File size: 16,454 Bytes
f6228f9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import math
from typing import Tuple, Type
import torch
from torch import Tensor, nn
from ultralytics.nn.modules import MLPBlock
class TwoWayTransformer(nn.Module):
"""
A Two-Way Transformer module for simultaneous attention to image and query points.
This class implements a specialized transformer decoder that attends to an input image using queries with
supplied positional embeddings. It's useful for tasks like object detection, image segmentation, and point
cloud processing.
Attributes:
depth (int): Number of layers in the transformer.
embedding_dim (int): Channel dimension for input embeddings.
num_heads (int): Number of heads for multihead attention.
mlp_dim (int): Internal channel dimension for the MLP block.
layers (nn.ModuleList): List of TwoWayAttentionBlock layers composing the transformer.
final_attn_token_to_image (Attention): Final attention layer from queries to image.
norm_final_attn (nn.LayerNorm): Layer normalization applied to final queries.
Methods:
forward: Processes image and point embeddings through the transformer.
Examples:
>>> transformer = TwoWayTransformer(depth=6, embedding_dim=256, num_heads=8, mlp_dim=2048)
>>> image_embedding = torch.randn(1, 256, 32, 32)
>>> image_pe = torch.randn(1, 256, 32, 32)
>>> point_embedding = torch.randn(1, 100, 256)
>>> output_queries, output_image = transformer(image_embedding, image_pe, point_embedding)
>>> print(output_queries.shape, output_image.shape)
"""
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
"""
Initialize a Two-Way Transformer for simultaneous attention to image and query points.
Args:
depth (int): Number of layers in the transformer.
embedding_dim (int): Channel dimension for input embeddings.
num_heads (int): Number of heads for multihead attention. Must divide embedding_dim.
mlp_dim (int): Internal channel dimension for the MLP block.
activation (Type[nn.Module]): Activation function to use in the MLP block.
attention_downsample_rate (int): Downsampling rate for attention mechanism.
Attributes:
depth (int): Number of layers in the transformer.
embedding_dim (int): Channel dimension for input embeddings.
num_heads (int): Number of heads for multihead attention.
mlp_dim (int): Internal channel dimension for the MLP block.
layers (nn.ModuleList): List of TwoWayAttentionBlock layers.
final_attn_token_to_image (Attention): Final attention layer from queries to image.
norm_final_attn (nn.LayerNorm): Layer normalization applied to final queries.
Examples:
>>> transformer = TwoWayTransformer(depth=6, embedding_dim=256, num_heads=8, mlp_dim=2048)
>>> image_embedding = torch.randn(1, 256, 32, 32)
>>> image_pe = torch.randn(1, 256, 32, 32)
>>> point_embedding = torch.randn(1, 100, 256)
>>> output_queries, output_image = transformer(image_embedding, image_pe, point_embedding)
>>> print(output_queries.shape, output_image.shape)
"""
super().__init__()
self.depth = depth
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.mlp_dim = mlp_dim
self.layers = nn.ModuleList()
for i in range(depth):
self.layers.append(
TwoWayAttentionBlock(
embedding_dim=embedding_dim,
num_heads=num_heads,
mlp_dim=mlp_dim,
activation=activation,
attention_downsample_rate=attention_downsample_rate,
skip_first_layer_pe=(i == 0),
)
)
self.final_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
self,
image_embedding: Tensor,
image_pe: Tensor,
point_embedding: Tensor,
) -> Tuple[Tensor, Tensor]:
"""
Processes image and point embeddings through the Two-Way Transformer.
Args:
image_embedding (torch.Tensor): Image to attend to, with shape (B, embedding_dim, H, W).
image_pe (torch.Tensor): Positional encoding to add to the image, with same shape as image_embedding.
point_embedding (torch.Tensor): Embedding to add to query points, with shape (B, N_points, embedding_dim).
Returns:
(Tuple[torch.Tensor, torch.Tensor]): Processed point_embedding and image_embedding.
Examples:
>>> transformer = TwoWayTransformer(depth=6, embedding_dim=256, num_heads=8, mlp_dim=2048)
>>> image_embedding = torch.randn(1, 256, 32, 32)
>>> image_pe = torch.randn(1, 256, 32, 32)
>>> point_embedding = torch.randn(1, 100, 256)
>>> output_queries, output_image = transformer(image_embedding, image_pe, point_embedding)
>>> print(output_queries.shape, output_image.shape)
"""
# BxCxHxW -> BxHWxC == B x N_image_tokens x C
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
# Apply transformer blocks and final layernorm
for layer in self.layers:
queries, keys = layer(
queries=queries,
keys=keys,
query_pe=point_embedding,
key_pe=image_pe,
)
# Apply the final attention layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm_final_attn(queries)
return queries, keys
class TwoWayAttentionBlock(nn.Module):
"""
A two-way attention block for simultaneous attention to image and query points.
This class implements a specialized transformer block with four main layers: self-attention on sparse inputs,
cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention of dense
inputs to sparse inputs.
Attributes:
self_attn (Attention): Self-attention layer for queries.
norm1 (nn.LayerNorm): Layer normalization after self-attention.
cross_attn_token_to_image (Attention): Cross-attention layer from queries to keys.
norm2 (nn.LayerNorm): Layer normalization after token-to-image attention.
mlp (MLPBlock): MLP block for transforming query embeddings.
norm3 (nn.LayerNorm): Layer normalization after MLP block.
norm4 (nn.LayerNorm): Layer normalization after image-to-token attention.
cross_attn_image_to_token (Attention): Cross-attention layer from keys to queries.
skip_first_layer_pe (bool): Whether to skip positional encoding in the first layer.
Methods:
forward: Applies self-attention and cross-attention to queries and keys.
Examples:
>>> embedding_dim, num_heads = 256, 8
>>> block = TwoWayAttentionBlock(embedding_dim, num_heads)
>>> queries = torch.randn(1, 100, embedding_dim)
>>> keys = torch.randn(1, 1000, embedding_dim)
>>> query_pe = torch.randn(1, 100, embedding_dim)
>>> key_pe = torch.randn(1, 1000, embedding_dim)
>>> processed_queries, processed_keys = block(queries, keys, query_pe, key_pe)
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
"""
Initializes a TwoWayAttentionBlock for simultaneous attention to image and query points.
This block implements a specialized transformer layer with four main components: self-attention on sparse
inputs, cross-attention of sparse inputs to dense inputs, MLP block on sparse inputs, and cross-attention
of dense inputs to sparse inputs.
Args:
embedding_dim (int): Channel dimension of the embeddings.
num_heads (int): Number of attention heads in the attention layers.
mlp_dim (int): Hidden dimension of the MLP block.
activation (Type[nn.Module]): Activation function for the MLP block.
attention_downsample_rate (int): Downsampling rate for the attention mechanism.
skip_first_layer_pe (bool): Whether to skip positional encoding in the first layer.
Examples:
>>> embedding_dim, num_heads = 256, 8
>>> block = TwoWayAttentionBlock(embedding_dim, num_heads)
>>> queries = torch.randn(1, 100, embedding_dim)
>>> keys = torch.randn(1, 1000, embedding_dim)
>>> query_pe = torch.randn(1, 100, embedding_dim)
>>> key_pe = torch.randn(1, 1000, embedding_dim)
>>> processed_queries, processed_keys = block(queries, keys, query_pe, key_pe)
"""
super().__init__()
self.self_attn = Attention(embedding_dim, num_heads)
self.norm1 = nn.LayerNorm(embedding_dim)
self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
self.norm2 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm3 = nn.LayerNorm(embedding_dim)
self.norm4 = nn.LayerNorm(embedding_dim)
self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)
self.skip_first_layer_pe = skip_first_layer_pe
def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:
"""Applies two-way attention to process query and key embeddings in a transformer block."""
# Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(queries)
# Cross attention block, tokens attending to image embedding
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm2(queries)
# MLP block
mlp_out = self.mlp(queries)
queries = queries + mlp_out
queries = self.norm3(queries)
# Cross attention block, image embedding attending to tokens
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
keys = keys + attn_out
keys = self.norm4(keys)
return queries, keys
class Attention(nn.Module):
"""
An attention layer with downscaling capability for embedding size after projection.
This class implements a multi-head attention mechanism with the option to downsample the internal
dimension of queries, keys, and values.
Attributes:
embedding_dim (int): Dimensionality of input embeddings.
kv_in_dim (int): Dimensionality of key and value inputs.
internal_dim (int): Internal dimension after downsampling.
num_heads (int): Number of attention heads.
q_proj (nn.Linear): Linear projection for queries.
k_proj (nn.Linear): Linear projection for keys.
v_proj (nn.Linear): Linear projection for values.
out_proj (nn.Linear): Linear projection for output.
Methods:
_separate_heads: Separates input tensor into attention heads.
_recombine_heads: Recombines separated attention heads.
forward: Computes attention output for given query, key, and value tensors.
Examples:
>>> attn = Attention(embedding_dim=256, num_heads=8, downsample_rate=2)
>>> q = torch.randn(1, 100, 256)
>>> k = v = torch.randn(1, 50, 256)
>>> output = attn(q, k, v)
>>> print(output.shape)
torch.Size([1, 100, 256])
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
kv_in_dim: int = None,
) -> None:
"""
Initializes the Attention module with specified dimensions and settings.
This class implements a multi-head attention mechanism with optional downsampling of the internal
dimension for queries, keys, and values.
Args:
embedding_dim (int): Dimensionality of input embeddings.
num_heads (int): Number of attention heads.
downsample_rate (int): Factor by which internal dimensions are downsampled. Defaults to 1.
kv_in_dim (int | None): Dimensionality of key and value inputs. If None, uses embedding_dim.
Raises:
AssertionError: If num_heads does not evenly divide the internal dim (embedding_dim / downsample_rate).
Examples:
>>> attn = Attention(embedding_dim=256, num_heads=8, downsample_rate=2)
>>> q = torch.randn(1, 100, 256)
>>> k = v = torch.randn(1, 50, 256)
>>> output = attn(q, k, v)
>>> print(output.shape)
torch.Size([1, 100, 256])
"""
super().__init__()
self.embedding_dim = embedding_dim
self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
self.internal_dim = embedding_dim // downsample_rate
self.num_heads = num_heads
assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
@staticmethod
def _separate_heads(x: Tensor, num_heads: int) -> Tensor:
"""Separates the input tensor into the specified number of attention heads."""
b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
@staticmethod
def _recombine_heads(x: Tensor) -> Tensor:
"""Recombines separated attention heads into a single tensor."""
b, n_heads, n_tokens, c_per_head = x.shape
x = x.transpose(1, 2)
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
"""Applies multi-head attention to query, key, and value tensors with optional downsampling."""
# Input projections
q = self.q_proj(q)
k = self.k_proj(k)
v = self.v_proj(v)
# Separate into heads
q = self._separate_heads(q, self.num_heads)
k = self._separate_heads(k, self.num_heads)
v = self._separate_heads(v, self.num_heads)
# Attention
_, _, _, c_per_head = q.shape
attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
attn = attn / math.sqrt(c_per_head)
attn = torch.softmax(attn, dim=-1)
# Get output
out = attn @ v
out = self._recombine_heads(out)
return self.out_proj(out)
|