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35839a1 8a6ed33 35839a1 8a6ed33 35839a1 8a6ed33 35839a1 8a6ed33 35839a1 8a6ed33 35839a1 8a6ed33 35839a1 | 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 | import torch.nn as nn
import torch
class ResidualBlock(nn.Module):
def __init__(self, in_channels: int, out_channels: int,is_res: bool = False) -> None:
super(ResidualBlock,self).__init__()
self.same_channesls = in_channels == out_channels
self.is_res = is_res
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels,out_channels,3,1,1),
nn.BatchNorm2d(out_channels),
nn.GELU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(out_channels,out_channels,3,1,1),
nn.BatchNorm2d(out_channels),
nn.GELU(),
)
def forward(self,x):
if self.is_res:
x1 = self.conv1(x)
x2 = self.conv2(x1)
if self.same_channesls:
out = x1 + x2
else:
shortcut = nn.Conv2d(x.shape[1],x2.shape[1],1,1,0).to(x.device)
out = shortcut(x) + x2
return out / 1.414
else:
x1 = self.conv1(x)
x2 = self.conv2(x1)
return x2
class UnetUp(nn.Module):
def __init__(self, in_channels, out_channels):
super(UnetUp, self).__init__()
# Create a list of layers for the upsampling block
# The block consists of a ConvTranspose2d layer for upsampling, followed by two ResidualConvBlock layers
layers = [
nn.ConvTranspose2d(in_channels, out_channels, 2, 2),
ResidualBlock(out_channels, out_channels),
ResidualBlock(out_channels, out_channels),
]
# Use the layers to create a sequential model
self.model = nn.Sequential(*layers)
def forward(self, x, skip):
# Concatenate the input tensor x with the skip connection tensor along the channel dimension
x = torch.cat((x, skip), 1)
# Pass the concatenated tensor through the sequential model and return the output
x = self.model(x)
return x
class UnetDown(nn.Module):
def __init__(self, input_channels, out_channels) -> None:
super(UnetDown,self).__init__()
self.model = nn.Sequential(
ResidualBlock(input_channels,out_channels),
ResidualBlock(out_channels,out_channels),
nn.MaxPool2d(2)
)
def forward(self,x):
return self.model(x)
class EmbedFC(nn.Module):
def __init__(self, input_dim,embed_dm) -> None:
super(EmbedFC,self).__init__()
self.input_dim = input_dim
self.model = nn.Sequential(
nn.Linear(input_dim,embed_dm),
nn.GELU(),
nn.Linear(embed_dm,embed_dm),
)
def forward(self,x):
x = x.view(-1,self.input_dim)
return self.model(x)
class ContextUnet(nn.Module):
def __init__(self,in_channels, n_feat = 256,n_cfeat = 10, height = 28) -> None:
super(ContextUnet,self).__init__()
self.in_channels = in_channels
self.n_feat = n_feat
self.n_cfeat = n_cfeat
self.h = height
self.init_conv = ResidualBlock(in_channels,n_feat,is_res=True)
self.down1 = UnetDown(n_feat,n_feat)
self.down2 = UnetDown(n_feat,n_feat * 2)
self.to_vec = nn.Sequential(nn.AvgPool2d((4)),nn.GELU())
self.timeembed1 = EmbedFC(1, 2 *n_feat)
self.timeembed2 = EmbedFC(1,embed_dm=1*n_feat)
self.contextembed1 = EmbedFC(n_cfeat,2 * n_feat)
self.contextembed2 = EmbedFC(n_cfeat,1*n_feat)
self.up0 = nn.Sequential(
nn.ConvTranspose2d(2 * n_feat,2*n_feat,self.h // 4,self.h // 4),
nn.GroupNorm(8, 2*n_feat),
nn.ReLU(),
)
self.up1 = UnetUp(4 * n_feat,n_feat)
self.up2 = UnetUp(2 * n_feat,n_feat)
self.out = nn.Sequential(
nn.Conv2d(2 * n_feat, n_feat,3,1,1),
nn.GroupNorm(8,n_feat),
nn.ReLU(),
nn.Conv2d(n_feat,self.in_channels,3,1,1)
)
def forward(self,x,t,c=None):
x = self.init_conv(x)
down1 = self.down1(x)
down2 = self.down2(down1)
hidden_vec = self.to_vec(down2)
if c is None:
c = torch.zeros(x.shape[0],self.n_cfeat).to(x)
cemb1 = self.contextembed1(c).view(-1,self.n_feat*2,1,1)
temb1 = self.timeembed1(t).view(-1,self.n_feat * 2,1,1)
cemb2 = self.contextembed2(c).view(-1,self.n_feat,1,1)
temb2 = self.timeembed2(t).view(-1,self.n_feat,1,1)
up1 = self.up0(hidden_vec)
up2 = self.up1(cemb1*up1 + temb1, down2) # add and multiply embeddings
up3 = self.up2(cemb2*up2 + temb2, down1)
out = self.out(torch.cat((up3, x), 1))
return out |