def _ann_to_snn_helper(prev, current, nxt):
if isinstance(current, nn.Linear):
layer = SubtractiveResetIFNodes(n=current.out_features, reset=0, thresh=1, refrac=0)
connection = topology.Connection(
source=prev, target=layer, w=current.weight.t(), b=current.bias
)
elif isinstance(current, nn.Conv2d):
input_height, input_width = prev.shape[2], prev.shape[3]
out_channels, output_height, output_width = current.out_channels, prev.shape[2], prev.shape[3]
width = (input_height - current.kernel_size[0] + 2 * current.padding[0]) / current.stride[0] + 1
height = (input_width - current.kernel_size[1] + 2 * current.padding[1]) / current.stride[1] + 1
shape = (1, out_channels, int(width), int(height))
layer = SubtractiveResetIFNodes(
shape=shape, reset=0, thresh=1, refrac=0
)
connection = topology.Conv2dConnection(
source=prev, target=layer, kernel_size=current.kernel_size, stride=current.stride,
padding=current.padding, dilation=current.dilation, w=current.weight, b=current.bias
)
elif isinstance(current, nn.MaxPool2d):
input_height, input_width = prev.shape[2], prev.shape[3]
current.kernel_size = _pair(current.kernel_size)
current.padding = _pair(current.padding)
current.stride = _pair(current.stride)
width = (input_height - current.kernel_size[0] + 2 * current.padding[0]) / current.stride[0] + 1
height = (input_width - current.kernel_size[1] + 2 * current.padding[1]) / current.stride[1] + 1
shape = (1, prev.shape[1], int(width), int(height))
layer = PassThroughNodes(
shape=shape
)
connection = topology.MaxPool2dConnection(
source=prev, target=layer, kernel_size=current.kernel_size, stride=current.stride,
padding=current.padding, dilation=current.dilation, decay=1
)
else:
return None, None
return layer, connection
def _ann_to_snn_helper(prev, current, node_type, **kwargs):
# language=rst
"""
Helper function for main ``ann_to_snn`` method.
:param prev: Previous PyTorch module in artificial neural network.
:param current: Current PyTorch module in artificial neural network.
:return: Spiking neural network layer and connection corresponding to ``prev`` and ``current`` PyTorch modules.
"""
if isinstance(current, nn.Linear):
layer = node_type(n=current.out_features,
reset=0,
thresh=1,
refrac=0,
**kwargs)
bias = current.bias if current.bias is not None else torch.zeros(
layer.n)
connection = topology.Connection(source=prev,
target=layer,
w=current.weight.t(),
b=bias)
elif isinstance(current, nn.Conv2d):
input_height, input_width = prev.shape[2], prev.shape[3]
out_channels, output_height, output_width = (
current.out_channels,
prev.shape[2],
prev.shape[3],
)
width = (input_height - current.kernel_size[0] +
2 * current.padding[0]) / current.stride[0] + 1
height = (input_width - current.kernel_size[1] +
2 * current.padding[1]) / current.stride[1] + 1
shape = (1, out_channels, int(width), int(height))
layer = node_type(shape=shape, reset=0, thresh=1, refrac=0, **kwargs)
bias = current.bias if current.bias is not None else torch.zeros(
layer.shape[1])
connection = topology.Conv2dConnection(
source=prev,
target=layer,
kernel_size=current.kernel_size,
stride=current.stride,
padding=current.padding,
dilation=current.dilation,
w=current.weight,
b=bias,
)
elif isinstance(current, nn.MaxPool2d):
input_height, input_width = prev.shape[2], prev.shape[3]
current.kernel_size = _pair(current.kernel_size)
current.padding = _pair(current.padding)
current.stride = _pair(current.stride)
width = (input_height - current.kernel_size[0] +
2 * current.padding[0]) / current.stride[0] + 1
height = (input_width - current.kernel_size[1] +
2 * current.padding[1]) / current.stride[1] + 1
shape = (1, prev.shape[1], int(width), int(height))
layer = PassThroughNodes(shape=shape)
connection = topology.MaxPool2dConnection(
source=prev,
target=layer,
kernel_size=current.kernel_size,
stride=current.stride,
padding=current.padding,
dilation=current.dilation,
decay=1,
)
elif isinstance(current, Permute):
layer = PassThroughNodes(shape=[
prev.shape[current.dims[0]],
prev.shape[current.dims[1]],
prev.shape[current.dims[2]],
prev.shape[current.dims[3]],
])
connection = PermuteConnection(source=prev,
target=layer,
dims=current.dims)
elif isinstance(current, nn.ConstantPad2d):
layer = PassThroughNodes(shape=[
prev.shape[0],
prev.shape[1],
current.padding[0] + current.padding[1] + prev.shape[2],
current.padding[2] + current.padding[3] + prev.shape[3],
])
connection = ConstantPad2dConnection(source=prev,
target=layer,
padding=current.padding)
else:
return None, None
return layer, connection
time = 1000
# Create network object.
network = Network()
# Create input and output groups of neurons.
input_group = nodes.Input(n=n_input) # 100 input nodes.
output_group = nodes.LIFNodes(n=n_output) # 500 output nodes.
network.add_layer(input_group, name='input')
network.add_layer(output_group, name='output')
# Input -> output connection.
# Unit Gaussian feed-forward weights.
w = torch.randn(n_input, n_output)
forward_conn = topology.Connection(input_group, output_group, w=w)
# Output -> output connection.
# Random, inhibitory recurrent weights.
w = torch.bernoulli(torch.rand(n_output, n_output)) - torch.diag(torch.ones(n_output))
recurrent_conn = topology.Connection(output_group, output_group, w=w)
network.add_connection(forward_conn, source='input', target='output')
network.add_connection(recurrent_conn, source='output', target='output')
# Monitor input and output spikes during the simulation.
for l in network.layers:
monitor = monitors.Monitor(network.layers[l], state_vars=['s'], time=time)
network.add_monitor(monitor, name=l)
# Create input ~ Bernoulli(0.1) for 1,000 timesteps.
def _ann_to_snn_helper(prev, current, scale):
# language=rst
"""
Helper function for main ``ann_to_snn`` method.
:param prev: Previous PyTorch module in artificial neural network.
:param current: Current PyTorch module in artificial neural network.
:return: Spiking neural network layer and connection corresponding to ``prev`` and ``current`` PyTorch modules.
"""
if isinstance(current, nn.Linear):
layer = LIFNodes(n=current.out_features,
refrac=0,
traces=True,
thresh=-52,
rest=-65.0,
decay=1e-2)
connection = topology.Connection(source=prev,
target=layer,
w=current.weight.t() * scale)
elif isinstance(current, nn.Conv2d):
input_height, input_width = prev.shape[2], prev.shape[3]
out_channels, output_height, output_width = current.out_channels, prev.shape[
2], prev.shape[3]
width = (input_height - current.kernel_size[0] +
2 * current.padding[0]) / current.stride[0] + 1
height = (input_width - current.kernel_size[1] +
2 * current.padding[1]) / current.stride[1] + 1
shape = (1, out_channels, int(width), int(height))
layer = LIFNodes(
shape=shape,
refrac=0,
traces=True,
thresh=-52,
rest=-65.0,
decay=1e-2,
)
connection = topology.Conv2dConnection(source=prev,
target=layer,
kernel_size=current.kernel_size,
stride=current.stride,
padding=current.padding,
dilation=current.dilation,
w=current.weight * scale)
elif isinstance(current, Permute):
layer = PassThroughNodes(shape=[
prev.shape[current.dims[0]], prev.shape[current.dims[1]],
prev.shape[current.dims[2]], prev.shape[current.dims[3]]
])
connection = PermuteConnection(source=prev,
target=layer,
dims=current.dims)
elif isinstance(current, nn.ConstantPad2d):
layer = PassThroughNodes(shape=[
prev.shape[0], prev.shape[1], current.padding[0] +
current.padding[1] + prev.shape[2], current.padding[2] +
current.padding[3] + prev.shape[3]
])
connection = ConstantPad2dConnection(source=prev,
target=layer,
padding=current.padding)
else:
return None, None
return layer, connection