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import torch.nn as nn
import torch.nn.functional as F
# ---------------------------------------------------------------------------
# 0. Normalization helpers
# ---------------------------------------------------------------------------
def get_norm_layer(norm_type: str, num_channels: int):
if norm_type == "instance":
return nn.InstanceNorm2d(num_channels, affine=True)
elif norm_type == "batch":
return nn.BatchNorm2d(num_channels)
else:
raise ValueError(f"Invalid norm_type: {norm_type}")
# ---------------------------------------------------------------------------
# 1. Basic Conv Block: Conv3x3 -> Norm -> ReLU (x2)
# ---------------------------------------------------------------------------
class BasicBlock(nn.Module):
def __init__(self, in_ch: int, out_ch: int, norm_type: str = "instance", dilation: int = 1):
super().__init__()
pad = dilation
self.conv1 = nn.Conv2d(in_ch, out_ch, 3, padding=pad, dilation=dilation)
self.norm1 = get_norm_layer(norm_type, out_ch)
self.conv2 = nn.Conv2d(out_ch, out_ch, 3, padding=pad, dilation=dilation)
self.norm2 = get_norm_layer(norm_type, out_ch)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.relu(self.norm1(self.conv1(x)))
x = self.relu(self.norm2(self.conv2(x)))
return x
# ---------------------------------------------------------------------------
# 2. DCSA Block (paper Sec. 3.2)
# Channel: avg-pool + two 1x1 convs, ADD back to features
# Spatial: avg+max concat → standard 3x3 path + dilated 3x3 path (×0.1) → sum
# ---------------------------------------------------------------------------
class ChannelAttention(nn.Module):
"""Paper: average pooling + two consecutive 1x1 convs; correlations added back."""
def __init__(self, channels: int, reduction: int = 8):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
hidden = max(channels // reduction, 4)
self.mlp = nn.Sequential(
nn.Conv2d(channels, hidden, 1),
nn.ReLU(inplace=True),
nn.Conv2d(hidden, channels, 1),
)
def forward(self, x):
attn = self.mlp(self.avg_pool(x))
return x + attn
class SpatialAttention(nn.Module):
"""Paper: avg+max concat, then standard and dilated 3x3-norm-relu paths; dilated ×0.1."""
def __init__(self, channels: int, dilation: int = 3, factor: float = 0.1, norm_type: str = "batch"):
super().__init__()
self.factor = factor
# Input to both paths is 2-channel (avg || max); map to `channels` then back to attention map
self.std_path = nn.Sequential(
nn.Conv2d(2, channels, 3, padding=1, dilation=1),
get_norm_layer(norm_type, channels),
nn.ReLU(inplace=True),
nn.Conv2d(channels, 1, 1),
)
pad_d = dilation
self.dil_path = nn.Sequential(
nn.Conv2d(2, channels, 3, padding=pad_d, dilation=dilation),
get_norm_layer(norm_type, channels),
nn.ReLU(inplace=True),
nn.Conv2d(channels, 1, 1),
)
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
concat = torch.cat([avg_out, max_out], dim=1)
attn = self.std_path(concat) + self.factor * self.dil_path(concat)
return x + attn
class DCSABlock(nn.Module):
def __init__(self, channels: int, factor: float = 0.1, norm_type: str = "batch"):
super().__init__()
self.channel_attn = ChannelAttention(channels)
self.spatial_attn = SpatialAttention(channels, factor=factor, norm_type=norm_type)
def forward(self, x):
x = self.channel_attn(x)
x = self.spatial_attn(x)
return x
# ---------------------------------------------------------------------------
# 3. Reconstruction Sub-network (Encoder-Decoder + Instance Norm + DCSA)
# ---------------------------------------------------------------------------
class ReconstructionNet(nn.Module):
def __init__(self, in_ch: int = 3, base_ch: int = 32, norm_type: str = "instance",
dcsa_factor: float = 0.1):
super().__init__()
chs = [base_ch, base_ch * 2, base_ch * 4, base_ch * 8]
self.level_channels = [chs[0], chs[1], chs[2]]
self.enc1 = BasicBlock(in_ch, chs[0], norm_type)
self.dcsa1 = DCSABlock(chs[0], factor=dcsa_factor, norm_type=norm_type)
self.enc2 = BasicBlock(chs[0], chs[1], norm_type)
self.dcsa2 = DCSABlock(chs[1], factor=dcsa_factor, norm_type=norm_type)
self.enc3 = BasicBlock(chs[1], chs[2], norm_type)
self.dcsa3 = DCSABlock(chs[2], factor=dcsa_factor, norm_type=norm_type)
self.enc4 = BasicBlock(chs[2], chs[3], norm_type)
self.dcsa4 = DCSABlock(chs[3], factor=dcsa_factor, norm_type=norm_type)
self.pool = nn.MaxPool2d(2)
self.up3 = nn.ConvTranspose2d(chs[3], chs[2], 2, stride=2)
self.dec3 = BasicBlock(chs[2] * 2, chs[2], norm_type)
self.dcsa_d3 = DCSABlock(chs[2], factor=dcsa_factor, norm_type=norm_type)
self.up2 = nn.ConvTranspose2d(chs[2], chs[1], 2, stride=2)
self.dec2 = BasicBlock(chs[1] * 2, chs[1], norm_type)
self.dcsa_d2 = DCSABlock(chs[1], factor=dcsa_factor, norm_type=norm_type)
self.up1 = nn.ConvTranspose2d(chs[1], chs[0], 2, stride=2)
self.dec1 = BasicBlock(chs[0] * 2, chs[0], norm_type)
self.dcsa_d1 = DCSABlock(chs[0], factor=dcsa_factor, norm_type=norm_type)
self.out_img = nn.Conv2d(chs[0], in_ch, 1)
self.up3_jnd = nn.ConvTranspose2d(chs[3], chs[2], 2, stride=2)
self.dec3_jnd = BasicBlock(chs[2] * 2, chs[2], norm_type)
self.dcsa_d3j = DCSABlock(chs[2], factor=dcsa_factor, norm_type=norm_type)
self.up2_jnd = nn.ConvTranspose2d(chs[2], chs[1], 2, stride=2)
self.dec2_jnd = BasicBlock(chs[1] * 2, chs[1], norm_type)
self.dcsa_d2j = DCSABlock(chs[1], factor=dcsa_factor, norm_type=norm_type)
self.up1_jnd = nn.ConvTranspose2d(chs[1], chs[0], 2, stride=2)
self.dec1_jnd = BasicBlock(chs[0] * 2, chs[0], norm_type)
self.dcsa_d1j = DCSABlock(chs[0], factor=dcsa_factor, norm_type=norm_type)
self.out_jnd = nn.Conv2d(chs[0], 1, 1)
def forward(self, x):
e1 = self.dcsa1(self.enc1(x))
p1 = self.pool(e1)
e2 = self.dcsa2(self.enc2(p1))
p2 = self.pool(e2)
e3 = self.dcsa3(self.enc3(p2))
p3 = self.pool(e3)
e4 = self.dcsa4(self.enc4(p3))
d3 = self.dcsa_d3(self.dec3(torch.cat([self.up3(e4), e3], dim=1)))
d2 = self.dcsa_d2(self.dec2(torch.cat([self.up2(d3), e2], dim=1)))
d1 = self.dcsa_d1(self.dec1(torch.cat([self.up1(d2), e1], dim=1)))
rec_img = torch.sigmoid(self.out_img(d1))
d3j = self.dcsa_d3j(self.dec3_jnd(torch.cat([self.up3_jnd(e4), e3], dim=1)))
d2j = self.dcsa_d2j(self.dec2_jnd(torch.cat([self.up2_jnd(d3j), e2], dim=1)))
d1j = self.dcsa_d1j(self.dec1_jnd(torch.cat([self.up1_jnd(d2j), e1], dim=1)))
rec_jnd = torch.sigmoid(self.out_jnd(d1j))
feats_inp = [e1, e2, e3]
feats_rec = [d1, d2, d3]
feats_jnd = [d1j, d2j, d3j]
return rec_img, rec_jnd, feats_inp, feats_rec, feats_jnd
# ---------------------------------------------------------------------------
# 4. DiffNeck — (Rec,Inp), (JND,Inp), (Rec,JND,Inp); separate paths ×0.1
# ---------------------------------------------------------------------------
class DiffNeckLevel(nn.Module):
def __init__(self, in_ch: int, out_ch: int, factor: float = 0.1, norm_type: str = "batch"):
super().__init__()
self.factor = factor
self.block_rec_inp = BasicBlock(in_ch * 2, out_ch, norm_type) # (Rec, Inp)
self.block_jnd_inp = BasicBlock(in_ch * 2, out_ch, norm_type) # (JND, Inp)
self.block_all = BasicBlock(in_ch * 3, out_ch, norm_type) # (Rec, JND, Inp)
def forward(self, rec_feat, inp_feat, jnd_feat):
d_rec_inp = self.block_rec_inp(torch.cat([rec_feat, inp_feat], dim=1))
d_jnd_inp = self.block_jnd_inp(torch.cat([jnd_feat, inp_feat], dim=1))
d_all = self.block_all(torch.cat([rec_feat, jnd_feat, inp_feat], dim=1))
# Separate paths balanced by factor 0.1; unified path kept at full weight
return self.factor * (d_rec_inp + d_jnd_inp) + d_all
class DiffNeckMultiScale(nn.Module):
"""Alias-friendly name; also exposed as DiffNeck."""
def __init__(self, channels_list, out_ch: int, factor: float = 0.1, norm_type: str = "batch"):
super().__init__()
self.levels = nn.ModuleList([
DiffNeckLevel(ch, out_ch, factor=factor, norm_type=norm_type) for ch in channels_list
])
self.out_ch = out_ch
def forward(self, feats_rec, feats_inp, feats_jnd):
target_size = feats_inp[0].shape[-2:]
descriptor = None
for level_module, rec_f, inp_f, jnd_f in zip(self.levels, feats_rec, feats_inp, feats_jnd):
level_out = level_module(rec_f, inp_f, jnd_f)
if level_out.shape[-2:] != target_size:
level_out = F.interpolate(level_out, size=target_size, mode="bilinear", align_corners=False)
descriptor = level_out if descriptor is None else descriptor + level_out
return descriptor
# Public alias so notebooks can `from models.model import DiffNeck`
DiffNeck = DiffNeckMultiScale
# ---------------------------------------------------------------------------
# 5. Localization Sub-network (6 scales, dilated Basic+DCSA at scales 3–6, BN)
# ---------------------------------------------------------------------------
class LocalizationNet(nn.Module):
def __init__(self, in_ch: int = 6, base_ch: int = 32, num_classes: int = 4,
dilation_rates=(1, 1, 2, 4, 8, 16), norm_type: str = "batch",
dcsa_factor: float = 0.1):
super().__init__()
chs = [base_ch, base_ch * 2, base_ch * 4, base_ch * 4, base_ch * 8, base_ch * 8]
rates = list(dilation_rates)
self.enc1 = BasicBlock(in_ch, chs[0], norm_type, dilation=rates[0])
self.dcsa1 = DCSABlock(chs[0], factor=dcsa_factor, norm_type=norm_type)
self.enc2 = BasicBlock(chs[0], chs[1], norm_type, dilation=rates[1])
self.dcsa2 = DCSABlock(chs[1], factor=dcsa_factor, norm_type=norm_type)
self.enc3 = BasicBlock(chs[1], chs[2], norm_type, dilation=rates[2])
self.dcsa3 = DCSABlock(chs[2], factor=dcsa_factor, norm_type=norm_type)
self.enc4 = BasicBlock(chs[2], chs[3], norm_type, dilation=rates[3])
self.dcsa4 = DCSABlock(chs[3], factor=dcsa_factor, norm_type=norm_type)
self.enc5 = BasicBlock(chs[3], chs[4], norm_type, dilation=rates[4])
self.dcsa5 = DCSABlock(chs[4], factor=dcsa_factor, norm_type=norm_type)
self.enc6 = BasicBlock(chs[4], chs[5], norm_type, dilation=rates[5])
self.dcsa6 = DCSABlock(chs[5], factor=dcsa_factor, norm_type=norm_type)
self.pool = nn.MaxPool2d(2)
self.up5 = nn.ConvTranspose2d(chs[5], chs[4], 2, stride=2)
self.dec5 = BasicBlock(chs[4] * 2, chs[4], norm_type)
self.dcsa_d5 = DCSABlock(chs[4], factor=dcsa_factor, norm_type=norm_type)
self.up4 = nn.ConvTranspose2d(chs[4], chs[3], 2, stride=2)
self.dec4 = BasicBlock(chs[3] * 2, chs[3], norm_type)
self.dcsa_d4 = DCSABlock(chs[3], factor=dcsa_factor, norm_type=norm_type)
self.up3 = nn.ConvTranspose2d(chs[3], chs[2], 2, stride=2)
self.dec3 = BasicBlock(chs[2] * 2, chs[2], norm_type)
self.dcsa_d3 = DCSABlock(chs[2], factor=dcsa_factor, norm_type=norm_type)
self.up2 = nn.ConvTranspose2d(chs[2], chs[1], 2, stride=2)
self.dec2 = BasicBlock(chs[1] * 2, chs[1], norm_type)
self.dcsa_d2 = DCSABlock(chs[1], factor=dcsa_factor, norm_type=norm_type)
self.up1 = nn.ConvTranspose2d(chs[1], chs[0], 2, stride=2)
self.dec1 = BasicBlock(chs[0] * 2, chs[0], norm_type)
self.dcsa_d1 = DCSABlock(chs[0], factor=dcsa_factor, norm_type=norm_type)
self.out_seg = nn.Conv2d(chs[0], num_classes, 1)
self.enc_channels = chs
def forward(self, x):
e1 = self.dcsa1(self.enc1(x))
e2 = self.dcsa2(self.enc2(self.pool(e1)))
e3 = self.dcsa3(self.enc3(self.pool(e2)))
e4 = self.dcsa4(self.enc4(self.pool(e3)))
e5 = self.dcsa5(self.enc5(self.pool(e4)))
e6 = self.dcsa6(self.enc6(self.pool(e5)))
d5 = self.dcsa_d5(self.dec5(torch.cat([self.up5(e6), e5], dim=1)))
d4 = self.dcsa_d4(self.dec4(torch.cat([self.up4(d5), e4], dim=1)))
d3 = self.dcsa_d3(self.dec3(torch.cat([self.up3(d4), e3], dim=1)))
d2 = self.dcsa_d2(self.dec2(torch.cat([self.up2(d3), e2], dim=1)))
d1 = self.dcsa_d1(self.dec1(torch.cat([self.up1(d2), e1], dim=1)))
return self.out_seg(d1)
# ---------------------------------------------------------------------------
# 6. Full OmniAL model
# ---------------------------------------------------------------------------
class OmniAL(nn.Module):
def __init__(self, cfg: dict):
super().__init__()
m = cfg["model"]
base_ch = m["base_channels"]
factor = m.get("diffneck_factor", 0.1)
self.reconstruction = ReconstructionNet(
in_ch=m["in_channels"],
base_ch=base_ch,
norm_type=m["reconstruction_norm"],
dcsa_factor=factor,
)
self.diffneck = DiffNeckMultiScale(
channels_list=self.reconstruction.level_channels,
out_ch=base_ch,
factor=factor,
norm_type=m["localization_norm"],
)
# Loc input: RGB anomaly image + DiffNeck descriptor
self.localization = LocalizationNet(
in_ch=m["in_channels"] + base_ch,
base_ch=base_ch,
num_classes=4,
dilation_rates=tuple(m["dilation_rates"]),
norm_type=m["localization_norm"],
dcsa_factor=factor,
)
def forward(self, anomaly_img: torch.Tensor):
rec_img, rec_jnd, feats_inp, feats_rec, feats_jnd = self.reconstruction(anomaly_img)
descriptor = self.diffneck(feats_rec, feats_inp, feats_jnd)
if descriptor.shape[-2:] != anomaly_img.shape[-2:]:
descriptor = F.interpolate(
descriptor, size=anomaly_img.shape[-2:], mode="bilinear", align_corners=False
)
loc_input = torch.cat([anomaly_img, descriptor], dim=1)
seg_logits = self.localization(loc_input)
return {
"rec_img": rec_img,
"rec_jnd": rec_jnd,
"seg_logits": seg_logits,
}
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