import torch
from torch import nn
from torchvision import models
[docs]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)
[docs] 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