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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# This script is from the following repositories
# https://github.com/ermongroup/ddim
# https://github.com/bahjat-kawar/ddrm
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels,in_channels,kernel_size=3,stride=1,padding=1)
#padding=1. The convoution won't change the input shape
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
#scale_factor. Perform up sampling to the input. The image size becomes (image_h*2) * (image_w*2)
if self.with_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels,in_channels,kernel_size=3,stride=2,padding=0)
def forward(self, x):
if self.with_conv:#whether use convoution to perform downsampling
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
#This is down sampling, The image size becomes image_h/2 * image_w/2
class ResnetBlock(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=1,padding=1)
self.temb_proj = torch.nn.Linear(temb_channels,out_channels)#projection
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv2d(out_channels,out_channels,kernel_size=3,stride=1,padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:#whether use convoution to perform residual
self.conv_shortcut = torch.nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=1,padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=1,padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
#temb=token embedding dimension
h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 1, stride = 1, padding = 0)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 1, stride = 1, padding = 0)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 1, stride = 1, padding = 0)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 1, stride = 1, padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h*w)
q = q.permute(0, 2, 1) # b,hw,c
k = k.reshape(b, c, h*w) # b,c,hw
w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h*w)
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
# b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = torch.bmm(v, w_)
h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
return x+h_
class MultiHeadAttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
num_heads = 8
assert in_channels % num_heads == 0, "in_channels must be divisible by num_heads"
self.in_channels = in_channels
self.num_heads = num_heads
self.head_dim = in_channels // num_heads
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# Split heads
b, c, h, w = q.shape
q = q.reshape(b, self.num_heads, self.head_dim, h * w).permute(0, 1, 3, 2) # b, num_heads, hw, head_dim
k = k.reshape(b, self.num_heads, self.head_dim, h * w) # b, num_heads, head_dim, hw
v = v.reshape(b, self.num_heads, self.head_dim, h * w) # b, num_heads, head_dim, hw
# Compute attention
w_ = torch.einsum('bnqd,bnkd->bnqk', q, k) # b, num_heads, hw, hw
w_ = w_ * (self.head_dim ** -0.5)
w_ = torch.nn.functional.softmax(w_, dim=-1)
# Attend to values
h_ = torch.einsum('bnqk,bnvd->bnqd', w_, v)
h_ = h_.permute(0, 1, 3, 2).reshape(b, c, h, w) # Merge heads
h_ = self.proj_out(h_)
return x + h_
class DiffusionUNet(nn.Module):
def __init__(self,ch, num_res_blocks, image_size, drop_out):
super().__init__()
self.dropout=drop_out
ch_mult = [1, 1, 2, 2, 4, 4]
attn_resolutions = [32,16,]
#only when the image becomes attn_resolutions*attn_resolutions, use the self attention.
resamp_with_conv = True
#dropout = config.model.dropout
#in_channels = config.model.in_channels * 2 if config.data.conditional else config.model.in_channels
in_channels = 2
out_ch = 1
resolution = image_size
self.ch = ch
self.temb_ch = self.ch*4 #the time step embedding dimension
self.num_resolutions = len(ch_mult) #the nubmer of down-sampling and up-sampling
#the ch_mult is a tuple, specify the channels in different level of feature extraction (down-sampling or sampling)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
#self.shearemb = nn.Module()
#self.shearemb.dense = nn.ModuleList([
# torch.nn.Linear(3, self.temb_ch),
# torch.nn.Linear(self.temb_ch, self.temb_ch),])
self.shear_emb = nn.Embedding(num_embeddings=3, embedding_dim=self.temb_ch)
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList([
torch.nn.Linear(self.ch, self.temb_ch),
torch.nn.Linear(self.temb_ch, self.temb_ch),])
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size = 3, stride = 1, padding = 1) #the first feature extraction
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.down = nn.ModuleList()
block_in = None
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock(in_channels = block_in, out_channels = block_out, temb_channels = self.temb_ch, dropout = self.dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions-1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2 #afte one down-sampling, the resolution becomes half.
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,out_channels=block_in,temb_channels=self.temb_ch,dropout=self.dropout)
self.mid.attn_1 = AttnBlock(block_in)
self.mid.block_2 = ResnetBlock(in_channels=block_in,out_channels=block_in,temb_channels=self.temb_ch,dropout=self.dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
skip_in = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
if i_block == self.num_res_blocks:
skip_in = ch*in_ch_mult[i_level]
block.append(ResnetBlock(in_channels=block_in+skip_in,out_channels=block_out,temb_channels=self.temb_ch,dropout=self.dropout))
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock(block_in))
up = nn.Module()
up.block = block
up.attn = attn
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2 #after one up-sampling, the resolution becomes twice.
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,out_ch,kernel_size=3,stride=1,padding=1)
def forward(self, x, t, shear):
#condition is the initial condition used as key and value in cross attention
assert x.shape[2] == x.shape[3] == self.resolution
#print(x.shape)
# timestep embedding
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = self.temb.dense[1](temb)
#shear embedding
shear_emb = self.shear_emb(shear)
'''
shear_emb=nn.functional.one_hot(shear, num_classes=3).type(torch.float) #one hot
shear_emb=self.shearemb.dense[0](shear_emb)
shear_emb = nonlinearity(shear_emb)
shear_emb=self.shearemb.dense[1](shear_emb)
'''
temb = temb + shear_emb
# downsampling
hs = [self.conv_in(x)] #the first feature extraction
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb) #perform one Restblock
if len(self.down[i_level].attn) > 0: #check if there is Attblock
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1])) #perform down-sampling
#store the each level down-sampling ouput for skip connection when up-sampling
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](
torch.cat([h, hs.pop()], dim=1), temb) #this is skip connection
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
h = self.up[i_level].upsample(h)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h |