Source code for sinabs.layers.to_spike

from typing import Tuple

import torch
from torch import nn


[docs] class Img2SpikeLayer(nn.Module): """Layer to convert images to spikes. Parameters: image_shape: tuple image shape tw: int Time window length max_rate: maximum firing rate of neurons layer_name: string layer name norm: the supposed maximum value of the input (default 255.0) squeeze: whether to remove singleton dimensions from the input negative_spikes: whether to allow negative spikes in response to negative input """ def __init__( self, image_shape, tw: int = 100, max_rate: float = 1000, norm: float = 255.0, squeeze: bool = False, negative_spikes: bool = False, ): super().__init__() self.tw = tw self.max_rate = max_rate self.norm = norm self.squeeze = squeeze self.negative_spikes = negative_spikes
[docs] def forward(self, img_input): if self.squeeze: img_input = img_input.squeeze() random_tensor = torch.rand(self.tw, *tuple(img_input.shape)).to( img_input.device ) if not self.negative_spikes: firing_probs = (img_input / self.norm) * (self.max_rate / 1000) spk_img = (random_tensor < firing_probs).float() else: firing_probs = (img_input.abs() / self.norm) * (self.max_rate / 1000) spk_img = (random_tensor < firing_probs).float() * img_input.sign().float() self.spikes_number = spk_img.abs().sum() self.tw = len(spk_img) return spk_img
def get_output_shape(self, input_shape: Tuple): # The time dimension is not included in the shape # NOTE: This is not true if the squeeze is false but input_shape has a batch_size # TODO: Fix this return input_shape # (self.tw, *input_shape)
[docs] class Sig2SpikeLayer(torch.nn.Module): """Layer to convert analog Signals to spikes. Parameters: channels_in: number of channels in the analog signal tw: int number of time steps for each sample of the signal (up sampling) layer_name: string layer name """ def __init__( self, channels_in, tw: int = 1, norm_level: float = 1, spk_out: bool = True, ): super().__init__() self.tw = tw self.norm_level = norm_level self.spk_out = spk_out def get_output_shape(self, input_shape: Tuple): channels, time_steps = input_shape return (self.tw * time_steps, channels)
[docs] def forward(self, signal): channels, time_steps = signal.shape if self.tw != 1: signal = signal.view(-1, 1).repeat(1, self.tw).view(channels, -1) signal = signal.transpose(1, 0) if self.spk_out: random_tensor = ( torch.rand(self.tw * time_steps, channels).to(signal.device) * self.norm_level ) spk_sig = (random_tensor < signal).float() else: # If there is no conversion to spikes # just replicate the signal as current injection spk_sig = signal self.spikes_number = spk_sig.abs().sum() return spk_sig