU @ 2.30GHz 126 GB Memory 4 x NVIDIA Titan RTX

Software

sh python==3.7.10 CUDA==10.2 cudnn==7.6.5 nvidia-drivers==440.4 (other refer to ./requirements.txt)

Data setup

```sh

DOWNLOAD DATA

kaggle competitions download -c uw-madison-gi-tract-image-segmentation

mkdir -p ./data/tract mv uw-madison-gi-tract-image-segmentation.zip ./data/tract

cd ./data/tract unzip uw-madison-gi-tract-image-segmentation.zip cd ../.. ```

The expected after unzip should be:

sh ./data/tract ├── sample_submission.csv ├── test ├── train ├── train.csv Install base requirements, mmsegmentation and monai toolboxes

```sh

INSTALL PYTHON REQUIREMENTS

pip install -r requirements.txt pip install "monai[ignite,skimage,nibabel]==0.8.1" pip install mmcv-full==1.3.17 --force-reinstall -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html pip install -v -e . ```

Data preprocess

sh python data_preprocess.py

Training

NOTE: make sure internet connection for public pretrained weights downloading

```sh mkdir -p savedweights/cls savedweights/seg saved_weights/3d

DOWNLOAD PRETRAINED WEIGHTS

mkdir weights cd weights wget https://dl.fbaipublicfiles.com/convnext/ade20k/convnextbase22k224.pth wget https://dl.fbaipublicfiles.com/convnext/ade20k/convnextsmall1k224_ema.pth cd ..

TRAIN CLASSIFICATION MODELS

id=1 for config in $(find ./workconfigs/tract/finalsolution/classificationconfigs/cls*.py | sort); do ./tools/disttrain.sh $config 2 lastworkdir=$(ls ./workdirs/tract/ -rt | tail -n 1) lastweight=$(ls ./workdirs/tract/$lastworkdir/*.pth -rt | tail -n 1) lastconfig=$(ls ./workdirs/tract/$lastworkdir/*.py -rt | tail -n 1) mv ./workdirs/tract/$lastworkdir/$lastweight ./savedweights/cls/cls${id}.pth mv ./workdirs/tract/$lastworkdir/$lastconfig ./savedweights/cls/cls_${id}.py id=$[id+1] done

TRAIN SEGMENTATION MODELS

id=1 for config in $(find ./workconfigs/tract/finalsolution/segmentationconfigs/seg*.py | sort); do ./tools/disttrain.sh $config 2 lastworkdir=$(ls ./workdirs/tract/ -rt | tail -n 1) lastweight=$(ls ./workdirs/tract/$lastworkdir/*.pth -rt | tail -n 1) lastconfig=$(ls ./workdirs/tract/$lastworkdir/*.py -rt | tail -n 1) mv ./workdirs/tract/$lastworkdir/$lastweight ./savedweights/seg/seg${id}.pth mv ./workdirs/tract/$lastworkdir/$lastconfig ./savedweights/seg/seg_${id}.py id=$[id+1] done

TRAIN 3D MODELS

cd ./monai fold=-1 for n in (12 20 32); do mkdir -p ./output/segres${n}all/all python multilabeltrain.py \ -c segres${n}all \ -f $fold \ > ./output/segres${n}all/all/output.txt

mkdir -p  ./output/segres${n}_all_round2/all
python multilabel_train.py \
    -c segres${n}_all_round2 \
    -f $fold \
    -w ./output/segres${n}_all/all/last.pth \
    > ./output/segres${n}_all_round2/all/output.txt
mv ./output/segres${n}_all_round2/all/last.pth ../saved_weights/3d/segres${n}.pth

done cd .. ```

Inferencing

python inference.py