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import torch.utils.model_zoo as model_zoo
import torch
import clip
import open_clip
from .srm_filter_kernel import all_normalized_hpf_list
import numpy as np
class HPF(nn.Module):
def __init__(self):
super(HPF, self).__init__()
#Load 30 SRM Filters
all_hpf_list_5x5 = []
for hpf_item in all_normalized_hpf_list:
if hpf_item.shape[0] == 3:
hpf_item = np.pad(hpf_item, pad_width=((1, 1), (1, 1)), mode='constant')
all_hpf_list_5x5.append(hpf_item)
hpf_weight = torch.Tensor(all_hpf_list_5x5).view(30, 1, 5, 5).contiguous()
hpf_weight = torch.nn.Parameter(hpf_weight.repeat(1, 3, 1, 1), requires_grad=False)
self.hpf = nn.Conv2d(3, 30, kernel_size=5, padding=2, bias=False)
self.hpf.weight = hpf_weight
def forward(self, input):
output = self.hpf(input)
return output
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=True):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(30, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
class AIDE_Model(nn.Module):
def __init__(self, resnet_path, convnext_path):
super(AIDE_Model, self).__init__()
self.hpf = HPF()
self.model_min = ResNet(Bottleneck, [3, 4, 6, 3])
self.model_max = ResNet(Bottleneck, [3, 4, 6, 3])
if resnet_path is not None:
pretrained_dict = torch.load(resnet_path, map_location='cpu')
model_min_dict = self.model_min.state_dict()
model_max_dict = self.model_max.state_dict()
for k in pretrained_dict.keys():
if k in model_min_dict and pretrained_dict[k].size() == model_min_dict[k].size():
model_min_dict[k] = pretrained_dict[k]
model_max_dict[k] = pretrained_dict[k]
else:
print(f"Skipping layer {k} because of size mismatch")
self.fc = Mlp(2048 + 256 , 1024, 2)
print("build model with convnext_xxl")
self.openclip_convnext_xxl, _, _ = open_clip.create_model_and_transforms(
"convnext_xxlarge", pretrained=convnext_path
)
self.openclip_convnext_xxl = self.openclip_convnext_xxl.visual.trunk
self.openclip_convnext_xxl.head.global_pool = nn.Identity()
self.openclip_convnext_xxl.head.flatten = nn.Identity()
self.openclip_convnext_xxl.eval()
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.convnext_proj = nn.Sequential(
nn.Linear(3072, 256),
)
for param in self.openclip_convnext_xxl.parameters():
param.requires_grad = False
def forward(self, x):
b, t, c, h, w = x.shape
x_minmin = x[:, 0] #[b, c, h, w]
x_maxmax = x[:, 1]
x_minmin1 = x[:, 2]
x_maxmax1 = x[:, 3]
tokens = x[:, 4]
x_minmin = self.hpf(x_minmin)
x_maxmax = self.hpf(x_maxmax)
x_minmin1 = self.hpf(x_minmin1)
x_maxmax1 = self.hpf(x_maxmax1)
with torch.no_grad():
clip_mean = torch.Tensor([0.48145466, 0.4578275, 0.40821073])
clip_mean = clip_mean.to(tokens, non_blocking=True).view(3, 1, 1)
clip_std = torch.Tensor([0.26862954, 0.26130258, 0.27577711])
clip_std = clip_std.to(tokens, non_blocking=True).view(3, 1, 1)
dinov2_mean = torch.Tensor([0.485, 0.456, 0.406]).to(tokens, non_blocking=True).view(3, 1, 1)
dinov2_std = torch.Tensor([0.229, 0.224, 0.225]).to(tokens, non_blocking=True).view(3, 1, 1)
local_convnext_image_feats = self.openclip_convnext_xxl(
tokens * (dinov2_std / clip_std) + (dinov2_mean - clip_mean) / clip_std
) #[b, 3072, 8, 8]
assert local_convnext_image_feats.size()[1:] == (3072, 8, 8)
local_convnext_image_feats = self.avgpool(local_convnext_image_feats).view(tokens.size(0), -1)
x_0 = self.convnext_proj(local_convnext_image_feats)
x_min = self.model_min(x_minmin)
x_max = self.model_max(x_maxmax)
x_min1 = self.model_min(x_minmin1)
x_max1 = self.model_max(x_maxmax1)
x_1 = (x_min + x_max + x_min1 + x_max1) / 4
x = torch.cat([x_0, x_1], dim=1)
x = self.fc(x)
return x
def AIDE(resnet_path, convnext_path):
model = AIDE_Model(resnet_path, convnext_path)
return model
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