SDNNs and SNNs

[Kristi1217]

Hi @bamsumit @tangores ,

I tried using psp0 to convert the inputs into binary spikes then the psp0 is sent SDNN layers.

1. Is there a way to verify that the input was converted? because I don't see much difference while training a network, if it takes in normal channels or binary spikes, it computes at the same rate.

2. My forward operations looks something like this. Secondly I cant change number of output channels, I have to then to create separate blocks.

      self.blocks = torch.nn.ModuleList([
          slayer.block.sigma_delta.Conv(sdnn_params1,  2, 2, 3, padding=1),            
          slayer.block.sigma_delta.Conv(sdnn_params1,  2, 2, 1, padding=0), 
      ])
     
      
  def forward(self, x):
      count = []
      event_cost = 0
      device = torch.device("cuda")  # or "cpu" if you want to use the CPU

      scale = 1#<<12 # scale factor for integer simulation
      decay = torch.FloatTensor([0.1 * scale]).to(device)
      initial_state = torch.FloatTensor([0]).to(device)
      threshold = 0.5
      
      B, C, H, W, T = x.shape
      
      psp = slayer.neuron.dynamics.leaky_integrator._li_dynamics_fwd(x, decay=decay, state=initial_state, w_scale=scale,threshold= threshold)  
      
         
      for block in self.blocks:
          x = block(psp)
          if hasattr(block, 'neuron'):
              event_cost += event_rate_loss(x)
              count.append(torch.sum(torch.abs((x[..., 1:]) > 0).to(x.dtype)).item())
              
      return x, event_cost, torch.FloatTensor(count).reshape((1, -1)).to(x.device)

Originally posted by @Kristi1217 in #225 (comment)