export PRJROOT=/path/to/your/project/directory/STAC
export DATAROOT=/path/to/your/dataroot
export COCODIR=$DATAROOT/coco
export VOCDIR=$DATAROOT/voc
export PYTHONPATH=$PYTHONPATH:${PRJROOT}/third_party/FasterRCNN:${PRJROOT}/third_party/auto_augment:${PRJROOT}/third_party/tensorpack

cd ${PRJROOT}

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

# Make sure your tensorflow version is 1.14 not only in virtual environment but also in
# your machine, 1.15 can cause OOM issues.
python -c 'import tensorflow as tf; print(tf.__version__)'

# install coco apis
pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

cd ${COCODIR}
wget http://models.tensorpack.com/FasterRCNN/ImageNet-R50-AlignPadding.npz

cd ${PRJROOT}/detection

# Labeled and Unlabeled datasets
[email protected]
UNLABELED_DATASET=${DATASET}-unlabeled

# PATH to save trained models
CKPT_PATH=result/${DATASET}

# PATH to save pseudo labels for unlabeled data
PSEUDO_PATH=${CKPT_PATH}/PSEUDO_DATA

# Train with 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

# --simple_path makes train_log/${DATASET}/${EXPNAME} as exact location to save
python3 train_stg1.py \
    --logdir ${CKPT_PATH} --simple_path --config \
    BACKBONE.WEIGHTS=${COCODIR}/ImageNet-R50-AlignPadding.npz \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${DATASET}',)" \
    MODE_MASK=False \
    FRCNN.BATCH_PER_IM=64 \
    PREPROC.TRAIN_SHORT_EDGE_SIZE="[500,800]" \
    TRAIN.EVAL_PERIOD=20 \
    TRAIN.AUGTYPE_LAB='default'

# Check pseudo path
if [ ! -d ${PSEUDO_PATH} ]; then
    mkdir -p ${PSEUDO_PATH}
fi

# Evaluate the model for sanity check
# model-180000 is the last checkpoint
# save eval.json at $PSEUDO_PATH

python3 predict.py \
    --evaluate ${PSEUDO_PATH}/eval.json \
    --load "${CKPT_PATH}"/model-180000 \
    --config \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${UNLABELED_DATASET}',)"

# Extract pseudo label
python3 predict.py \
    --predict_unlabeled ${PSEUDO_PATH} \
    --load "${CKPT_PATH}"/model-180000 \
    --config \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${UNLABELED_DATASET}',)" \
    EVAL.PSEUDO_INFERENCE=True

python3 train_stg2.py \
    --logdir=${CKPT_PATH}/STAC --simple_path \
    --pseudo_path=${PSEUDO_PATH} \
    --config \
    BACKBONE.WEIGHTS=${COCODIR}/ImageNet-R50-AlignPadding.npz \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${DATASET}',)" \
    DATA.UNLABEL="('${UNLABELED_DATASET}',)" \
    MODE_MASK=False \
    FRCNN.BATCH_PER_IM=64 \
    PREPROC.TRAIN_SHORT_EDGE_SIZE="[500,800]" \
    TRAIN.EVAL_PERIOD=20 \
    TRAIN.AUGTYPE_LAB='default' \
    TRAIN.AUGTYPE='strong' \
    TRAIN.CONFIDENCE=0.9 \
    TRAIN.WU=2

tensorboard --logdir=${PRJROOT}/detection/train_log

@inproceedings{sohn2020detection,
  title={A Simple Semi-Supervised Learning Framework for Object Detection},
  author={Kihyuk Sohn and Zizhao Zhang and Chun-Liang Li and Han Zhang and Chen-Yu Lee and Tomas Pfister},
  year={2020},
  booktitle={arXiv:2005.04757}
}