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depthnet_real-world_baseline.py
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'''
Code of 'Searching Central Difference Convolutional Networks for Face Anti-Spoofing'
By Zitong Yu & Zhuo Su, 2019
If you use the code, please cite:
@inproceedings{yu2020searching,
title={Searching Central Difference Convolutional Networks for Face Anti-Spoofing},
author={Yu, Zitong and Zhao, Chenxu and Wang, Zezheng and Qin, Yunxiao and Su, Zhuo and Li, Xiaobai and Zhou, Feng and Zhao, Guoying},
booktitle= {CVPR},
year = {2020}
}
Only for research purpose, and commercial use is not allowed.
MIT License
Copyright (c) 2020
'''
import math
import sys
sys.path.insert(0,"/data/home/scv7305/run/chenjiawei/adv_branch/CDCN")
from dataset import MyDataset
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import math
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Parameter
class Conv2d_basic(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, dilation=1, groups=1, bias=False):
super(Conv2d_basic, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
def forward(self, x):
out_normal = self.conv(x)
return out_normal
class Depthnet(nn.Module):
def __init__(self, basic_conv=Conv2d_basic, map_size=16, multi_heads=False, input_channel=3):
super(Depthnet, self).__init__()
self.conv1 = nn.Sequential(
basic_conv(input_channel, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.Block1 = nn.Sequential(
basic_conv(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
basic_conv(128, int(128*1.6), kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(int(128*1.6)),
nn.ReLU(inplace=True),
basic_conv(int(128*1.6), 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),#éçť´
)
self.Block2 = nn.Sequential(
basic_conv(128, int(128*1.2), kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(int(128*1.2)),
nn.ReLU(inplace=True),
basic_conv(int(128*1.2), 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
basic_conv(128, int(128*1.4), kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(int(128*1.4)),
nn.ReLU(inplace=True),
basic_conv(int(128*1.4), 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.Block3 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
basic_conv(128, int(128*1.2), kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(int(128*1.2)),
nn.ReLU(inplace=True),
basic_conv(int(128*1.2), 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
# Original
self.downconv1 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
basic_conv(128, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
self.downconv2 = nn.Sequential(
basic_conv(128, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
self.lastconv1 = nn.Sequential(
basic_conv(128*3, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
self.decoder1 = nn.Sequential(
basic_conv(128, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
basic_conv(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
nn.ReLU(inplace=True),
)
self.decoder2 = nn.Sequential(
basic_conv(128, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
basic_conv(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
nn.ReLU(inplace=True),
)
self.decoder3 = nn.Sequential(
basic_conv(128, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
basic_conv(64, 1, kernel_size=3, stride=1, padding=1, bias=False),
nn.ReLU(inplace=True),
)
self.average = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
self.spoofer = nn.Linear(map_size * map_size, 1)
def forward(self, x): # x [3, 112, 112]
x = self.conv1(x)
x_Block1 = self.Block1(x)#13*128*64*64
x_Block1_32x32 = self.downconv1(x_Block1)#13*128*16*16
x_Block2 = self.Block2(x_Block1)#13*128*32*32
x_Block2_32x32 = self.downconv2(x_Block2)#13*128*16*16
x_Block3 = self.Block3(x_Block2)#13*128*16*16
x_concat = torch.cat((x_Block1_32x32,x_Block2_32x32,x_Block3), dim=1) #13*384*16*16
#pdb.set_trace()
embedding = self.lastconv1(x_concat)#13*128*16*16
map_x = self.decoder1(embedding)#13*1*16*16
map_x = map_x.squeeze(1)#13*16*16
embedding_1 = self.average(embedding)
map_x_1 = self.decoder2(embedding_1)
map_x_1 = map_x_1.squeeze(1)
embedding_2 = self.average(embedding_1)
map_x_2 = self.decoder3(embedding_2)
map_x_2 = map_x_2.squeeze(1)
output = map_x.view(map_x.size(0), -1)
spoof_output = self.spoofer(output)
spoof_output = torch.sigmoid(spoof_output)
return map_x, map_x_1, map_x_2, spoof_output
if __name__ == '__main__':
model = Depthnet().cuda()
#a=torch.load('../checkpoint/Depthnet_AWP_two_class_patch-AT_best.pt')
a=torch.load('../checkpoint/Depthnet_two_class_patch-AT_last.pt')
# new_state_dict = {}
# for k,v in a.items():
# new_state_dict[k[7:]] = v
model.load_state_dict(a)
with torch.no_grad():
model.eval()
test_data = MyDataset('/data/home/scv7305/run/chenjiawei/fasdata/fakeglass/facepath.txt', transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean =[0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])]))
dataloader_test = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=1)
n = 0
for images in dataloader_test:
# get the inputs
images = images.cuda()
map_x, map_x_1, map_x_2, spoof_output = model(images)
map_score = (torch.clamp(torch.mean(map_x, dim=(1,2)),0,1)+torch.clamp(torch.mean(map_x_1, dim=(1,2)),0,1)
+torch.clamp(torch.mean(map_x_2, dim=(1,2)),0,1)+spoof_output)/4
#socre_all = map_score.squeeze(-1).float()*adv_out.squeeze(-1).float()
if map_score < 0.9:
n = n+1
print(n)
#print(spoof_out)