Organizing the baselines

bob/bio/face/config/baseline/afffe.py

-import bob.bio.base
-from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.embeddings.pytorch import AFFFE_2021
-from bob.pipelines import wrap
-import scipy.spatial
-from bob.bio.base.pipelines.vanilla_biometrics import Distance
-from sklearn.pipeline import make_pipeline
-from bob.pipelines import wrap
-from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
+from bob.bio.face.embeddings.pytorch import afffe_baseline
+from bob.bio.face.utils import lookup_config_from_database
 
 
-memory_demanding = False
-if "database" in locals():
-    annotation_type = database.annotation_type
-    fixed_positions = database.fixed_positions
-    memory_demanding = (
-        database.memory_demanding if hasattr(database, "memory_demanding") else False
-    )
-
-else:
-    annotation_type = None
-    fixed_positions = None
-
-cropped_positions = {"leye": (110, 144), "reye": (110, 96)}
-
-preprocessor_transformer = FaceCrop(
-    cropped_image_size=(224, 224),
-    cropped_positions=cropped_positions,
-    color_channel="rgb",
-    fixed_positions=fixed_positions,
-    allow_upside_down_normalized_faces=True,
+annotation_type, fixed_positions, _ = lookup_config_from_database(
+    locals().get("database")
 )
 
-transform_extra_arguments = (
-    None
-    if (cropped_positions is None or fixed_positions is not None)
-    else (("annotations", "annotations"),)
-)
 
-extractor_transformer = AFFFE_2021()
+def load(annotation_type, fixed_positions=None):
+    return afffe_baseline(annotation_type, fixed_positions)
 
-# Algorithm
-algorithm = Distance(
-    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
-)
-
-# Chain the Transformers together
-transformer = make_pipeline(
-    wrap(
-        ["sample"],
-        preprocessor_transformer,
-        transform_extra_arguments=transform_extra_arguments,
-    ),
-    wrap(["sample"], extractor_transformer)
-    # Add more transformers here if needed
-)
 
+pipeline = load(annotation_type, fixed_positions)
 
-# Assemble the Vanilla Biometric pipeline and execute
-pipeline = VanillaBiometricsPipeline(transformer, algorithm)
-transformer = pipeline.transformer

bob/bio/face/config/baseline/arcface_insightface.py

-from bob.bio.face.embeddings.mxnet import ArcFaceInsightFace_LResNet100
-from bob.bio.face.config.baseline.helpers import embedding_transformer_112x112
-from bob.bio.base.pipelines.vanilla_biometrics import (
-    Distance,
-    VanillaBiometricsPipeline,
-)
-
-
-if "database" in locals():
-    annotation_type = database.annotation_type
-    fixed_positions = database.fixed_positions
-    memory_demanding = (
-        database.memory_demanding if hasattr(database, "memory_demanding") else False
-    )
-else:
-    annotation_type = None
-    fixed_positions = None
-    memory_demanding = False
+from bob.bio.face.embeddings.mxnet import arcface_baseline
+from bob.bio.face.utils import lookup_config_from_database
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
@@ -23,19 +7,12 @@ annotation_type, fixed_positions, memory_demanding = lookup_config_from_database
 
 
 def load(annotation_type, fixed_positions=None):
-    transformer = embedding_transformer_112x112(
-        ArcFaceInsightFace_LResNet100(memory_demanding=memory_demanding),
-        annotation_type,
-        fixed_positions,
-        color_channel="rgb",
-    )
 
     return arcface_baseline(
-        embedding=ArcFaceInsightFace(memory_demanding=memory_demanding),
+        embedding=ArcFaceInsightFace_LResNet100(memory_demanding=memory_demanding),
         annotation_type=annotation_type,
         fixed_positions=fixed_positions,
     )
 
 
 pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer

bob/bio/face/config/baseline/facenet_sanderberg.py

-from bob.bio.face.embeddings.tf2_inception_resnet import (
-    FaceNetSanderberg_20170512_110547,
-)
+from bob.bio.face.embeddings.tensorflow import facenet_sanderberg_20170512_110547
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
@@ -10,12 +7,9 @@ annotation_type, fixed_positions, memory_demanding = lookup_config_from_database
 
 
 def load(annotation_type, fixed_positions=None):
-    return facenet_baseline(
-        embedding=FaceNetSanderberg_20170512_110547(memory_demanding=memory_demanding),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+    return facenet_sanderberg_20170512_110547(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
 pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer

bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py

-from bob.bio.face.embeddings.tf2_inception_resnet import (
-    InceptionResnetv1_Casia_CenterLoss_2018,
-)
+from bob.bio.face.embeddings.tensorflow import inception_resnet_v1_casia_centerloss_2018
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return facenet_baseline(
-        embedding=InceptionResnetv1_Casia_CenterLoss_2018(
-            memory_demanding=memory_demanding
-        ),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return inception_resnet_v1_casia_centerloss_2018(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/inception_resnetv1_msceleb.py

-from bob.bio.face.embeddings.tf2_inception_resnet import (
-    InceptionResnetv1_MsCeleb_CenterLoss_2018,
+from bob.bio.face.embeddings.tensorflow import (
+    inception_resnet_v1_msceleb_centerloss_2018,
 )
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
-
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return facenet_baseline(
-        embedding=InceptionResnetv1_MsCeleb_CenterLoss_2018(
-            memory_demanding=memory_demanding
-        ),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return inception_resnet_v1_msceleb_centerloss_2018(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py

-from bob.bio.face.embeddings.tf2_inception_resnet import (
-    InceptionResnetv2_Casia_CenterLoss_2018,
-)
+from bob.bio.face.embeddings.tensorflow import inception_resnet_v2_casia_centerloss_2018
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
-
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return facenet_baseline(
-        embedding=InceptionResnetv2_Casia_CenterLoss_2018(
-            memory_demanding=memory_demanding
-        ),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return inception_resnet_v2_casia_centerloss_2018(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/inception_resnetv2_msceleb.py

-from bob.bio.face.embeddings.tf2_inception_resnet import (
-    InceptionResnetv2_MsCeleb_CenterLoss_2018,
+from bob.bio.face.embeddings.tensorflow import (
+    inception_resnet_v2_msceleb_centerloss_2018,
 )
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return facenet_baseline(
-        embedding=InceptionResnetv2_MsCeleb_CenterLoss_2018(
-            memory_demanding=memory_demanding
-        ),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return inception_resnet_v2_msceleb_centerloss_2018(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/mobilenetv2_msceleb_arcface_2021.py

-from bob.bio.face.embeddings.mobilenet_v2 import MobileNetv2_MsCeleb_ArcFace_2021
+from bob.bio.face.embeddings.tensorflow import mobilenetv2_msceleb_arcface_2021
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import arcface_baseline
-
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-
-    return arcface_baseline(
-        embedding=MobileNetv2_MsCeleb_ArcFace_2021(memory_demanding=memory_demanding),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return mobilenetv2_msceleb_arcface_2021(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/mxnet_pipe.py

-import bob.bio.base
-from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import MxNetModel
-from bob.bio.base.algorithm import Distance
-from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
-import scipy.spatial
-from bob.bio.base.pipelines.vanilla_biometrics import Distance
-from sklearn.pipeline import make_pipeline
-from bob.pipelines import wrap
-from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
-
-
-memory_demanding = False
-if "database" in locals():
-    annotation_type = database.annotation_type
-    fixed_positions = database.fixed_positions
-    memory_demanding = (
-        database.memory_demanding if hasattr(database, "memory_demanding") else False
-    )
-
-else:
-    annotation_type = None
-    fixed_positions = None
-
-
-cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
-
-preprocessor_transformer = FaceCrop(
-    cropped_image_size=(112, 112),
-    cropped_positions={"leye": (49, 72), "reye": (49, 38)},
-    color_channel="rgb",
-    fixed_positions=fixed_positions,
-)
-
-transform_extra_arguments = (
-    None
-    if (cropped_positions is None or fixed_positions is not None)
-    else (("annotations", "annotations"),)
-)
-
-
-extractor_transformer = MxNetModel()
-
-algorithm = Distance(
-    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
-)
-
-
-# Chain the Transformers together
-transformer = make_pipeline(
-    wrap(
-        ["sample"],
-        preprocessor_transformer,
-        transform_extra_arguments=transform_extra_arguments,
-    ),
-    wrap(["sample"], extractor_transformer)
-    # Add more transformers here if needed
-)
-
-
-# Assemble the Vanilla Biometric pipeline and execute
-pipeline = VanillaBiometricsPipeline(transformer, algorithm)
-transformer = pipeline.transformer
-
-

bob/bio/face/config/baseline/mxnet_tinyface.py

@@ -14,19 +14,24 @@ from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
 
 annotator_transformer = BobIpTinyface()
 
-preprocessor_transformer = FaceCrop(cropped_image_size=(112,112), cropped_positions={'leye':(49,72), 'reye':(49,38)}, color_channel='rgb',annotator=annotator_transformer)
+preprocessor_transformer = FaceCrop(
+    cropped_image_size=(112, 112),
+    cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+    color_channel="rgb",
+    annotator=annotator_transformer,
+)
 
 extractor_transformer = MxNetModel()
 
 
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+algorithm = Distance(
+    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
+)
 
 transformer = make_pipeline(
-    wrap(["sample"], preprocessor_transformer),
-    wrap(["sample"], extractor_transformer)
+    wrap(["sample"], preprocessor_transformer), wrap(["sample"], extractor_transformer)
 )
 
 pipeline = VanillaBiometricsPipeline(transformer, algorithm)
 transformer = pipeline.transformer
 
-

bob/bio/face/config/baseline/pytorch_pipe_v2.py

-import bob.bio.base
-from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import PyTorchLibraryModel
-from facenet_pytorch import InceptionResnetV1
-from bob.bio.base.algorithm import Distance
-from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
-import scipy.spatial
-from bob.bio.base.pipelines.vanilla_biometrics import Distance
-from sklearn.pipeline import make_pipeline
-from bob.pipelines import wrap
-from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
-
-
-memory_demanding = False
-if "database" in locals():
-    annotation_type = database.annotation_type
-    fixed_positions = database.fixed_positions
-    memory_demanding = (
-        database.memory_demanding if hasattr(database, "memory_demanding") else False
-    )
-
-else:
-    annotation_type = None
-    fixed_positions = None
-
-
-cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
-
-cropped_positions = {"leye": (110, 144), "reye": (110, 96)}
-
-preprocessor_transformer = FaceCrop(
-    cropped_image_size=(224, 224),
-    cropped_positions={"leye": (110, 144), "reye": (110, 96)},
-    color_channel="rgb",
-    fixed_positions=fixed_positions,
-)
-
-transform_extra_arguments = (
-    None
-    if (cropped_positions is None or fixed_positions is not None)
-    else (("annotations", "annotations"),)
-)
-
-
-transform_extra_arguments = (
-    None
-    if (cropped_positions is None or fixed_positions is not None)
-    else (("annotations", "annotations"),)
-)
-
-
-model = InceptionResnetV1(pretrained="vggface2").eval()
-extractor_transformer = PyTorchLibraryModel(model=model)
-
-
-algorithm = Distance(
-    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
-)
-
-
-# Chain the Transformers together
-transformer = make_pipeline(
-    wrap(
-        ["sample"],
-        preprocessor_transformer,
-        transform_extra_arguments=transform_extra_arguments,
-    ),
-    wrap(["sample"], extractor_transformer)
-    # Add more transformers here if needed
-)
-
-
-# Assemble the Vanilla Biometric pipeline and execute
-pipeline = VanillaBiometricsPipeline(transformer, algorithm)
-transformer = pipeline.transformer

bob/bio/face/config/baseline/resnet50_msceleb_arcface_2021.py

-from bob.bio.face.embeddings.resnet50 import Resnet50_MsCeleb_ArcFace_2021
+from bob.bio.face.embeddings.tensorflow import resnet50_msceleb_arcface_2021
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import arcface_baseline
-
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return arcface_baseline(
-        embedding=Resnet50_MsCeleb_ArcFace_2021(memory_demanding=memory_demanding),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return resnet50_msceleb_arcface_2021(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/resnet50_vgg2_arcface_2021.py

-from bob.bio.face.embeddings.resnet50 import Resnet50_VGG2_ArcFace_2021
+from bob.bio.face.embeddings.tensorflow import resnet50_vgg2_arcface_2021
 from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import arcface_baseline
-
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
     locals().get("database")
 )
 
 
-def load(annotation_type, fixed_positions=None):
-    return arcface_baseline(
-        embedding=Resnet50_VGG2_ArcFace_2021(memory_demanding=memory_demanding),
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
+def load(annotation_type, fixed_positions=None, memory_demanding=None):
+    return resnet50_vgg2_arcface_2021(
+        annotation_type, fixed_positions, memory_demanding
     )
 
 
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer
+pipeline = load(annotation_type, fixed_positions, memory_demanding)

bob/bio/face/config/baseline/templates.py

-from bob.bio.face.utils import (
-    dnn_default_cropping,
-    embedding_transformer,
-)
-from bob.bio.base.pipelines.vanilla_biometrics import (
-    Distance,
-    VanillaBiometricsPipeline,
-)
-
-
-def arcface_baseline(embedding, annotation_type, fixed_positions=None):
-    # DEFINE CROPPING
-    cropped_image_size = (112, 112)
-    if annotation_type == "eyes-center":
-        # Hard coding eye positions for backward consistency
-        cropped_positions = {
-            "leye": (55, 81),
-            "reye": (55, 42),
-        }
-    else:
-        cropped_positions = dnn_default_cropping(cropped_image_size, annotation_type)
-
-    transformer = embedding_transformer(
-        cropped_image_size=cropped_image_size,
-        embedding=embedding,
-        cropped_positions=cropped_positions,
-        fixed_positions=fixed_positions,
-        color_channel="rgb",
-        annotator="mtcnn",
-    )
-
-    algorithm = Distance()
-
-    return VanillaBiometricsPipeline(transformer, algorithm)
-
-
-def facenet_baseline(embedding, annotation_type, fixed_positions=None):
-    # DEFINE CROPPING
-    cropped_image_size = (160, 160)
-    cropped_positions = dnn_default_cropping(cropped_image_size, annotation_type)
-
-    # ASSEMBLE TRANSFORMER
-    transformer = embedding_transformer(
-        cropped_image_size=cropped_image_size,
-        embedding=embedding,
-        cropped_positions=cropped_positions,
-        fixed_positions=fixed_positions,
-        color_channel="rgb",
-        annotator="mtcnn",
-    )
-
-    algorithm = Distance()
-
-    return VanillaBiometricsPipeline(transformer, algorithm)

bob/bio/face/config/baseline/tf2_inception_resnet.py

-from bob.extension import rc
-from bob.bio.face.embeddings.tf2_inception_resnet import InceptionResnetv2
-from bob.bio.face.utils import lookup_config_from_database
-from bob.bio.face.config.baseline.templates import facenet_baseline
-
-annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
-    locals().get("database")
-)
-
-
-def load(annotation_type, fixed_positions=None):
-    extractor_path = rc["bob.bio.face.tf2.casia-webface-inception-v2"]
-    embedding = InceptionResnetv2(
-        checkpoint_path=extractor_path, memory_demanding=memory_demanding
-    )
-    return facenet_baseline(
-        embedding=embedding,
-        annotation_type=annotation_type,
-        fixed_positions=fixed_positions,
-    )
-
-
-pipeline = load(annotation_type, fixed_positions)
-transformer = pipeline.transformer