def __init__(self, backbone: nn.Module) -> None:
    super().__init__()
    self.backbone = backbone
    (c3, c4) = self.backbone.channels[(- 2):]
    self.arm16 = AttentionRefinmentModule(c3, 128)
    self.arm32 = AttentionRefinmentModule(c4, 128)
    self.global_context = nn.Sequential(nn.AdaptiveAvgPool2d(1), ConvModule(c4, 128, 1, 1, 0))
    self.up16 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True)
    self.up32 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True)
    self.refine16 = ConvModule(128, 128, 3, 1, 1)
    self.refine32 = ConvModule(128, 128, 3, 1, 1)
def __init__(self, backbone: nn.Module) -> None:
    super().__init__()
    self.backbone = backbone
    (c3, c4) = self.backbone.channels[(- 2):]
    self.arm16 = AttentionRefinmentModule(c3, 128)
    self.arm32 = AttentionRefinmentModule(c4, 128)
    self.global_context = nn.Sequential(nn.AdaptiveAvgPool2d(1), ConvModule(c4, 128, 1, 1, 0))
    self.up16 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True)
    self.up32 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True)
    self.refine16 = ConvModule(128, 128, 3, 1, 1)
    self.refine32 = ConvModule(128, 128, 3, 1, 1)