| import math |
|
|
| from mmcv.cnn import build_conv_layer, build_norm_layer |
|
|
| from ..builder import BACKBONES |
| from .detectors_resnet import Bottleneck as _Bottleneck |
| from .detectors_resnet import DetectoRS_ResNet |
|
|
|
|
| class Bottleneck(_Bottleneck): |
| expansion = 4 |
|
|
| def __init__(self, |
| inplanes, |
| planes, |
| groups=1, |
| base_width=4, |
| base_channels=64, |
| **kwargs): |
| """Bottleneck block for ResNeXt. |
| |
| If style is "pytorch", the stride-two layer is the 3x3 conv layer, if |
| it is "caffe", the stride-two layer is the first 1x1 conv layer. |
| """ |
| super(Bottleneck, self).__init__(inplanes, planes, **kwargs) |
|
|
| if groups == 1: |
| width = self.planes |
| else: |
| width = math.floor(self.planes * |
| (base_width / base_channels)) * groups |
|
|
| self.norm1_name, norm1 = build_norm_layer( |
| self.norm_cfg, width, postfix=1) |
| self.norm2_name, norm2 = build_norm_layer( |
| self.norm_cfg, width, postfix=2) |
| self.norm3_name, norm3 = build_norm_layer( |
| self.norm_cfg, self.planes * self.expansion, postfix=3) |
|
|
| self.conv1 = build_conv_layer( |
| self.conv_cfg, |
| self.inplanes, |
| width, |
| kernel_size=1, |
| stride=self.conv1_stride, |
| bias=False) |
| self.add_module(self.norm1_name, norm1) |
| fallback_on_stride = False |
| self.with_modulated_dcn = False |
| if self.with_dcn: |
| fallback_on_stride = self.dcn.pop('fallback_on_stride', False) |
| if self.with_sac: |
| self.conv2 = build_conv_layer( |
| self.sac, |
| width, |
| width, |
| kernel_size=3, |
| stride=self.conv2_stride, |
| padding=self.dilation, |
| dilation=self.dilation, |
| groups=groups, |
| bias=False) |
| elif not self.with_dcn or fallback_on_stride: |
| self.conv2 = build_conv_layer( |
| self.conv_cfg, |
| width, |
| width, |
| kernel_size=3, |
| stride=self.conv2_stride, |
| padding=self.dilation, |
| dilation=self.dilation, |
| groups=groups, |
| bias=False) |
| else: |
| assert self.conv_cfg is None, 'conv_cfg must be None for DCN' |
| self.conv2 = build_conv_layer( |
| self.dcn, |
| width, |
| width, |
| kernel_size=3, |
| stride=self.conv2_stride, |
| padding=self.dilation, |
| dilation=self.dilation, |
| groups=groups, |
| bias=False) |
|
|
| self.add_module(self.norm2_name, norm2) |
| self.conv3 = build_conv_layer( |
| self.conv_cfg, |
| width, |
| self.planes * self.expansion, |
| kernel_size=1, |
| bias=False) |
| self.add_module(self.norm3_name, norm3) |
|
|
|
|
| @BACKBONES.register_module() |
| class DetectoRS_ResNeXt(DetectoRS_ResNet): |
| """ResNeXt backbone for DetectoRS. |
| |
| Args: |
| groups (int): The number of groups in ResNeXt. |
| base_width (int): The base width of ResNeXt. |
| """ |
|
|
| arch_settings = { |
| 50: (Bottleneck, (3, 4, 6, 3)), |
| 101: (Bottleneck, (3, 4, 23, 3)), |
| 152: (Bottleneck, (3, 8, 36, 3)) |
| } |
|
|
| def __init__(self, groups=1, base_width=4, **kwargs): |
| self.groups = groups |
| self.base_width = base_width |
| super(DetectoRS_ResNeXt, self).__init__(**kwargs) |
|
|
| def make_res_layer(self, **kwargs): |
| return super().make_res_layer( |
| groups=self.groups, |
| base_width=self.base_width, |
| base_channels=self.base_channels, |
| **kwargs) |
|
|
