### Introduction

Hello!

Below you can find a outline of how to reproduce my solution for the [UW-Madison GI Tract Image Segmentation | Kaggle](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation/discussion/337197#1864282)

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 saved_weights/cls saved_weights/seg saved_weights/3d

# DOWNLOAD PRETRAINED WEIGHTS

mkdir weights

cd weights

wget https://dl.fbaipublicfiles.com/convnext/ade20k/convnext_base_22k_224.pth

wget https://dl.fbaipublicfiles.com/convnext/ade20k/convnext_small_1k_224_ema.pth

cd ..

# TRAIN CLASSIFICATION MODELS

id=1

for config in $(find ./work_configs/tract/final_solution/classification_configs/cls*.py | sort); do

    ./tools/dist_train.sh $config 2

    last_work_dir=$(ls ./work_dirs/tract/ -rt | tail -n 1)

    last_weight=$(ls ./work_dirs/tract/$last_work_dir/*.pth -rt | tail -n 1)

    last_config=$(ls ./work_dirs/tract/$last_work_dir/*.py -rt | tail -n 1)

    mv ./work_dirs/tract/$last_work_dir/$last_weight ./saved_weights/cls/cls_${id}.pth

    mv ./work_dirs/tract/$last_work_dir/$last_config ./saved_weights/cls/cls_${id}.py

    id=$[id+1]

done

# TRAIN SEGMENTATION MODELS

id=1

for config in $(find ./work_configs/tract/final_solution/segmentation_configs/seg*.py | sort); do

    ./tools/dist_train.sh $config 2

    last_work_dir=$(ls ./work_dirs/tract/ -rt | tail -n 1)

    last_weight=$(ls ./work_dirs/tract/$last_work_dir/*.pth -rt | tail -n 1)

    last_config=$(ls ./work_dirs/tract/$last_work_dir/*.py -rt | tail -n 1)

    mv ./work_dirs/tract/$last_work_dir/$last_weight ./saved_weights/seg/seg_${id}.pth

    mv ./work_dirs/tract/$last_work_dir/$last_config ./saved_weights/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 multilabel_train.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

```