def FullConvolutionalNetIntegratedLinear()

in sparseconvnet/networkArchitectures.py [0:0]

• 38 lines of code
• 5 McCabe index (conditional complexity)

def FullConvolutionalNetIntegratedLinear(dimension, reps, nPlanes, nClasses=-1, residual=False, downsample=[2,2], leakiness=0):
    if nClasses==-1:
        nClasses=reps[0]
    def l(x):
        return x+nPlanes
    def foo(m,np):
        for _ in range(reps):
            if residual: #ResNet style blocks
                m.add(scn.ConcatTable()
                      .add(scn.Identity())
                      .add(scn.Sequential()
                        .add(scn.BatchNormLeakyReLU(np,leakiness=leakiness))
                        .add(scn.SubmanifoldConvolution(dimension, np, np, 3, False))
                        .add(scn.BatchNormLeakyReLU(np,leakiness=leakiness))
                        .add(scn.SubmanifoldConvolution(dimension, np, np, 3, False)))
                 ).add(scn.AddTable())
            else: #VGG style blocks
                m.add(scn.BatchNormLeakyReLU(np,leakiness=leakiness)
                ).add(scn.SubmanifoldConvolution(dimension, np, np, 3, False))
    def bar(m,nPlanes,bias):
        m.add(scn.BatchNormLeakyReLU(nPlanes,leakiness=leakiness))
        m.add(scn.NetworkInNetwork(nPlanes,nClasses,bias)) #accumulte softmax input, only one set of biases
    def baz(nPlanes):
        m=scn.Sequential()
        foo(m,nPlanes[0])
        if len(nPlanes)==1:
            bar(m,nPlanes[0],True)
        else:
            a=scn.Sequential()
            bar(a,nPlanes,False)
            b=scn.Sequential(
                scn.BatchNormLeakyReLU(nPlanes,leakiness=leakiness),
                scn.Convolution(dimension, nPlanes[0], nPlanes[1], downsample[0], downsample[1], False),
                baz(nPlanes[1:]),
                scn.UnPooling(dimension, downsample[0], downsample[1]))
            m.add(ConcatTable(a,b))
            m.add(scn.AddTable())
    return baz(nPlanes)