diff --git a/bob/bio/face/config/baseline/facenet_sanderberg.py b/bob/bio/face/config/baseline/facenet_sanderberg.py
index de9873e28c536af0ec26aef963b7d19d0c152cf2..e3d1dc269922f9633feceab0b988514ff42b37d2 100644
--- a/bob/bio/face/config/baseline/facenet_sanderberg.py
+++ b/bob/bio/face/config/baseline/facenet_sanderberg.py
@@ -7,10 +7,14 @@ 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
@@ -19,7 +23,9 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- FaceNetSanderberg_20170512_110547(), annotation_type, fixed_positions
+ FaceNetSanderberg_20170512_110547(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py b/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
index d1ae02f10d04d7eb727489ff21bab21e7f1cea87..acc6f981aecc0ae7312097e68536055605c50ce6 100644
--- a/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
+++ b/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
@@ -7,10 +7,14 @@ 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
@@ -18,7 +22,9 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv1_Casia_CenterLoss_2018(), annotation_type, fixed_positions
+ InceptionResnetv1_Casia_CenterLoss_2018(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py b/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
index acf3e255bf755abeff682ce8fcdc0aa57e3cbdfe..70a1a58fdee70f1566a1bfb63e10493cf5599809 100644
--- a/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
+++ b/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
@@ -7,10 +7,14 @@ 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
@@ -18,7 +22,9 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv1_MsCeleb_CenterLoss_2018(), annotation_type, fixed_positions
+ InceptionResnetv1_MsCeleb_CenterLoss_2018(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py b/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
index 5b476ed855018dd307c2ad8c538cfa6fe13b52bb..3dd2704330cadf49873af96489cc2d9d51e27cfd 100644
--- a/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
+++ b/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
@@ -7,10 +7,14 @@ 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
@@ -18,7 +22,9 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv2_Casia_CenterLoss_2018(), annotation_type, fixed_positions
+ InceptionResnetv2_Casia_CenterLoss_2018(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py b/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
index f516d7917d39ac6c9dbf6247da8aa307e8582a78..ba339bcf11dd9928ed37f1f9d95eeba7f559f8fc 100644
--- a/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
+++ b/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
@@ -7,10 +7,14 @@ 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
@@ -18,7 +22,9 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv2_MsCeleb_CenterLoss_2018(), annotation_type, fixed_positions
+ InceptionResnetv2_MsCeleb_CenterLoss_2018(memory_demanding=memory_demanding),
+ annotation_type,
+ fixed_positions,
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/tf2_inception_resnet.py b/bob/bio/face/config/baseline/tf2_inception_resnet.py
index 1d68f0bad2ec215b6663edbace89665cea0c5ec6..9804831574e62e555bc70213915f340f965bba17 100644
--- a/bob/bio/face/config/baseline/tf2_inception_resnet.py
+++ b/bob/bio/face/config/baseline/tf2_inception_resnet.py
@@ -3,7 +3,7 @@ from bob.bio.face.embeddings.tf2_inception_resnet import InceptionResnetv2
from bob.bio.face.preprocessor import FaceCrop
from bob.bio.face.config.baseline.helpers import (
embedding_transformer_default_cropping,
- embedding_transformer
+ embedding_transformer,
)
from sklearn.pipeline import make_pipeline
@@ -13,32 +13,43 @@ 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
+
def load(annotation_type, fixed_positions=None):
CROPPED_IMAGE_SIZE = (160, 160)
- CROPPED_POSITIONS = embedding_transformer_default_cropping(CROPPED_IMAGE_SIZE,
- annotation_type=annotation_type)
+ CROPPED_POSITIONS = embedding_transformer_default_cropping(
+ CROPPED_IMAGE_SIZE, annotation_type=annotation_type
+ )
- extractor_path = rc['bob.bio.face.tf2.casia-webface-inception-v2']
+ extractor_path = rc["bob.bio.face.tf2.casia-webface-inception-v2"]
- embedding = InceptionResnetv2(checkpoint_path=extractor_path)
+ embedding = InceptionResnetv2(
+ checkpoint_path=extractor_path, memory_demanding=memory_demanding
+ )
- transformer = embedding_transformer(CROPPED_IMAGE_SIZE,
- embedding,
- annotation_type,
- CROPPED_POSITIONS,
- fixed_positions)
+ transformer = embedding_transformer(
+ CROPPED_IMAGE_SIZE,
+ embedding,
+ annotation_type,
+ CROPPED_POSITIONS,
+ fixed_positions,
+ )
algorithm = Distance()
return VanillaBiometricsPipeline(transformer, algorithm)
+
pipeline = load(annotation_type, fixed_positions)
transformer = pipeline.transformer
diff --git a/bob/bio/face/embeddings/tf2_inception_resnet.py b/bob/bio/face/embeddings/tf2_inception_resnet.py
index e475bdafcc6f20a21f3b8ebb1bd78d32cf75ee07..34b1c904df445c6eb0ecf4efdd9722664f41e445 100644
--- a/bob/bio/face/embeddings/tf2_inception_resnet.py
+++ b/bob/bio/face/embeddings/tf2_inception_resnet.py
@@ -10,6 +10,7 @@ from functools import partial
import pkg_resources
import os
from bob.bio.face.embeddings import download_model
+import numpy as np
def sanderberg_rescaling():
@@ -34,13 +35,19 @@ class InceptionResnet(TransformerMixin, BaseEstimator):
preprocessor:
Preprocessor function
+ memory_demanding bool
+ If `True`, the `transform` method will run one sample at the time.
+ This is useful when there is not enough memory available to forward big chucks of data.
"""
- def __init__(self, checkpoint_path, preprocessor=None, **kwargs):
+ def __init__(
+ self, checkpoint_path, preprocessor=None, memory_demanding=False, **kwargs
+ ):
super().__init__(**kwargs)
self.checkpoint_path = checkpoint_path
self.model = None
self.preprocessor = preprocessor
+ self.memory_demanding = memory_demanding
def load_model(self):
self.model = tf.keras.models.load_model(self.checkpoint_path)
@@ -54,19 +61,26 @@ class InceptionResnet(TransformerMixin, BaseEstimator):
return embeddings
def transform(self, X):
+ def _transform(X):
+ X = tf.convert_to_tensor(X)
+ X = to_channels_last(X)
+
+ if X.shape[-3:] != self.model.input_shape[-3:]:
+ raise ValueError(
+ f"Image shape {X.shape} not supported. Expected {self.model.input_shape}"
+ )
+
+ return self.inference(X).numpy()
+
if self.model is None:
self.load_model()
X = check_array(X, allow_nd=True)
- X = tf.convert_to_tensor(X)
- X = to_channels_last(X)
- if X.shape[-3:] != self.model.input_shape[-3:]:
- raise ValueError(
- f"Image shape {X.shape} not supported. Expected {self.model.input_shape}"
- )
-
- return self.inference(X).numpy()
+ if self.memory_demanding:
+ return np.array([_transform(x[None, ...]) for x in X])
+ else:
+ return _transform(X)
def __getstate__(self):
# Handling unpicklable objects
@@ -89,7 +103,7 @@ class InceptionResnetv2_MsCeleb_CenterLoss_2018(InceptionResnet):
"""
- def __init__(self):
+ def __init__(self, memory_demanding=False):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "inceptionresnetv2_msceleb_centerloss_2018"),
)
@@ -111,7 +125,9 @@ class InceptionResnetv2_MsCeleb_CenterLoss_2018(InceptionResnet):
)
super(InceptionResnetv2_MsCeleb_CenterLoss_2018, self).__init__(
- checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ checkpoint_path,
+ preprocessor=tf.image.per_image_standardization,
+ memory_demanding=memory_demanding,
)
@@ -123,7 +139,7 @@ class InceptionResnetv2_Casia_CenterLoss_2018(InceptionResnet):
"""
- def __init__(self):
+ def __init__(self, memory_demanding=False):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "inceptionresnetv2_casia_centerloss_2018"),
)
@@ -144,7 +160,9 @@ class InceptionResnetv2_Casia_CenterLoss_2018(InceptionResnet):
)
super(InceptionResnetv2_Casia_CenterLoss_2018, self).__init__(
- checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ checkpoint_path,
+ preprocessor=tf.image.per_image_standardization,
+ memory_demanding=memory_demanding,
)
@@ -156,7 +174,7 @@ class InceptionResnetv1_Casia_CenterLoss_2018(InceptionResnet):
"""
- def __init__(self):
+ def __init__(self, memory_demanding=False):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "inceptionresnetv1_casia_centerloss_2018"),
)
@@ -177,7 +195,9 @@ class InceptionResnetv1_Casia_CenterLoss_2018(InceptionResnet):
)
super(InceptionResnetv1_Casia_CenterLoss_2018, self).__init__(
- checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ checkpoint_path,
+ preprocessor=tf.image.per_image_standardization,
+ memory_demanding=memory_demanding,
)
@@ -189,7 +209,7 @@ class InceptionResnetv1_MsCeleb_CenterLoss_2018(InceptionResnet):
"""
- def __init__(self):
+ def __init__(self, memory_demanding=False):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "inceptionresnetv1_msceleb_centerloss_2018"),
)
@@ -211,7 +231,9 @@ class InceptionResnetv1_MsCeleb_CenterLoss_2018(InceptionResnet):
)
super(InceptionResnetv1_MsCeleb_CenterLoss_2018, self).__init__(
- checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ checkpoint_path,
+ preprocessor=tf.image.per_image_standardization,
+ memory_demanding=memory_demanding,
)
@@ -237,7 +259,7 @@ class FaceNetSanderberg_20170512_110547(InceptionResnet):
)
"""
- def __init__(self):
+ def __init__(self, memory_demanding=False):
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "facenet_sanderberg_20170512_110547"),
)
@@ -257,5 +279,8 @@ class FaceNetSanderberg_20170512_110547(InceptionResnet):
)
super(FaceNetSanderberg_20170512_110547, self).__init__(
- checkpoint_path, tf.image.per_image_standardization,
+ checkpoint_path,
+ tf.image.per_image_standardization,
+ memory_demanding=memory_demanding,
)
+
diff --git a/bob/bio/face/test/test_embeddings.py b/bob/bio/face/test/test_embeddings.py
index e97e3b0028fc81db6bb03728c67f2a9a3e1c1287..8fed0d688e20de63c84b4f033a5cc99888b6561c 100644
--- a/bob/bio/face/test/test_embeddings.py
+++ b/bob/bio/face/test/test_embeddings.py
@@ -32,6 +32,33 @@ def test_idiap_inceptionv2_msceleb():
assert output.size == 128, output.shape
+@is_library_available("tensorflow")
+def test_idiap_inceptionv2_msceleb_memory_demanding():
+ from bob.bio.face.embeddings.tf2_inception_resnet import (
+ InceptionResnetv2_MsCeleb_CenterLoss_2018,
+ )
+
+ reference = bob.io.base.load(
+ pkg_resources.resource_filename(
+ "bob.bio.face.test", "data/inception_resnet_v2_rgb.hdf5"
+ )
+ )
+ np.random.seed(10)
+
+ transformer = InceptionResnetv2_MsCeleb_CenterLoss_2018(memory_demanding=True)
+ data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
+ output = transformer.transform([data])[0]
+ assert output.size == 128, output.shape
+
+ # Sample Batch
+ sample = Sample(data)
+ transformer_sample = wrap(["sample"], transformer)
+ output = [s.data for s in transformer_sample.transform([sample])][0]
+
+ np.testing.assert_allclose(output[0], reference.flatten(), rtol=1e-5, atol=1e-4)
+ assert output.size == 128, output.shape
+
+
@is_library_available("tensorflow")
def test_idiap_inceptionv2_casia():
from bob.bio.face.embeddings import InceptionResnetv2_Casia_CenterLoss_2018