sinabs.layers¶
All the layers implemented in this package can be used similar to torch.nn layers in your implementations.
Main spiking layer¶
SpikingLayer¶
-
class
SpikingLayer(threshold: float = 1.0, threshold_low: Optional[float] = - 1.0, membrane_subtract: Optional[float] = None, batch_size: Optional[int] = None, membrane_reset=False)¶ Pytorch implementation of a spiking neuron with learning enabled. This class is the base class for any layer that need to implement integrate-and-fire operations.
- Parameters
threshold – Spiking threshold of the neuron.
threshold_low – Lower bound for membrane potential.
membrane_subtract – The amount to subtract from the membrane potential upon spiking. Default is equal to threshold. Ignored if membrane_reset is set.
negative_spikes – Implement a linear transfer function through negative spiking. Ignored if membrane_reset is set.
batch_size – The batch size. Needed to distinguish between timesteps and batch dimension.
membrane_reset – bool, if True, reset the membrane to 0 on spiking.
layer_name – The name of the layer.
-
get_output_shape(in_shape)¶ Returns the output shape for passthrough implementation
- Parameters
in_shape –
- Returns
out_shape
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reset_states(shape=None, randomize=False)¶ Reset the state of all neurons in this layer
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synaptic_output(input_spikes: torch.Tensor) → torch.Tensor¶ This method needs to be overridden/defined by the child class Default implementation is pass through
- Parameters
input_spikes – torch.Tensor input to the layer.
- Returns
torch.Tensor - synaptic output current
Auxiliary spiking layers¶
Cropping2dLayer¶
-
class
Cropping2dLayer(cropping: Union[numpy.ndarray, List, Tuple] = 0, 0, 0, 0)¶ Crop input image by
Crop input to the the rectangle dimensions
- Parameters
cropping – ((top, bottom), (left, right))
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get_output_shape(input_shape: Tuple) → Tuple¶ Retuns the output dimensions
- Parameters
input_shape – (channels, height, width)
- Returns
(channels, height, width)
SpikingMaxPooling2dLayer¶
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class
SpikingMaxPooling2dLayer(pool_size: Union[numpy.ndarray, List, Tuple], strides: Optional[Union[numpy.ndarray, List, Tuple]] = None, padding: Union[numpy.ndarray, List, Tuple] = 0, 0, 0, 0)¶ Torch implementation of SpikingMaxPooling
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get_output_shape(input_shape: Tuple) → Tuple¶ Returns the shape of output, given an input to this layer
- Parameters
input_shape – (channels, height, width)
- Returns
(channelsOut, height_out, width_out)
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InputLayer¶
-
class
InputLayer(input_shape: Union[numpy.ndarray, List, Tuple], layer_name='input')¶ Place holder layer, used typically to acquire some statistics on the input
- Parameters
image_shape – Input image dimensions
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get_output_shape(input_shape: Tuple) → Tuple¶ Retuns the output dimensions
- Parameters
input_shape – (channels, height, width)
- Returns
(channels, height, width)
Hybrid layers¶
The hybrid layers have inputs and outputs of different formats (eg. take analog values as inputs and produce spikes as outputs.)
Img2SpikeLayer¶
-
class
Img2SpikeLayer(image_shape, tw: int = 100, max_rate: float = 1000, norm: float = 255.0, squeeze: bool = False, negative_spikes: bool = False)¶ Layer to convert Images to Spikes
Layer converts 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
Sig2SpikeLayer¶
-
class
Sig2SpikeLayer(channels_in, tw: int = 1, norm_level: float = 1, spk_out: bool = True)¶ Layer to convert analog Signals to Spikes
Layer converts 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
ANN layers¶
These are utility layers used in the training of ANNs, in order to provide specific features suitable for SNN conversion.
NeuromorphicReLU¶
-
class
NeuromorphicReLU(quantize=True, fanout=1, stochastic_rounding=False)¶ 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
Initializes internal Module state, shared by both nn.Module and ScriptModule.
QuantizeLayer¶
-
class
QuantizeLayer(quantize=True)¶ Layer that quantizes the input, i.e. returns floor(input).
- Parameters
quantize – If False, this layer will do nothing.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
SumPool2d¶
-
class
SumPool2d(kernel_size, stride=None, ceil_mode=False)¶ Non-spiking sumpooling layer to be used in analogue Torch models. It is identical to torch.nn.LPPool2d with p=1.
- Parameters
kernel_size – the size of the window
stride – the stride of the window. Default value is kernel_size
ceil_mode – when True, will use ceil instead of floor to compute the output shape
Initializes internal Module state, shared by both nn.Module and ScriptModule.
sinabs.layers.functional¶
Quantization tools¶
Thresholding tools¶
threshold_subtract¶
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threshold_subtract(data, threshold=1, window=0.5)¶ PyTorch-compatible function that returns the number of spikes emitted, given a membrane potential value and in a “threshold subtracting” regime. In other words, the integer division of the input by the threshold is returned. In the backward pass, the gradient is zero if the membrane is at least threshold - window, and is passed through otherwise.