Support glob expression for predict.src_las (#15)

* Support glob expression for predict.src_las

* Add condition to save docke rimage on push only

.github/workflows/cicd.yaml

@@ -64,8 +64,6 @@ jobs:
         --ignore=actions-runner
         --ignore="notebooks"
 
-
-
     # Everything ran so we tag the valid docker image to keep it
     # This happens for push events, which are in particular 
     # triggered when a pull request is merged.
@@ -87,6 +85,7 @@ jobs:
 
     # This needs writing rights to the mounted path
     - name: Save the docker image as lidar_deep_im_${BRANCH_NAME}.tar
+      if: github.event_name == 'push'
       run: docker save lidar_deep_im:${BRANCH_NAME} -o /var/data/cicd/CICD_github_assets/CICD_docker_images/lidar_deep_im_${BRANCH_NAME}.tar
 
     - name: Clean dangling docker images

configs/predict/default.yaml

@@ -1,11 +1,20 @@
-src_las: "/path/to/input.las"
-output_dir: "/path/to/output_dir/"
-resume_from_checkpoint: "/path/to/lightning_model.ckpt"
+src_las: "/path/to/input.las"  # Any glob pattern can be used to predict on multiple files.
+output_dir: "/path/to/output_dir/"  # Predictions are saved in a new file which shares src_las basename.
+resume_from_checkpoint: "/path/to/lightning_model.ckpt"  # Checkpoint of trained model.
 gpus: 0  # 0 for none, 1 for one, [gpu_id] to specify which gpu to use e.g [1]
 
-probas_to_save: "all"  # override with a list of string matching class names to select specific probas to save
+# Speifying the ouptut:
+# A list of string matching class names to select specific probas to save
+# OR keyword "all" to save all probabilities.
+# In addition, these dimensions are always created:
+# - `PredictedClassification`: predicted classification based on argmax, with classes 
+#   specified by datamodule.dataset_description class dictionary. 
+# - entropy`: Shannon entropy of predicted probabilities 
+probas_to_save: "all"  
 
-# Relative to how probas are interpolated
-# e.g. subtile_overlap=25 to use a sliding window of inference of whihc predictions will be merged.
+# Probas interpolation parameters
+# subtile_overlap=25 to use a sliding window of inference of which predictions will be merged.
+# This comes with a computing cost as the effective predicted area is multiplied.
 subtile_overlap: 0
+# Higher interpolation_k to use more context
 interpolation_k: 10

docs/source/tutorials/make_predictions.md

@@ -2,13 +2,13 @@
 
 Refer to [this tutorial](./setup_install.md) for how to setup a virtual environment and install the library.
 
-## Run inference from installed package
-
 To run inference, you will need:
 - A source cloud point in LAS format on which to infer classes and probabilites.
 - A checkpoint of a trained lightning module implementing model logic (class `lidar_multiclass.models.model.Model`)
 - A minimal yaml configuration specifying parameters. We use [hydra](https://hydra.cc/) to manage configurations, and this yaml results from the model training. The `datamodule` and `model` parameters groups must match datset characteristics and model training settings.  The `predict` parameters group specifies path to models and data as well as batch size (N=50 works well, the larger the faster) and use of gpu (optionnal).
 
+## Run inference from installed package
+
 Fill out the {missing parameters} below and run: 
 
 ```bash
@@ -29,6 +29,8 @@ To show you current inference config, simply add a `--help` flag:
 python -m lidar_multiclass.predict --config-path {/path/to/.hydra} --config-name {config.yaml} --help
 ```
 
+Note that `predict.src_las` may be any valid glob pattern (e.g. `/path/to/multiple_files/*.las`), in order to **predict on multiple files successively**.
+
 ## Run inference from sources
 
 From the line for package-based inference above, simply change `python -m lidar_multiclass.predict` to `python run.py` to run directly from sources.

lidar_multiclass/predict.py

@@ -1,7 +1,8 @@
+from glob import glob
 import os
 import hydra
 import torch
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 from pytorch_lightning import LightningDataModule, LightningModule
 from tqdm import tqdm
 
@@ -62,9 +63,15 @@ def predict(config: DictConfig) -> str:
 
 @hydra.main(config_path="../configs/", config_name="config.yaml")
 def main(config: DictConfig):
-    """See function predict
+    """This wrapper allows to specify a hydra configuration from command line.
 
-    :meta private:
+    The config file logged during training should be used for prediction. It should
+    be edited so that it does not rely on unnecessary environment variables (`oc.env:` prefix).
+    Parameters in configuration group `predict` can be specified directly in the config file
+    or overriden via CLI at runtime.
+
+    This wrapper supports running predictions for all files specified
+    by a glob pattern as specified via config parameter predict.src_las.
 
     """
     # Imports should be nested inside @hydra.main to optimize tab completion
@@ -78,10 +85,21 @@ def main(config: DictConfig):
     if config.get("print_config"):
         utils.print_config(config, resolve=False)
 
-    return predict(config)
+    # Parameter predict.src_las can be a path or a glob pattern
+    # e.g. /path/to/files_*.las
+    src_las_iterable = glob(config.predict.src_las)
+
+    if not src_las_iterable:
+        raise FileNotFoundError(
+            f"Globing pattern {config.predict.src_las} (param predict.src_las) did not return any file."
+        )
+
+    # Iterate over the files and predict.
+    for config.predict.src_las in tqdm(src_las_iterable):
+        predict(config)
 
 
 if __name__ == "__main__":
     # cf. https://github.com/facebookresearch/hydra/issues/1283
-    OmegaConf.register_new_resolver("get_method", hydra.utils.get_method, replace=True)
+    # OmegaConf.register_new_resolver("get_method", hydra.utils.get_method, replace=True)
     main()