def __init__()

in segmentation/model/cnsn_resnet.py [0:0]

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

    def __init__(self, inplanes, planes, pos, beta, crop, cnsn_type, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlockCustom, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

        assert cnsn_type in ['sn', 'cn', 'cnsn']

        if 'cn' in cnsn_type:
            crossnorm = CrossNorm(beta=beta, crop=crop)
        else:
            crossnorm = None

        if 'sn' in cnsn_type:
            print('using SelfNorm module')
            if pos == 'pre' and not self.is_in_equal_out:
                selfnorm = SelfNorm(in_planes)
            else:
                selfnorm = SelfNorm(out_planes)
        else:
            selfnorm = None

        self.cnsn = CNSN(selfnorm=selfnorm, crossnorm=crossnorm)
        
        self.pos = pos
        if pos is not None:
            print('{} in residual module: {}'.format(cnsn_type, pos))
            assert pos in ['residual', 'identity', 'pre', 'post']