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import torch.nn as nn
import math
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, time):
device = time.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = time[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, time_emb_dim):
super().__init__()
self.time_mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(time_emb_dim, out_channels * 2)
)
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
self.norm1 = nn.GroupNorm(32, out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
self.norm2 = nn.GroupNorm(32, out_channels)
self.residual_conv = nn.Conv2d(in_channels, out_channels, 1) if in_channels != out_channels else nn.Identity()
self.act = nn.SiLU()
def forward(self, x, time_emb):
h = self.conv1(x)
h = self.norm1(h)
h = self.act(h)
# Add time embedding
time_emb = self.time_mlp(time_emb)
time_emb = time_emb[:, :, None, None]
scale, shift = time_emb.chunk(2, dim=1)
h = h * (scale + 1) + shift
h = self.conv2(h)
h = self.norm2(h)
h = self.act(h)
return h + self.residual_conv(x)
class SelfAttention(nn.Module):
def __init__(self, channels):
super().__init__()
self.norm = nn.GroupNorm(32, channels)
self.qkv = nn.Conv2d(channels, channels * 3, 1)
self.out = nn.Conv2d(channels, channels, 1)
self.scale = 1.0 / math.sqrt(channels)
def forward(self, x):
b, c, h, w = x.shape
h_norm = self.norm(x)
qkv = self.qkv(h_norm)
q, k, v = qkv.chunk(3, dim=1)
q = q.reshape(b, c, h * w).transpose(-2, -1)
k = k.reshape(b, c, h * w)
v = v.reshape(b, c, h * w).transpose(-2, -1)
attn = torch.softmax(q @ k * self.scale, dim=-1)
out = attn @ v
out = out.transpose(-2, -1).reshape(b, c, h, w)
return x + self.out(out)
class UNet(nn.Module):
def __init__(self, img_size=64, in_channels=3, out_channels=3, base_channels=128, ch_mult=(1, 2, 4)):
super().__init__()
self.time_embed = SinusoidalPosEmb(base_channels)
self.time_mlp = nn.Sequential(
nn.Linear(base_channels, base_channels * 4),
nn.SiLU(),
nn.Linear(base_channels * 4, base_channels * 4)
)
# Initial convolution
self.init_conv = nn.Conv2d(in_channels, base_channels, 3, padding=1)
# Downsampling - store channel dims for skip connections
self.down_channels = []
self.down_blocks = nn.ModuleList([])
channels = base_channels
for i, mult in enumerate(ch_mult):
out_ch = base_channels * mult
self.down_channels.append(out_ch)
self.down_blocks.append(nn.ModuleList([
ResidualBlock(channels, out_ch, base_channels * 4),
ResidualBlock(out_ch, out_ch, base_channels * 4),
]))
channels = out_ch
if i < len(ch_mult) - 1:
self.down_blocks[-1].append(nn.Conv2d(out_ch, out_ch, 3, stride=2, padding=1))
else:
self.down_blocks[-1].append(nn.Identity())
# Bottleneck
self.bottleneck = nn.ModuleList([
ResidualBlock(channels, channels, base_channels * 4),
SelfAttention(channels),
ResidualBlock(channels, channels, base_channels * 4)
])
# Upsampling
self.up_blocks = nn.ModuleList([])
for i, mult in reversed(list(enumerate(ch_mult))):
out_ch = base_channels * mult
# Skip connections: match corresponding down block
# up_block[i] connects to down_block[i] (same resolution)
skip_ch = self.down_channels[i]
in_ch = channels + skip_ch
self.up_blocks.append(nn.ModuleList([
ResidualBlock(in_ch, out_ch, base_channels * 4),
ResidualBlock(out_ch, out_ch, base_channels * 4),
]))
channels = out_ch
if i > 0:
self.up_blocks[-1].append(nn.Upsample(scale_factor=2))
else:
self.up_blocks[-1].append(nn.Identity())
# Final convolution
self.final_conv = nn.Sequential(
nn.GroupNorm(32, base_channels),
nn.SiLU(),
nn.Conv2d(base_channels, out_channels, 3, padding=1)
)
def forward(self, x, t):
# Time embedding
t_emb = self.time_embed(t)
t_emb = self.time_mlp(t_emb)
# Initial conv
h = self.init_conv(x)
# Downsampling with skip connections
skips = []
for down_block in self.down_blocks:
res1, res2, downsample = down_block
h = res1(h, t_emb)
h = res2(h, t_emb)
skips.append(h)
h = downsample(h)
# Bottleneck
for layer in self.bottleneck:
if isinstance(layer, SelfAttention):
h = layer(h)
else:
h = layer(h, t_emb)
# Upsampling with skip connections
for i, up_block in enumerate(self.up_blocks):
res1, res2, upsample = up_block
# Concatenate skip connection (reverse order)
skip_idx = len(skips) - 1 - i
if skip_idx >= 0:
h = torch.cat([h, skips[skip_idx]], dim=1)
h = res1(h, t_emb)
h = res2(h, t_emb)
h = upsample(h)
return self.final_conv(h)
if __name__ == "__main__":
# Test the model with smaller batch size
print("Initializing UNet...")
model = UNet(img_size=64, base_channels=128)
total_params = sum(p.numel() for p in model.parameters())
print(f"Total parameters: {total_params:,}")
# Test with small batch
print("\nTesting forward pass...")
x = torch.randn(1, 3, 64, 64)
t = torch.randint(0, 1000, (1,))
with torch.no_grad():
output = model(x, t)
print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")
print("\nModel architecture verified successfully!")
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