Implemented the checkpoint_dir feature

bob/bio/base/pipelines/vanilla_biometrics/vanilla_biometrics.py

@@ -42,6 +42,7 @@ def execute_vanilla_biometrics(
     checkpoint,
     dask_partition_size,
     dask_n_workers,
+    checkpoint_dir=None,
     **kwargs,
 ):
     """
@@ -72,26 +73,39 @@ def execute_vanilla_biometrics(
         Groups of the dataset that will be requested from the database interface.
 
     output: str
-        Path where the results and checkpoints will be saved to.
+        Path where the scores will be saved.
 
     write_metadata_scores: bool
         Use the CSVScoreWriter instead of the FourColumnScoreWriter when True.
 
     checkpoint: bool
         Whether checkpoint files will be created for every step of the pipelines.
+
+    checkpoint_dir: str
+        If `checkpoint` is set, this path will be used to save the checkpoints.
+        If `None`, the content of `output` will be used.
     """
     if not os.path.exists(output):
         os.makedirs(output, exist_ok=True)
 
+    # Setting the `checkpoint_dir`
+    if checkpoint_dir is None:
+        checkpoint_dir = output
+    else:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+
+    # Scores are written on `output`
     if write_metadata_scores:
         pipeline.score_writer = CSVScoreWriter(os.path.join(output, "./tmp"))
     else:
         pipeline.score_writer = FourColumnsScoreWriter(os.path.join(output, "./tmp"))
 
-    # Check if it's already checkpointed
+    # Checkpoint if it's already checkpointed
     if checkpoint and not is_checkpointed(pipeline):
         hash_fn = database.hash_fn if hasattr(database, "hash_fn") else None
-        pipeline = checkpoint_vanilla_biometrics(pipeline, output, hash_fn=hash_fn)
+        pipeline = checkpoint_vanilla_biometrics(
+            pipeline, checkpoint_dir, hash_fn=hash_fn
+        )
 
     # Load the background model samples only if the transformer requires fitting
     if all([is_estimator_stateless(step) for step in pipeline.transformer]):
@@ -162,6 +176,7 @@ def execute_vanilla_biometrics_ztnorm(
     checkpoint,
     dask_partition_size,
     dask_n_workers,
+    checkpoint_dir=None,
     **kwargs,
 ):
     """
@@ -209,6 +224,10 @@ def execute_vanilla_biometrics_ztnorm(
 
     consider_genuines: float
         If set, will consider genuine scores in the ZT score normalization
+    checkpoint_dir: str
+        If `checkpoint` is set, this path will be used to save the checkpoints.
+        If `None`, the content of `output` will be used.
+
     """
 
     def _merge_references_ztnorm(biometric_references, probes, zprobes, treferences):
@@ -225,6 +244,13 @@ def execute_vanilla_biometrics_ztnorm(
     if not os.path.exists(output):
         os.makedirs(output, exist_ok=True)
 
+    # Setting the `checkpoint_dir`
+    if checkpoint_dir is None:
+        checkpoint_dir = output
+    else:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+
+    # Scores are written on `output`
     if write_metadata_scores:
         pipeline.score_writer = CSVScoreWriter(os.path.join(output, "./tmp"))
     else:
@@ -232,13 +258,13 @@ def execute_vanilla_biometrics_ztnorm(
 
     # Check if it's already checkpointed
     if checkpoint and not is_checkpointed(pipeline):
-        pipeline = checkpoint_vanilla_biometrics(pipeline, output)
+        pipeline = checkpoint_vanilla_biometrics(pipeline, checkpoint_dir)
 
     # Patching the pipeline in case of ZNorm and checkpointing it
     pipeline = ZTNormPipeline(pipeline)
     if checkpoint:
         pipeline.ztnorm_solver = ZTNormCheckpointWrapper(
-            pipeline.ztnorm_solver, os.path.join(output, "normed-scores")
+            pipeline.ztnorm_solver, os.path.join(checkpoint_dir, "normed-scores")
         )
 
     background_model_samples = database.background_model_samples()

bob/bio/base/script/vanilla_biometrics.py

@@ -96,7 +96,7 @@ It is possible to do it via configuration file
     "--output",
     show_default=True,
     default="results",
-    help="Name of output directory where output scores will be saved. In case --checkpoint is set, checkpoints will be saved in this directory.",
+    help="Name of output directory where output scores will be saved.",
     cls=ResourceOption,
 )
 @click.option(
@@ -113,6 +113,14 @@ It is possible to do it via configuration file
     help="If set, it will checkpoint all steps of the pipeline. Checkpoints will be saved in `--output`.",
     cls=ResourceOption,
 )
+@click.option(
+    "-c",
+    "--checkpoint-dir",
+    show_default=True,
+    default=None,
+    help="Name of output directory where the checkpoints will be saved. In case --checkpoint is set, checkpoints will be saved in this directory.",
+    cls=ResourceOption,
+)
 @click.option(
     "--dask-partition-size",
     "-s",
@@ -142,6 +150,7 @@ def vanilla_biometrics(
     output,
     write_metadata_scores,
     checkpoint,
+    checkpoint_dir,
     dask_partition_size,
     dask_n_workers,
     **kwargs,
@@ -212,6 +221,7 @@ def vanilla_biometrics(
         checkpoint,
         dask_partition_size,
         dask_n_workers,
+        checkpoint_dir=checkpoint_dir,
         **kwargs,
     )
 

doc/vanilla_biometrics_features.rst

@@ -355,11 +355,13 @@ To enable the checkpointing of a Transformer or :any:`bob.bio.base.pipelines.van
 This class takes a Transformer as input and returns the same Transformer with the ability to automatically create checkpoint files.
 The :py:class:`bob.pipelines.CheckpointWrapper` class is available in the :py:mod:`bob.pipelines`.
 
-The ``--checkpoint`` option is a command-line option that automatically wraps every steps of the pipeline with checkpointing::
+The ``--checkpoint`` option is a command-line option that automatically wraps every steps of the pipeline with checkpointing.
+If set, the ``--checkpoint-dir`` sets the path for such a checkpoints::
 
-$ bob bio pipelines vanilla-biometrics <database> <pipeline> --checkpoint --output <output_dir>
+$ bob bio pipelines vanilla-biometrics <database> <pipeline> --checkpoint --output <output_dir> --checkpoint-dir <checkpoint_dir>
 
-When doing so, the output of each Transformer of the pipeline will be saved to the disk in the ``<output_dir>`` folder specified with the ``--output`` option.
+When doing so, the output of each Transformer of the pipeline will be saved to the disk in the ``<checkpoint_dir>`` folder specified with the ``--checkpoint-dir`` option.
+Output scores will be saved on ``<output_dir>``.
 
 
 .. WARNING::
@@ -371,7 +373,7 @@ When doing so, the output of each Transformer of the pipeline will be saved to t
   **You** have to take care of removing invalid checkpoints files.
 
   When changing the pipeline or the dataset of an experiment, you should change
-  the output folder (``--output``) accordingly. Otherwise, the system could try to
+  the output folder (``--checkpoint-dir``) accordingly. Otherwise, the system could try to
   load a checkpoint of an older experiment, or samples from another dataset.