BroadcastReduce¶

class continual.BroadcastReduce(*args: CoModule, reduce: Union[Reduction, Callable[[Sequence[Tensor]], Tensor], str] = 'sum', auto_delay=True)[source]¶

class continual.BroadcastReduce(arg: OrderedDict[str, CoModule], reduce: Union[Reduction, Callable[[Sequence[Tensor]], Tensor], str] = 'sum', auto_delay=True)

Broadcast an input to parallel modules and reduce. This module is a shorthand for:

co.Sequential(co.Broadcast(), co.Parallel(*args), co.Reduce(reduce))

For instance, it can be used to succinctly create a continual 3D Inception Module:

def norm_relu(module, channels):
    return co.Sequential(
        module,
        nn.BatchNorm3d(channels),
        nn.ReLU(),
    )

inception_module = co.BroadcastReduce(
    co.Conv3d(192, 64, kernel_size=1),
    co.Sequential(
        norm_relu(co.Conv3d(192, 96, kernel_size=1), 96),
        norm_relu(co.Conv3d(96, 128, kernel_size=3, padding=1), 128),
    ),
    co.Sequential(
        norm_relu(co.Conv3d(192, 16, kernel_size=1), 16),
        norm_relu(co.Conv3d(16, 32, kernel_size=5, padding=2), 32),
    ),
    co.Sequential(
        co.MaxPool3d(kernel_size=(1, 3, 3), padding=(0, 1, 1), stride=1),
        norm_relu(co.Conv3d(192, 32, kernel_size=1), 32),
    ),
    reduce="concat",
)

Parameters:

arg (OrderedDict[str, CoModule]) – An OrderedDict or modules to be applied in parallel.
*args (CoModule) – Modules to be applied in parallel.
reduce (ReductionFuncOrEnum, optional) – Function used to reduce the parallel outputs. Sum or concatenation can be specified by passing “sum” or “concat” respectively. Custom reduce functions can also be passed. Defaults to “sum”.
auto_delay (bool, optional) – Automatically add delay to modules in order to match the longest delay. Defaults to True.