From fe38ce3f566a88f1138783a055a24fecbb374423 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Tue, 16 Jun 2020 09:35:36 +0200
Subject: [PATCH] Created a wrapper that wraps vanilla biometrics pipelines to
checkpointin.
---
.../pipelines/vanilla_biometrics/wrappers.py | 24 ++++++++++++++++---
1 file changed, 21 insertions(+), 3 deletions(-)
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py b/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py
index 29ef62f3..15eb02dd 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/wrappers.py
+++ b/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(
--
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