File size: 23,286 Bytes
caa56d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 |
"""
# author: Kangran ZHAO
# email: [email protected]
# date: 2024-0401
# description: Class for the Multi-attention Detector
Functions in the Class are summarized as:
1. __init__: Initialization
2. build_backbone: Backbone-building
3. build_loss: Loss-function-building
4. features: Feature-extraction
5. classifier: Classification
6. get_losses: Loss-computation
7. get_train_metrics: Training-metrics-computation
8. get_test_metrics: Testing-metrics-computation
9. forward: Forward-propagation
Reference:
@INPROCEEDINGS{9577592,
author={Zhao, Hanqing and Wei, Tianyi and Zhou, Wenbo and Zhang, Weiming and Chen, Dongdong and Yu, Nenghai},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Multi-attentional Deepfake Detection},
year={2021},
volume={},
number={},
pages={2185-2194},
keywords={Measurement;Semantics;Feature extraction;Forgery;Pattern recognition;Feeds;Task analysis},
doi={10.1109/CVPR46437.2021.00222}
}
Codes are modified based on GitHub repo https://github.com/yoctta/multiple-attention
"""
import random
import kornia
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from detectors import DETECTOR
from loss import LOSSFUNC
from metrics.base_metrics_class import calculate_metrics_for_train
from networks import BACKBONE
from sklearn import metrics
from .base_detector import AbstractDetector
@DETECTOR.register_module(module_name='multi_attention')
class MultiAttentionDetector(AbstractDetector):
def __init__(self, config):
super().__init__()
self.config = config
self.block_layer = {"b1": 1, "b2": 5, "b3": 9, "b4": 15, "b5": 21, "b6": 29, "b7": 31}
self.mid_dim = config["mid_dim"]
self.backbone = self.build_backbone(config)
self.loss_func = self.build_loss(config)
self.batch_cnt = 0
with torch.no_grad():
layer_outputs = self.features({"image": torch.zeros(1, 3, config["resolution"], config["resolution"])})
self.feature_layer = config["feature_layer"]
self.attention_layer = config["attention_layer"]
self.num_classes = config["backbone_config"]["num_classes"]
self.num_shallow_features = layer_outputs[self.feature_layer].shape[1]
self.num_attention_features = layer_outputs[self.attention_layer].shape[1]
self.num_final_features = layer_outputs["final"].shape[1]
self.num_attentions = config["num_attentions"]
self.AGDA = AGDA(kernel_size=config["AGDA"]["kernel_size"],
dilation=config["AGDA"]["dilation"],
sigma=config["AGDA"]["sigma"],
threshold=config["AGDA"]["threshold"],
zoom=config["AGDA"]["zoom"],
scale_factor=config["AGDA"]["scale_factor"],
noise_rate=config["AGDA"]["noise_rate"])
self.attention_generation = AttentionMap(self.num_attention_features, self.num_attentions)
self.attention_pooling = AttentionPooling()
self.texture_enhance = TextureEnhanceV1(self.num_shallow_features, self.num_attentions) # Todo
self.num_enhanced_features = self.texture_enhance.output_features
self.num_features_d = self.texture_enhance.output_features_d
self.projection_local = nn.Sequential(nn.Linear(self.num_attentions * self.num_enhanced_features, self.mid_dim),
nn.Hardswish(),
nn.Linear(self.mid_dim, self.mid_dim),
nn.Hardswish())
self.projection_final = nn.Sequential(nn.Linear(self.num_final_features, self.mid_dim),
nn.Hardswish())
self.ensemble_classifier_fc = nn.Sequential(nn.Linear(self.mid_dim * 2, self.mid_dim),
nn.Hardswish(),
nn.Linear(self.mid_dim, self.num_classes))
self.dropout = nn.Dropout(config["dropout_rate"], inplace=True)
self.dropout_final = nn.Dropout(config["dropout_rate_final"], inplace=True)
def build_backbone(self, config):
backbone_class = BACKBONE[config['backbone_name']]
model_config = config['backbone_config']
model_config['pretrained'] = self.config.get('pretrained', None)
backbone = backbone_class(model_config)
return backbone
def build_loss(self, config):
cls_loss_class = LOSSFUNC[config["loss_func"]["cls_loss"]]
ril_loss_class = LOSSFUNC[config["loss_func"]["ril_loss"]]
cls_loss_func = cls_loss_class()
ril_loss_func = ril_loss_class(M=config["num_attentions"],
N=config["loss_func"]["ril_params"]["N"],
alpha=config["loss_func"]["ril_params"]["alpha"],
alpha_decay=config["loss_func"]["ril_params"]["alpha_decay"],
decay_batch=config["batch_per_epoch"],
inter_margin=config["loss_func"]["ril_params"]["inter_margin"],
intra_margin=config["loss_func"]["ril_params"]["intra_margin"])
return {"cls": cls_loss_func, "ril": ril_loss_func, "weights": config["loss_func"]["weights"]}
def features(self, data_dict: dict) -> torch.tensor:
x = data_dict["image"]
layer_output = {}
for name, module in self.backbone.efficientnet.named_children():
if name == "_avg_pooling":
layer_output["final"] = x
break
elif name != "_blocks":
x = module(x)
else:
for i in range(len(module)):
x = module[i](x)
if i == self.block_layer["b1"]:
layer_output["b1"] = x
elif i == self.block_layer["b2"]:
layer_output["b2"] = x
elif i == self.block_layer["b3"]:
layer_output["b3"] = x
elif i == self.block_layer["b4"]:
layer_output["b4"] = x
elif i == self.block_layer["b5"]:
layer_output["b5"] = x
elif i == self.block_layer["b6"]:
layer_output["b6"] = x
elif i == self.block_layer["b7"]:
layer_output["b7"] = x
x = F.adaptive_avg_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
layer_output["logit"] = self.backbone.last_layer(x)
return layer_output
def classifier(self, features: torch.tensor) -> torch.tensor:
pass # do not overwrite this, since classifier structure has been written in self.forward()
def get_losses(self, data_dict: dict, pred_dict: dict) -> dict:
if self.batch_cnt <= self.config["backbone_nEpochs"] * self.config["batch_per_epoch"]:
label = data_dict["label"]
pred = pred_dict["cls"]
ce_loss = self.loss_func["cls"](pred, label)
return {"overall": ce_loss, "ce_loss": ce_loss}
else:
label = data_dict["label"]
pred = pred_dict["cls"]
feature_maps_d = pred_dict["feature_maps_d"]
attention_maps = pred_dict["attentions"]
ce_loss = self.loss_func["cls"](pred, label)
ril_loss = self.loss_func["ril"](feature_maps_d, attention_maps, label)
weights = self.loss_func["weights"]
over_all_loss = weights[0] * ce_loss + weights[1] * ril_loss
return {"overall": over_all_loss, "ce_loss": ce_loss, "ril_loss": ril_loss}
def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict:
label = data_dict['label']
pred = pred_dict['cls']
auc, eer, acc, ap = calculate_metrics_for_train(label.detach(), pred.detach())
metric_batch_dict = {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap}
return metric_batch_dict
def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict:
label = data_dict['label']
pred = pred_dict['cls']
auc, eer, acc, ap = calculate_metrics_for_train(label.detach(), pred.detach())
metric_batch_dict = {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap}
return metric_batch_dict
def forward(self, data_dict: dict, inference=False) -> dict:
self.batch_cnt += 1
if self.batch_cnt <= self.config["backbone_nEpochs"] * self.config["batch_per_epoch"]:
layer_output = self.features(data_dict)
pred = layer_output["logit"]
prob = torch.softmax(pred, dim=1)[:, 1]
pred_dict = {"cls": pred,
"prob": prob,
"feat": layer_output["final"]}
else:
if not inference: # use AGDA when training
with torch.no_grad():
layer_output = self.features(data_dict)
raw_attentions = layer_output[self.attention_layer]
attention_maps = self.attention_generation(raw_attentions)
data_dict["image"], _ = self.AGDA.agda(data_dict["image"], attention_maps)
# Get Attention Maps
layer_output = self.features(data_dict)
raw_attentions = layer_output[self.attention_layer]
attention_maps = self.attention_generation(raw_attentions)
# Get Textural Feature Matrix P
shallow_features = layer_output[self.feature_layer]
enhanced_features, feature_maps_d = self.texture_enhance(shallow_features, attention_maps)
textural_feature_matrix_p = self.attention_pooling(enhanced_features, attention_maps)
B, M, N = textural_feature_matrix_p.size()
feature_matrix = self.dropout(textural_feature_matrix_p).view(B, -1)
feature_matrix = self.projection_local(feature_matrix)
# Get Global Feature G
final = layer_output["final"]
attention_maps2 = attention_maps.sum(dim=1, keepdim=True) # [B, 1, H_A, W_A]
final = self.attention_pooling(final, attention_maps2, norm=1).squeeze(1) # [B, C_F]
final = self.projection_final(final)
final = F.hardswish(final)
# Get the Prediction by Ensemble Classifier
feature_matrix = torch.cat((feature_matrix, final), dim=1) # [B, 2 * mid_dim]
pred = self.ensemble_classifier_fc(feature_matrix) # [B, 2]
# Get probability
prob = torch.softmax(pred, dim=1)[:, 1]
pred_dict = {"cls": pred,
"prob": prob,
"feat": layer_output["final"],
"attentions": attention_maps,
"feature_maps_d": feature_maps_d}
return pred_dict
class AttentionMap(nn.Module):
def __init__(self, in_channels, num_attention):
super(AttentionMap, self).__init__()
self.register_buffer('mask', torch.zeros([1, 1, 24, 24]))
self.mask[0, 0, 2:-2, 2:-2] = 1
self.num_attentions = num_attention
self.conv_extract = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv2 = nn.Conv2d(in_channels, num_attention, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(num_attention)
def forward(self, x):
"""
Convert deep feature to attention map
Args:
x: extracted features
Returns:
attention_maps: conventionally 4 attention maps
"""
if self.num_attentions == 0:
return torch.ones([x.shape[0], 1, 1, 1], device=x.device)
x = self.conv_extract(x)
x = self.bn1(x)
x = F.relu(x, inplace=True)
x = self.conv2(x)
x = self.bn2(x)
x = F.elu(x) + 1
mask = F.interpolate(self.mask, (x.shape[2], x.shape[3]), mode='nearest')
return x * mask
class AttentionPooling(nn.Module):
def __init__(self):
super().__init__()
def forward(self, features, attentions, norm=2):
"""
Bilinear Attention Pooing, when used for
Args:
features: [Tensor in [B, C_F, H_F, W_F]] extracted feature maps, either shallow ones or deep ones ???
attentions: [Tensor in [B, M, H, W]] attention maps, conventionally 4 attention maps (M = 4)
norm: [int, default=2] 1 for deep features, 2 for shallow features
Returns:
feature_matrix: [Tensor in [B, M, C_F] or [B, M, 1]] P (shallow feature) or G (deep feature) ???
"""
feature_size = features.size()[-2:]
attention_size = attentions.size()[-2:]
if feature_size != attention_size:
attentions = F.interpolate(attentions, size=feature_size, mode='bilinear', align_corners=True)
if len(features.shape) == 4:
# In TextureEnhanceV1, in accordance with paper
feature_matrix = torch.einsum('imjk,injk->imn', attentions, features) # [B, M, C_F]
else:
# In TextureEnhanceV2
feature_matrix = torch.einsum('imjk,imnjk->imn', attentions, features)
if norm == 1: # Used for deep feature BAP
w = torch.sum(attentions + 1e-8, dim=(2, 3)).unsqueeze(-1)
feature_matrix /= w
elif norm == 2: # Used for shallow feature BAP
feature_matrix = F.normalize(feature_matrix, p=2, dim=-1)
return feature_matrix
class TextureEnhanceV1(nn.Module):
def __init__(self, num_features, num_attentions):
super().__init__()
# self.output_features=num_features
self.output_features = num_features * 4
self.output_features_d = num_features
self.conv0 = nn.Conv2d(num_features, num_features, 1)
self.conv1 = nn.Conv2d(num_features, num_features, 3, padding=1)
self.bn1 = nn.BatchNorm2d(num_features)
self.conv2 = nn.Conv2d(num_features * 2, num_features, 3, padding=1)
self.bn2 = nn.BatchNorm2d(2 * num_features)
self.conv3 = nn.Conv2d(num_features * 3, num_features, 3, padding=1)
self.bn3 = nn.BatchNorm2d(3 * num_features)
self.conv_last = nn.Conv2d(num_features * 4, num_features * 4, 1)
self.bn4 = nn.BatchNorm2d(4 * num_features)
self.bn_last = nn.BatchNorm2d(num_features * 4)
def forward(self, feature_maps, attention_maps=(1, 1)):
"""
Texture Enhancement Block V1, in accordance with description in paper
1. Local average pooling.
2. Residual local features.
3. Dense Net
Args:
feature_maps: [Tensor in [B, C', H', W']] extracted shallow features
attention_maps: [Tensor in [B, M, H_A, W_A]] calculated attention maps, or
[Tuple with two float elements] local average grid scale,
used for conduct local average pooling, local patch size is decided by attention map size.
Returns:
feature_maps: [Tensor in [B, C_1, H_1, W_1]] enhanced feature maps
feature_maps_d: [Tensor in [B, C', H_A, W_A]] textural information
"""
B, N, H, W = feature_maps.shape
if type(attention_maps) == tuple:
attention_size = (int(H * attention_maps[0]), int(W * attention_maps[1]))
else:
attention_size = (attention_maps.shape[2], attention_maps.shape[3])
feature_maps_d = F.adaptive_avg_pool2d(feature_maps, attention_size)
feature_maps = feature_maps - F.interpolate(feature_maps_d, (feature_maps.shape[2], feature_maps.shape[3]),
mode='nearest')
feature_maps0 = self.conv0(feature_maps)
feature_maps1 = self.conv1(F.relu(self.bn1(feature_maps0), inplace=True))
feature_maps1_ = torch.cat([feature_maps0, feature_maps1], dim=1)
feature_maps2 = self.conv2(F.relu(self.bn2(feature_maps1_), inplace=True))
feature_maps2_ = torch.cat([feature_maps1_, feature_maps2], dim=1)
feature_maps3 = self.conv3(F.relu(self.bn3(feature_maps2_), inplace=True))
feature_maps3_ = torch.cat([feature_maps2_, feature_maps3], dim=1)
feature_maps = self.bn_last(self.conv_last(F.relu(self.bn4(feature_maps3_), inplace=True)))
return feature_maps, feature_maps_d
class TextureEnhanceV2(nn.Module):
def __init__(self, num_features, num_attentions):
super().__init__()
self.output_features = num_features
self.output_features_d = num_features
self.conv_extract = nn.Conv2d(num_features, num_features, 3, padding=1)
self.conv0 = nn.Conv2d(num_features * num_attentions, num_features * num_attentions, 5, padding=2,
groups=num_attentions)
self.conv1 = nn.Conv2d(num_features * num_attentions, num_features * num_attentions, 3, padding=1,
groups=num_attentions)
self.bn1 = nn.BatchNorm2d(num_features * num_attentions)
self.conv2 = nn.Conv2d(num_features * 2 * num_attentions, num_features * num_attentions, 3, padding=1,
groups=num_attentions)
self.bn2 = nn.BatchNorm2d(2 * num_features * num_attentions)
self.conv3 = nn.Conv2d(num_features * 3 * num_attentions, num_features * num_attentions, 3, padding=1,
groups=num_attentions)
self.bn3 = nn.BatchNorm2d(3 * num_features * num_attentions)
self.conv_last = nn.Conv2d(num_features * 4 * num_attentions, num_features * num_attentions, 1,
groups=num_attentions)
self.bn4 = nn.BatchNorm2d(4 * num_features * num_attentions)
self.bn_last = nn.BatchNorm2d(num_features * num_attentions)
self.M = num_attentions
def cat(self, a, b):
B, C, H, W = a.shape
c = torch.cat([a.reshape(B, self.M, -1, H, W), b.reshape(B, self.M, -1, H, W)], dim=2).reshape(B, -1, H, W)
return c
def forward(self, feature_maps, attention_maps=(1, 1)):
"""
Args:
feature_maps: [Tensor in [B, N, H, W]] extracted feature maps from shallow layer
attention_maps: [Tensor in [B, M, H_A, W_A] or float of (H_ratio, W_ratio)] either extracted attention maps
or average pooling down-sampling ratio
Returns:
feature_maps, feature_maps_d: [Tensor in [B, M, N, H, W], Tensor in [B, N, H, W]] feature maps after dense
network and non-textural feature map D
"""
B, N, H, W = feature_maps.shape
if type(attention_maps) == tuple:
attention_size = (int(H * attention_maps[0]), int(W * attention_maps[1]))
else:
attention_size = (attention_maps.shape[2], attention_maps.shape[3])
feature_maps = self.conv_extract(feature_maps)
feature_maps_d = F.adaptive_avg_pool2d(feature_maps, attention_size)
if feature_maps.size(2) > feature_maps_d.size(2):
feature_maps = feature_maps - F.interpolate(feature_maps_d, (feature_maps.shape[2], feature_maps.shape[3]),
mode='nearest')
attention_maps = (
torch.tanh(F.interpolate(attention_maps.detach(), (H, W), mode='bilinear', align_corners=True))).unsqueeze(
2) if type(attention_maps) != tuple else 1
feature_maps = feature_maps.unsqueeze(1)
feature_maps = (feature_maps * attention_maps).reshape(B, -1, H, W)
feature_maps0 = self.conv0(feature_maps)
feature_maps1 = self.conv1(F.relu(self.bn1(feature_maps0), inplace=True))
feature_maps1_ = self.cat(feature_maps0, feature_maps1)
feature_maps2 = self.conv2(F.relu(self.bn2(feature_maps1_), inplace=True))
feature_maps2_ = self.cat(feature_maps1_, feature_maps2)
feature_maps3 = self.conv3(F.relu(self.bn3(feature_maps2_), inplace=True))
feature_maps3_ = self.cat(feature_maps2_, feature_maps3)
feature_maps = F.relu(self.bn_last(self.conv_last(F.relu(self.bn4(feature_maps3_), inplace=True))),
inplace=True)
feature_maps = feature_maps.reshape(B, -1, N, H, W)
return feature_maps, feature_maps_d
class AGDA(nn.Module):
def __init__(self, kernel_size, dilation, sigma, threshold, zoom, scale_factor, noise_rate):
super().__init__()
self.kernel_size = kernel_size
self.dilation = dilation
self.sigma = sigma
self.noise_rate = noise_rate
self.scale_factor = scale_factor
self.threshold = threshold
self.zoom = zoom
self.filter = kornia.filters.GaussianBlur2d((self.kernel_size, self.kernel_size), (self.sigma, self.sigma))
def mod_func(self, x):
threshold = random.uniform(*self.threshold) if type(self.threshold) == list else self.threshold
zoom = random.uniform(*self.zoom) if type(self.zoom) == list else self.zoom
bottom = torch.sigmoid((torch.tensor(0.) - threshold) * zoom)
return (torch.sigmoid((x - threshold) * zoom) - bottom) / (1 - bottom)
def soft_drop2(self, x, attention_map):
with torch.no_grad():
attention_map = self.mod_func(attention_map)
B, C, H, W = x.size()
xs = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=True)
xs = self.filter(xs)
xs += torch.randn_like(xs) * self.noise_rate
xs = F.interpolate(xs, (H, W), mode='bilinear', align_corners=True)
x = x * (1 - attention_map) + xs * attention_map
return x
def agda(self, X, attention_map):
with torch.no_grad():
attention_weight = torch.sum(attention_map, dim=(2, 3))
attention_map = F.interpolate(attention_map, (X.size(2), X.size(3)), mode="bilinear", align_corners=True)
attention_weight = torch.sqrt(attention_weight + 1)
index = torch.distributions.categorical.Categorical(attention_weight).sample()
index1 = index.view(-1, 1, 1, 1).repeat(1, 1, X.size(2), X.size(3))
attention_map = torch.gather(attention_map, 1, index1)
atten_max = torch.max(attention_map.view(attention_map.shape[0], 1, -1), 2)[0] + 1e-8
attention_map = attention_map / atten_max.view(attention_map.shape[0], 1, 1, 1)
return self.soft_drop2(X, attention_map), index
|