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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import math | |
| from mmcv.cnn import build_conv_layer, build_norm_layer | |
| from mmdet.registry import MODELS | |
| 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) | |
| 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) | |