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models.py
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models.py
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import torch
import numpy as np
class SNN(torch.nn.Module):
def __init__(self, layers):
super(SNN, self).__init__()
self.layers = torch.nn.ModuleList(layers)
def forward(self, x):
loss_seq = []
for l in self.layers:
x, loss = l(x)
loss_seq.append(loss)
return x, loss_seq
def clamp(self):
for l in self.layers:
l.clamp()
def reset_parameters(self):
for l in self.layers:
l.reset_parameters()
class SpikingDenseLayer(torch.nn.Module):
def __init__(self, nb_inputs, nb_outputs, spike_fn, w_init_mean, w_init_std, eps=1e-8):
super(SpikingDenseLayer, self).__init__()
self.nb_inputs = nb_inputs
self.nb_outputs = nb_outputs
self.spike_fn = spike_fn
self.eps = eps
self.w_init_mean = w_init_mean
self.w_init_std = w_init_std
self.w = torch.nn.Parameter(torch.empty((nb_inputs, nb_outputs)), requires_grad=True)
self.beta = torch.nn.Parameter(torch.empty(1), requires_grad=True)
self.b = torch.nn.Parameter(torch.empty(nb_outputs), requires_grad=True)
self.reset_parameters()
self.clamp()
self.spk_rec_hist = None
self.training = True
def forward(self, x):
batch_size = x.shape[0]
h = torch.einsum("abc,cd->abd", x, self.w)
nb_steps = h.shape[1]
# membrane potential
mem = torch.zeros((batch_size, self.nb_outputs), dtype=x.dtype, device=x.device)
# output spikes
spk = torch.zeros((batch_size, self.nb_outputs), dtype=x.dtype, device=x.device)
# output spikes recording
spk_rec = torch.zeros((batch_size, nb_steps, self.nb_outputs), dtype=x.dtype, device=x.device)
d = torch.einsum("ab, ac -> bc", self.w, self.w)
norm = (self.w**2).sum(0)
for t in range(nb_steps):
# reset term
rst = torch.einsum("ab,bc ->ac", spk, d)
# membrane potential update
mem = (mem-rst)*self.beta + h[:,t,:]*(1.-self.beta)
mthr = torch.einsum("ab,b->ab",mem, 1./(norm+self.eps))-self.b
spk = self.spike_fn(mthr)
spk_rec[:,t,:] = spk
# save spk_rec for plotting
self.spk_rec_hist = spk_rec.detach().cpu().numpy()
loss = 0.5*(spk_rec**2).mean()
return spk_rec, loss
def reset_parameters(self):
torch.nn.init.normal_(self.w, mean=self.w_init_mean, std=self.w_init_std*np.sqrt(1./self.nb_inputs))
torch.nn.init.normal_(self.beta, mean=0.7, std=0.01)
torch.nn.init.normal_(self.b, mean=1., std=0.01)
def clamp(self):
self.beta.data.clamp_(0.,1.)
self.b.data.clamp_(min=0.)
class SpikingConvLayer(torch.nn.Module):
def __init__(self, nb_inputs, nb_outputs,
in_channels, out_channels, kernel_size, dilation,
spike_fn, w_init_mean, w_init_std, eps=1e-8, stride=(1,1),flatten_output=False):
super(SpikingConvLayer, self).__init__()
self.kernel_size = np.array(kernel_size)
self.dilation = np.array(dilation)
self.stride = np.array(stride)
self.in_channels = in_channels
self.out_channels = out_channels
self.nb_inputs = nb_inputs
self.nb_outputs = nb_outputs
self.spike_fn = spike_fn
self.eps = eps
self.flatten_output = flatten_output
self.w_init_mean = w_init_mean
self.w_init_std = w_init_std
self.w = torch.nn.Parameter(torch.empty((out_channels, in_channels, *kernel_size)), requires_grad=True)
self.beta = torch.nn.Parameter(torch.empty(1), requires_grad=True)
self.b = torch.nn.Parameter(torch.empty(out_channels), requires_grad=True)
self.reset_parameters()
self.clamp()
self.spk_rec_hist = None
self.training = True
def forward(self, x):
batch_size = x.shape[0]
conv_x = torch.nn.functional.conv2d(x, self.w, padding=tuple(np.ceil(((self.kernel_size-1)*self.dilation)/2).astype(int)),
dilation=tuple(self.dilation),
stride=tuple(self.stride))
conv_x = conv_x[:,:,:,:self.nb_outputs]
nb_steps = conv_x.shape[2]
# membrane potential
mem = torch.zeros((batch_size, self.out_channels, self.nb_outputs), dtype=x.dtype, device=x.device)
# output spikes
spk = torch.zeros((batch_size, self.out_channels, self.nb_outputs), dtype=x.dtype, device=x.device)
# output spikes recording
spk_rec = torch.zeros((batch_size, self.out_channels, nb_steps, self.nb_outputs), dtype=x.dtype, device=x.device)
d = torch.einsum("abcd, ebcd -> ae", self.w, self.w)
b = self.b.unsqueeze(1).repeat((1,self.nb_outputs))
norm = (self.w**2).sum((1,2,3))
for t in range(nb_steps):
# reset term
rst = torch.einsum("abc,bd ->adc", spk, d)
# membrane potential update
mem = (mem-rst)*self.beta + conv_x[:,:,t,:]*(1.-self.beta)
mthr = torch.einsum("abc,b->abc",mem, 1./(norm+self.eps))-b
spk = self.spike_fn(mthr)
spk_rec[:,:,t,:] = spk
# save spk_rec for plotting
self.spk_rec_hist = spk_rec.detach().cpu().numpy()
loss = 0.5*(spk_rec**2).mean()
if self.flatten_output:
output = torch.transpose(spk_rec, 1, 2).contiguous()
output = output.view(batch_size, nb_steps, self.out_channels*self.nb_outputs)
else:
output = spk_rec
return output, loss
def reset_parameters(self):
torch.nn.init.normal_(self.w, mean=self.w_init_mean, std=self.w_init_std*np.sqrt(1./(self.in_channels*np.prod(self.kernel_size))))
torch.nn.init.normal_(self.beta, mean=0.7, std=0.01)
torch.nn.init.normal_(self.b, mean=1., std=0.01)
def clamp(self):
self.beta.data.clamp_(0.,1.)
self.b.data.clamp_(min=0.)
class ReadoutLayer(torch.nn.Module):
"Fully connected readout"
def __init__(self, nb_inputs, nb_outputs, w_init_mean, w_init_std, eps=1e-8, time_reduction="mean"):
assert time_reduction in ["mean", "max"], 'time_reduction should be "mean" or "max"'
super(ReadoutLayer, self).__init__()
self.nb_inputs = nb_inputs
self.nb_outputs = nb_outputs
self.w_init_mean = w_init_mean
self.w_init_std = w_init_std
self.eps = eps
self.time_reduction = time_reduction
self.w = torch.nn.Parameter(torch.empty((nb_inputs, nb_outputs)), requires_grad=True)
if time_reduction == "max":
self.beta = torch.nn.Parameter(torch.tensor(0.7*np.ones((1))), requires_grad=True)
self.b = torch.nn.Parameter(torch.empty(nb_outputs), requires_grad=True)
self.reset_parameters()
self.clamp()
self.mem_rec_hist = None
def forward(self, x):
batch_size = x.shape[0]
h = torch.einsum("abc,cd->abd", x, self.w)
norm = (self.w**2).sum(0)
if self.time_reduction == "max":
nb_steps = x.shape[1]
# membrane potential
mem = torch.zeros((batch_size, self.nb_outputs), dtype=x.dtype, device=x.device)
# memrane potential recording
mem_rec = torch.zeros((batch_size, nb_steps, self.nb_outputs), dtype=x.dtype, device=x.device)
for t in range(nb_steps):
# membrane potential update
mem = mem*self.beta + (1-self.beta)*h[:,t,:]
mem_rec[:,t,:] = mem
output = torch.max(mem_rec, 1)[0]/(norm+1e-8) - self.b
elif self.time_reduction == "mean":
mem_rec = h
output = torch.mean(mem_rec, 1)/(norm+1e-8) - self.b
# save mem_rec for plotting
self.mem_rec_hist = mem_rec.detach().cpu().numpy()
loss = None
return output, loss
def reset_parameters(self):
torch.nn.init.normal_(self.w, mean=self.w_init_mean,
std=self.w_init_std*np.sqrt(1./(self.nb_inputs)))
if self.time_reduction == "max":
torch.nn.init.normal_(self.beta, mean=0.7, std=0.01)
torch.nn.init.normal_(self.b, mean=1., std=0.01)
def clamp(self):
if self.time_reduction == "max":
self.beta.data.clamp_(0.,1.)
class SurrogateHeaviside(torch.autograd.Function):
# Activation function with surrogate gradient
sigma = 10.0
@staticmethod
def forward(ctx, input):
output = torch.zeros_like(input)
output[input > 0] = 1.0
ctx.save_for_backward(input)
return output
@staticmethod
def backward(ctx, grad_output):
input, = ctx.saved_tensors
grad_input = grad_output.clone()
# approximation of the gradient using sigmoid function
grad = grad_input*torch.sigmoid(SurrogateHeaviside.sigma*input)*torch.sigmoid(-SurrogateHeaviside.sigma*input)
return grad