if [[ "$IF_KUNLUN3" == "ON" ]]; then

#install paddlex

git clone --depth 1000 https://gitee.com/paddlepaddle/PaddleX.git

cd PaddleX

pip install -e .

#install paddle x dependency

paddlex --install PaddleClas

#download paddle dataset

wget -q https://paddle-model-ecology.bj.bcebos.com/paddlex/data/cls_flowers_examples.tar -P ./dataset

tar -xf ./dataset/cls_flowers_examples.tar -C ./dataset/

#train Reset50

echo "Starting to train ResNet50 model..."

python main.py -c paddlex/configs/image_classification/ResNet50.yaml \

-o Global.mode=train \

-o Global.dataset_dir=./dataset/cls_flowers_examples \

-o Global.output=resnet50_output \

-o Global.device="xpu:${CUDA_VISIBLE_DEVICES}"

echo "Training Resnet50 completed!"

#inference Reset50

IFS=',' read -ra DEVICES <<< "$CUDA_VISIBLE_DEVICES"

echo ${DEVICES[0]}

echo "Starting to predict ResNet50 model..."

python main.py -c paddlex/configs/image_classification/ResNet50.yaml \

-o Global.mode=predict \

-o Predict.model_dir="./resnet50_output/best_model" \

-o Predict.input_path="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg" \

-o Global.device="xpu:${DEVICES[0]}"

echo "Predicting Resnet50 completed!"

cd ..

fi