from_torch#

This module provides support for importing models into the sinabs from pytorch.

class sinabs.from_torch.SpkConverter(input_shape: typing.Optional[typing.Tuple] = None, spike_threshold=1.0, spike_fn: typing.Callable = <sinabs.activation.spike_generation.MultiSpike object>, reset_fn: typing.Callable = MembraneSubtract(subtract_value=None), surrogate_grad_fn: typing.Callable = SingleExponential(grad_width=0.5, grad_scale=1.0), min_v_mem: float = -1.0, bias_rescaling: float = 1.0, num_timesteps: typing.Optional[int] = None, batch_size: int = 1, synops: bool = False, add_spiking_output: bool = False, backend: str = 'bptt', kwargs_backend: typing.Optional[dict] = None)#

Converts a Torch model and returns a Sinabs network object. The modules in the model are analyzed, and a copy is returned, with all ReLUs, LeakyReLUs and NeuromorphicReLUs turned into SpikingLayers.

Parameters
  • input_shape – If provided, the layer dimensions are computed. Otherwise they will computed at the first forward pass.

  • spike_threshold – The membrane potential threshold for spiking layers (same for all layers).

  • spike_fn (Callable) – The spike dynamics to determine the number of spikes out

  • reset_fn (Callable) – The reset mechanism of the neuron (like reset to zero, or subtract)

  • surrogate_grad_fn (Callable) – The surrogate gradient method for the spiking dynamics

  • min_v_mem – The lower bound of the potential in convolutional and linear layers (same for all layers).

  • bias_rescaling – Biases are divided by this value.

  • num_timesteps – Number of timesteps per sample. If None, batch_size must be provided to seperate batch and time dimensions.

  • batch_size – Must be provided if num_timesteps is None and is ignored otherwise.

  • synops – If True (default: False), register hooks for counting synaptic operations during foward passes.

  • add_spiking_output – If True (default: False), add a spiking layer to the end of a sequential model if not present.

  • backend – String defining the simulation backend (currently sinabs or exodus)

  • kwargs_backend – Dict with additional kwargs for the simulation backend

convert(model: torch.nn.modules.module.Module) sinabs.network.Network#

Converts the Torch model and returns a Sinabs network object. :param model: A torch module.

Returns

The Sinabs network object created by conversion.

Return type

network

sinabs.from_torch.from_model(model, input_shape=None, spike_threshold=1.0, spike_fn: typing.Callable = <class 'sinabs.activation.spike_generation.MultiSpike'>, reset_fn: typing.Callable = MembraneSubtract(subtract_value=None), surrogate_grad_fn: typing.Callable = SingleExponential(grad_width=0.5, grad_scale=1.0), min_v_mem=-1.0, bias_rescaling=1.0, num_timesteps=None, batch_size=1, synops=False, add_spiking_output=False, backend='sinabs', kwargs_backend=None)#

Converts a Torch model and returns a Sinabs network object. The modules in the model are analyzed, and a copy is returned, with all ReLUs, LeakyReLUs and NeuromorphicReLUs turned into SpikingLayers.

Parameters
  • model – Torch model

  • input_shape – If provided, the layer dimensions are computed. Otherwise they will be computed at the first forward pass.

  • spike_threshold – The membrane potential threshold for spiking (same for all layers).

  • spike_fn (Callable) – The spike dynamics to determine the number of spikes out

  • reset_fn (Callable) – The reset mechanism of the neuron (like reset to zero, or subtract)

  • surrogate_grad_fn (Callable) – The surrogate gradient method for the spiking dynamics

  • min_v_mem – The lower bound of the potential in (same for all layers).

  • bias_rescaling – Biases are divided by this value.

  • num_timesteps – Number of timesteps per sample. If None, batch_size must be provided to seperate batch and time dimensions.

  • batch_size – Must be provided if num_timesteps is None and is ignored otherwise.

  • synops – If True (default: False), register hooks for counting synaptic operations during forward passes.

  • add_spiking_output – If True (default: False), add a spiking layer to the end of a sequential model if not present.

  • backend – String defining the simulation backend (currently sinabs or exodus)

  • kwargs_backend – Dict with additional kwargs for the simulation backend