Class CEBlock

segmentation/backbones/bisenetv2.py:302–361

Context Embedding Block for large receptive filed in Semantic Branch. Args: in_channels (int): Number of input channels. Default: 3. out_channels (int): Number of output channels. Default: 16. conv_cfg (dict | None): Config of conv layers.

class CEBlock(BaseModule):
    """Context Embedding Block for large receptive filed in Semantic Branch.

    Args:
        in_channels (int): Number of input channels.
            Default: 3.
        out_channels (int): Number of output channels.
            Default: 16.
        conv_cfg (dict | None): Config of conv layers.
            Default: None.
        norm_cfg (dict | None): Config of norm layers.
            Default: dict(type='BN').
        act_cfg (dict): Config of activation layers.
            Default: dict(type='ReLU').
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None.
    Returns:
        x (torch.Tensor): Last feature map in Semantic Branch.
    """

    def __init__(self,
                 in_channels=3,
                 out_channels=16,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 init_cfg=None):
        super(CEBlock, self).__init__(init_cfg=init_cfg)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.gap = nn.Sequential(
            nn.AdaptiveAvgPool2d((1, 1)),
            build_norm_layer(norm_cfg, self.in_channels)[1])
        self.conv_gap = ConvModule(
            in_channels=self.in_channels,
            out_channels=self.out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg)
        # Note: in paper here is naive conv2d, no bn-relu
        self.conv_last = ConvModule(
            in_channels=self.out_channels,
            out_channels=self.out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg)

    def forward(self, x):
        identity = x
        x = self.gap(x)
        x = self.conv_gap(x)
        x = identity + x