Mc deep pixbis

Adds a net architecture, extractor and tests

bob/learn/pytorch/architectures/MCDeepPixBiS.py

+import torch
+from torch import nn
+from torchvision import models
+import numpy as np
+
+
+class MCDeepPixBiS(nn.Module):
+
+    """ The class defining Multi-Channel Deep Pixelwise Binary Supervision for Face Presentation
+    Attack Detection:
+
+    This extends the following paper to multi-channel/ multi-spectral images with cross modal pretraining.
+
+    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.
+
+    The initialization uses `Cross modality pre-training` idea from the following paper:
+
+    Wang L, Xiong Y, Wang Z, Qiao Y, Lin D, Tang X, Van Gool L. Temporal segment networks: 
+    Towards good practices for deep action recognition. InEuropean conference on computer 
+    vision 2016 Oct 8 (pp. 20-36). Springer, Cham.
+
+
+    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 
+    num_channels: int
+        Number of channels in the input.      
+    """
+
+    def __init__(self, pretrained=True, num_channels=4):
+
+        """ 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
+        num_channels: int
+            Number of channels in the input. 
+        """
+        super(MCDeepPixBiS, self).__init__()
+
+        dense = models.densenet161(pretrained=pretrained)
+
+        features = list(dense.features.children())
+
+        temp_layer = features[0]
+
+        # No bias in this architecture
+
+        mean_weight = np.mean(temp_layer.weight.data.detach().numpy(),axis=1) # for 96 filters
+
+        new_weight = np.zeros((96,num_channels,7,7))
+  
+        for i in range(num_channels):
+            new_weight[:,i,:,:]=mean_weight
+
+        features[0]=nn.Conv2d(num_channels, 96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+
+        features[0].weight.data = torch.Tensor(new_weight)
+
+        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 Multi-channel images of size num_channelsx224x224
+
+        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