def __init__()

in siammot/modelling/backbone/dla.py [0:0]

• 31 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, levels, block, in_channels, out_channels, stride=1,
                 dilation=1, cardinality=1, base_width=64,
                 level_root=False, root_dim=0, root_kernel_size=1, root_residual=False,
                 batch_norm=FrozenBatchNorm2d, with_dcn=False):
        super(DlaTree, self).__init__()
        if root_dim == 0:
            root_dim = 2 * out_channels
        if level_root:
            root_dim += in_channels
        cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width, batch_norm=batch_norm, with_dcn=with_dcn)
        if levels == 1:
            self.tree1 = block(in_channels, out_channels, stride, **cargs)
            self.tree2 = block(out_channels, out_channels, 1, **cargs)
        else:
            cargs.update(dict(root_kernel_size=root_kernel_size, root_residual=root_residual))
            self.tree1 = DlaTree(
                levels - 1, block, in_channels, out_channels, stride, root_dim=0, **cargs)
            self.tree2 = DlaTree(
                levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, **cargs)
        if levels == 1:
            self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_residual, batch_norm=batch_norm)
        self.level_root = level_root
        self.root_dim = root_dim
        self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else None
        self.project = None
        if in_channels != out_channels:
            self.project = nn.Sequential(
                Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
                batch_norm(out_channels)
            )
        self.levels = levels