Commit 23a4b535 authored by Anjith GEORGE's avatar Anjith GEORGE Committed by Anjith GEORGE
Browse files

Added DeepPixBis architecture for PAD

parent 2c7b52d3
Pipeline #28113 passed with stage
in 31 minutes and 38 seconds
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.
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
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)
def forward(self, x):
""" Propagate data through the network
img: :py:class:`torch.Tensor`
The data to forward through the network. Expects RGB image of size 3x224x224
dec: :py:class:`torch.Tensor`
Binary map of size 1x14x14
op: :py:class:`torch.Tensor`
Final binary score.
enc = self.enc(x)
return dec,op
......@@ -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
......@@ -92,6 +92,15 @@ def test_architectures():
output = net.forward(t)
assert output.shape == torch.Size([1, 1])
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])
d = numpy.random.rand(1, 3, 64, 64).astype("float32")
t = torch.from_numpy(d)
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