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DeepfakeGenome_Codebase / training /networks /mesonet.py
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'''
# author: Zhiyuan Yan
# email: zhiyuanyan@link.cuhk.edu.cn
# date: 2023-0706
The code is mainly modified from the below link:
https://github.com/HongguLiu/MesoNet-Pytorch
'''
import os
import argparse
import logging
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init
from typing import Union
from metrics.registry import BACKBONE
logger = logging.getLogger(__name__)
@BACKBONE.register_module(module_name="meso4")
class Meso4(nn.Module):
def __init__(self, meso4_config):
super(Meso4, self).__init__()
self.num_classes = meso4_config["num_classes"]
inc = meso4_config["inc"]
self.conv1 = nn.Conv2d(inc, 8, 3, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(8)
self.relu = nn.ReLU(inplace=True)
self.leakyrelu = nn.LeakyReLU(0.1)
self.conv2 = nn.Conv2d(8, 8, 5, padding=2, bias=False)
self.bn2 = nn.BatchNorm2d(16)
self.conv3 = nn.Conv2d(8, 16, 5, padding=2, bias=False)
self.conv4 = nn.Conv2d(16, 16, 5, padding=2, bias=False)
self.maxpooling1 = nn.MaxPool2d(kernel_size=(2, 2))
self.maxpooling2 = nn.MaxPool2d(kernel_size=(4, 4))
#flatten: x = x.view(x.size(0), -1)
self.dropout = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(16*8*8, 16)
self.fc2 = nn.Linear(16, self.num_classes)
def features(self, input):
x = self.conv1(input) #(8, 256, 256)
x = self.relu(x)
x = self.bn1(x)
x = self.maxpooling1(x) #(8, 128, 128)
x = self.conv2(x) #(8, 128, 128)
x = self.relu(x)
x = self.bn1(x)
x = self.maxpooling1(x) #(8, 64, 64)
x = self.conv3(x) #(16, 64, 64)
x = self.relu(x)
x = self.bn2(x)
x = self.maxpooling1(x) #(16, 32, 32)
x = self.conv4(x) #(16, 32, 32)
x = self.relu(x)
x = self.bn2(x)
x = self.maxpooling2(x) #(16, 8, 8)
x = x.view(x.size(0), -1) #(Batch, 16*8*8)
return x
def classifier(self, feature):
out = self.dropout(feature)
out = self.fc1(out) #(Batch, 16)
out = self.leakyrelu(out)
out = self.dropout(out)
out = self.fc2(out)
return out
def forward(self, input):
x = self.features(input)
out = self.classifier(x)
return out, x
@BACKBONE.register_module(module_name="meso4Inception")
class MesoInception4(nn.Module):
def __init__(self, mesoInception4_config):
super(MesoInception4, self).__init__()
self.num_classes = mesoInception4_config["num_classes"]
inc = mesoInception4_config["inc"]
#InceptionLayer1
self.Incption1_conv1 = nn.Conv2d(3, 1, 1, padding=0, bias=False)
self.Incption1_conv2_1 = nn.Conv2d(3, 4, 1, padding=0, bias=False)
self.Incption1_conv2_2 = nn.Conv2d(4, 4, 3, padding=1, bias=False)
self.Incption1_conv3_1 = nn.Conv2d(3, 4, 1, padding=0, bias=False)
self.Incption1_conv3_2 = nn.Conv2d(4, 4, 3, padding=2, dilation=2, bias=False)
self.Incption1_conv4_1 = nn.Conv2d(3, 2, 1, padding=0, bias=False)
self.Incption1_conv4_2 = nn.Conv2d(2, 2, 3, padding=3, dilation=3, bias=False)
self.Incption1_bn = nn.BatchNorm2d(11)
#InceptionLayer2
self.Incption2_conv1 = nn.Conv2d(11, 2, 1, padding=0, bias=False)
self.Incption2_conv2_1 = nn.Conv2d(11, 4, 1, padding=0, bias=False)
self.Incption2_conv2_2 = nn.Conv2d(4, 4, 3, padding=1, bias=False)
self.Incption2_conv3_1 = nn.Conv2d(11, 4, 1, padding=0, bias=False)
self.Incption2_conv3_2 = nn.Conv2d(4, 4, 3, padding=2, dilation=2, bias=False)
self.Incption2_conv4_1 = nn.Conv2d(11, 2, 1, padding=0, bias=False)
self.Incption2_conv4_2 = nn.Conv2d(2, 2, 3, padding=3, dilation=3, bias=False)
self.Incption2_bn = nn.BatchNorm2d(12)
#Normal Layer
self.conv1 = nn.Conv2d(12, 16, 5, padding=2, bias=False)
self.relu = nn.ReLU(inplace=True)
self.leakyrelu = nn.LeakyReLU(0.1)
self.bn1 = nn.BatchNorm2d(16)
self.maxpooling1 = nn.MaxPool2d(kernel_size=(2, 2))
self.conv2 = nn.Conv2d(16, 16, 5, padding=2, bias=False)
self.maxpooling2 = nn.MaxPool2d(kernel_size=(4, 4))
self.dropout = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(16*8*8, 16)
self.fc2 = nn.Linear(16, self.num_classes)
#InceptionLayer
def InceptionLayer1(self, input):
x1 = self.Incption1_conv1(input)
x2 = self.Incption1_conv2_1(input)
x2 = self.Incption1_conv2_2(x2)
x3 = self.Incption1_conv3_1(input)
x3 = self.Incption1_conv3_2(x3)
x4 = self.Incption1_conv4_1(input)
x4 = self.Incption1_conv4_2(x4)
y = torch.cat((x1, x2, x3, x4), 1)
y = self.Incption1_bn(y)
y = self.maxpooling1(y)
return y
def InceptionLayer2(self, input):
x1 = self.Incption2_conv1(input)
x2 = self.Incption2_conv2_1(input)
x2 = self.Incption2_conv2_2(x2)
x3 = self.Incption2_conv3_1(input)
x3 = self.Incption2_conv3_2(x3)
x4 = self.Incption2_conv4_1(input)
x4 = self.Incption2_conv4_2(x4)
y = torch.cat((x1, x2, x3, x4), 1)
y = self.Incption2_bn(y)
y = self.maxpooling1(y)
return y
def features(self, input):
x = self.InceptionLayer1(input) #(Batch, 11, 128, 128)
x = self.InceptionLayer2(x) #(Batch, 12, 64, 64)
x = self.conv1(x) #(Batch, 16, 64 ,64)
x = self.relu(x)
x = self.bn1(x)
x = self.maxpooling1(x) #(Batch, 16, 32, 32)
x = self.conv2(x) #(Batch, 16, 32, 32)
x = self.relu(x)
x = self.bn1(x)
x = self.maxpooling2(x) #(Batch, 16, 8, 8)
x = x.view(x.size(0), -1) #(Batch, 16*8*8)
return x
def classifier(self, feature):
out = self.dropout(feature)
out = self.fc1(out) #(Batch, 16)
out = self.leakyrelu(out)
out = self.dropout(out)
out = self.fc2(out)
return out
def forward(self, input):
x = self.features(input)
out = self.classifier(x)
return out, x