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""" |
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NPR: Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection@CVPR'2024 |
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Copyright (c) Beijing Jiaotong University and its affiliates. |
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Modified by Chuangchuang Tan from https://github.com/chuangchuangtan/NPR-DeepfakeDetection |
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""" |
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import torch.nn as nn |
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import torch |
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import torch.utils.model_zoo as model_zoo |
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from torch.nn import functional as F |
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from typing import Any, cast, Dict, List, Optional, Union |
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import numpy as np |
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', |
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'resnet152'] |
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model_urls = { |
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
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} |
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def conv3x3(in_planes, out_planes, stride=1): |
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"""3x3 convolution with padding""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
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padding=1, bias=False) |
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def conv1x1(in_planes, out_planes, stride=1): |
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"""1x1 convolution""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out += identity |
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out = self.relu(out) |
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return out |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(Bottleneck, self).__init__() |
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self.conv1 = conv1x1(inplanes, planes) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = conv3x3(planes, planes, stride) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.conv3 = conv1x1(planes, planes * self.expansion) |
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self.bn3 = nn.BatchNorm2d(planes * self.expansion) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out += identity |
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out = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__(self, block, layers, num_classes=1, zero_init_residual=False): |
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super(ResNet, self).__init__() |
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self.unfoldSize = 2 |
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self.unfoldIndex = 0 |
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assert self.unfoldSize > 1 |
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assert -1 < self.unfoldIndex and self.unfoldIndex < self.unfoldSize*self.unfoldSize |
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self.inplanes = 64 |
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(64) |
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self.relu = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64 , layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
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self.fc1 = nn.Linear(512, num_classes) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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if zero_init_residual: |
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for m in self.modules(): |
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if isinstance(m, Bottleneck): |
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nn.init.constant_(m.bn3.weight, 0) |
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elif isinstance(m, BasicBlock): |
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nn.init.constant_(m.bn2.weight, 0) |
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def _make_layer(self, block, planes, blocks, stride=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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conv1x1(self.inplanes, planes * block.expansion, stride), |
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nn.BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample)) |
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self.inplanes = planes * block.expansion |
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for _ in range(1, blocks): |
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layers.append(block(self.inplanes, planes)) |
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return nn.Sequential(*layers) |
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def interpolate(self, img, factor): |
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return F.interpolate(F.interpolate(img, scale_factor=factor, mode='nearest', recompute_scale_factor=True), scale_factor=1/factor, mode='nearest', recompute_scale_factor=True) |
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def forward(self, x): |
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b, t, _, h, w = x.shape |
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x = x.view(b * t, 3, h, w) |
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NPR = x - self.interpolate(x, 0.5) |
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x = self.conv1(NPR*2.0/3.0) |
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x = self.bn1(x) |
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x = self.relu(x) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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x = self.fc1(x) |
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x = x.view(b, t, -1) |
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x = x.mean(1) |
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return x |
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def infer(self, x): |
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b, t, _, h, w = x.shape |
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x = x.view(b * t, 3, h, w) |
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NPR = x - self.interpolate(x, 0.5) |
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x = self.conv1(NPR*2.0/3.0) |
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x = self.bn1(x) |
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x = self.relu(x) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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x = self.fc1(x) |
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x = x.view(b, t, -1) |
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x = x.mean(1) |
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return x |
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def resnet18_npr(pretrained=False, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) |
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return model |
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def resnet34_npr(pretrained=False, **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) |
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return model |
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def resnet50_npr(pretrained=False, **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model_state = torch.load('/ossfs/workspace/aigc_video/weights/resnet50-19c8e357.pth') |
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model.load_state_dict(model_state, strict=False) |
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return model |
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def resnet101_npr(pretrained=False, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) |
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return model |
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def resnet152_npr(pretrained=False, **kwargs): |
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"""Constructs a ResNet-152 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) |
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return model |
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