Added FASNet architecture

Added FASNet architecture for face PAD.

bob/learn/pytorch/architectures/FASNet.py

+import torch
+from torch import nn
+from torchvision import models
+
+
+class FASNet(nn.Module):
+
+	"""PyTorch Reimplementation of Lucena, Oeslle, et al. "Transfer learning using 
+	convolutional neural networks for face anti-spoofing."
+	International Conference Image Analysis and Recognition. Springer, Cham, 2017.
+	eferenced from keras implementation: https://github.com/OeslleLucena/FASNet
+
+	Attributes:
+		pretrained: bool
+			if set `True` loads the pretrained vgg16 model. 
+		vgg: :py:class:`torch.nn.Module`
+			The VGG16 model
+		relu: :py:class:`torch.nn.Module`
+			ReLU activation
+		enc: :py:class:`torch.nn.Module`
+			Uses the layers for feature extraction
+		linear1: :py:class:`torch.nn.Module`
+			Fully connected layer
+		linear2: :py:class:`torch.nn.Module`
+			Fully connected layer
+		dropout: :py:class:`torch.nn.Module`
+			Dropout layer
+		sigmoid: :py:class:`torch.nn.Module`
+			Sigmoid activation
+	"""
+
+	def __init__(self, pretrained=True):
+
+		""" Init method
+
+		Parameters
+		----------
+		pretrained: bool
+			if set `True` loads the pretrained vgg16 model.
+
+		"""
+		super(FASNet, self).__init__()
+
+		vgg = models.vgg16(pretrained=pretrained)
+
+		features = list(vgg.features.children())
+
+		self.enc = nn.Sequential(*features)
+
+		self.linear1=nn.Linear(25088,256)
+
+		self.relu=nn.ReLU()
+
+		self.dropout= nn.Dropout(p=0.5)
+
+		self.linear2=nn.Linear(256,1)
+
+		self.sigmoid= nn.Sigmoid()
+
+
+	def forward(self, x):
+		""" Propagate data through the network
+
+		Parameters
+		----------
+		x: :py:class:`torch.Tensor` 
+		  The data to forward through the network
+
+		Returns
+		-------
+		x: :py:class:`torch.Tensor` 
+		  The last layer of the network
+		
+		"""
+	   
+		enc = self.enc(x)
+
+		x=enc.view(-1,25088)
+
+		x=self.linear1(x)
+
+		x=self.relu(x)
+
+		x=self.dropout(x)
+
+		x=self.linear2(x)
+
+		x=self.sigmoid(x)  
+
+		return x