| '''
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| # author: Zhiyuan Yan
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| # email: zhiyuanyan@link.cuhk.edu.cn
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| # date: 2023-0706
|
|
|
| The code is mainly modified from GitHub link below:
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| https://github.com/ondyari/FaceForensics/blob/master/classification/network/xception.py
|
| '''
|
|
|
| import os
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| import argparse
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| import logging
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|
|
| import math
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| import torch
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|
|
| import torch.nn as nn
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| import torch.nn.functional as F
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|
|
| import torch.utils.model_zoo as model_zoo
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| from torch.nn import init
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| from typing import Union
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| from utils.registry import BACKBONE
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|
|
| logger = logging.getLogger(__name__)
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|
|
|
|
|
|
| class SeparableConv2d(nn.Module):
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| def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
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| super(SeparableConv2d, self).__init__()
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|
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| self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size,
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| stride, padding, dilation, groups=in_channels, bias=bias)
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| self.pointwise = nn.Conv2d(
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| in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)
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|
|
| def forward(self, x):
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| x = self.conv1(x)
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| x = self.pointwise(x)
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| return x
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|
|
|
|
| class Block(nn.Module):
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| def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
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| super(Block, self).__init__()
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|
|
| if out_filters != in_filters or strides != 1:
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| self.skip = nn.Conv2d(in_filters, out_filters,
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| 1, stride=strides, bias=False)
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| self.skipbn = nn.BatchNorm2d(out_filters)
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| else:
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| self.skip = None
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|
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| self.relu = nn.ReLU(inplace=True)
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| rep = []
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|
|
| filters = in_filters
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| if grow_first:
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| rep.append(self.relu)
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| rep.append(SeparableConv2d(in_filters, out_filters,
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| 3, stride=1, padding=1, bias=False))
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| rep.append(nn.BatchNorm2d(out_filters))
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| filters = out_filters
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|
|
| for i in range(reps-1):
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| rep.append(self.relu)
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| rep.append(SeparableConv2d(filters, filters,
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| 3, stride=1, padding=1, bias=False))
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| rep.append(nn.BatchNorm2d(filters))
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|
|
| if not grow_first:
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| rep.append(self.relu)
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| rep.append(SeparableConv2d(in_filters, out_filters,
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| 3, stride=1, padding=1, bias=False))
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| rep.append(nn.BatchNorm2d(out_filters))
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|
|
| if not start_with_relu:
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| rep = rep[1:]
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| else:
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| rep[0] = nn.ReLU(inplace=False)
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|
|
| if strides != 1:
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| rep.append(nn.MaxPool2d(3, strides, 1))
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| self.rep = nn.Sequential(*rep)
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|
|
| def forward(self, inp):
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| x = self.rep(inp)
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|
|
| if self.skip is not None:
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| skip = self.skip(inp)
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| skip = self.skipbn(skip)
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| else:
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| skip = inp
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|
|
| x += skip
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| return x
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|
|
| def add_gaussian_noise(ins, mean=0, stddev=0.2):
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| noise = ins.data.new(ins.size()).normal_(mean, stddev)
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| return ins + noise
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|
|
|
|
| @BACKBONE.register_module(module_name="xception")
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| class Xception(nn.Module):
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| """
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| Xception optimized for the ImageNet dataset, as specified in
|
| https://arxiv.org/pdf/1610.02357.pdf
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| """
|
|
|
| def __init__(self, xception_config):
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| """ Constructor
|
| Args:
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| xception_config: configuration file with the dict format
|
| """
|
| super(Xception, self).__init__()
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| self.num_classes = xception_config["num_classes"]
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| self.mode = xception_config["mode"]
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| inc = xception_config["inc"]
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| dropout = xception_config["dropout"]
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|
|
|
|
| self.conv1 = nn.Conv2d(inc, 32, 3, 2, 0, bias=False)
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|
|
| self.bn1 = nn.BatchNorm2d(32)
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| self.relu = nn.ReLU(inplace=True)
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|
|
| self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
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| self.bn2 = nn.BatchNorm2d(64)
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|
|
|
|
| self.block1 = Block(
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| 64, 128, 2, 2, start_with_relu=False, grow_first=True)
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| self.block2 = Block(
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| 128, 256, 2, 2, start_with_relu=True, grow_first=True)
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| self.block3 = Block(
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| 256, 728, 2, 2, start_with_relu=True, grow_first=True)
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|
|
|
|
| self.block4 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block5 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block6 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block7 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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|
|
| self.block8 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block9 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block10 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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| self.block11 = Block(
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| 728, 728, 3, 1, start_with_relu=True, grow_first=True)
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|
|
|
|
| self.block12 = Block(
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| 728, 1024, 2, 2, start_with_relu=True, grow_first=False)
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|
|
| self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
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| self.bn3 = nn.BatchNorm2d(1536)
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|
|
|
|
| self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
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| self.bn4 = nn.BatchNorm2d(2048)
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|
|
| self.last_linear = nn.Linear(2048, self.num_classes)
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| if dropout:
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| self.last_linear = nn.Sequential(
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| nn.Dropout(p=dropout),
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| nn.Linear(2048, self.num_classes)
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| )
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|
|
| self.adjust_channel = nn.Sequential(
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| nn.Conv2d(2048, 512, 1, 1),
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| nn.BatchNorm2d(512),
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| nn.ReLU(inplace=True),
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| )
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|
|
| def fea_part1_0(self, x):
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| x = self.conv1(x)
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| x = self.bn1(x)
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| x = self.relu(x)
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|
|
| return x
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|
|
| def fea_part1_1(self, x):
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|
|
| x = self.conv2(x)
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| x = self.bn2(x)
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| x = self.relu(x)
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|
|
| return x
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|
|
| def fea_part1(self, x):
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| x = self.conv1(x)
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| x = self.bn1(x)
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| x = self.relu(x)
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|
|
| x = self.conv2(x)
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| x = self.bn2(x)
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| x = self.relu(x)
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|
|
| return x
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|
|
| def fea_part2(self, x):
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| x = self.block1(x)
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| x = self.block2(x)
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| x = self.block3(x)
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|
|
| return x
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|
|
| def fea_part3(self, x):
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| if self.mode == "shallow_xception":
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| return x
|
| else:
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| x = self.block4(x)
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| x = self.block5(x)
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| x = self.block6(x)
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| x = self.block7(x)
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| return x
|
|
|
| def fea_part4(self, x):
|
| if self.mode == "shallow_xception":
|
| x = self.block12(x)
|
| else:
|
| x = self.block8(x)
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| x = self.block9(x)
|
| x = self.block10(x)
|
| x = self.block11(x)
|
| x = self.block12(x)
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| return x
|
|
|
| def fea_part5(self, x):
|
| x = self.conv3(x)
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| x = self.bn3(x)
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| x = self.relu(x)
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|
|
| x = self.conv4(x)
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| x = self.bn4(x)
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|
|
| return x
|
|
|
| def features(self, input):
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| x = self.fea_part1(input)
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|
|
| x = self.fea_part2(x)
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| x = self.fea_part3(x)
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| x = self.fea_part4(x)
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|
|
| x = self.fea_part5(x)
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|
|
| if self.mode == 'adjust_channel':
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| x = self.adjust_channel(x)
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|
|
| return x
|
|
|
| def classifier(self, features):
|
| x = self.relu(features)
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|
|
| if len(x.shape) == 4:
|
| x = F.adaptive_avg_pool2d(x, (1, 1))
|
| x = x.view(x.size(0), -1)
|
| out = self.last_linear(x)
|
| return out
|
|
|
| def forward(self, input):
|
| x = self.features(input)
|
| out = self.classifier(x)
|
| return out, x
|
|
|
