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DC_CDN_IJCAI21.py
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DC_CDN_IJCAI21.py
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'''
Code of 'Dual-Cross Central Difference Network for Face Anti-Spoofing'
By Zitong Yu, 2021
If you use the code, please cite:
@inproceedings{yu2021dual,
title={Dual-Cross Central Difference Network for Face Anti-Spoofing},
author={Yu, Zitong and Qin, Yunxiao and ZHoa, Hengshuang and Li, Xiaobai and Zhao, Guoying},
booktitle= {IJCAI},
year = {2021}
}
Only for research purpose, and commercial use is not allowed.
MIT License
Copyright (c) 2021
'''
import math
import torch
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import Parameter
import pdb
import numpy as np
class Conv2d_Hori_Veri_Cross(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, dilation=1, groups=1, bias=False, theta=0.7):
super(Conv2d_Hori_Veri_Cross, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=(1, 5), stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
self.theta = theta
def forward(self, x):
[C_out,C_in,H_k,W_k] = self.conv.weight.shape
tensor_zeros = torch.FloatTensor(C_out, C_in, 1).fill_(0).cuda()
conv_weight = torch.cat((tensor_zeros, self.conv.weight[:,:,:,0], tensor_zeros, self.conv.weight[:,:,:,1], self.conv.weight[:,:,:,2], self.conv.weight[:,:,:,3], tensor_zeros, self.conv.weight[:,:,:,4], tensor_zeros), 2)
conv_weight = conv_weight.contiguous().view(C_out, C_in, 3, 3)
out_normal = F.conv2d(input=x, weight=conv_weight, bias=self.conv.bias, stride=self.conv.stride, padding=self.conv.padding)
if math.fabs(self.theta - 0.0) < 1e-8:
return out_normal
else:
#pdb.set_trace()
[C_out,C_in, kernel_size,kernel_size] = self.conv.weight.shape
kernel_diff = self.conv.weight.sum(2).sum(2)
kernel_diff = kernel_diff[:, :, None, None]
out_diff = F.conv2d(input=x, weight=kernel_diff, bias=self.conv.bias, stride=self.conv.stride, padding=0, groups=self.conv.groups)
return out_normal - self.theta * out_diff
class Conv2d_Diag_Cross(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, dilation=1, groups=1, bias=False, theta=0.7):
super(Conv2d_Diag_Cross, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=(1, 5), stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
self.theta = theta
def forward(self, x):
[C_out,C_in,H_k,W_k] = self.conv.weight.shape
tensor_zeros = torch.FloatTensor(C_out, C_in, 1).fill_(0).cuda()
conv_weight = torch.cat((self.conv.weight[:,:,:,0], tensor_zeros, self.conv.weight[:,:,:,1], tensor_zeros, self.conv.weight[:,:,:,2], tensor_zeros, self.conv.weight[:,:,:,3], tensor_zeros, self.conv.weight[:,:,:,4]), 2)
conv_weight = conv_weight.contiguous().view(C_out, C_in, 3, 3)
out_normal = F.conv2d(input=x, weight=conv_weight, bias=self.conv.bias, stride=self.conv.stride, padding=self.conv.padding)
if math.fabs(self.theta - 0.0) < 1e-8:
return out_normal
else:
#pdb.set_trace()
[C_out,C_in, kernel_size,kernel_size] = self.conv.weight.shape
kernel_diff = self.conv.weight.sum(2).sum(2)
kernel_diff = kernel_diff[:, :, None, None]
out_diff = F.conv2d(input=x, weight=kernel_diff, bias=self.conv.bias, stride=self.conv.stride, padding=0, groups=self.conv.groups)
return out_normal - self.theta * out_diff
class C_CDN(nn.Module):
def __init__(self, basic_conv=Conv2d_Hori_Veri_Cross, theta=0.8):
super(C_CDN, self).__init__()
self.conv1 = nn.Sequential(
basic_conv(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1 = nn.Sequential(
basic_conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block2 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block3 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.lastconv1 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv2 = nn.Sequential(
basic_conv(128, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.lastconv3 = nn.Sequential(
basic_conv(64, 1, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
#nn.Conv2d(64, 1, kernel_size=1, stride=1, padding=0, bias=False),
nn.ReLU(),
)
self.downsample32x32 = nn.Upsample(size=(32, 32), mode='bilinear')
def forward(self, x): # x [3, 256, 256]
x_input = x
x = self.conv1(x)
x_Block1 = self.Block1(x) # x [128, 128, 128]
x_Block1_32x32 = self.downsample32x32(x_Block1) # x [128, 32, 32]
x_Block2 = self.Block2(x_Block1) # x [128, 64, 64]
x_Block2_32x32 = self.downsample32x32(x_Block2) # x [128, 32, 32]
x_Block3 = self.Block3(x_Block2) # x [128, 32, 32]
x_Block3_32x32 = self.downsample32x32(x_Block3) # x [128, 32, 32]
x_concat = torch.cat((x_Block1_32x32,x_Block2_32x32,x_Block3_32x32), dim=1) # x [128*3, 32, 32]
#pdb.set_trace()
x = self.lastconv1(x_concat) # x [128, 32, 32]
x = self.lastconv2(x) # x [64, 32, 32]
x = self.lastconv3(x) # x [1, 32, 32]
depth = x.squeeze(1)
return depth
class DC_CDN(nn.Module):
def __init__(self, basic_conv1=Conv2d_Hori_Veri_Cross, basic_conv2=Conv2d_Diag_Cross, theta=0.8):
super(DC_CDN, self).__init__()
self.conv1 = nn.Sequential(
basic_conv1(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1 = nn.Sequential(
basic_conv1(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv1(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv1(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block2 = nn.Sequential(
basic_conv1(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv1(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv1(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block3 = nn.Sequential(
basic_conv1(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv1(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv1(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.lastconv1 = nn.Sequential(
basic_conv1(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv2 = nn.Sequential(
basic_conv1(128, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.lastconv3 = nn.Sequential(
#basic_conv1(64, 1, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.Conv2d(128, 1, kernel_size=1, stride=1, padding=0, bias=False),
nn.ReLU(),
)
# 2nd stream
self.conv1_2 = nn.Sequential(
basic_conv2(3, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.Block1_2 = nn.Sequential(
basic_conv2(64, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv2(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv2(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block2_2 = nn.Sequential(
basic_conv2(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv2(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv2(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.Block3_2 = nn.Sequential(
basic_conv2(128, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
basic_conv2(128, 196, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(196),
nn.ReLU(),
basic_conv2(196, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0),
)
self.lastconv1_2 = nn.Sequential(
basic_conv2(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.lastconv2_2 = nn.Sequential(
basic_conv2(128, 64, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
nn.BatchNorm2d(64),
nn.ReLU(),
)
#self.lastconv3_2 = nn.Sequential(
# basic_conv2(64, 1, kernel_size=3, stride=1, padding=1, bias=False, theta= theta),
# #nn.Conv2d(64, 1, kernel_size=1, stride=1, padding=0, bias=False),
# nn.ReLU(),
#)
#self.HP_branch1 = Parameter(torch.ones([3,1]))
self.HP_branch1 = Parameter(torch.zeros([3,1]))
#self.HP_branch2 = Parameter(torch.ones([3,1]))
self.HP_branch2 = Parameter(torch.zeros([3,1]))
self.downsample32x32 = nn.Upsample(size=(32, 32), mode='bilinear')
def forward(self, x): # x [3, 256, 256]
x_input = x
# 1st stream
x = self.conv1(x_input)
x_2 = self.conv1_2(x_input)
x_Block1 = self.Block1(x) # x [128, 128, 128]
x_Block1_2 = self.Block1_2(x_2) # x [128, 128, 128]
# fusion1
x_Block1_new = F.sigmoid(self.HP_branch1[0])*x_Block1 + (1-F.sigmoid(self.HP_branch1[0]))*x_Block1_2
x_Block1_2_new = F.sigmoid(self.HP_branch2[0])*x_Block1_2 + (1-F.sigmoid(self.HP_branch2[0]))*x_Block1
x_Block2 = self.Block2(x_Block1) # x [128, 64, 64]
x_Block2_2 = self.Block2_2(x_Block1_2) # x [128, 64, 64]
# fusion2
x_Block2_new = F.sigmoid(self.HP_branch1[1])*x_Block2 + (1-F.sigmoid(self.HP_branch1[1]))*x_Block2_2
x_Block2_2_new = F.sigmoid(self.HP_branch2[1])*x_Block2_2 + (1-F.sigmoid(self.HP_branch2[1]))*x_Block2
x_Block3 = self.Block3(x_Block2) # x [128, 32, 32]
x_Block3_2 = self.Block3_2(x_Block2_2) # x [128, 32, 32]
# fusion3
x_Block3_new = F.sigmoid(self.HP_branch1[2])*x_Block3 + (1-F.sigmoid(self.HP_branch1[2]))*x_Block3_2
x_Block3_2_new = F.sigmoid(self.HP_branch2[2])*x_Block3_2 + (1-F.sigmoid(self.HP_branch2[2]))*x_Block3
x_Block1_32x32 = self.downsample32x32(x_Block1_new) # x [128, 32, 32]
x_Block2_32x32 = self.downsample32x32(x_Block2_new) # x [128, 32, 32]
x_Block3_32x32 = self.downsample32x32(x_Block3_new) # x [128, 32, 32]
x_concat = torch.cat((x_Block1_32x32,x_Block2_32x32,x_Block3_32x32), dim=1) # x [128*3, 32, 32]
x = self.lastconv1(x_concat) # x [128, 32, 32]
depth1 = self.lastconv2(x) # x [64, 32, 32]
#x = self.lastconv3(x) # x [1, 32, 32]
#map_x_1 = x.squeeze(1)
# 2nd stream
x_Block1_32x32 = self.downsample32x32(x_Block1_2_new) # x [128, 32, 32]
x_Block2_32x32 = self.downsample32x32(x_Block2_2_new) # x [128, 32, 32]
x_Block3_32x32 = self.downsample32x32(x_Block3_2_new) # x [128, 32, 32]
x_concat = torch.cat((x_Block1_32x32,x_Block2_32x32,x_Block3_32x32), dim=1) # x [128*3, 32, 32]
x = self.lastconv1_2(x_concat) # x [128, 32, 32]
depth2 = self.lastconv2_2(x) # x [64, 32, 32]
# fusion
depth = torch.cat((depth1,depth2), dim=1)
depth = self.lastconv3(depth) # x [1, 32, 32]
depth = depth.squeeze(1)
return depth
if __name__ == '__main__':
inputs = torch.randn(1,3,256,256).cuda()
model_C_CDN = C_CDN(basic_conv=Conv2d_Hori_Veri_Cross, theta=0.8).cuda()
depth = model_C_CDN(inputs)
model_C_CDN = C_CDN(basic_conv=Conv2d_Diag_Cross, theta=0.8).cuda()
depth = model_C_CDN(inputs)
model_DC_CDN = DC_CDN(basic_conv1=Conv2d_Hori_Veri_Cross, basic_conv2=Conv2d_Diag_Cross, theta=0.8).cuda()
depth = model_DC_CDN(inputs)
pdb.set_trace()