Spaces:
Sleeping
Sleeping
File size: 8,518 Bytes
aec5c02 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 |
import math
import torch
from torch import nn
from einops import rearrange
from inspect import isfunction
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
try:
m.weight.data.normal_(0.0, 0.02)
except:
pass
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, *args, **kwargs):
return self.fn(x, *args, **kwargs) + x
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class LayerNorm(nn.Module):
def __init__(self, dim, eps = 1e-5):
super().__init__()
self.eps = eps
self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
mean = torch.mean(x, dim = 1, keepdim = True)
return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.fn = fn
self.norm = LayerNorm(dim)
# self.norm = nn.BatchNorm2d(dim)
# self.norm = nn.GroupNorm(dim // 32, dim)
def forward(self, x):
x = self.norm(x)
return self.fn(x)
# building block modules
class ConvNextBlock(nn.Module):
""" https://arxiv.org/abs/2201.03545 """
def __init__(self, dim, dim_out, *, time_emb_dim = None, mult = 2, norm = True):
super().__init__()
self.mlp = nn.Sequential(
nn.GELU(),
nn.Linear(time_emb_dim, dim*2)
) if exists(time_emb_dim) else None
self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
self.net = nn.Sequential(
LayerNorm(dim) if norm else nn.Identity(),
nn.Conv2d(dim, dim_out * mult, 3, 1, 1),
nn.GELU(),
nn.Conv2d(dim_out * mult, dim_out, 3, 1, 1),
)
# self.noise_adding = NoiseInjection(dim_out)
self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
def forward(self, x, time_emb = None):
h = self.ds_conv(x)
if exists(self.mlp):
assert exists(time_emb), 'time emb must be passed in'
condition = self.mlp(time_emb)
condition = rearrange(condition, 'b c -> b c 1 1')
weight, bias = torch.split(condition, x.shape[1],dim=1)
h = h * (1 + weight) + bias
h = self.net(h)
# h = self.noise_adding(h)
return h + self.res_conv(x)
class ConvNextBlock_dis(nn.Module):
""" https://arxiv.org/abs/2201.03545 """
def __init__(self, dim, dim_out, *, time_emb_dim = None, mult = 2, norm = True):
super().__init__()
self.mlp = nn.Sequential(
nn.GELU(),
nn.Linear(time_emb_dim, dim*2)
) if exists(time_emb_dim) else None
self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
self.net = nn.Sequential(
nn.BatchNorm2d(dim) if norm else nn.Identity(),
# LayerNorm(dim) if norm else nn.Identity(),
nn.Conv2d(dim, dim_out * mult, 3, 1, 1),
nn.GELU(),
nn.Conv2d(dim_out * mult, dim_out, 3, 1, 1),
)
self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
def forward(self, x):
h = self.ds_conv(x)
h = self.net(h)
return h + self.res_conv(x)
class LinearAttention(nn.Module):
def __init__(self, dim, heads = 4, dim_head = 32):
super().__init__()
self.scale = dim_head ** -0.5
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x).chunk(3, dim = 1)
q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
q = q * self.scale
k = k.softmax(dim = -1)
context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
return self.to_out(out)
# model
class UNet(nn.Module):
def __init__(
self,
dim = 32,
dim_mults=(1, 2, 4, 8, 16, 32, 32),
channels = 3,
):
super().__init__()
self.channels = dim
dims = [dim, *map(lambda m: dim * m, dim_mults)]
in_out = list(zip(dims[:-1], dims[1:]))
self.model_depth = len(dim_mults)
time_dim = dim
self.time_mlp = nn.Sequential(
SinusoidalPosEmb(dim),
nn.Linear(dim, dim * 2),
nn.GELU(),
nn.Linear(dim * 2, dim)
)
self.downs = nn.ModuleList([])
self.ups = nn.ModuleList([])
num_resolutions = len(in_out)
self.initial = nn.Conv2d(channels, dim, 7,1,3, bias=False)
for ind, (dim_in, dim_out) in enumerate(in_out):
self.downs.append(nn.ModuleList([
ConvNextBlock(dim_in, dim_out, time_emb_dim = time_dim, norm = ind != 0),
nn.AvgPool2d(2),
Residual(PreNorm(dim_out, LinearAttention(dim_out))) if ind >= (num_resolutions - 3) else nn.Identity(),
ConvNextBlock(dim_out, dim_out, time_emb_dim=time_dim),
]))
for ind, (dim_in, dim_out) in enumerate(reversed(in_out)):
self.ups.append(nn.ModuleList([
ConvNextBlock(dim_out * 2, dim_in, time_emb_dim = time_dim),
nn.Upsample(scale_factor=2, mode='nearest'),
Residual(PreNorm(dim_in, LinearAttention(dim_in))) if ind < 3 else nn.Identity(),
ConvNextBlock(dim_in, dim_in, time_emb_dim=time_dim),
]))
self.final_conv = nn.Conv2d(dim, 3, 1, bias=False)
def forward(self, x, time):
x = self.initial(x)
t = self.time_mlp(time) if exists(self.time_mlp) else None
h = []
for convnext, downsample, attn, convnext2 in self.downs:
x = convnext(x, t)
x = downsample(x)
h.append(x)
x = attn(x)
x = convnext2(x, t)
for convnext, upsample, attn, convnext2 in self.ups:
x = torch.cat((x, h.pop()), dim=1)
x = convnext(x, t)
x = upsample(x)
x = attn(x)
x = convnext2(x, t)
return self.final_conv(x)
class Discriminator(nn.Module):
def __init__(
self,
dim=32,
dim_mults=(1, 2, 4, 8, 16, 32, 32),
channels=3,
with_time_emb=True,
):
super().__init__()
self.channels = dim
dims = [dim, *map(lambda m: dim * m, dim_mults)]
in_out = list(zip(dims[:-1], dims[1:]))
self.model_depth = len(dim_mults)
self.downs = nn.ModuleList([])
num_resolutions = len(in_out)
self.initial = nn.Conv2d(channels, dim, 7,1,3, bias=False)
for ind, (dim_in, dim_out) in enumerate(in_out):
is_last = ind >= (num_resolutions - 1)
self.downs.append(nn.ModuleList([
ConvNextBlock_dis(dim_in, dim_out, norm=ind != 0),
nn.AvgPool2d(2),
ConvNextBlock_dis(dim_out, dim_out),
]))
dim_out = dim_mults[-1] * dim
self.out = nn.Conv2d(dim_out, 1, 1, bias=False)
def forward(self, x):
x = self.initial(x)
for convnext, downsample, convnext2 in self.downs:
x = convnext(x)
x = downsample(x)
x = convnext2(x)
return self.out(x).view(x.shape[0], -1)
|