Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,612 Bytes
0b23d5a |
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 |
import math
import torch
import torch.nn as nn
from einops import rearrange, repeat
class ImageProjModel(nn.Module):
"""Projection Model"""
def __init__(
self,
cross_attention_dim=1024,
clip_embeddings_dim=1024,
clip_extra_context_tokens=4,
):
super().__init__()
self.cross_attention_dim = cross_attention_dim
self.clip_extra_context_tokens = clip_extra_context_tokens
self.proj = nn.Linear(
clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim
)
self.norm = nn.LayerNorm(cross_attention_dim)
def forward(self, image_embeds):
# embeds = image_embeds
embeds = image_embeds.type(list(self.proj.parameters())[0].dtype)
clip_extra_context_tokens = self.proj(embeds).reshape(
-1, self.clip_extra_context_tokens, self.cross_attention_dim
)
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
return clip_extra_context_tokens
# FFN
def FeedForward(dim, mult=4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
def reshape_tensor(x, heads):
bs, length, width = x.shape
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
x = x.view(bs, length, heads, -1)
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
x = x.transpose(1, 2)
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
x = x.reshape(bs, heads, length, -1)
return x
class PerceiverAttention(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8):
super().__init__()
self.scale = dim_head**-0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head * heads
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, n1, D)
latent (torch.Tensor): latent features
shape (b, n2, D)
"""
x = self.norm1(x)
latents = self.norm2(latents)
b, l, _ = latents.shape
q = self.to_q(latents)
kv_input = torch.cat((x, latents), dim=-2)
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
q = reshape_tensor(q, self.heads)
k = reshape_tensor(k, self.heads)
v = reshape_tensor(v, self.heads)
# attention
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
# More stable with f16 than dividing afterwards
weight = (q * scale) @ (k * scale).transpose(-2, -1)
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
out = weight @ v
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
return self.to_out(out)
class Resampler(nn.Module):
def __init__(
self,
dim=1024,
depth=8,
dim_head=64,
heads=16,
num_queries=8,
embedding_dim=768,
output_dim=1024,
ff_mult=4,
video_length=None,
):
super().__init__()
self.num_queries = num_queries
self.video_length = video_length
if video_length is not None:
num_queries = num_queries * video_length
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
self.proj_in = nn.Linear(embedding_dim, dim)
self.proj_out = nn.Linear(dim, output_dim)
self.norm_out = nn.LayerNorm(output_dim)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
FeedForward(dim=dim, mult=ff_mult),
]
)
)
def forward(self, x):
latents = self.latents.repeat(x.size(0), 1, 1) # B (T L) C
x = self.proj_in(x)
for attn, ff in self.layers:
latents = attn(x, latents) + latents
latents = ff(latents) + latents
latents = self.proj_out(latents)
latents = self.norm_out(latents) # B L C or B (T L) C
return latents
class CameraPoseQueryTransformer(nn.Module):
def __init__(
self,
dim=1024,
depth=8,
dim_head=64,
heads=16,
num_queries=8,
embedding_dim=768,
output_dim=1024,
ff_mult=4,
num_views=None,
use_multi_view_attention=True,
):
super().__init__()
self.num_queries = num_queries
self.num_views = num_views
assert num_views is not None, "video_length must be given."
self.use_multi_view_attention = use_multi_view_attention
self.camera_pose_embedding_layers = nn.Sequential(
nn.Linear(12, dim),
nn.SiLU(),
nn.Linear(dim, dim),
nn.SiLU(),
nn.Linear(dim, dim),
)
nn.init.zeros_(self.camera_pose_embedding_layers[-1].weight)
nn.init.zeros_(self.camera_pose_embedding_layers[-1].bias)
self.latents = nn.Parameter(
torch.randn(1, num_views * num_queries, dim) / dim**0.5
)
self.proj_in = nn.Linear(embedding_dim, dim)
self.proj_out = nn.Linear(dim, output_dim)
self.norm_out = nn.LayerNorm(output_dim)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
FeedForward(dim=dim, mult=ff_mult),
]
)
)
def forward(self, x, camera_poses):
# camera_poses: (b, t, 12)
batch_size, num_views, _ = camera_poses.shape
# latents: (1, t*q, d) -> (b, t*q, d)
latents = self.latents.repeat(batch_size, 1, 1)
x = self.proj_in(x)
# camera_poses: (b*t, 12)
camera_poses = rearrange(camera_poses, "b t d -> (b t) d", t=num_views)
camera_poses = self.camera_pose_embedding_layers(
camera_poses
) # camera_poses: (b*t, d)
# camera_poses: (b, t, d)
camera_poses = rearrange(camera_poses, "(b t) d -> b t d", t=num_views)
# camera_poses: (b, t*q, d)
camera_poses = repeat(camera_poses, "b t d -> b (t q) d", q=self.num_queries)
latents = latents + camera_poses # b, t*q, d
latents = rearrange(
latents,
"b (t q) d -> (b t) q d",
b=batch_size,
t=num_views,
q=self.num_queries,
) # (b*t, q, d)
_, x_seq_size, _ = x.shape
for layer_idx, (attn, ff) in enumerate(self.layers):
if self.use_multi_view_attention and layer_idx % 2 == 1:
# latents: (b*t, q, d)
latents = rearrange(
latents,
"(b t) q d -> b (t q) d",
b=batch_size,
t=num_views,
q=self.num_queries,
)
# x: (b*t, s, d)
x = rearrange(
x, "(b t) s d -> b (t s) d", b=batch_size, t=num_views, s=x_seq_size
)
# print("After rearrange: latents.shape=", latents.shape)
# print("After rearrange: x.shape=", camera_poses.shape)
latents = attn(x, latents) + latents
latents = ff(latents) + latents
if self.use_multi_view_attention and layer_idx % 2 == 1:
# latents: (b*q, t, d)
latents = rearrange(
latents,
"b (t q) d -> (b t) q d",
b=batch_size,
t=num_views,
q=self.num_queries,
)
# x: (b*s, t, d)
x = rearrange(
x, "b (t s) d -> (b t) s d", b=batch_size, t=num_views, s=x_seq_size
)
latents = self.proj_out(latents)
latents = self.norm_out(latents) # B L C or B (T L) C
return latents
|