Class CGNet

segmentation/backbones/cgnet.py:187–372

CGNet backbone. This backbone is the implementation of `A Light-weight Context Guided Network for Semantic Segmentation `_. Args: in_channels (int): Number of input image channels. Normally 3. num_channels (tuple[int]): Numbers of featu

@BACKBONES.register_module()
class CGNet(BaseModule):
    """CGNet backbone.

    This backbone is the implementation of `A Light-weight Context Guided
    Network for Semantic Segmentation <https://arxiv.org/abs/1811.08201>`_.

    Args:
        in_channels (int): Number of input image channels. Normally 3.
        num_channels (tuple[int]): Numbers of feature channels at each stages.
            Default: (32, 64, 128).
        num_blocks (tuple[int]): Numbers of CG blocks at stage 1 and stage 2.
            Default: (3, 21).
        dilations (tuple[int]): Dilation rate for surrounding context
            extractors at stage 1 and stage 2. Default: (2, 4).
        reductions (tuple[int]): Reductions for global context extractors at
            stage 1 and stage 2. Default: (8, 16).
        conv_cfg (dict): Config dict for convolution layer.
            Default: None, which means using conv2d.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='BN', requires_grad=True).
        act_cfg (dict): Config dict for activation layer.
            Default: dict(type='PReLU').
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        pretrained (str, optional): model pretrained path. Default: None
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None
    """

    def __init__(self,
                 in_channels=3,
                 num_channels=(32, 64, 128),
                 num_blocks=(3, 21),
                 dilations=(2, 4),
                 reductions=(8, 16),
                 conv_cfg=None,
                 norm_cfg=dict(type='BN', requires_grad=True),
                 act_cfg=dict(type='PReLU'),
                 norm_eval=False,
                 with_cp=False,
                 pretrained=None,
                 init_cfg=None):

        super(CGNet, self).__init__(init_cfg)

        assert not (init_cfg and pretrained), \
            'init_cfg and pretrained cannot be setting at the same time'
        if isinstance(pretrained, str):
            warnings.warn('DeprecationWarning: pretrained is a deprecated, '
                          'please use "init_cfg" instead')
            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
        elif pretrained is None:
            if init_cfg is None:
                self.init_cfg = [
                    dict(type='Kaiming', layer=['Conv2d', 'Linear']),