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from functools import partial
import numpy as np
import torch
from timm.models.efficientnet import tf_efficientnet_b4_ns, tf_efficientnet_b3_ns, \
tf_efficientnet_b5_ns, tf_efficientnet_b2_ns, tf_efficientnet_b6_ns, tf_efficientnet_b7_ns
from torch import nn
from torch.nn.modules.dropout import Dropout
from torch.nn.modules.linear import Linear
from torch.nn.modules.pooling import AdaptiveAvgPool2d
encoder_params = {
"tf_efficientnet_b3_ns": {
"features": 1536,
"init_op": partial(tf_efficientnet_b3_ns, pretrained=True, drop_path_rate=0.2)
},
"tf_efficientnet_b2_ns": {
"features": 1408,
"init_op": partial(tf_efficientnet_b2_ns, pretrained=False, drop_path_rate=0.2)
},
"tf_efficientnet_b4_ns": {
"features": 1792,
"init_op": partial(tf_efficientnet_b4_ns, pretrained=True, drop_path_rate=0.5)
},
"tf_efficientnet_b5_ns": {
"features": 2048,
"init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.2)
},
"tf_efficientnet_b4_ns_03d": {
"features": 1792,
"init_op": partial(tf_efficientnet_b4_ns, pretrained=True, drop_path_rate=0.3)
},
"tf_efficientnet_b5_ns_03d": {
"features": 2048,
"init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.3)
},
"tf_efficientnet_b5_ns_04d": {
"features": 2048,
"init_op": partial(tf_efficientnet_b5_ns, pretrained=True, drop_path_rate=0.4)
},
"tf_efficientnet_b6_ns": {
"features": 2304,
"init_op": partial(tf_efficientnet_b6_ns, pretrained=True, drop_path_rate=0.2)
},
"tf_efficientnet_b7_ns": {
"features": 2560,
"init_op": partial(tf_efficientnet_b7_ns, pretrained=True, drop_path_rate=0.2)
},
"tf_efficientnet_b6_ns_04d": {
"features": 2304,
"init_op": partial(tf_efficientnet_b6_ns, pretrained=True, drop_path_rate=0.4)
},
}
def setup_srm_weights(input_channels: int = 3) -> torch.Tensor:
"""Creates the SRM kernels for noise analysis."""
# note: values taken from Zhou et al., "Learning Rich Features for Image Manipulation Detection", CVPR2018
srm_kernel = torch.from_numpy(np.array([
[ # srm 1/2 horiz
[0., 0., 0., 0., 0.], # noqa: E241,E201
[0., 0., 0., 0., 0.], # noqa: E241,E201
[0., 1., -2., 1., 0.], # noqa: E241,E201
[0., 0., 0., 0., 0.], # noqa: E241,E201
[0., 0., 0., 0., 0.], # noqa: E241,E201
], [ # srm 1/4
[0., 0., 0., 0., 0.], # noqa: E241,E201
[0., -1., 2., -1., 0.], # noqa: E241,E201
[0., 2., -4., 2., 0.], # noqa: E241,E201
[0., -1., 2., -1., 0.], # noqa: E241,E201
[0., 0., 0., 0., 0.], # noqa: E241,E201
], [ # srm 1/12
[-1., 2., -2., 2., -1.], # noqa: E241,E201
[2., -6., 8., -6., 2.], # noqa: E241,E201
[-2., 8., -12., 8., -2.], # noqa: E241,E201
[2., -6., 8., -6., 2.], # noqa: E241,E201
[-1., 2., -2., 2., -1.], # noqa: E241,E201
]
])).float()
srm_kernel[0] /= 2
srm_kernel[1] /= 4
srm_kernel[2] /= 12
return srm_kernel.view(3, 1, 5, 5).repeat(1, input_channels, 1, 1)
def setup_srm_layer(input_channels: int = 3) -> torch.nn.Module:
"""Creates a SRM convolution layer for noise analysis."""
weights = setup_srm_weights(input_channels)
conv = torch.nn.Conv2d(input_channels, out_channels=3, kernel_size=5, stride=1, padding=2, bias=False)
with torch.no_grad():
conv.weight = torch.nn.Parameter(weights, requires_grad=False)
return conv
class DeepFakeClassifierSRM(nn.Module):
def __init__(self, encoder, dropout_rate=0.5) -> None:
super().__init__()
self.encoder = encoder_params[encoder]["init_op"]()
self.avg_pool = AdaptiveAvgPool2d((1, 1))
self.srm_conv = setup_srm_layer(3)
self.dropout = Dropout(dropout_rate)
self.fc = Linear(encoder_params[encoder]["features"], 1)
def forward(self, x):
noise = self.srm_conv(x)
x = self.encoder.forward_features(noise)
x = self.avg_pool(x).flatten(1)
x = self.dropout(x)
x = self.fc(x)
return x
class GlobalWeightedAvgPool2d(nn.Module):
"""
Global Weighted Average Pooling from paper "Global Weighted Average
Pooling Bridges Pixel-level Localization and Image-level Classification"
"""
def __init__(self, features: int, flatten=False):
super().__init__()
self.conv = nn.Conv2d(features, 1, kernel_size=1, bias=True)
self.flatten = flatten
def fscore(self, x):
m = self.conv(x)
m = m.sigmoid().exp()
return m
def norm(self, x: torch.Tensor):
return x / x.sum(dim=[2, 3], keepdim=True)
def forward(self, x):
input_x = x
x = self.fscore(x)
x = self.norm(x)
x = x * input_x
x = x.sum(dim=[2, 3], keepdim=not self.flatten)
return x
class DeepFakeClassifier(nn.Module):
def __init__(self, encoder, dropout_rate=0.0) -> None:
super().__init__()
self.encoder = encoder_params[encoder]["init_op"]()
self.avg_pool = AdaptiveAvgPool2d((1, 1))
self.dropout = Dropout(dropout_rate)
self.fc = Linear(encoder_params[encoder]["features"], 1)
def forward(self, x):
x = self.encoder.forward_features(x)
x = self.avg_pool(x).flatten(1)
x = self.dropout(x)
x = self.fc(x)
return x
class DeepFakeClassifierGWAP(nn.Module):
def __init__(self, encoder, dropout_rate=0.5) -> None:
super().__init__()
self.encoder = encoder_params[encoder]["init_op"]()
self.avg_pool = GlobalWeightedAvgPool2d(encoder_params[encoder]["features"])
self.dropout = Dropout(dropout_rate)
self.fc = Linear(encoder_params[encoder]["features"], 1)
def forward(self, x):
x = self.encoder.forward_features(x)
x = self.avg_pool(x).flatten(1)
x = self.dropout(x)
x = self.fc(x)
return x |