Added DeepPixBis architecture for PAD

bob/learn/pytorch/architectures/DeepPixBiS.py

+import torch
+from torch import nn
+from torchvision import models
+
+
+class DeepPixBiS(nn.Module):
+    """ The class defining Deep Pixelwise Binary Supervision for Face Presentation
+    Attack Detection:
+
+    Reference: Anjith George and Sébastien Marcel. "Deep Pixel-wise Binary Supervision for 
+    Face Presentation Attack Detection." In 2019 International Conference on Biometrics (ICB).IEEE, 2019.
+
+    Attributes
+    ----------
+    pretrained: bool
+        If set to `True` uses the pretrained DenseNet model as the base. If set to `False`, the network
+        will be trained from scratch. 
+        default: True      
+    """
+
+    def __init__(self, pretrained=True):
+
+        """ Init function
+
+        Parameters
+        ----------
+        pretrained: bool
+            If set to `True` uses the pretrained densenet model as the base. Else, it uses the default network
+            default: True
+        """
+        super(DeepPixBiS, self).__init__()
+
+        dense = models.densenet161(pretrained=pretrained)
+
+        features = list(dense.features.children())
+
+        self.enc = nn.Sequential(*features[0:8])
+
+        self.dec=nn.Conv2d(384, 1, kernel_size=1, padding=0)
+
+        self.linear=nn.Linear(14*14,1)
+
+
+    def forward(self, x):
+        """ Propagate data through the network
+
+        Parameters
+        ----------
+        img: :py:class:`torch.Tensor` 
+          The data to forward through the network. Expects RGB image of size 3x224x224
+
+        Returns
+        -------
+        dec: :py:class:`torch.Tensor` 
+            Binary map of size 1x14x14
+        op: :py:class:`torch.Tensor`
+            Final binary score.  
+
+        """
+        enc = self.enc(x)
+
+        dec=self.dec(enc)
+
+        dec=nn.Sigmoid()(dec)
+
+        dec_flat=dec.view(-1,14*14)
+
+        op=self.linear(dec_flat)
+
+        op=nn.Sigmoid()(op)
+ 
+        return dec,op

bob/learn/pytorch/architectures/__init__.py

@@ -7,6 +7,7 @@ from .MCCNN import MCCNN
 from .MCCNNv2 import MCCNNv2
 from .FASNet import FASNet
 from .DeepMSPAD import DeepMSPAD
+from .DeepPixBiS import DeepPixBiS
 
 from .DCGAN import DCGAN_generator
 from .DCGAN import DCGAN_discriminator

bob/learn/pytorch/test/test.py

@@ -92,6 +92,15 @@ def test_architectures():
   output = net.forward(t)
   assert output.shape == torch.Size([1, 1])
 
+  #DeepPixBiS 
+  a = numpy.random.rand(1, 3, 224, 224).astype("float32")
+  t = torch.from_numpy(a)
+  from ..architectures import DeepPixBiS
+  net = DeepPixBiS(pretrained=True)
+  output = net.forward(t)
+  assert output[0].shape == torch.Size([1, 1, 14, 14])
+  assert output[1].shape == torch.Size([1, 1])
+
   # DCGAN
   d = numpy.random.rand(1, 3, 64, 64).astype("float32")
   t = torch.from_numpy(d)