Created a wrapper that wraps vanilla biometrics pipelines to checkpointin.

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

@@ -11,7 +11,8 @@ import h5py
 import cloudpickle
 from .zt_norm import ZTNormPipeline, ZTNormDaskWrapper
 from .legacy import BioAlgorithmLegacy
-
+from bob.bio.base.transformers import PreprocessorTransformer, ExtractorTransformer, AlgorithmTransformer
+from bob.pipelines.wrappers import SampleWrapper
 
 class BioAlgorithmCheckpointWrapper(BioAlgorithm):
     """Wrapper used to checkpoint enrolled and Scoring samples.
@@ -278,13 +279,30 @@ def checkpoint_vanilla_biometrics(pipeline, base_dir):
     sk_pipeline = pipeline.transformer
     for i, name, estimator in sk_pipeline._iter():
 
+        # If they are legacy objects, we need to hook their load/save functions
+        save_func=None
+        load_func=None
+
+        if not isinstance(estimator, SampleWrapper):
+            raise ValueError(f"{estimator} needs to be the type `SampleWrapper` to be checkpointed")
+
+        if isinstance(estimator.estimator, PreprocessorTransformer):
+            save_func = estimator.estimator.instance.write_data
+            load_func = estimator.estimator.instance.read_data
+        elif any([isinstance(estimator.estimator, ExtractorTransformer),
+                  isinstance(estimator.estimator, AlgorithmTransformer)]):
+            save_func = estimator.estimator.instance.write_feature
+            load_func = estimator.estimator.instance.read_feature
+
         wraped_estimator = bob.pipelines.wrap(
-            ["checkpoint"], estimator, features_dir=os.path.join(base_dir, name)
+            ["checkpoint"], estimator, features_dir=os.path.join(base_dir, name),
+            load_func=load_func,
+            save_func=save_func
         )
 
         sk_pipeline.steps[i] = (name, wraped_estimator)
 
-    if isinstance(pipeline.biometric_algorithm, BioAlgorithmLegacy):
+    if isinstance(pipeline.biometric_algorithm, BioAlgorithmLegacy):        
         pipeline.biometric_algorithm.base_dir = base_dir
     else:
         pipeline.biometric_algorithm = BioAlgorithmCheckpointWrapper(