Prediction on lidar-prod optimization dataset #35
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| # 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 |