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