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b417724 | 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 | """OpenHTR visual encoder with local and global feature mixing."""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import kaiming_normal_, ones_, trunc_normal_, zeros_
from .layers import DropPath, Identity
__all__ = ['OpenHTR_Encoder']
class ConvBNLayer(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=0,
bias=False,
groups=1,
act=nn.GELU,
):
super().__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
bias=bias,
)
self.norm = nn.BatchNorm2d(out_channels)
self.act = act()
def forward(self, inputs):
out = self.conv(inputs)
out = self.norm(out)
out = self.act(out)
return out
class LastStage(nn.Module):
def __init__(self, in_channels, out_channels, last_drop, out_char_num=0):
super().__init__()
self.last_conv = nn.Linear(in_channels, out_channels, bias=False)
self.hardswish = nn.Hardswish()
self.dropout = nn.Dropout(p=last_drop)
def forward(self, x, sz):
x = x.reshape(-1, sz[0], sz[1], x.shape[-1])
x = x.mean(1)
x = self.last_conv(x)
x = self.hardswish(x)
x = self.dropout(x)
return x, [1, sz[1]]
class Feat2D(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, sz):
C = x.shape[-1]
x = x.transpose(1, 2).reshape(-1, C, sz[0], sz[1])
return x, sz
def build_rope_2d(head_dim, H, W, device, dtype=torch.float32):
"""2D RoPE angles for a h-major flattened grid (H*W, w fastest).
Returns cos/sin of shape [N, head_dim // 2] for the interleaved
rotation convention (token dims are rotated in (2i, 2i+1) pairs).
The head dim is split evenly: first half of the rotation frequencies
encode the H axis, second half the W axis.
"""
half = head_dim // 2
assert half % 2 == 0, 'head_dim must be divisible by 4 for 2D RoPE'
inv_freq = 1.0 / (10000.0**(torch.arange(0, half, 2, device=device,
dtype=dtype) / half))
h_pos = torch.arange(H, device=device, dtype=dtype)
w_pos = torch.arange(W, device=device, dtype=dtype)
ang_h = h_pos[:, None] * inv_freq[None, :] # [H, half//2]
ang_w = w_pos[:, None] * inv_freq[None, :] # [W, half//2]
ang_h = ang_h.repeat_interleave(W, dim=0) # [H*W, half//2] (h-major)
ang_w = ang_w.repeat(H, 1) # [H*W, half//2]
angles = torch.cat([ang_h, ang_w], dim=1) # [N, half]
return angles.cos(), angles.sin()
def rotate_interleaved(x, cos, sin):
"""RoPE rotation, interleaved-pair convention.
x: [B, heads, N, D]; cos/sin: [N, D//2] (broadcastable).
"""
x1 = x[..., 0::2]
x2 = x[..., 1::2]
out1 = x1 * cos - x2 * sin
out2 = x1 * sin + x2 * cos
return torch.stack([out1, out2], dim=-1).flatten(-2)
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
dtype = x.dtype
x = x.float()
x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return self.weight * x.to(dtype)
class SwiGLU(nn.Module):
def __init__(self, dim, hidden_dim, drop=0.0):
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim, bias=False) # gate
self.w3 = nn.Linear(dim, hidden_dim, bias=False) # up
self.w2 = nn.Linear(hidden_dim, dim, bias=False) # down
self.drop = nn.Dropout(drop)
def forward(self, x):
return self.drop(self.w2(F.silu(self.w1(x)) * self.w3(x)))
class ConvNeXtBlock(nn.Module):
"""ConvNeXt block operating on [B, C, H, W] feature maps."""
def __init__(self, dim, kernel_size=7, mlp_ratio=4, drop_path=0.0):
super().__init__()
self.dwconv = nn.Conv2d(dim,
dim,
kernel_size,
1,
kernel_size // 2,
groups=dim)
self.norm = nn.LayerNorm(dim, eps=1e-6)
hidden = int(dim * mlp_ratio)
self.pw1 = nn.Linear(dim, hidden)
self.act = nn.GELU()
self.pw2 = nn.Linear(hidden, dim)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
def forward(self, x):
B, C, H, W = x.shape
y = self.dwconv(x).permute(0, 2, 3, 1) # B H W C
y = self.norm(y)
y = self.pw2(self.act(self.pw1(y)))
y = y.permute(0, 3, 1, 2)
return x + self.drop_path(y)
class GlobalAttentionBlock(nn.Module):
"""Pre-norm attention block with 2D RoPE, RMSNorm and SwiGLU.
Input/output: [B, C, H, W]; attention runs on the flattened (h-major)
sequence so global mixing covers the whole line.
"""
def __init__(self,
dim,
num_heads,
mlp_ratio=2.67,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
qkv_bias=True):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
assert self.head_dim * num_heads == dim
assert self.head_dim % 4 == 0, 'head_dim must be divisible by 4'
self.norm1 = RMSNorm(dim)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(drop)
self.attn_drop = attn_drop
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else Identity()
self.norm2 = RMSNorm(dim)
hidden = max(64, int(dim * mlp_ratio) // 64 * 64)
self.mlp = SwiGLU(dim, hidden, drop=drop)
def forward(self, x):
# x: [B, C, H, W]
B, C, H, W = x.shape
y = x.flatten(2).transpose(1, 2) # B N C
y2 = self.norm1(y)
qkv = self.qkv(y2).reshape(B, -1, 3, self.num_heads,
self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
cos, sin = build_rope_2d(self.head_dim, H, W, x.device,
q.dtype)
cos = cos.to(q.dtype)[None, None] # 1 1 N hd/2
sin = sin.to(q.dtype)[None, None]
q = rotate_interleaved(q, cos, sin)
k = rotate_interleaved(k, cos, sin)
y = F.scaled_dot_product_attention(
q, k, v, dropout_p=self.attn_drop if self.training else 0.0)
y = y.transpose(1, 2).reshape(B, -1, C)
y = self.proj_drop(self.proj(y))
x = x.flatten(2).transpose(1, 2) + self.drop_path(y)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x.transpose(1, 2).reshape(B, C, H, W)
class EncoderStage(nn.Module):
def __init__(self,
dim=64,
out_dim=256,
depth=3,
mixer=['Conv'] * 3,
kernel_sizes=[7] * 3,
sub_k=[2, 1],
num_heads=2,
mlp_ratio=4,
attn_mlp_ratio=2.67,
qkv_bias=True,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path=[0.1] * 3,
downsample=None,
**kwargs):
super().__init__()
self.blocks = nn.ModuleList()
for i in range(depth):
if mixer[i] == 'Conv':
self.blocks.append(
ConvNeXtBlock(dim=dim,
kernel_size=kernel_sizes[i],
mlp_ratio=mlp_ratio,
drop_path=drop_path[i]))
else:
self.blocks.append(
GlobalAttentionBlock(
dim=dim,
num_heads=num_heads,
mlp_ratio=attn_mlp_ratio,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=drop_path[i],
qkv_bias=qkv_bias,
))
if downsample:
self.downsample = EncoderDownsample(dim, out_dim, stride=sub_k)
else:
self.downsample = None
def forward(self, x, sz):
for blk in self.blocks:
x = blk(x)
if self.downsample is not None:
x, sz = self.downsample(x, sz)
return x, sz
class EncoderDownsample(nn.Module):
"""Strided 3x3 conv + RMSNorm, operating on [B, C, H, W] (stays 4D)."""
def __init__(self, in_channels, out_channels, stride=[2, 1]):
super().__init__()
self.conv = nn.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1)
self.norm = RMSNorm(out_channels)
def forward(self, x, sz):
x = self.conv(x)
C, H, W = x.shape[1:]
y = self.norm(x.permute(0, 2, 3, 1)) # B H W C
return y.permute(0, 3, 1, 2).contiguous(), [H, W]
class OpenHTR_Encoder(nn.Module):
"""Encode local strokes and global context as a sequence of visual tokens."""
def __init__(self,
max_sz=[32, 128],
in_channels=3,
out_channels=192,
depths=[3, 6, 3],
dims=[64, 128, 256],
mixer=[['Conv'] * 3, ['Conv'] * 3 + ['Global'] * 3,
['Global'] * 3],
sub_k=[[1, 1], [2, 1], [1, 1]],
num_heads=[2, 4, 8],
mlp_ratio=4,
attn_mlp_ratio=2.67,
qkv_bias=True,
drop_rate=0.0,
last_drop=0.1,
attn_drop_rate=0.0,
drop_path_rate=0.1,
last_stage=False,
feat2d=False,
num_convs=None, # unused, kept for config compat
kernel_sizes=[[7] * 3, [7] * 3 + [7] * 3, [7] * 3],
**kwargs):
super().__init__()
num_stages = len(depths)
self.num_features = dims[-1]
# Two stride-2 convolutions reduce the input resolution by a factor of four.
self.pope = nn.Sequential(
ConvBNLayer(in_channels=in_channels,
out_channels=dims[0] // 2,
kernel_size=3,
stride=2,
padding=1,
act=nn.GELU),
ConvBNLayer(in_channels=dims[0] // 2,
out_channels=dims[0],
kernel_size=3,
stride=2,
padding=1,
act=nn.GELU),
)
dpr = np.linspace(0, drop_path_rate, sum(depths))
self.stages = nn.ModuleList()
for i_stage in range(num_stages):
stage = EncoderStage(
dim=dims[i_stage],
out_dim=dims[i_stage + 1] if i_stage < num_stages - 1 else 0,
depth=depths[i_stage],
mixer=mixer[i_stage],
kernel_sizes=kernel_sizes[i_stage] if len(
kernel_sizes[i_stage]) == len(mixer[i_stage]) else [7] *
len(mixer[i_stage]),
sub_k=sub_k[i_stage],
num_heads=num_heads[i_stage],
mlp_ratio=mlp_ratio,
attn_mlp_ratio=attn_mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_stage]):sum(depths[:i_stage + 1])],
downsample=False if i_stage == num_stages - 1 else True,
)
self.stages.append(stage)
self.out_channels = self.num_features
self.last_stage = last_stage
self.last_stage_layer = None
self.feat2d_layer = None
if last_stage:
self.out_channels = out_channels
self.last_stage_layer = LastStage(self.num_features, out_channels,
last_drop)
if feat2d:
self.feat2d_layer = Feat2D()
self.apply(self._init_weights)
def _init_weights(self, m: nn.Module):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, mean=0, std=0.02)
if m.bias is not None:
zeros_(m.bias)
if isinstance(m, nn.LayerNorm):
zeros_(m.bias)
ones_(m.weight)
if isinstance(m, nn.Conv2d):
kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
@torch.jit.ignore
def no_weight_decay(self):
return {'patch_embed', 'downsample', 'pos_embed'}
def forward(self, x):
if len(x.shape) == 5:
x = x.flatten(0, 1)
sz = x.shape[2:]
x = self.pope(x)
sz = [sz[0] // 4, sz[1] // 4]
for stage in self.stages:
x, sz = stage(x, sz)
# All EncoderStage blocks keep [B, C, H, W]; convert once at the end.
if self.last_stage_layer is not None:
x, sz = self.last_stage_layer(x.flatten(2).transpose(1, 2), sz)
elif self.feat2d_layer is not None:
pass # already [B, C, H, W]
else:
x = x.flatten(2).transpose(1, 2) # B N C
return x
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