def __init__()

in seamseg/modules/residual.py [0:0]

• 47 lines of code
• 11 McCabe index (conditional complexity)

    def __init__(self,
                 in_channels,
                 channels,
                 stride=1,
                 dilation=1,
                 groups=1,
                 norm_act=ABN,
                 dropout=None):
        super(ResidualBlock, self).__init__()

        # Check parameters for inconsistencies
        if len(channels) != 2 and len(channels) != 3:
            raise ValueError("channels must contain either two or three values")
        if len(channels) == 2 and groups != 1:
            raise ValueError("groups > 1 are only valid if len(channels) == 3")

        is_bottleneck = len(channels) == 3
        need_proj_conv = stride != 1 or in_channels != channels[-1]

        if not is_bottleneck:
            bn2 = norm_act(channels[1])
            bn2.activation = "identity"
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 3, stride=stride, padding=dilation, bias=False,
                                    dilation=dilation)),
                ("bn1", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=1, padding=dilation, bias=False,
                                    dilation=dilation)),
                ("bn2", bn2)
            ]
            if dropout is not None:
                layers = layers[0:2] + [("dropout", dropout())] + layers[2:]
        else:
            bn3 = norm_act(channels[2])
            bn3.activation = "identity"
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 1, stride=1, padding=0, bias=False)),
                ("bn1", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=stride, padding=dilation, bias=False,
                                    groups=groups, dilation=dilation)),
                ("bn2", norm_act(channels[1])),
                ("conv3", nn.Conv2d(channels[1], channels[2], 1, stride=1, padding=0, bias=False)),
                ("bn3", bn3)
            ]
            if dropout is not None:
                layers = layers[0:4] + [("dropout", dropout())] + layers[4:]
        self.convs = nn.Sequential(OrderedDict(layers))

        if need_proj_conv:
            self.proj_conv = nn.Conv2d(in_channels, channels[-1], 1, stride=stride, padding=0, bias=False)
            self.proj_bn = norm_act(channels[-1])
            self.proj_bn.activation = "identity"