Spaces:
Runtime error
Runtime error
File size: 14,148 Bytes
f85e212 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 |
import torch.nn.functional as F
import torch.nn as nn
import torch
from monai.networks.blocks import TransformerBlock
from monai.networks.layers.utils import get_norm_layer, get_dropout_layer
from monai.networks.layers.factories import Conv
from einops import rearrange
class GEGLU(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.norm = nn.LayerNorm(in_channels)
self.proj = nn.Linear(in_channels, out_channels*2, bias=True)
def forward(self, x):
# x expected to be [B, C, *]
# Workaround as layer norm can't currently be applied on arbitrary dimension: https://github.com/pytorch/pytorch/issues/71465
b, c, *spatial = x.shape
x = x.reshape(b, c, -1).transpose(1, 2) # -> [B, C, N] -> [B, N, C]
x = self.norm(x)
x, gate = self.proj(x).chunk(2, dim=-1)
x = x * F.gelu(gate)
return x.transpose(1, 2).reshape(b, -1, *spatial) # -> [B, C, N] -> [B, C, *]
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def compute_attention(q,k,v , num_heads, scale):
q, k, v = map(lambda t: rearrange(t, 'b (h d) n -> (b h) d n', h=num_heads), (q, k, v)) # [(BxHeads), Dim_per_head, N]
attn = (torch.einsum('b d i, b d j -> b i j', q*scale, k*scale)).softmax(dim=-1) # Matrix product = [(BxHeads), Dim_per_head, N] * [(BxHeads), Dim_per_head, N'] =[(BxHeads), N, N']
out = torch.einsum('b i j, b d j-> b d i', attn, v) # Matrix product: [(BxHeads), N, N'] * [(BxHeads), Dim_per_head, N'] = [(BxHeads), Dim_per_head, N]
out = rearrange(out, '(b h) d n-> b (h d) n', h=num_heads) # -> [B, (Heads x Dim_per_head), N]
return out
class LinearTransformerNd(nn.Module):
""" Combines multi-head self-attention and multi-head cross-attention.
Multi-Head Self-Attention:
Similar to multi-head self-attention (https://arxiv.org/abs/1706.03762) without Norm+MLP (compare Monai TransformerBlock)
Proposed here: https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
Similar to: https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/ldm/modules/diffusionmodules/openaimodel.py#L278
Similar to: https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/ldm/modules/attention.py#L80
Similar to: https://github.com/lucidrains/denoising-diffusion-pytorch/blob/dfbafee555bdae80b55d63a989073836bbfc257e/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py#L209
Similar to: https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/modules/diffusionmodules/model.py#L150
CrossAttention:
Proposed here: https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/ldm/modules/attention.py#L152
"""
def __init__(
self,
spatial_dims,
in_channels,
out_channels, # WARNING: if out_channels != in_channels, skip connection is disabled
num_heads=8,
ch_per_head=32, # rule of thumb: 32 or 64 channels per head (see stable-diffusion / diffusion models beat GANs)
norm_name=("GROUP", {'num_groups':32, "affine": True}), # Or use LayerNorm but be aware of https://github.com/pytorch/pytorch/issues/71465 (=> GroupNorm with num_groups=1)
dropout=None,
emb_dim=None,
):
super().__init__()
hid_channels = num_heads*ch_per_head
self.num_heads = num_heads
self.scale = ch_per_head**-0.25 # Should be 1/sqrt("queries and keys of dimension"), Note: additional sqrt needed as it follows OpenAI: (q * scale) * (k * scale) instead of (q *k) * scale
self.norm_x = get_norm_layer(norm_name, spatial_dims=spatial_dims, channels=in_channels)
emb_dim = in_channels if emb_dim is None else emb_dim
Convolution = Conv["conv", spatial_dims]
self.to_q = Convolution(in_channels, hid_channels, 1)
self.to_k = Convolution(emb_dim, hid_channels, 1)
self.to_v = Convolution(emb_dim, hid_channels, 1)
self.to_out = nn.Sequential(
zero_module(Convolution(hid_channels, out_channels, 1)),
nn.Identity() if dropout is None else get_dropout_layer(name=dropout, dropout_dim=spatial_dims)
)
def forward(self, x, embedding=None):
# x expected to be [B, C, *] and embedding is None or [B, C*] or [B, C*, *]
# if no embedding is given, cross-attention defaults to self-attention
# Normalize
b, c, *spatial = x.shape
x_n = self.norm_x(x)
# Attention: embedding (cross-attention) or x (self-attention)
if embedding is None:
embedding = x_n # WARNING: This assumes that emb_dim==in_channels
else:
if embedding.ndim == 2:
embedding = embedding.reshape(*embedding.shape[:2], *[1]*(x.ndim-2)) # [B, C*] -> [B, C*, *]
# Why no normalization for embedding here?
# Convolution
q = self.to_q(x_n) # -> [B, (Heads x Dim_per_head), *]
k = self.to_k(embedding) # -> [B, (Heads x Dim_per_head), *]
v = self.to_v(embedding) # -> [B, (Heads x Dim_per_head), *]
# Flatten
q = q.reshape(b, c, -1) # -> [B, (Heads x Dim_per_head), N]
k = k.reshape(*embedding.shape[:2], -1) # -> [B, (Heads x Dim_per_head), N']
v = v.reshape(*embedding.shape[:2], -1) # -> [B, (Heads x Dim_per_head), N']
# Apply attention
out = compute_attention(q, k, v, self.num_heads, self.scale)
out = out.reshape(*out.shape[:2], *spatial) # -> [B, (Heads x Dim_per_head), *]
out = self.to_out(out) # -> [B, C', *]
if x.shape == out.shape:
out = x + out
return out # [B, C', *]
class LinearTransformer(nn.Module):
""" See LinearTransformer, however this implementation is fixed to Conv1d/Linear"""
def __init__(
self,
spatial_dims,
in_channels,
out_channels, # WARNING: if out_channels != in_channels, skip connection is disabled
num_heads,
ch_per_head=32, # rule of thumb: 32 or 64 channels per head (see stable-diffusion / diffusion models beat GANs)
norm_name=("GROUP", {'num_groups':32, "affine": True}),
dropout=None,
emb_dim=None
):
super().__init__()
hid_channels = num_heads*ch_per_head
self.num_heads = num_heads
self.scale = ch_per_head**-0.25 # Should be 1/sqrt("queries and keys of dimension"), Note: additional sqrt needed as it follows OpenAI: (q * scale) * (k * scale) instead of (q *k) * scale
self.norm_x = get_norm_layer(norm_name, spatial_dims=spatial_dims, channels=in_channels)
emb_dim = in_channels if emb_dim is None else emb_dim
# Note: Conv1d and Linear are interchangeable but order of input changes [B, C, N] <-> [B, N, C]
self.to_q = nn.Conv1d(in_channels, hid_channels, 1)
self.to_k = nn.Conv1d(emb_dim, hid_channels, 1)
self.to_v = nn.Conv1d(emb_dim, hid_channels, 1)
# self.to_qkv = nn.Conv1d(emb_dim, hid_channels*3, 1)
self.to_out = nn.Sequential(
zero_module(nn.Conv1d(hid_channels, out_channels, 1)),
nn.Identity() if dropout is None else get_dropout_layer(name=dropout, dropout_dim=spatial_dims)
)
def forward(self, x, embedding=None):
# x expected to be [B, C, *] and embedding is None or [B, C*] or [B, C*, *]
# if no embedding is given, cross-attention defaults to self-attention
# Normalize
b, c, *spatial = x.shape
x_n = self.norm_x(x)
# Attention: embedding (cross-attention) or x (self-attention)
if embedding is None:
embedding = x_n # WARNING: This assumes that emb_dim==in_channels
else:
if embedding.ndim == 2:
embedding = embedding.reshape(*embedding.shape[:2], *[1]*(x.ndim-2)) # [B, C*] -> [B, C*, *]
# Why no normalization for embedding here?
# Flatten
x_n = x_n.reshape(b, c, -1) # [B, C, *] -> [B, C, N]
embedding = embedding.reshape(*embedding.shape[:2], -1) # [B, C*, *] -> [B, C*, N']
# Convolution
q = self.to_q(x_n) # -> [B, (Heads x Dim_per_head), N]
k = self.to_k(embedding) # -> [B, (Heads x Dim_per_head), N']
v = self.to_v(embedding) # -> [B, (Heads x Dim_per_head), N']
# qkv = self.to_qkv(x_n)
# q,k,v = qkv.split(qkv.shape[1]//3, dim=1)
# Apply attention
out = compute_attention(q, k, v, self.num_heads, self.scale)
out = self.to_out(out) # -> [B, C', N]
out = out.reshape(*out.shape[:2], *spatial) # -> [B, C', *]
if x.shape == out.shape:
out = x + out
return out # [B, C', *]
class BasicTransformerBlock(nn.Module):
def __init__(
self,
spatial_dims,
in_channels,
out_channels, # WARNING: if out_channels != in_channels, skip connection is disabled
num_heads,
ch_per_head=32,
norm_name=("GROUP", {'num_groups':32, "affine": True}),
dropout=None,
emb_dim=None
):
super().__init__()
self.self_atn = LinearTransformer(spatial_dims, in_channels, in_channels, num_heads, ch_per_head, norm_name, dropout, None)
if emb_dim is not None:
self.cros_atn = LinearTransformer(spatial_dims, in_channels, in_channels, num_heads, ch_per_head, norm_name, dropout, emb_dim)
self.proj_out = nn.Sequential(
GEGLU(in_channels, in_channels*4),
nn.Identity() if dropout is None else get_dropout_layer(name=dropout, dropout_dim=spatial_dims),
Conv["conv", spatial_dims](in_channels*4, out_channels, 1, bias=True)
)
def forward(self, x, embedding=None):
# x expected to be [B, C, *] and embedding is None or [B, C*] or [B, C*, *]
x = self.self_atn(x)
if embedding is not None:
x = self.cros_atn(x, embedding=embedding)
out = self.proj_out(x)
if out.shape[1] == x.shape[1]:
return out + x
return x
class SpatialTransformer(nn.Module):
""" Proposed here: https://github.com/CompVis/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/ldm/modules/attention.py#L218
Unrelated to: https://arxiv.org/abs/1506.02025
"""
def __init__(
self,
spatial_dims,
in_channels,
out_channels, # WARNING: if out_channels != in_channels, skip connection is disabled
num_heads,
ch_per_head=32, # rule of thumb: 32 or 64 channels per head (see stable-diffusion / diffusion models beat GANs)
norm_name = ("GROUP", {'num_groups':32, "affine": True}),
dropout=None,
emb_dim=None,
depth=1
):
super().__init__()
self.in_channels = in_channels
self.norm = get_norm_layer(norm_name, spatial_dims=spatial_dims, channels=in_channels)
conv_class = Conv["conv", spatial_dims]
hid_channels = num_heads*ch_per_head
self.proj_in = conv_class(
in_channels,
hid_channels,
kernel_size=1,
stride=1,
padding=0,
)
self.transformer_blocks = nn.ModuleList([
BasicTransformerBlock(spatial_dims, hid_channels, hid_channels, num_heads, ch_per_head, norm_name, dropout=dropout, emb_dim=emb_dim)
for _ in range(depth)]
)
self.proj_out = conv_class( # Note: zero_module is used in original code
hid_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
)
def forward(self, x, embedding=None):
# x expected to be [B, C, *] and embedding is None or [B, C*] or [B, C*, *]
# Note: if no embedding is given, cross-attention is disabled
h = self.norm(x)
h = self.proj_in(h)
for block in self.transformer_blocks:
h = block(h, embedding=embedding)
h = self.proj_out(h) # -> [B, C'', *]
if h.shape == x.shape:
return h + x
return h
class Attention(nn.Module):
def __init__(
self,
spatial_dims,
in_channels,
out_channels,
num_heads=8,
ch_per_head=32, # rule of thumb: 32 or 64 channels per head (see stable-diffusion / diffusion models beat GANs)
norm_name = ("GROUP", {'num_groups':32, "affine": True}),
dropout=0,
emb_dim=None,
depth=1,
attention_type='linear'
) -> None:
super().__init__()
if attention_type == 'spatial':
self.attention = SpatialTransformer(
spatial_dims=spatial_dims,
in_channels=in_channels,
out_channels=out_channels,
num_heads=num_heads,
ch_per_head=ch_per_head,
depth=depth,
norm_name=norm_name,
dropout=dropout,
emb_dim=emb_dim
)
elif attention_type == 'linear':
self.attention = LinearTransformer(
spatial_dims=spatial_dims,
in_channels=in_channels,
out_channels=out_channels,
num_heads=num_heads,
ch_per_head=ch_per_head,
norm_name=norm_name,
dropout=dropout,
emb_dim=emb_dim
)
def forward(self, x, emb=None):
if hasattr(self, 'attention'):
return self.attention(x, emb)
else:
return x |