Spaces:
Sleeping
Sleeping
File size: 17,416 Bytes
3cc4a06 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 |
import math
from collections import OrderedDict
from functools import partial
from typing import Any, Callable, List, NamedTuple, Optional
import torch
import torch.nn as nn
# from .._internally_replaced_utils import load_state_dict_from_url
from .vision_transformer_misc import ConvNormActivation
from .vision_transformer_utils import _log_api_usage_once
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
# __all__ = [
# "VisionTransformer",
# "vit_b_16",
# "vit_b_32",
# "vit_l_16",
# "vit_l_32",
# ]
model_urls = {
"vit_b_16": "https://download.pytorch.org/models/vit_b_16-c867db91.pth",
"vit_b_32": "https://download.pytorch.org/models/vit_b_32-d86f8d99.pth",
"vit_l_16": "https://download.pytorch.org/models/vit_l_16-852ce7e3.pth",
"vit_l_32": "https://download.pytorch.org/models/vit_l_32-c7638314.pth",
}
class ConvStemConfig(NamedTuple):
out_channels: int
kernel_size: int
stride: int
norm_layer: Callable[..., nn.Module] = nn.BatchNorm2d
activation_layer: Callable[..., nn.Module] = nn.ReLU
class MLPBlock(nn.Sequential):
"""Transformer MLP block."""
def __init__(self, in_dim: int, mlp_dim: int, dropout: float):
super().__init__()
self.linear_1 = nn.Linear(in_dim, mlp_dim)
self.act = nn.GELU()
self.dropout_1 = nn.Dropout(dropout)
self.linear_2 = nn.Linear(mlp_dim, in_dim)
self.dropout_2 = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.linear_1.weight)
nn.init.xavier_uniform_(self.linear_2.weight)
nn.init.normal_(self.linear_1.bias, std=1e-6)
nn.init.normal_(self.linear_2.bias, std=1e-6)
class EncoderBlock(nn.Module):
"""Transformer encoder block."""
def __init__(
self,
num_heads: int,
hidden_dim: int,
mlp_dim: int,
dropout: float,
attention_dropout: float,
norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
):
super().__init__()
self.num_heads = num_heads
# Attention block
self.ln_1 = norm_layer(hidden_dim)
self.self_attention = nn.MultiheadAttention(hidden_dim, num_heads, dropout=attention_dropout, batch_first=True)
self.dropout = nn.Dropout(dropout)
# MLP block
self.ln_2 = norm_layer(hidden_dim)
self.mlp = MLPBlock(hidden_dim, mlp_dim, dropout)
def forward(self, input: torch.Tensor):
torch._assert(input.dim() == 3, f"Expected (seq_length, batch_size, hidden_dim) got {input.shape}")
x = self.ln_1(input)
x, _ = self.self_attention(query=x, key=x, value=x, need_weights=False)
x = self.dropout(x)
x = x + input
y = self.ln_2(x)
y = self.mlp(y)
return x + y
class Encoder(nn.Module):
"""Transformer Model Encoder for sequence to sequence translation."""
def __init__(
self,
seq_length: int,
num_layers: int,
num_heads: int,
hidden_dim: int,
mlp_dim: int,
dropout: float,
attention_dropout: float,
norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
):
super().__init__()
# Note that batch_size is on the first dim because
# we have batch_first=True in nn.MultiAttention() by default
self.pos_embedding = nn.Parameter(torch.empty(1, seq_length, hidden_dim).normal_(std=0.02)) # from BERT
self.dropout = nn.Dropout(dropout)
layers: OrderedDict[str, nn.Module] = OrderedDict()
for i in range(num_layers):
layers[f"encoder_layer_{i}"] = EncoderBlock(
num_heads,
hidden_dim,
mlp_dim,
dropout,
attention_dropout,
norm_layer,
)
self.layers = nn.Sequential(layers)
self.ln = norm_layer(hidden_dim)
def forward(self, input: torch.Tensor):
torch._assert(input.dim() == 3, f"Expected (batch_size, seq_length, hidden_dim) got {input.shape}")
input = input + self.pos_embedding
return self.ln(self.layers(self.dropout(input)))
class VisionTransformer(nn.Module):
"""Vision Transformer as per https://arxiv.org/abs/2010.11929."""
def __init__(
self,
image_size: int,
patch_size: int,
num_layers: int,
num_heads: int,
hidden_dim: int,
mlp_dim: int,
dropout: float = 0.0,
attention_dropout: float = 0.0,
num_classes: int = 1000,
representation_size: Optional[int] = None,
norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
conv_stem_configs: Optional[List[ConvStemConfig]] = None,
):
super().__init__()
_log_api_usage_once(self)
torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!")
self.image_size = image_size
self.patch_size = patch_size
self.hidden_dim = hidden_dim
self.mlp_dim = mlp_dim
self.attention_dropout = attention_dropout
self.dropout = dropout
self.num_classes = num_classes
self.representation_size = representation_size
self.norm_layer = norm_layer
if conv_stem_configs is not None:
# As per https://arxiv.org/abs/2106.14881
seq_proj = nn.Sequential()
prev_channels = 3
for i, conv_stem_layer_config in enumerate(conv_stem_configs):
seq_proj.add_module(
f"conv_bn_relu_{i}",
ConvNormActivation(
in_channels=prev_channels,
out_channels=conv_stem_layer_config.out_channels,
kernel_size=conv_stem_layer_config.kernel_size,
stride=conv_stem_layer_config.stride,
norm_layer=conv_stem_layer_config.norm_layer,
activation_layer=conv_stem_layer_config.activation_layer,
),
)
prev_channels = conv_stem_layer_config.out_channels
seq_proj.add_module(
"conv_last", nn.Conv2d(in_channels=prev_channels, out_channels=hidden_dim, kernel_size=1)
)
self.conv_proj: nn.Module = seq_proj
else:
self.conv_proj = nn.Conv2d(
in_channels=3, out_channels=hidden_dim, kernel_size=patch_size, stride=patch_size
)
seq_length = (image_size // patch_size) ** 2
# Add a class token
self.class_token = nn.Parameter(torch.zeros(1, 1, hidden_dim))
seq_length += 1
self.encoder = Encoder(
seq_length,
num_layers,
num_heads,
hidden_dim,
mlp_dim,
dropout,
attention_dropout,
norm_layer,
)
self.seq_length = seq_length
heads_layers: OrderedDict[str, nn.Module] = OrderedDict()
if representation_size is None:
heads_layers["head"] = nn.Linear(hidden_dim, num_classes)
else:
heads_layers["pre_logits"] = nn.Linear(hidden_dim, representation_size)
heads_layers["act"] = nn.Tanh()
heads_layers["head"] = nn.Linear(representation_size, num_classes)
self.heads = nn.Sequential(heads_layers)
if isinstance(self.conv_proj, nn.Conv2d):
# Init the patchify stem
fan_in = self.conv_proj.in_channels * self.conv_proj.kernel_size[0] * self.conv_proj.kernel_size[1]
nn.init.trunc_normal_(self.conv_proj.weight, std=math.sqrt(1 / fan_in))
if self.conv_proj.bias is not None:
nn.init.zeros_(self.conv_proj.bias)
elif self.conv_proj.conv_last is not None and isinstance(self.conv_proj.conv_last, nn.Conv2d):
# Init the last 1x1 conv of the conv stem
nn.init.normal_(
self.conv_proj.conv_last.weight, mean=0.0, std=math.sqrt(2.0 / self.conv_proj.conv_last.out_channels)
)
if self.conv_proj.conv_last.bias is not None:
nn.init.zeros_(self.conv_proj.conv_last.bias)
if hasattr(self.heads, "pre_logits") and isinstance(self.heads.pre_logits, nn.Linear):
fan_in = self.heads.pre_logits.in_features
nn.init.trunc_normal_(self.heads.pre_logits.weight, std=math.sqrt(1 / fan_in))
nn.init.zeros_(self.heads.pre_logits.bias)
if isinstance(self.heads.head, nn.Linear):
nn.init.zeros_(self.heads.head.weight)
nn.init.zeros_(self.heads.head.bias)
def _process_input(self, x: torch.Tensor) -> torch.Tensor:
n, c, h, w = x.shape
p = self.patch_size
torch._assert(h == self.image_size, "Wrong image height!")
torch._assert(w == self.image_size, "Wrong image width!")
n_h = h // p
n_w = w // p
# (n, c, h, w) -> (n, hidden_dim, n_h, n_w)
x = self.conv_proj(x)
# (n, hidden_dim, n_h, n_w) -> (n, hidden_dim, (n_h * n_w))
x = x.reshape(n, self.hidden_dim, n_h * n_w)
# (n, hidden_dim, (n_h * n_w)) -> (n, (n_h * n_w), hidden_dim)
# The self attention layer expects inputs in the format (N, S, E)
# where S is the source sequence length, N is the batch size, E is the
# embedding dimension
x = x.permute(0, 2, 1)
return x
def forward(self, x: torch.Tensor):
out = {}
# Reshape and permute the input tensor
x = self._process_input(x)
n = x.shape[0]
# Expand the class token to the full batch
batch_class_token = self.class_token.expand(n, -1, -1)
x = torch.cat([batch_class_token, x], dim=1)
x = self.encoder(x)
img_feature = x[:,1:]
H = W = int(self.image_size / self.patch_size)
out['f4'] = img_feature.view(n, H, W, self.hidden_dim).permute(0,3,1,2)
# Classifier "token" as used by standard language architectures
x = x[:, 0]
out['penultimate'] = x
x = self.heads(x) # I checked that for all pretrained ViT, this is just a fc
out['logits'] = x
return out
def _vision_transformer(
arch: str,
patch_size: int,
num_layers: int,
num_heads: int,
hidden_dim: int,
mlp_dim: int,
pretrained: bool,
progress: bool,
**kwargs: Any,
) -> VisionTransformer:
image_size = kwargs.pop("image_size", 224)
model = VisionTransformer(
image_size=image_size,
patch_size=patch_size,
num_layers=num_layers,
num_heads=num_heads,
hidden_dim=hidden_dim,
mlp_dim=mlp_dim,
**kwargs,
)
if pretrained:
if arch not in model_urls:
raise ValueError(f"No checkpoint is available for model type '{arch}'!")
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model
def vit_b_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer:
"""
Constructs a vit_b_16 architecture from
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vision_transformer(
arch="vit_b_16",
patch_size=16,
num_layers=12,
num_heads=12,
hidden_dim=768,
mlp_dim=3072,
pretrained=pretrained,
progress=progress,
**kwargs,
)
def vit_b_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer:
"""
Constructs a vit_b_32 architecture from
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vision_transformer(
arch="vit_b_32",
patch_size=32,
num_layers=12,
num_heads=12,
hidden_dim=768,
mlp_dim=3072,
pretrained=pretrained,
progress=progress,
**kwargs,
)
def vit_l_16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer:
"""
Constructs a vit_l_16 architecture from
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vision_transformer(
arch="vit_l_16",
patch_size=16,
num_layers=24,
num_heads=16,
hidden_dim=1024,
mlp_dim=4096,
pretrained=pretrained,
progress=progress,
**kwargs,
)
def vit_l_32(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VisionTransformer:
"""
Constructs a vit_l_32 architecture from
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" <https://arxiv.org/abs/2010.11929>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _vision_transformer(
arch="vit_l_32",
patch_size=32,
num_layers=24,
num_heads=16,
hidden_dim=1024,
mlp_dim=4096,
pretrained=pretrained,
progress=progress,
**kwargs,
)
def interpolate_embeddings(
image_size: int,
patch_size: int,
model_state: "OrderedDict[str, torch.Tensor]",
interpolation_mode: str = "bicubic",
reset_heads: bool = False,
) -> "OrderedDict[str, torch.Tensor]":
"""This function helps interpolating positional embeddings during checkpoint loading,
especially when you want to apply a pre-trained model on images with different resolution.
Args:
image_size (int): Image size of the new model.
patch_size (int): Patch size of the new model.
model_state (OrderedDict[str, torch.Tensor]): State dict of the pre-trained model.
interpolation_mode (str): The algorithm used for upsampling. Default: bicubic.
reset_heads (bool): If true, not copying the state of heads. Default: False.
Returns:
OrderedDict[str, torch.Tensor]: A state dict which can be loaded into the new model.
"""
# Shape of pos_embedding is (1, seq_length, hidden_dim)
pos_embedding = model_state["encoder.pos_embedding"]
n, seq_length, hidden_dim = pos_embedding.shape
if n != 1:
raise ValueError(f"Unexpected position embedding shape: {pos_embedding.shape}")
new_seq_length = (image_size // patch_size) ** 2 + 1
# Need to interpolate the weights for the position embedding.
# We do this by reshaping the positions embeddings to a 2d grid, performing
# an interpolation in the (h, w) space and then reshaping back to a 1d grid.
if new_seq_length != seq_length:
# The class token embedding shouldn't be interpolated so we split it up.
seq_length -= 1
new_seq_length -= 1
pos_embedding_token = pos_embedding[:, :1, :]
pos_embedding_img = pos_embedding[:, 1:, :]
# (1, seq_length, hidden_dim) -> (1, hidden_dim, seq_length)
pos_embedding_img = pos_embedding_img.permute(0, 2, 1)
seq_length_1d = int(math.sqrt(seq_length))
torch._assert(seq_length_1d * seq_length_1d == seq_length, "seq_length is not a perfect square!")
# (1, hidden_dim, seq_length) -> (1, hidden_dim, seq_l_1d, seq_l_1d)
pos_embedding_img = pos_embedding_img.reshape(1, hidden_dim, seq_length_1d, seq_length_1d)
new_seq_length_1d = image_size // patch_size
# Perform interpolation.
# (1, hidden_dim, seq_l_1d, seq_l_1d) -> (1, hidden_dim, new_seq_l_1d, new_seq_l_1d)
new_pos_embedding_img = nn.functional.interpolate(
pos_embedding_img,
size=new_seq_length_1d,
mode=interpolation_mode,
align_corners=True,
)
# (1, hidden_dim, new_seq_l_1d, new_seq_l_1d) -> (1, hidden_dim, new_seq_length)
new_pos_embedding_img = new_pos_embedding_img.reshape(1, hidden_dim, new_seq_length)
# (1, hidden_dim, new_seq_length) -> (1, new_seq_length, hidden_dim)
new_pos_embedding_img = new_pos_embedding_img.permute(0, 2, 1)
new_pos_embedding = torch.cat([pos_embedding_token, new_pos_embedding_img], dim=1)
model_state["encoder.pos_embedding"] = new_pos_embedding
if reset_heads:
model_state_copy: "OrderedDict[str, torch.Tensor]" = OrderedDict()
for k, v in model_state.items():
if not k.startswith("heads"):
model_state_copy[k] = v
model_state = model_state_copy
return model_state
|