| 279 | |
| 280 | |
| 281 | class ResNet(nn.Module): |
| 282 | def __init__( |
| 283 | self, |
| 284 | block: Type[Union[SEBasicBlock, SEBottleneck]], |
| 285 | layers: List[int], |
| 286 | num_classes: int = 1000, |
| 287 | zero_init_residual: bool = False, |
| 288 | groups: int = 1, |
| 289 | width_per_group: int = 64, |
| 290 | replace_stride_with_dilation: Optional[List[bool]] = None, |
| 291 | norm_layer: Optional[Callable[..., nn.Module]] = None, |
| 292 | ) -> None: |
| 293 | super().__init__() |
| 294 | if norm_layer is None: |
| 295 | norm_layer = nn.BatchNorm2d |
| 296 | self._norm_layer = norm_layer |
| 297 | |
| 298 | self.inplanes = 64 |
| 299 | self.dilation = 1 |
| 300 | if replace_stride_with_dilation is None: |
| 301 | # each element in the tuple indicates if we should replace |
| 302 | # the 2x2 stride with a dilated convolution instead |
| 303 | replace_stride_with_dilation = [False, False, False] |
| 304 | if len(replace_stride_with_dilation) != 3: |
| 305 | raise ValueError( |
| 306 | "replace_stride_with_dilation should be None " |
| 307 | f"or a 3-element tuple, got {replace_stride_with_dilation}" |
| 308 | ) |
| 309 | self.groups = groups |
| 310 | self.base_width = width_per_group |
| 311 | |
| 312 | self.stem = nn.Sequential( |
| 313 | nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), |
| 314 | norm_layer(self.inplanes), |
| 315 | nn.ReLU(inplace=True), |
| 316 | nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| 317 | ) |
| 318 | |
| 319 | self.layer1 = self._make_layer(block, 64, layers[0]) |
| 320 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) |
| 321 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) |
| 322 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) |
| 323 | |
| 324 | self.fc = nn.Sequential( |
| 325 | nn.AdaptiveAvgPool2d((1, 1)), |
| 326 | nn.Flatten(), |
| 327 | nn.Linear(512 * block.expansion, num_classes) |
| 328 | ) |
| 329 | |
| 330 | for m in self.modules(): |
| 331 | if isinstance(m, nn.Conv2d): |
| 332 | nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") |
| 333 | elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
| 334 | nn.init.constant_(m.weight, 1) |
| 335 | nn.init.constant_(m.bias, 0) |
| 336 | |
| 337 | # Zero-initialize the last BN in each residual branch, |
| 338 | # so that the residual branch starts with zeros, and each residual block behaves like an identity. |
no outgoing calls
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