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21e2ec3 | 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 | """ShadeNet-2 generator (standalone, no Lightning dependency).
ParallelUNet: dual parallel encoders (vanilla UNet + frozen MobileNetV2),
fused decoder -> 8ch intrinsic maps in [-1, 1]:
[0:3] albedo | [3:4] relative depth (0=near) | [4:7] normal | [7:8] shading
"""
import torch
import torch.nn as nn
import torchvision
from torchvision.models.feature_extraction import create_feature_extractor
OUT_CH = 8
WIDTH_MULT = 1.45
def _num_groups(ch: int) -> int:
g = min(32, ch)
while ch % g != 0:
g -= 1
return g
class ChannelLinear(nn.Module):
"""Per-channel learnable affine: y = x * weight + bias."""
def __init__(self, channels: int, init_scale: float = 0.01):
super().__init__()
self.weight = nn.Parameter(
torch.empty(1, channels, 1, 1).uniform_(-init_scale, init_scale) + 1.0
)
self.bias = nn.Parameter(
torch.empty(1, channels, 1, 1).uniform_(-init_scale, init_scale)
)
def forward(self, x):
return x * self.weight + self.bias
class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
g = _num_groups(out_ch)
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, (1, 3), padding=(0, 1), bias=False),
nn.GroupNorm(g, out_ch),
nn.ELU(inplace=True),
nn.Conv2d(out_ch, out_ch, (3, 1), padding=(1, 0), bias=False),
nn.GroupNorm(g, out_ch),
nn.ELU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class Down(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.pool = nn.AvgPool2d(2)
self.conv = DoubleConv(in_ch, out_ch)
def forward(self, x):
return self.conv(self.pool(x))
class Up(nn.Module):
def __init__(self, in_ch, out_ch, skip_ch=None):
super().__init__()
skip_ch = skip_ch or in_ch
self.up = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2)
self.conv = DoubleConv(out_ch + skip_ch, out_ch)
def forward(self, x, skip):
x = self.up(x)
return self.conv(torch.cat([skip, x], dim=1))
class ParallelUNet(nn.Module):
def __init__(self, in_ch=3, out_ch=OUT_CH, dropout=0.0,
width_mult=WIDTH_MULT):
super().__init__()
w = float(width_mult)
def C(n):
return max(8, int(round(n * w / 8.0) * 8))
self.u_inc = DoubleConv(in_ch, C(64))
self.u_down1 = Down(C(64), C(128))
self.u_down2 = Down(C(128), C(256))
self.u_down3 = Down(C(256), C(512))
self.u_down4 = Down(C(512), C(256))
self.u_down5 = Down(C(256), C(512))
mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
self.register_buffer("m_shift", 1.0 - 2.0 * mean)
self.register_buffer("m_scale", 1.0 / (2.0 * std))
mbnet = torchvision.models.mobilenet_v2(weights="IMAGENET1K_V1")
self.mobile = create_feature_extractor(
mbnet,
return_nodes={
"features.0": "m0",
"features.2": "m1",
"features.6": "m2",
"features.13": "m3",
"features.17": "m4",
},
)
for p in self.mobile.parameters():
p.requires_grad = False
self.bottleneck_fusion = DoubleConv(C(512) + 320, C(512))
self.dropout = nn.Dropout2d(p=dropout) if dropout > 0 else nn.Identity()
self.mem_u1 = ChannelLinear(C(64))
self.mem_u2 = ChannelLinear(C(128))
self.mem_u3 = ChannelLinear(C(256))
self.mem_u4 = ChannelLinear(C(512))
self.mem_u5 = ChannelLinear(C(256))
self.mem_u6 = ChannelLinear(C(512))
self.mem_b = ChannelLinear(C(512))
self.mem_d0 = ChannelLinear(C(256))
self.mem_d1 = ChannelLinear(C(256))
self.mem_d2 = ChannelLinear(C(256))
self.mem_d3 = ChannelLinear(C(128))
self.mem_d4 = ChannelLinear(C(64))
self.up0 = Up(C(512), C(256), skip_ch=C(256) + 96)
self.up1 = Up(C(256), C(256), skip_ch=C(512) + 32)
self.up2 = Up(C(256), C(256), skip_ch=C(256) + 24)
self.up3 = Up(C(256), C(128), skip_ch=C(128) + 32)
self.up4 = Up(C(128), C(64), skip_ch=C(64))
self.head = DoubleConv(C(64) + 3, C(32))
self.head_out = nn.Conv2d(C(32), out_ch, 3, padding=1)
def forward(self, x):
u1 = self.mem_u1(self.u_inc(x))
u2 = self.mem_u2(self.u_down1(u1))
u3 = self.mem_u3(self.u_down2(u2))
u4 = self.mem_u4(self.u_down3(u3))
u5 = self.mem_u5(self.u_down4(u4))
u6 = self.mem_u6(self.u_down5(u5))
x_imagenet = (x + self.m_shift) * self.m_scale
mf = self.mobile(x_imagenet)
m0, m1, m2, m3, m4 = mf["m0"], mf["m1"], mf["m2"], mf["m3"], mf["m4"]
b = self.mem_b(self.dropout(
self.bottleneck_fusion(torch.cat([u6, m4], dim=1))))
d0 = self.mem_d0(self.dropout(self.up0(b, torch.cat([u5, m3], dim=1))))
d1 = self.mem_d1(self.dropout(self.up1(d0, torch.cat([u4, m2], dim=1))))
d2 = self.mem_d2(self.dropout(self.up2(d1, torch.cat([u3, m1], dim=1))))
d3 = self.mem_d3(self.dropout(self.up3(d2, torch.cat([u2, m0], dim=1))))
d4 = self.mem_d4(self.dropout(self.up4(d3, u1)))
h = self.head(torch.cat([d4, x], dim=1))
return torch.tanh(self.head_out(h))
def load_shadenet2(checkpoint_path, device="cpu", use_ema=True,
width_mult=WIDTH_MULT) -> ParallelUNet:
"""Build the generator and load shadenet2.ckpt (Lightning or raw format).
Strips the `generator.` prefix from Lightning checkpoints and applies the
EMA shadow (validated best) unless use_ema=False.
"""
model = ParallelUNet(out_ch=OUT_CH, width_mult=width_mult)
ckpt = torch.load(checkpoint_path, map_location=device, weights_only=False)
sd = ckpt.get("state_dict", ckpt)
if any(k.startswith("generator.") for k in sd):
sd = {k[len("generator."):]: v for k, v in sd.items()
if k.startswith("generator.")}
model.load_state_dict(sd, strict=False)
if use_ema:
ema = ckpt.get("ema_generator") or {}
if ema:
model.load_state_dict(ema, strict=False)
print(f"Using EMA weights ({len(ema)} tensors).")
else:
print("No EMA shadow in checkpoint, using raw weights.")
model.eval().to(device)
return model
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