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from functools import partial
import torch
from torch import nn
import torch.nn.functional as F
from .Advanced_Network_Helpers import *
class Unet(nn.Module):
def __init__(
self,
dim,
init_dim=None,
out_dim=None,
dim_mults=(1, 2, 4, 8),
channels=3,
with_time_emb=True,
resnet_block_groups=8,
use_convnext=True,
convnext_mult=2,
):
super().__init__()
# determine dimensions
self.channels = channels # since we are concatenating the images and the conditionings along the channel dimension
init_dim = default(init_dim, dim // 3 * 2)
self.init_conv = nn.Conv2d(self.channels * 2, init_dim, 7, padding=3)
self.conditioning_init = nn.Conv2d(self.channels, init_dim, 7, padding=3)
dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
in_out = list(zip(dims[:-1], dims[1:]))
self.in_out = in_out
if use_convnext:
block_klass = partial(ConvNextBlock, mult=convnext_mult)
else:
block_klass = partial(ResnetBlock, groups=resnet_block_groups)
# time embeddings
if with_time_emb:
time_dim = dim * 4
self.time_mlp = nn.Sequential(
SinusoidalPositionEmbeddings(dim),
nn.Linear(dim, time_dim),
nn.GELU(),
nn.Linear(time_dim, time_dim),
)
else:
time_dim = None
self.time_mlp = None
# layers
self.downs = nn.ModuleList([])
self.ups = nn.ModuleList([])
self.conditioning_encoder = nn.ModuleList([])
num_resolutions = len(in_out)
self.num_resolutions = num_resolutions
# conditioning encoder
for ind, (dim_in, dim_out) in enumerate(in_out):
is_last = ind >= (num_resolutions - 1)
self.conditioning_encoder.append(
nn.ModuleList(
[
block_klass(dim_in, dim_out),
Residual(PreNorm(dim_out, LinearAttention(dim_out))),
Downsample(dim_out) if not is_last else nn.Identity(),
]
)
)
for ind, (dim_in, dim_out) in enumerate(in_out):
is_last = ind >= (num_resolutions - 1)
self.downs.append(
nn.ModuleList(
[
block_klass(dim_in, dim_out, time_emb_dim=time_dim),
block_klass(dim_out, dim_out, time_emb_dim=time_dim),
Residual(PreNorm(dim_out, LinearAttention(dim_out))),
Downsample(dim_out) if not is_last else nn.Identity(),
]
)
)
mid_dim = dims[-1]
self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)
self.cross_attention_1 = Residual(
PreNorm(mid_dim, LinearCrossAttention(mid_dim))
)
self.cross_attention_2 = Residual(
PreNorm(mid_dim, LinearCrossAttention(mid_dim))
)
self.cross_attention_3 = Residual(
PreNorm(mid_dim, LinearCrossAttention(mid_dim))
)
self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim=time_dim)
for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
is_last = ind >= (num_resolutions - 1)
self.ups.append(
nn.ModuleList(
[
block_klass(dim_out * 2, dim_in, time_emb_dim=time_dim),
block_klass(dim_in, dim_in, time_emb_dim=time_dim),
Residual(PreNorm(dim_in, LinearAttention(dim_in))),
Upsample(dim_in) if not is_last else nn.Identity(),
]
)
)
out_dim = default(out_dim, channels)
self.final_conv = nn.Sequential(
block_klass(dim, dim), nn.Conv2d(dim, out_dim, 1)
)
def forward(self, x, time, implicit_conditioning, explicit_conditioning):
x = torch.cat((x, explicit_conditioning), dim=1)
x = self.init_conv(x)
conditioning = self.conditioning_init(implicit_conditioning)
t = self.time_mlp(time) if exists(self.time_mlp) else None
h = []
# conditioning encoder
for block1, attn, downsample in self.conditioning_encoder:
conditioning = block1(conditioning)
conditioning = attn(conditioning)
conditioning = downsample(conditioning)
for block1, block2, attn, downsample in self.downs:
x = block1(x, t)
x = block2(x, t)
x = attn(x)
h.append(x)
x = downsample(x)
# reverse the c list
# bottleneck
x = self.cross_attention_1(x, conditioning)
x = self.mid_block1(x, t)
x = self.cross_attention_2(x, conditioning)
x = self.mid_block2(x, t)
x = self.cross_attention_3(x, conditioning)
for block1, block2, attn, upsample in self.ups:
x = torch.cat((x, h.pop()), dim=1)
x = block1(x, t)
x = block2(x, t)
x = attn(x)
x = upsample(x)
return self.final_conv(x)
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