File size: 6,399 Bytes
35ec68e | 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 | """Backbone (IR-50) split into F_c / F_s, and the U-Net feature-inversion network.
The IR-50 definition follows face.evoLVe / AdaFace exactly so the pretrained
AdaFace MS1Mv2 checkpoint loads without surgery. The client encoder F_c is the
stem plus the first residual stage, whose output is 64x56x56 -- the intermediate
feature used throughout DecoyFace (paper Sec. V-B). F_s is the remainder.
"""
from collections import namedtuple
import torch
import torch.nn as nn
# ----------------------------- IR-50 backbone ------------------------------ #
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class BasicBlockIR(nn.Module):
def __init__(self, in_channel, depth, stride):
super().__init__()
if in_channel == depth:
self.shortcut_layer = nn.MaxPool2d(1, stride)
else:
self.shortcut_layer = nn.Sequential(
nn.Conv2d(in_channel, depth, (1, 1), stride, bias=False),
nn.BatchNorm2d(depth))
self.res_layer = nn.Sequential(
nn.BatchNorm2d(in_channel),
nn.Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
nn.BatchNorm2d(depth),
nn.PReLU(depth),
nn.Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
nn.BatchNorm2d(depth))
def forward(self, x):
return self.res_layer(x) + self.shortcut_layer(x)
Bottleneck = namedtuple('Block', ['in_channel', 'depth', 'stride'])
def get_block(in_channel, depth, num_units, stride=2):
return [Bottleneck(in_channel, depth, stride)] + \
[Bottleneck(depth, depth, 1) for _ in range(num_units - 1)]
def get_blocks_ir50():
return [
get_block(64, 64, 3), # stage 1 -> 64 x 56 x 56
get_block(64, 128, 4), # stage 2 -> 128 x 28 x 28
get_block(128, 256, 14), # stage 3 -> 256 x 14 x 14
get_block(256, 512, 3), # stage 4 -> 512 x 7 x 7
]
class IR50(nn.Module):
"""Full IR-50 recognizer (input 112x112 -> 512-d embedding)."""
def __init__(self):
super().__init__()
self.input_layer = nn.Sequential(
nn.Conv2d(3, 64, (3, 3), 1, 1, bias=False),
nn.BatchNorm2d(64), nn.PReLU(64))
modules = []
for block in get_blocks_ir50():
for b in block:
modules.append(BasicBlockIR(b.in_channel, b.depth, b.stride))
self.body = nn.Sequential(*modules)
self.output_layer = nn.Sequential(
nn.BatchNorm2d(512), nn.Dropout(0.4), Flatten(),
nn.Linear(512 * 7 * 7, 512), nn.BatchNorm1d(512, affine=False))
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
return self.output_layer(x)
# The first residual stage (stage 1) spans body[0:3]; splitting there yields the
# 64x56x56 intermediate feature the paper transmits.
SPLIT_INDEX = 3
def adaface_preprocess(x_rgb01):
"""RGB image in [0,1] -> AdaFace input (BGR, normalized to [-1,1])."""
x_bgr = x_rgb01.flip(1) # RGB -> BGR on the channel dim
return (x_bgr - 0.5) / 0.5
class ClientEncoder(nn.Module):
"""F_c: AdaFace stem + stage 1. Frozen. Consumes RGB [0,1], emits 64x56x56."""
def __init__(self, ir50: IR50):
super().__init__()
self.input_layer = ir50.input_layer
self.stage1 = ir50.body[:SPLIT_INDEX]
def forward(self, x_rgb01):
x = adaface_preprocess(x_rgb01)
x = self.input_layer(x)
return self.stage1(x)
class ServerBackbone(nn.Module):
"""F_s: AdaFace stage 2-4 + output layer. 64x56x56 -> 512-d embedding."""
def __init__(self, ir50: IR50):
super().__init__()
self.rest = ir50.body[SPLIT_INDEX:]
self.output_layer = ir50.output_layer
def forward(self, feat):
return self.output_layer(self.rest(feat))
def load_ir50(ckpt_path, device):
net = IR50()
sd = torch.load(ckpt_path, map_location="cpu")
if isinstance(sd, dict) and "state_dict" in sd:
sd = sd["state_dict"]
missing, unexpected = net.load_state_dict(sd, strict=True)
net.eval().to(device)
return net
# ------------------------------- U-Net ------------------------------------- #
class ConvBlock(nn.Module):
def __init__(self, cin, cout, norm="batch"):
super().__init__()
if norm == "batch":
norm1 = nn.BatchNorm2d(cout)
norm2 = nn.BatchNorm2d(cout)
elif norm == "instance":
norm1 = nn.InstanceNorm2d(cout)
norm2 = nn.InstanceNorm2d(cout)
else:
raise ValueError(norm)
self.net = nn.Sequential(
nn.Conv2d(cin, cout, 3, 1, 1, bias=False),
norm1, nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(cout, cout, 3, 1, 1, bias=False),
norm2, nn.LeakyReLU(0.2, inplace=True))
def forward(self, x):
return self.net(x)
class UNetInverter(nn.Module):
"""Feature-inversion U-Net: 64x56x56 feature -> 3x112x112 RGB image in [0,1].
Encoder-decoder with skip connections over the feature map, plus a final
upsampling stage to reach the 112x112 image resolution.
"""
def __init__(self, in_ch=64, norm="batch"):
super().__init__()
self.enc0 = ConvBlock(in_ch, 64, norm) # 56
self.down1 = nn.MaxPool2d(2) # 28
self.enc1 = ConvBlock(64, 128, norm)
self.down2 = nn.MaxPool2d(2) # 14
self.enc2 = ConvBlock(128, 256, norm)
self.bott = ConvBlock(256, 256, norm)
self.up2 = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
self.dec2 = ConvBlock(256 + 128, 128, norm) # 28
self.up1 = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
self.dec1 = ConvBlock(128 + 64, 64, norm) # 56
self.up0 = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
self.dec0 = ConvBlock(64, 32, norm) # 112
self.head = nn.Conv2d(32, 3, 3, 1, 1)
def forward(self, feat):
e0 = self.enc0(feat)
e1 = self.enc1(self.down1(e0))
e2 = self.enc2(self.down2(e1))
b = self.bott(e2)
d2 = self.dec2(torch.cat([self.up2(b), e1], 1))
d1 = self.dec1(torch.cat([self.up1(d2), e0], 1))
d0 = self.dec0(self.up0(d1))
return torch.sigmoid(self.head(d0))
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