WIP: add workflow to run prediction on lidar-prod optimization dataset

.github/workflows/predict-for-lidar-prod-optimization.yml

@@ -0,0 +1,90 @@
+# Workflow name
+name: "Prediction on lidar-prod optimization dataset"
+
+on:
+  # Run workflow on user request
+  workflow_dispatch:
+    inputs:
+      sampling_name:
+        description: >
+          Sampling name (nom du dataset sur lequel le modèle a été entraîné).
+          Eg. YYYYMMDD_MonBeauDataset
+        required: true
+      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
+      checkpoint_name:
+        description: >
+          Nom du checkpoint (sans l'extension) à utiliser pour les prédictions
+          Exemple : 20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0
+        required: true
+      predict_config_name:
+        description: >
+          Nom du fichier de config de myriad utilisé pour la prédiction
+          (doit exister dans projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
+          Exemple: 20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0_predict_config_V3.5.0.yaml
+        required: true
+
+jobs:
+  predict-validation-dataset:
+    runs-on: self-hosted
+    env:
+      WORKDIR: /var/data/LIDAR-PROD-OPTIMIZATION/
+      DATA: /var/data/LIDAR-PROD-OPTIMIZATION/20221018_lidar-prod-optimization-on-151-proto/Comparison/
+      LOGS_DIR: /var/data/MYRIA3D-SHARED-WORKSPACE/${{ github.event.inputs.user }}/${{ github.event.inputs.sampling_name }}/
+
+    steps:
+      - name: Log configuration
+        run: |
+          echo "Run prediction on lidar-prod optimzation 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.checkpoint_name }}"
+          echo "predict_config_name name ${{ github.event.inputs.predict_config_name }}"
+          echo "workdir: ${{env.WORKDIR}}"
+          echo "data: ${{env.DATA}}"
+          echo "logs dir: ${{env.LOGS_DIR}}"
+
+      - name: Checkout branch
+        uses: actions/checkout@v4
+
+      # See https://github.com/marketplace/actions/setup-micromamba
+      - name: setup-micromamba
+        uses: mamba-org/[email protected]
+        with:
+          environment-file: environment.yml
+          environment-name: myria3d # activate the environment
+          cache-environment-key: environment-myria3d-predict-validation-dataset # create cache for this pipeline only
+          cache-downloads-key: downloads-myria3d-predict-validation-dataset # create cache for this pipeline only
+          generate-run-shell: true
+          download-micromamba: false
+          micromamba-binary-path: /var/data/.local/bin/micromamba
+          micromamba-root-path: /var/data/micromamba
+
+      - name: Run prediction on validation dataset
+        shell: micromamba-shell {0}
+        run: >
+          python run.py
+          --config-path ${{env.LOGS_DIR}}
+          --config-name ${{ github.event.inputs.predict_config_name }}
+          task.task_name=predict
+          predict.src_las=${{env.DATA}}/val/*.laz
+          model.ckpt_path=${{env.LOGS_DIR}}${{ github.event.inputs.checkpoint_name }}.ckpt
+          predict.output_dir=${{env.WORKDIR}}/${{ github.event.inputs.sampling_name }}/preds-valset/
+          predict.interpolator.probas_to_save=[building]
+          predict.gpus=[0]
+
+      - name: Run prediction on test dataset
+        shell: micromamba-shell {0}
+        run: >
+          python run.py
+          --config-path ${{env.LOGS_DIR}}
+          --config-name ${{ github.event.inputs.predict_config_name }}
+          task.task_name=predict
+          predict.src_las=${{env.DATA}}/test/*.laz
+          model.ckpt_path=${{env.LOGS_DIR}}${{ github.event.inputs.checkpoint_name }}.ckpt
+          predict.output_dir=${{env.WORKDIR}}/${{ github.event.inputs.sampling_name }}/preds-testset/
+          predict.interpolator.probas_to_save=[building]
+          predict.gpus=[0]