Working out embeddings

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

@@ -2,7 +2,7 @@ import bob.bio.base
 from bob.bio.base.pipelines.vanilla_biometrics import Distance
 from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
 from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.embeddings.GenericOpenCV import OpenCVModel
+from bob.bio.face.embeddings.pytorch import AFFFE_2021
 from bob.pipelines import wrap
 import scipy.spatial
 from sklearn.pipeline import make_pipeline
@@ -20,26 +20,36 @@ else:
     annotation_type = None
     fixed_positions = None
 
-cropped_positions={'leye':(100,140), 'reye':(100,95)}
-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)
+cropped_positions = {"leye": (110, 144), "reye": (110, 96)}
 
-transform_extra_arguments = (None if (cropped_positions is None or fixed_positions is not None) else (("annotations", "annotations"),))
-
-#Extractor
-
-weights = None # PATH/TO/WEIGHTS
-config = None # PATH/TO/CONFIG
-
-extractor_transformer = OpenCVModel(weights=weights,config=config)
+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,
+)
 
+transform_extra_arguments = (
+    None
+    if (cropped_positions is None or fixed_positions is not None)
+    else (("annotations", "annotations"),)
+)
 
+extractor_transformer = AFFFE_2021()
 
-#Algorithm
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+# 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"],
+        preprocessor_transformer,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
     wrap(["sample"], extractor_transformer)
     # Add more transformers here if needed
 )