import torch
from torch import nn
from torchvision import models
[docs]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.
Referenced 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()
[docs] 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