Added facexzoo models

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

+from bob.bio.face.embeddings.pytorch import AttentionNet
+from bob.bio.face.utils import lookup_config_from_database
+
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+    locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=False):
+    return AttentionNet(annotation_type, fixed_positions, memory_demanding)
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
+

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

+from bob.bio.face.embeddings.pytorch import EfficientNet
+from bob.bio.face.utils import lookup_config_from_database
+
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+    locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=False):
+    return EfficientNet(annotation_type, fixed_positions, memory_demanding)
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
+

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

+from bob.bio.face.embeddings.pytorch import MobileFaceNet
+from bob.bio.face.utils import lookup_config_from_database
+
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+    locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=False):
+    return MobileFaceNet(annotation_type, fixed_positions, memory_demanding)
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
+

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

+from bob.bio.face.embeddings.pytorch import ResNeSt
+from bob.bio.face.utils import lookup_config_from_database
+
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+    locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=False):
+    return ResNeSt(annotation_type, fixed_positions, memory_demanding)
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
+

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

+from bob.bio.face.embeddings.pytorch import ResNet
+from bob.bio.face.utils import lookup_config_from_database
+
+
+annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(
+    locals().get("database")
+)
+
+
+def load(annotation_type, fixed_positions=None, memory_demanding=False):
+    return ResNet(annotation_type, fixed_positions, memory_demanding)
+
+
+pipeline = load(annotation_type, fixed_positions, memory_demanding)
+

bob/bio/face/embeddings/pytorch.py

@@ -5,7 +5,7 @@
 
 import imp
 import os
-
+import torch
 import numpy as np
 from bob.bio.base.pipelines.vanilla_biometrics import Distance
 from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometricsPipeline
@@ -16,6 +16,7 @@ from sklearn.base import BaseEstimator
 from sklearn.base import TransformerMixin
 from sklearn.utils import check_array
 
+from bob.learn.pytorch.architectures.facexzoo import FaceXZooModelFactory
 
 class PyTorchModel(TransformerMixin, BaseEstimator):
     """
@@ -276,6 +277,68 @@ class IResnet100(PyTorchModel):
         self.model.eval()
         self.place_model_on_device()
 
+class FaceXZooModel(PyTorchModel):
+    """
+    FaceXZoo models
+    """
+
+    def __init__(
+        self,
+        preprocessor=lambda x: (x - 127.5) / 128.0,
+        memory_demanding=False,
+        device=None,
+        arch='AttentionNet',
+        **kwargs,
+    ):
+
+        self.arch=arch
+
+        _model= FaceXZooModelFactory(self.arch)
+
+        # self.model = _model.get_model()
+        filename = _model.get_facexzoo_file()
+        checkpoint_name = _model.get_checkpoint_name()
+        config=None
+        path = os.path.dirname(filename)
+        checkpoint_path = os.path.join(path, self.arch+'.pt')
+
+        super(FaceXZooModel, self).__init__(
+            checkpoint_path,
+            config,
+            memory_demanding=memory_demanding,
+            preprocessor=preprocessor,
+            device=device,
+            **kwargs,
+        )
+
+    def _load_model(self):
+
+        _model= FaceXZooModelFactory(self.arch)
+
+        self.model = _model.get_model()
+
+        print('self.checkpoint_path',self.checkpoint_path)
+        print('self.arch',self.arch)
+        #print('self.model', self.model)
+
+     
+        model_dict = self.model.state_dict()
+        pretrained_dict = torch.load(self.checkpoint_path, map_location=torch.device('cpu'))['state_dict']
+
+        pretrained_dict_keys=pretrained_dict.keys()
+        model_dict_keys=model_dict.keys()
+
+        new_pretrained_dict = {}
+        for k in model_dict:
+            new_pretrained_dict[k] = pretrained_dict['backbone.'+k] 
+        model_dict.update(new_pretrained_dict)
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+        self.place_model_on_device()
+
+
+
+
 
 def iresnet_template(embedding, annotation_type, fixed_positions=None):
     # DEFINE CROPPING
@@ -303,6 +366,137 @@ def iresnet_template(embedding, annotation_type, fixed_positions=None):
     return VanillaBiometricsPipeline(transformer, algorithm)
 
 
+
+
+def AttentionNet(annotation_type, fixed_positions=None, memory_demanding=False):
+    """
+    Get the AttentionNet pipeline which will crop the face :math:`112 \times 112` and
+    use the :py:class:`AttentionNet` to extract the features
+
+
+    Parameters
+    ----------
+
+      annotation_type: str
+         Type of the annotations (e.g. `eyes-center')
+
+      fixed_positions: dict
+         Set it if in your face images are registered to a fixed position in the image
+
+      memory_demanding: bool
+
+    """
+
+    return iresnet_template(
+        embedding=FaceXZooModel(arch='AttentionNet', memory_demanding=memory_demanding),
+        annotation_type=annotation_type,
+        fixed_positions=fixed_positions,
+    )
+
+
+
+def ResNeSt(annotation_type, fixed_positions=None, memory_demanding=False):
+    """
+    Get the ResNeSt pipeline which will crop the face :math:`112 \times 112` and
+    use the :py:class:`ResNeSt` to extract the features
+
+
+    Parameters
+    ----------
+
+      annotation_type: str
+         Type of the annotations (e.g. `eyes-center')
+
+      fixed_positions: dict
+         Set it if in your face images are registered to a fixed position in the image
+
+      memory_demanding: bool
+
+    """
+
+    return iresnet_template(
+        embedding=FaceXZooModel(arch='ResNeSt', memory_demanding=memory_demanding),
+        annotation_type=annotation_type,
+        fixed_positions=fixed_positions,
+    )
+
+
+def MobileFaceNet(annotation_type, fixed_positions=None, memory_demanding=False):
+    """
+    Get the MobileFaceNet pipeline which will crop the face :math:`112 \times 112` and
+    use the :py:class:`MobileFaceNet` to extract the features
+
+
+    Parameters
+    ----------
+
+      annotation_type: str
+         Type of the annotations (e.g. `eyes-center')
+
+      fixed_positions: dict
+         Set it if in your face images are registered to a fixed position in the image
+
+      memory_demanding: bool
+
+    """
+
+    return iresnet_template(
+        embedding=FaceXZooModel(arch='MobileFaceNet', memory_demanding=memory_demanding),
+        annotation_type=annotation_type,
+        fixed_positions=fixed_positions,
+    )
+
+def ResNet(annotation_type, fixed_positions=None, memory_demanding=False):
+    """
+    Get the ResNet pipeline which will crop the face :math:`112 \times 112` and
+    use the :py:class:`ResNet` to extract the features
+
+
+    Parameters
+    ----------
+
+      annotation_type: str
+         Type of the annotations (e.g. `eyes-center')
+
+      fixed_positions: dict
+         Set it if in your face images are registered to a fixed position in the image
+
+      memory_demanding: bool
+
+    """
+
+    return iresnet_template(
+        embedding=FaceXZooModel(arch='ResNet', memory_demanding=memory_demanding),
+        annotation_type=annotation_type,
+        fixed_positions=fixed_positions,
+    )
+
+def EfficientNet(annotation_type, fixed_positions=None, memory_demanding=False):
+    """
+    Get the EfficientNet pipeline which will crop the face :math:`112 \times 112` and
+    use the :py:class:`EfficientNet` to extract the features
+
+
+    Parameters
+    ----------
+
+      annotation_type: str
+         Type of the annotations (e.g. `eyes-center')
+
+      fixed_positions: dict
+         Set it if in your face images are registered to a fixed position in the image
+
+      memory_demanding: bool
+
+    """
+
+    return iresnet_template(
+        embedding=FaceXZooModel(arch='EfficientNet', memory_demanding=memory_demanding),
+        annotation_type=annotation_type,
+        fixed_positions=fixed_positions,
+    )
+
+
 def iresnet34(annotation_type, fixed_positions=None, memory_demanding=False):
     """
     Get the Resnet34 pipeline which will crop the face :math:`112 \times 112` and

setup.py

@@ -143,6 +143,11 @@ setup(
             "resnet50-msceleb-arcface-20210521     = bob.bio.face.config.baseline.resnet50_msceleb_arcface_20210521:pipeline",
             "resnet50-vgg2-arcface-2021            = bob.bio.face.config.baseline.resnet50_vgg2_arcface_2021:pipeline",
             "vgg16-oxford                          = bob.bio.face.config.baseline.vgg16_oxford:pipeline",
+            "attentionnet                          = bob.bio.face.config.baseline.AttentionNet:pipeline",
+            "resnest                               = bob.bio.face.config.baseline.ResNeSt:pipeline",
+            "mobilefacenet                         = bob.bio.face.config.baseline.MobileFaceNet:pipeline",
+            "resnet                                = bob.bio.face.config.baseline.ResNet:pipeline",
+            "efficientnet                          = bob.bio.face.config.baseline.EfficientNet:pipeline",
         ],
         "bob.bio.config": [
             # pipelines
@@ -166,6 +171,11 @@ setup(
             "resnet50-msceleb-arcface-20210521     = bob.bio.face.config.baseline.resnet50_msceleb_arcface_20210521:pipeline",
             "resnet50-vgg2-arcface-2021            = bob.bio.face.config.baseline.resnet50_vgg2_arcface_2021",
             "vgg16-oxford                          = bob.bio.face.config.baseline.vgg16_oxford",
+            "attentionnet                          = bob.bio.face.config.baseline.AttentionNet",
+            "resnest                               = bob.bio.face.config.baseline.ResNeSt",
+            "mobilefacenet                         = bob.bio.face.config.baseline.MobileFaceNet",
+            "resnet                                = bob.bio.face.config.baseline.ResNet",
+            "efficientnet                          = bob.bio.face.config.baseline.EfficientNet",
             # databases
             "arface                                = bob.bio.face.config.database.arface",
             "atnt                                  = bob.bio.face.config.database.atnt",