Add workflow to run prediction on lidar-prod optimization dataset

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

@@ -0,0 +1,98 @@
+# 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
+      checkpoint_name:
+        description: |
+          Nom du fichier checkpoint à utiliser pour les prédictions
+          (doit exister dans projet-LHD/IA/MYRIA3D-SHARED-WORKSPACE/$USER/$SAMPLING_NAME/)
+          Exemple : YYYMMDD_MonBeauSampling_epochXXX_Myria3Dx.y.z.ckpt
+        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:
+      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
+          # Do not restore downloads as they are already stored by micromamba
+          # cache-downloads-key: downloads-myria3d-predict-validation-dataset
+          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
+          predict.ckpt_path=${{env.LOGS_DIR}}${{ github.event.inputs.checkpoint_name }}
+          predict.output_dir=${{env.WORKDIR}}/${{ github.event.inputs.sampling_name }}/preds-valset/
+          predict.interpolator.probas_to_save=[building]
+          predict.gpus=0
+          datamodule.batch_size=10
+
+      - 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
+          predict.ckpt_path=${{env.LOGS_DIR}}${{ github.event.inputs.checkpoint_name }}
+          predict.output_dir=${{env.WORKDIR}}/${{ github.event.inputs.sampling_name }}/preds-testset/
+          predict.interpolator.probas_to_save=[building]
+          predict.gpus=0
+          datamodule.batch_size=10

CHANGELOG.md

@@ -1,5 +1,8 @@
 # CHANGELOG
 
+- Add a github action workflow to run a trained model on the lidar-prod thresholds optimisation dataset
+(in order to automate thresholds optimisation)
+
 ### 3.8.4
 - fix: move IoU appropriately to fix wrong device error created by a breaking change in torch when using DDP.