Source code for sinabs.layers.neuromorphic_relu

import torch

from sinabs.activation import Quantize, StochasticRounding

[docs]class NeuromorphicReLU(torch.nn.Module): """NeuromorphicReLU layer. This layer is NOT used for Sinabs networks; it's useful while training analogue pyTorch networks for future use with Sinabs. Parameters: quantize: Whether or not to quantize the output (i.e. floor it to \ the integer below), in order to mimic spiking behavior. fanout: Useful when computing the number of SynOps of a quantized \ NeuromorphicReLU. The activity can be accessed through \ NeuromorphicReLU.activity, and is multiplied by the value of fanout. stochastic_rounding: Upon quantization, should the value be rounded stochastically or floored Only done during training. During evaluation mode, the value is simply floored """ def __init__(self, quantize=True, fanout=1, stochastic_rounding=False): super().__init__() self.quantize = quantize self.stochastic_rounding = stochastic_rounding self.fanout = fanout
[docs] def forward(self, inp): output = torch.nn.functional.relu(inp) if self.quantize: if self.stochastic_rounding: if output = StochasticRounding.apply(output) else: output = output.round() else: output = Quantize.apply(output) self.activity = output.sum() / len(output) * self.fanout return output