Spaces:
Runtime error
Runtime error
File size: 17,291 Bytes
56ef371 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 |
import math
import torch
import torch.utils.checkpoint
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from collections import OrderedDict
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from transformers.utils import (
logging,
)
logger = logging.get_logger(__name__)
class MySequential(nn.Sequential):
def forward(self, *inputs):
for module in self._modules.values():
if type(inputs) == tuple:
inputs = module(*inputs)
else:
inputs = module(inputs)
return inputs
class PreNorm(nn.Module):
def __init__(self, norm, fn, drop_path=None):
super().__init__()
self.norm = norm
self.fn = fn
self.drop_path = drop_path
def forward(self, x, *args, **kwargs):
shortcut = x
if self.norm != None:
x, size = self.fn(self.norm(x), *args, **kwargs)
else:
x, size = self.fn(x, *args, **kwargs)
if self.drop_path:
x = self.drop_path(x)
x = shortcut + x
return x, size
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.net = nn.Sequential(OrderedDict([
("fc1", nn.Linear(in_features, hidden_features)),
("act", act_layer()),
("fc2", nn.Linear(hidden_features, out_features))
]))
def forward(self, x, size):
return self.net(x), size
class DepthWiseConv2d(nn.Module):
def __init__(
self,
dim_in,
kernel_size,
padding,
stride,
bias=True,
):
super().__init__()
self.dw = nn.Conv2d(
dim_in, dim_in,
kernel_size=kernel_size,
padding=padding,
groups=dim_in,
stride=stride,
bias=bias
)
def forward(self, x, size):
B, N, C = x.shape
H, W = size
assert N == H * W
x = self.dw(x.transpose(1, 2).view(B, C, H, W))
size = (x.size(-2), x.size(-1))
x = x.flatten(2).transpose(1, 2)
return x, size
class ConvEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(
self,
patch_size=7,
in_chans=3,
embed_dim=64,
stride=4,
padding=2,
norm_layer=None,
pre_norm=True
):
super().__init__()
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim,
kernel_size=patch_size,
stride=stride,
padding=padding
)
dim_norm = in_chans if pre_norm else embed_dim
self.norm = norm_layer(dim_norm) if norm_layer else None
self.pre_norm = pre_norm
def forward(self, x, size):
H, W = size
if len(x.size()) == 3:
if self.norm and self.pre_norm:
x = self.norm(x)
x = rearrange(
x, 'b (h w) c -> b c h w',
h=H, w=W
)
x = self.proj(x)
_, _, H, W = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
if self.norm and not self.pre_norm:
x = self.norm(x)
return x, (H, W)
class ChannelAttention(nn.Module):
def __init__(self, dim, groups=8, qkv_bias=True):
super().__init__()
self.groups = groups
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
def forward(self, x, size):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.groups, C // self.groups).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * (float(N) ** -0.5)
attention = q.transpose(-1, -2) @ k
attention = attention.softmax(dim=-1)
x = (attention @ v.transpose(-1, -2)).transpose(-1, -2)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x, size
class ChannelBlock(nn.Module):
def __init__(self, dim, groups, mlp_ratio=4., qkv_bias=True,
drop_path_rate=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
conv_at_attn=True, conv_at_ffn=True):
super().__init__()
drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None
self.channel_attn = PreNorm(
norm_layer(dim),
ChannelAttention(dim, groups=groups, qkv_bias=qkv_bias),
drop_path
)
self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None
self.ffn = PreNorm(
norm_layer(dim),
Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer),
drop_path
)
def forward(self, x, size):
if self.conv1:
x, size = self.conv1(x, size)
x, size = self.channel_attn(x, size)
if self.conv2:
x, size = self.conv2(x, size)
x, size = self.ffn(x, size)
return x, size
def window_partition(x, window_size: int):
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, batch_size: int, window_size: int, H: int, W: int):
B = batch_size
# this will cause onnx conversion failed for dynamic axis, because treated as constant
# int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
def __init__(self, dim, num_heads, window_size, qkv_bias=True):
super().__init__()
self.dim = dim
self.window_size = window_size
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = float(head_dim) ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, size):
H, W = size
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
x = x.view(B, H, W, C)
pad_l = pad_t = 0
pad_r = (self.window_size - W % self.window_size) % self.window_size
pad_b = (self.window_size - H % self.window_size) % self.window_size
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
_, Hp, Wp, _ = x.shape
x = window_partition(x, self.window_size)
x = x.view(-1, self.window_size * self.window_size, C)
# W-MSA/SW-MSA
# attn_windows = self.attn(x_windows)
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = self.softmax(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
# merge windows
x = x.view(
-1, self.window_size, self.window_size, C
)
x = window_reverse(x, B, self.window_size, Hp, Wp)
if pad_r > 0 or pad_b > 0:
x = x[:, :H, :W, :].contiguous()
x = x.view(B, H * W, C)
return x, size
class SpatialBlock(nn.Module):
def __init__(self, dim, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, drop_path_rate=0., act_layer=nn.GELU,
norm_layer=nn.LayerNorm, conv_at_attn=True, conv_at_ffn=True):
super().__init__()
drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
self.conv1 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_attn else None
self.window_attn = PreNorm(
norm_layer(dim),
WindowAttention(dim, num_heads, window_size, qkv_bias=qkv_bias),
drop_path
)
self.conv2 = PreNorm(None, DepthWiseConv2d(dim, 3, 1, 1)) if conv_at_ffn else None
self.ffn = PreNorm(
norm_layer(dim),
Mlp(in_features=dim, hidden_features=int(dim*mlp_ratio), act_layer=act_layer),
drop_path
)
def forward(self, x, size):
if self.conv1:
x, size = self.conv1(x, size)
x, size = self.window_attn(x, size)
if self.conv2:
x, size = self.conv2(x, size)
x, size = self.ffn(x, size)
return x, size
class DaViT(nn.Module):
""" DaViT: Dual-Attention Transformer
Args:
in_chans (int): Number of input image channels. Default: 3.
num_classes (int): Number of classes for classification head. Default: 1000.
patch_size (tuple(int)): Patch size of convolution in different stages. Default: (7, 2, 2, 2).
patch_stride (tuple(int)): Patch stride of convolution in different stages. Default: (4, 2, 2, 2).
patch_padding (tuple(int)): Patch padding of convolution in different stages. Default: (3, 0, 0, 0).
patch_prenorm (tuple(bool)): If True, perform norm before convlution layer. Default: (True, False, False, False).
embed_dims (tuple(int)): Patch embedding dimension in different stages. Default: (64, 128, 192, 256).
num_heads (tuple(int)): Number of spatial attention heads in different stages. Default: (4, 8, 12, 16).
num_groups (tuple(int)): Number of channel groups in different stages. Default: (4, 8, 12, 16).
window_size (int): Window size. Default: 7.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True.
drop_path_rate (float): Stochastic depth rate. Default: 0.1.
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
enable_checkpoint (bool): If True, enable checkpointing. Default: False.
conv_at_attn (bool): If True, performe depthwise convolution before attention layer. Default: True.
conv_at_ffn (bool): If True, performe depthwise convolution before ffn layer. Default: True.
"""
def __init__(
self,
in_chans=3,
num_classes=1000,
depths=(1, 1, 3, 1),
patch_size=(7, 2, 2, 2),
patch_stride=(4, 2, 2, 2),
patch_padding=(3, 0, 0, 0),
patch_prenorm=(False, False, False, False),
embed_dims=(64, 128, 192, 256),
num_heads=(3, 6, 12, 24),
num_groups=(3, 6, 12, 24),
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
enable_checkpoint=False,
conv_at_attn=True,
conv_at_ffn=True
):
super().__init__()
self.num_classes = num_classes
self.embed_dims = embed_dims
self.num_heads = num_heads
self.num_groups = num_groups
self.num_stages = len(self.embed_dims)
self.enable_checkpoint = enable_checkpoint
assert self.num_stages == len(self.num_heads) == len(self.num_groups)
num_stages = len(embed_dims)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)*2)]
depth_offset = 0
convs = []
blocks = []
for i in range(num_stages):
conv_embed = ConvEmbed(
patch_size=patch_size[i],
stride=patch_stride[i],
padding=patch_padding[i],
in_chans=in_chans if i == 0 else self.embed_dims[i - 1],
embed_dim=self.embed_dims[i],
norm_layer=norm_layer,
pre_norm=patch_prenorm[i]
)
convs.append(conv_embed)
block = MySequential(
*[
MySequential(OrderedDict([
(
'spatial_block', SpatialBlock(
embed_dims[i],
num_heads[i],
window_size,
drop_path_rate=dpr[depth_offset+j*2],
qkv_bias=qkv_bias,
mlp_ratio=mlp_ratio,
conv_at_attn=conv_at_attn,
conv_at_ffn=conv_at_ffn,
)
),
(
'channel_block', ChannelBlock(
embed_dims[i],
num_groups[i],
drop_path_rate=dpr[depth_offset+j*2+1],
qkv_bias=qkv_bias,
mlp_ratio=mlp_ratio,
conv_at_attn=conv_at_attn,
conv_at_ffn=conv_at_ffn,
)
)
])) for j in range(depths[i])
]
)
blocks.append(block)
depth_offset += depths[i]*2
self.convs = nn.ModuleList(convs)
self.blocks = nn.ModuleList(blocks)
# self.norms = norm_layer(self.embed_dims[-1])
# self.avgpool = nn.AdaptiveAvgPool1d(1)
# self.head = nn.Linear(self.embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
@property
def dim_out(self):
return self.embed_dims[-1]
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, std=0.02)
for name, _ in m.named_parameters():
if name in ['bias']:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.weight, 1.0)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1.0)
nn.init.constant_(m.bias, 0)
def forward_features_unpool(self, x):
"""
forward until avg pooling
Args:
x (_type_): input image tensor
"""
input_size = (x.size(2), x.size(3))
for conv, block in zip(self.convs, self.blocks):
x, input_size = conv(x, input_size)
if self.enable_checkpoint:
x, input_size = checkpoint.checkpoint(block, x, input_size)
else:
x, input_size = block(x, input_size)
return x
# def forward_features(self, x):
# x = self.forward_features_unpool(x)
# # (batch_size, num_tokens, token_dim)
# x = self.avgpool(x.transpose(1, 2))
# # (batch_size, 1, num_tokens)
# x = torch.flatten(x, 1)
# x = self.norms(x)
# return x
def forward_features(self, x):
"""
forward until avg pooling
Args:
x (_type_): input image tensor
"""
outs = []
input_size = (x.size(2), x.size(3))
for i, (conv, block) in enumerate(zip(self.convs, self.blocks)):
x, input_size = conv(x, input_size)
if self.enable_checkpoint and self.training:
x, input_size = checkpoint.checkpoint(block, x, input_size, use_reentrant=False)
else:
x, input_size = block(x, input_size)
H, W = input_size
x_out = rearrange(x, 'b (h w) c -> b c h w', h=H, w=W)
outs.append(x_out)
# if i in self._out_features:
# norm_layer = getattr(self, f'norm{i}')
# x_out = norm_layer(x)
# H, W = input_size
# x_out = rearrange(x_out, 'b (h w) c -> b c h w', h=H, w=W)
# outs.append(x_out)
return {
"image_features": outs,
"last_feat": outs[-1],
}
def forward(self, x):
x = self.forward_features(x)
# x = self.head(x)
return x
@classmethod
def from_config(cls, config, enable_checkpoint=False):
return cls(
depths=config.depths,
embed_dims=config.dim_embed,
num_heads=config.num_heads,
num_groups=config.num_groups,
patch_size=config.patch_size,
patch_stride=config.patch_stride,
patch_padding=config.patch_padding,
patch_prenorm=config.patch_prenorm,
drop_path_rate=config.drop_path_rate,
window_size=config.window_size,
enable_checkpoint=enable_checkpoint
)
|