predict-for-lidar-prod-optimization.yml

# Workflow name
name: "Prediction on lidar-prod optimization dataset"

on:
  # Run workflow on user request
  workflow_dispatch:
    inputs:
      user:
        description: |
          Username :
          Utilisé pour générer un chemin standard pour les sorties dans le
          dossier IA du store (projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
        required: true
      sampling_name:
        description: |
          Sampling name :
          Nom du dataset sur lequel le modèle a été entraîné.
          Utilisé pour générer un chemin standard pour les sorties dans le
          dossier IA du store (projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
          Eg. YYYYMMDD_MonBeauDataset
        required: true
      model_id:
        description: |
          Identifiant du modèle :
          Il correspond au nom du fichier checkpoint à utiliser pour les prédictions (sans l'extension .ckpt !)
          ($MODEL_ID.ckpt doit exister dans projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
          Il est aussi utilisé pour générer le dossier de sortie
          (projet-LHD/IA/LIDAR-PROD-OPTIMIZATION/$SAMPLING_NAME/$MODEL_ID)
          Exemple : YYYMMDD_MonBeauSampling_epochXXX_Myria3Dx.y.z
        required: true
      predict_config_name:
        description: |
          Nom du fichier de config de myria3d (fichier .yaml) à utiliser pour la prédiction
          (doit exister dans projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
          Exemple: YYYMMDD_MonBeauSampling_epochXXX_Myria3Dx.y.z_predict_config_Vx.y.z.yaml
        required: true

jobs:
  predict-validation-dataset:
    runs-on: self-hosted
    env:
      OUTPUT_DIR: /var/data/LIDAR-PROD-OPTIMIZATION/${{ github.event.inputs.sampling_name }}/${{ github.event.inputs.model_id }}/
      DATA: /var/data/LIDAR-PROD-OPTIMIZATION/20221018_lidar-prod-optimization-on-151-proto/Comparison/
      CONFIG_DIR: /var/data/MYRIA3D-SHARED-WORKSPACE/${{ github.event.inputs.user }}/${{ github.event.inputs.sampling_name }}/
      BATCH_SIZE: 25

    steps:
      - name: Log configuration
        run: |
          echo "Run prediction on lidar-prod optimization datasets (val and test)"
          echo "Sampling name: ${{ github.event.inputs.sampling_name }}"
          echo "User name: ${{ github.event.inputs.user }}"
          echo "Checkpoint name: ${{ github.event.inputs.model_id }}"
          echo "Prediction config name: ${{ github.event.inputs.predict_config_name }}"
          echo "Output_dir: ${{env.OUTPUT_DIR}}"
          echo "Data: ${{env.DATA}}"
          echo "Config files dir: ${{env.CONFIG_DIR}}"

      - name: Checkout branch
        uses: actions/checkout@v4

      # get version number, to retrieve the docker image corresponding to the current version
      - name: Get version number
        run: |
          echo "VERSION=$(docker run myria3d python -m myria3d._version)" >> $GITHUB_ENV

      - name: pull docker image tagged with current version
        run: |
          docker login ${{ secrets.DOCKER_REGISTRY }} --username svc_lidarhd --password ${{ secrets.PASSWORD_SVC_LIDARHD }}
          docker pull ${{ secrets.DOCKER_REGISTRY }}/lidar_hd/myria3d:${{ env.VERSION }}

      - name: Run prediction on validation dataset
        run: >
          docker run --network host
          --shm-size='28g'
          -v ${{env.OUTPUT_DIR}}:/output_dir
          -v ${{env.DATA}}:/data
          -v ${{env.CONFIG_DIR}}:/config_dir
          ${{ secrets.DOCKER_REGISTRY }}/lidar_hd/myria3d:${{ env.VERSION }}
          python run.py
          --config-path /config_dir
          --config-name ${{ github.event.inputs.predict_config_name }}
          task.task_name=predict
          predict.src_las=/data/val/*.laz
          predict.ckpt_path=/config_dir/${{ github.event.inputs.model_id }}.ckpt
          predict.output_dir=/output_dir/preds-valset/
          predict.interpolator.probas_to_save=[building]
          predict.gpus=0
          datamodule.batch_size=${{env.BATCH_SIZE}}
          datamodule.tile_width=1000

      - name: Run prediction on test dataset
        run: >
          docker run --network host
          --shm-size='28g'
          -v ${{env.OUTPUT_DIR}}:/output_dir
          -v ${{env.DATA}}:/data
          -v ${{env.CONFIG_DIR}}:/config_dir
          ${{ secrets.DOCKER_REGISTRY }}/lidar_hd/myria3d:${{ env.VERSION }}
          python run.py
          --config-path /config_dir
          --config-name ${{ github.event.inputs.predict_config_name }}
          task.task_name=predict
          predict.src_las=/data/test/*.laz
          predict.ckpt_path=/config_dir/${{ github.event.inputs.model_id }}.ckpt
          predict.output_dir=/output_dir/preds-testset/
          predict.interpolator.probas_to_save=[building]
          predict.gpus=0
          datamodule.batch_size=${{env.BATCH_SIZE}}
          datamodule.tile_width=1000