norse.torch.module.lift module

class norse.torch.module.lift.Lift(module)

Bases: torch.nn.modules.module.Module

Lift applies a given torch.nn.Module over

a temporal sequence. In other words this module applies the given torch.nn.Module N times, where N is the outer dimension in the provided tensor.

Parameters

module (Module) – Module to apply

Examples

>>> batch_size = 16
>>> seq_length = 1000
>>> in_channels = 64
>>> out_channels = 32
>>> conv2d = Lift(torch.nn.Conv2d(in_channels, out_channels, 5, 1))
>>> data = torch.randn(seq_length, batch_size, 20, 30)
>>> output = conv2d(data)

>>> data = torch.randn(seq_length, batch_size, in_channels, 20, 30)
>>> module = torch.nn.Sequential(
>>>     Lift(torch.nn.Conv2d(in_channels, out_channels, 5, 1)),
>>>     LIF(),
>>> )
>>> output, _ = module(data)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

Apply the module over the input along the 0-th (time) dimension and accumulate the outputs in an output tensor.

Parameters

x (Union[Tensor, Tuple[Tensor, Tensor]]) – Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]

Note

If the input is a tuple of two tensors, the second tuple entry will be ignored.

Return type

Tensor

training: bool