Class BasicConvBlock

segmentation/backbones/unet.py:16–86

Basic convolutional block for UNet. This module consists of several plain convolutional layers. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. num_convs (int): Number of convolutional layers. Default: 2. str

class BasicConvBlock(nn.Module):
    """Basic convolutional block for UNet.

    This module consists of several plain convolutional layers.

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        num_convs (int): Number of convolutional layers. Default: 2.
        stride (int): Whether use stride convolution to downsample
            the input feature map. If stride=2, it only uses stride convolution
            in the first convolutional layer to downsample the input feature
            map. Options are 1 or 2. Default: 1.
        dilation (int): Whether use dilated convolution to expand the
            receptive field. Set dilation rate of each convolutional layer and
            the dilation rate of the first convolutional layer is always 1.
            Default: 1.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        conv_cfg (dict | None): Config dict for convolution layer.
            Default: None.
        norm_cfg (dict | None): Config dict for normalization layer.
            Default: dict(type='BN').
        act_cfg (dict | None): Config dict for activation layer in ConvModule.
            Default: dict(type='ReLU').
        dcn (bool): Use deformable convolution in convolutional layer or not.
            Default: None.
        plugins (dict): plugins for convolutional layers. Default: None.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_convs=2,
                 stride=1,
                 dilation=1,
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 dcn=None,
                 plugins=None):
        super(BasicConvBlock, self).__init__()
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'

        self.with_cp = with_cp
        convs = []
        for i in range(num_convs):
            convs.append(
                ConvModule(
                    in_channels=in_channels if i == 0 else out_channels,
                    out_channels=out_channels,
                    kernel_size=3,
                    stride=stride if i == 0 else 1,
                    dilation=1 if i == 0 else dilation,
                    padding=1 if i == 0 else dilation,
                    conv_cfg=conv_cfg,