File size: 16,828 Bytes
930952c 4f5ca28 30d2275 4f5ca28 b67aac7 4f5ca28 b67aac7 4f5ca28 930952c 4f5ca28 b67aac7 4f5ca28 30d2275 31cab2b 30d2275 31cab2b b67aac7 30d2275 80ba8d4 31cab2b 80ba8d4 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 4f5ca28 30d2275 4f5ca28 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 30d2275 97681c2 542860e 97681c2 b67aac7 97681c2 |
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 |
from typing import Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn.common_types import _size_2_t
from yolo.tools.module_helper import auto_pad, get_activation
class Conv(nn.Module):
"""A basic convolutional block that includes convolution, batch normalization, and activation."""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_2_t,
*,
activation: Optional[str] = "SiLU",
**kwargs
):
super().__init__()
kwargs.setdefault("padding", auto_pad(kernel_size, **kwargs))
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, **kwargs)
self.bn = nn.BatchNorm2d(out_channels)
self.act = get_activation(activation)
def forward(self, x: Tensor) -> Tensor:
return self.act(self.bn(self.conv(x)))
class Pool(nn.Module):
"""A generic pooling block supporting 'max' and 'avg' pooling methods."""
def __init__(self, method: str = "max", kernel_size: _size_2_t = 2, **kwargs):
super().__init__()
kwargs.setdefault("padding", auto_pad(kernel_size, **kwargs))
pool_classes = {"max": nn.MaxPool2d, "avg": nn.AvgPool2d}
self.pool = pool_classes[method.lower()](kernel_size=kernel_size, **kwargs)
def forward(self, x: Tensor) -> Tensor:
return self.pool(x)
class ADown(nn.Module):
"""Downsampling module combining average and max pooling with convolution for feature reduction."""
def __init__(self, in_channels: int, out_channels: int):
super().__init__()
half_in_channels = in_channels // 2
half_out_channels = out_channels // 2
mid_layer = {"kernel_size": 3, "stride": 2}
self.avg_pool = Pool("avg", kernel_size=2, stride=1)
self.conv1 = Conv(half_in_channels, half_out_channels, **mid_layer)
self.max_pool = Pool("max", **mid_layer)
self.conv2 = Conv(half_in_channels, half_out_channels, kernel_size=1)
def forward(self, x: Tensor) -> Tensor:
x = self.avg_pool(x)
x1, x2 = x.chunk(2, dim=1)
x1 = self.conv1(x1)
x2 = self.max_pool(x2)
x2 = self.conv2(x2)
return torch.cat((x1, x2), dim=1)
class CBLinear(nn.Module):
"""Convolutional block that outputs multiple feature maps split along the channel dimension."""
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 1, **kwargs):
super(CBLinear, self).__init__()
kwargs.setdefault("padding", auto_pad(kernel_size, **kwargs))
self.conv = nn.Conv2d(in_channels, sum(out_channels), kernel_size, **kwargs)
self.out_channels = out_channels
def forward(self, x: Tensor) -> Tuple[Tensor]:
x = self.conv(x)
return x.split(self.out_channels, dim=1)
class SPPELAN(nn.Module):
"""SPPELAN module comprising multiple pooling and convolution layers."""
def __init__(self, in_channels, out_channels, neck_channels=Optional[int]):
super(SPPELAN, self).__init__()
neck_channels = neck_channels or out_channels // 2
self.conv1 = Conv(in_channels, neck_channels, kernel_size=1)
self.pools = nn.ModuleList([Pool("max", 5, stride=1) for _ in range(3)])
self.conv5 = Conv(4 * neck_channels, out_channels, kernel_size=1)
def forward(self, x: Tensor) -> Tensor:
features = [self.conv1(x)]
for pool in self.pools:
features.append(pool(features[-1]))
return self.conv5(torch.cat(features, dim=1))
#### -- ####
# RepVGG
class RepConv(nn.Module):
# https://github.com/DingXiaoH/RepVGG
def __init__(
self, in_channels, out_channels, kernel_size=3, padding=None, stride=1, groups=1, act=nn.SiLU(), deploy=False
):
super().__init__()
self.deploy = deploy
self.conv1 = Conv(in_channels, out_channels, kernel_size, stride=stride, groups=groups, activation=False)
self.conv2 = Conv(in_channels, out_channels, 1, stride=stride, groups=groups, activation=False)
self.act = act if isinstance(act, nn.Module) else nn.Identity()
def forward(self, x):
return self.act(self.conv1(x) + self.conv2(x))
def forward_fuse(self, x):
return self.act(self.conv(x))
# to be implement
# def fuse_convs(self):
def fuse_conv_bn(self, conv, bn):
std = (bn.running_var + bn.eps).sqrt()
bias = bn.bias - bn.running_mean * bn.weight / std
t = (bn.weight / std).reshape(-1, 1, 1, 1)
weights = conv.weight * t
bn = nn.Identity()
conv = nn.Conv2d(
in_channels=conv.in_channels,
out_channels=conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
dilation=conv.dilation,
groups=conv.groups,
bias=True,
padding_mode=conv.padding_mode,
)
conv.weight = torch.nn.Parameter(weights)
conv.bias = torch.nn.Parameter(bias)
return conv
# ResNet
class Res(nn.Module):
# ResNet bottleneck
def __init__(self, in_channels, out_channels, groups=1, act=nn.ReLU(), ratio=0.25):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = Conv(h_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv3 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv3(self.cv2(self.cv1(x)))
class RepRes(nn.Module):
# RepResNet bottleneck
def __init__(self, in_channels, out_channels, groups=1, act=nn.ReLU(), ratio=0.25):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = RepConv(h_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv3 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv3(self.cv2(self.cv1(x)))
class ConvBlock(nn.Module):
# ConvBlock
def __init__(self, in_channels, repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = (
Conv(in_channels, in_channels, 3, 1, act=act)
if repeat == 1
else Conv(in_channels, h_channels, 3, 1, act=act)
)
self.cb = (
nn.Sequential(*(Conv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat - 2)))
if repeat > 2
else nn.Identity()
)
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return self.cv2(self.cb(self.cv1(x)))
class RepConvBlock(nn.Module):
# ConvBlock
def __init__(self, in_channels, repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = (
Conv(in_channels, in_channels, 3, 1, act=act)
if repeat == 1
else RepConv(in_channels, h_channels, 3, 1, act=act)
)
self.cb = (
nn.Sequential(*(RepConv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat - 2)))
if repeat > 2
else nn.Identity()
)
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return self.cv2(self.cb(self.cv1(x)))
class ResConvBlock(nn.Module):
# ResConvBlock
def __init__(self, in_channels, repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = (
Conv(in_channels, in_channels, 3, 1, act=act)
if repeat == 1
else Conv(in_channels, h_channels, 3, 1, act=act)
)
self.cb = (
nn.Sequential(*(Conv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat - 2)))
if repeat > 2
else nn.Identity()
)
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cb(self.cv1(x)))
class ResRepConvBlock(nn.Module):
# ResConvBlock
def __init__(self, in_channels, repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = (
Conv(in_channels, in_channels, 3, 1, act=act)
if repeat == 1
else RepConv(in_channels, h_channels, 3, 1, act=act)
)
self.cb = (
nn.Sequential(*(RepConv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat - 2)))
if repeat > 2
else nn.Identity()
)
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cb(self.cv1(x)))
# Darknet
class Dark(nn.Module):
# DarkNet bottleneck
def __init__(self, in_channels, out_channels, groups=1, act=nn.ReLU(), ratio=0.5):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = Conv(h_channels, out_channels, 3, 1, groups=groups, act=act)
def forward(self, x):
return x + self.cv2(self.cv1(x))
class RepDark(nn.Module):
# RepDarkNet bottleneck
def __init__(self, in_channels, out_channels, groups=1, act=nn.ReLU(), ratio=0.5):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = RepConv(in_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv2 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cv1(x))
# CSPNet
class CSP(nn.Module):
# CSPNet
def __init__(self, in_channels, out_channels, repeat=1, cb_repeat=2, act=nn.ReLU()):
super().__init__()
h_channels = in_channels // 2
self.cv1 = Conv(in_channels, in_channels, 1, 1, act=act)
self.cb = nn.Sequential(*(ResConvBlock(h_channels, act=act, repeat=cb_repeat) for _ in range(repeat)))
self.cv2 = Conv(2 * h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
x = list(self.cv1(x).chunk(2, 1))
x = torch.cat((self.cb(x[0]), x[1]), 1)
x = self.cv2(x)
return x
class CSPDark(nn.Module):
# CSPNet
def __init__(self, in_channels, out_channels, repeat=1, groups=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = in_channels // 2
self.cv1 = Conv(in_channels, in_channels, 1, 1, act=act)
self.cb = nn.Sequential(
*(Dark(h_channels, h_channels, groups=groups, act=act, ratio=ratio) for _ in range(repeat))
)
self.cv2 = Conv(2 * h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
return self.cv2(torch.cat((self.cb(y[0]), y[1]), 1))
# ELAN
class ELAN(nn.Module):
# ELAN
def __init__(self, in_channels, out_channels, med_channels, elan_repeat=2, cb_repeat=2, ratio=1.0):
super().__init__()
h_channels = med_channels // 2
self.cv1 = Conv(in_channels, med_channels, 1, 1)
self.cb = nn.ModuleList(ConvBlock(h_channels, repeat=cb_repeat, ratio=ratio) for _ in range(elan_repeat))
self.cv2 = Conv((2 + elan_repeat) * h_channels, out_channels, 1, 1)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
y.extend((m(y[-1])) for m in self.cb)
return self.cv2(torch.cat(y, 1))
class CSPELAN(nn.Module):
# ELAN
def __init__(self, in_channels, out_channels, med_channels, elan_repeat=2, cb_repeat=2, ratio=1.0):
super().__init__()
h_channels = med_channels // 2
self.cv1 = Conv(in_channels, med_channels, 1, 1)
self.cb = nn.ModuleList(CSP(h_channels, h_channels, repeat=cb_repeat, ratio=ratio) for _ in range(elan_repeat))
self.cv2 = Conv((2 + elan_repeat) * h_channels, out_channels, 1, 1)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
y.extend((m(y[-1])) for m in self.cb)
return self.cv2(torch.cat(y, 1))
class Concat(nn.Module):
def __init__(self, dim=1):
super(Concat, self).__init__()
self.dim = dim
def forward(self, x):
return torch.cat(x, self.dim)
# TODO: check if Mit
class SPPCSPConv(nn.Module):
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, in_channels, out_channels, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
super(SPPCSPConv, self).__init__()
c_ = int(2 * out_channels * e) # hidden channels
self.cv1 = Conv(in_channels, c_, 1)
self.cv2 = Conv(in_channels, c_, 1)
self.cv3 = Conv(c_, c_, 3)
self.cv4 = Conv(c_, c_, 1)
self.m = nn.ModuleList([Pool(method="max", kernel_size=x, stride=1, padding=x // 2) for x in k])
self.cv5 = Conv(4 * c_, c_, 1)
self.cv6 = Conv(c_, c_, 3)
self.cv7 = Conv(2 * c_, out_channels, 1)
def forward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
y2 = self.cv2(x)
return self.cv7(torch.cat((y1, y2), dim=1))
class ImplicitA(nn.Module):
"""
Implement YOLOR - implicit knowledge(Add), paper: https://arxiv.org/abs/2105.04206
"""
def __init__(self, channel: int, mean: float = 0.0, std: float = 0.02):
super().__init__()
self.channel = channel
self.mean = mean
self.std = std
self.implicit = nn.Parameter(torch.empty(1, channel, 1, 1))
nn.init.normal_(self.implicit, mean=mean, std=self.std)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.implicit + x
class ImplicitM(nn.Module):
"""
Implement YOLOR - implicit knowledge(multiply), paper: https://arxiv.org/abs/2105.04206
"""
def __init__(self, channel: int, mean: float = 1.0, std: float = 0.02):
super().__init__()
self.channel = channel
self.mean = mean
self.std = std
self.implicit = nn.Parameter(torch.empty(1, channel, 1, 1))
nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.implicit * x
class UpSample(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.UpSample = nn.Upsample(**kwargs)
def forward(self, x):
return self.UpSample(x)
class IDetect(nn.Module):
"""
#TODO: Add Detect class, change IDetect base class
"""
stride = None # strides computed during build
export = False # onnx export
end2end = False
include_nms = False
concat = False
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(IDetect, self).__init__()
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer("anchors", a) # shape(nl,na,2)
self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
self.ia = nn.ModuleList(ImplicitA(x) for x in ch)
self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch)
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](self.ia[i](x[i])) # conv
x[i] = self.im[i](x[i])
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
y = x[i].sigmoid()
y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1), x)
|