def SparseResNet()

in sparseconvnet/networkArchitectures.py [0:0]
64 lines of code
8 McCabe index (conditional complexity)

def SparseResNet(dimension, nInputPlanes, layers):
    """
    pre-activated ResNet
    e.g. layers = {{'basic',16,2,1},{'basic',32,2}}
    """
    nPlanes = nInputPlanes
    m = scn.Sequential()

    def residual(nIn, nOut, stride):
        if stride > 1:
            return scn.Convolution(dimension, nIn, nOut, 3, stride, False)
        elif nIn != nOut:
            return scn.NetworkInNetwork(nIn, nOut, False)
        else:
            return scn.Identity()
    for blockType, n, reps, stride in layers:
        for rep in range(reps):
            if blockType[0] == 'b':  # basic block
                if rep == 0:
                    m.add(scn.BatchNormReLU(nPlanes))
                    m.add(
                        scn.ConcatTable().add(
                            scn.Sequential().add(
                                scn.SubmanifoldConvolution(
                                    dimension,
                                    nPlanes,
                                    n,
                                    3,
                                    False) if stride == 1 else scn.Convolution(
                                    dimension,
                                    nPlanes,
                                    n,
                                    3,
                                    stride,
                                    False)) .add(
                                scn.BatchNormReLU(n)) .add(
                                scn.SubmanifoldConvolution(
                                    dimension,
                                    n,
                                    n,
                                    3,
                                    False))) .add(
                            residual(
                                nPlanes,
                                n,
                                stride)))
                else:
                    m.add(
                        scn.ConcatTable().add(
                            scn.Sequential().add(
                                scn.BatchNormReLU(nPlanes)) .add(
                                scn.SubmanifoldConvolution(
                                    dimension,
                                    nPlanes,
                                    n,
                                    3,
                                    False)) .add(
                                scn.BatchNormReLU(n)) .add(
                                scn.SubmanifoldConvolution(
                                    dimension,
                                    n,
                                    n,
                                    3,
                                    False))) .add(
                            scn.Identity()))
            nPlanes = n
            m.add(scn.AddTable())
    m.add(scn.BatchNormReLU(nPlanes))
    return m