spikingjelly.activation_based.lava_exchange package

Module contents

spikingjelly.activation_based.lava_exchange.step_quantize_forward(x: Tensor, step: float)

class spikingjelly.activation_based.lava_exchange.step_quantize_atgf(*args, **kwargs)

基类：Function

static forward(ctx, x: Tensor, step: float = 1.0)

static backward(ctx, grad_output)

spikingjelly.activation_based.lava_exchange.quantize_8b(x, scale, descale=False)

Denote k as an int, x[i] will be quantized to the nearest 2 * k / scale, and k = {-128, -127, ..., 126, 127}.

spikingjelly.activation_based.lava_exchange.right_shift_to_zero(x: Tensor, bits: int)

class spikingjelly.activation_based.lava_exchange.BatchNorm2d(num_features: int, eps: float = 1e-05, momentum: float = 0.1, track_running_stats: bool = True, weight_exp_bits: int = 3, pre_hook_fx: ~typing.Callable = <function BatchNorm2d.<lambda>>)

基类：Module

to_lava()

forward(x: Tensor)

training: bool

class spikingjelly.activation_based.lava_exchange.LeakyIntegratorStep(*args, **kwargs)

基类：Function

static forward(ctx, x, decay, state, w_scale)

static backward(ctx, grad_output)

class spikingjelly.activation_based.lava_exchange.CubaLIFNode(current_decay: Union[float, Tensor], voltage_decay: Union[float, Tensor], v_threshold: float = 1.0, v_reset: float = 0.0, scale=64, requires_grad=False, surrogate_function: Callable = Sigmoid(alpha=4.0, spiking=True), norm: Optional[BatchNorm2d] = None, detach_reset=False, step_mode='s', backend='torch', store_v_seq: bool = False, store_i_seq: bool = False)

基类：BaseNode

• API in English

参数:

• current_decay (float | torch.Tensor) – 电流衰减常数

• voltage_decay (float | torch.Tensor) – 电压衰减常数

• v_threshold (float) – 神经元阈值电压。默认为1。

• v_reset (float, None) – 重置电压，默认为0

• scale (float) – 量化参数，控制神经元的量化精度（参考了lava-dl的cuba.Neuron）。默认为 1<<6 。 等效于``w_scale=int(scale)``, s_scale=int(scale * (1<<6)), p_scale=1<<12。

• requires_grad (bool) – 指明 current_decay 和 voltage_decay 两个神经元参数是否可学习（是否需要梯度），默认为 False 。

• detach_reset (bool) – 是否将reset的计算图分离，默认为 False 。

• step_mode (str) – 步进模式，可以为 ‘s’ （单步）或 ‘m’ （多步），默认为 ‘s’ 。

• backend (str) – 使用哪种后端。不同的 step_mode 可能会带有不同的后端。可以通过打印 self.supported_backends 查看当前 使用的步进模式支持的后端。目前只支持torch

• store_v_seq (bool) – 在使用 step_mode = 'm' 时，给与 shape = [T, N, *] 的输入后，是否保存中间过程的 shape = [T, N, *] 的各个时间步的电压值 self.v_seq 。设置为 False 时计算完成后只保留最后一个时刻的电压，即 shape = [N, *] 的 self.voltage_state 。 通常设置成 False ，可以节省内存。

• store_i_seq (bool) – 在使用 step_mode = 'm' 时，给与 shape = [T, N, *] 的输入后，是否保存中间过程的 shape = [T, N, *] 的各个时间步的电流值 self.i_seq 。设置为 False 时计算完成后只保留最后一个时刻的电流，即 shape = [N, *] 的 self.current_state 。 通常设置成 False ，可以节省内存。

\[I[t] = (1 - \alpha_{I})I[t-1] + X[t] V[t] = (1 - \alpha_{V})V[t-1] + I[t]\]

• 中文API

参数:

• current_decay (float | torch.Tensor) – current decay constant

• voltage_decay (float | torch.Tensor) – voltage decay constant

• v_threshold (float) – threshold of the the neurons in this layer. Default to 1.

• v_reset (float) – reset potential of the neurons in this layer, 0 by default

• scale (float) – quantization precision (ref: lava-dl cuba.Neuron). Default to 1<<6 . Equivalent to w_scale=int(scale), s_scale=int(scale * (1<<6)), p_scale=1<<12.

• requires_grad (bool) – whether current_decay and voltage_decay are learnable. Default to False .

• detach_reset (bool) – whether to detach the computational graph of reset in backward pass. Default to False .

• step_mode (str) – the step mode, which can be s (single-step) or m (multi-step). Default to ‘s’ .

• backend – backend fot this neurons layer. Different step_mode may support for different backends. The user can

print self.supported_backends and check what backends are supported by the current step_mode. Only torch is supported. :type backend: str

参数:

• store_v_seq (bool) – when using step_mode = 'm' and given input with shape = [T, N, *], this option controls whether storing the voltage at each time-step to self.v_seq with shape = [T, N, *]. If set to False, only the voltage at last time-step will be stored to self.voltage_state with shape = [N, *], which can reduce the memory consumption. Default to False .

• store_i_seq (bool) – when using step_mode = 'm' and given input with shape = [T, N, *], this option controls whether storing the current at each time-step to self.i_seq with shape = [T, N, *]. If set to False, only the current at last time-step will be stored to self.current_state with shape = [N, *], which can reduce the memory consumption. Default to False .

\[I[t] = (1 - \alpha_{I})I[t-1] + X[t] V[t] = (1 - \alpha_{V})V[t-1] + I[t]\]

quantize_8bit(x, descale=False)

clamp_decay_parameters()

property scale

scale

Type:

Read-only attribute

property s_scale

s_scale

Type:

Read-only attribute

property p_scale

s_scale

Type:

Read-only attribute

property store_i_seq

property supported_backends

state_initialization(x: Tensor)

neuronal_charge(x: Tensor)

neuronal_fire()

neuronal_reset(spike)

single_step_forward(x)

multi_step_forward(x_seq: Tensor)

training: bool

spikingjelly.activation_based.lava_exchange.TNX_to_NXT(x_seq: Tensor)

spikingjelly.activation_based.lava_exchange.NXT_to_TNX(x_seq: Tensor)

spikingjelly.activation_based.lava_exchange.lava_neuron_forward(lava_neuron: Module, x_seq: Tensor, v: Tensor)

spikingjelly.activation_based.lava_exchange.step_quantize(x: Tensor, step: float = 1.0)

参数:

• x (torch.Tensor) – the input tensor

• step (float) – the quantize step

返回:

quantized tensor

返回类型:

torch.Tensor

The step quantize function. Here is an example:

# plt.style.use(['science', 'muted', 'grid'])
fig = plt.figure(dpi=200, figsize=(6, 4))
x = torch.arange(-4, 4, 0.001)
plt.plot(x, lava_exchange.step_quantize(x, 2.), label='quantize(x, step=2)')
plt.plot(x, x, label='y=x', ls='-.')
plt.legend()
plt.grid(ls='--')
plt.title('step quantize')
plt.xlabel('Input')
plt.ylabel('Output')
plt.savefig('./docs/source/_static/API/activation_based/lava_exchange/step_quantize.svg')
plt.savefig('./docs/source/_static/API/activation_based/lava_exchange/step_quantize.pdf')

spikingjelly.activation_based.lava_exchange.quantize_8bit(x: Tensor, scale, descale=False)

spikingjelly.activation_based.lava_exchange.check_instance(m, instance)

spikingjelly.activation_based.lava_exchange.check_no_bias(m)

spikingjelly.activation_based.lava_exchange.to_lava_neuron_param_dict(sj_ms_neuron: Module)

spikingjelly.activation_based.lava_exchange.to_lava_neuron(sj_ms_neuron: Module)

spikingjelly.activation_based.lava_exchange.linear_to_lava_synapse_dense(fc: Linear)

参数:

fc (nn.Linear) – a pytorch linear layer without bias

返回:

a lava slayer dense synapse

返回类型:

slayer.synapse.Dense

Codes example:

T = 4
N = 2
layer_nn = nn.Linear(8, 4, bias=False)
layer_sl = lava_exchange.linear_to_lava_synapse_dense(layer_nn)
x_seq = torch.rand([T, N, 8])
with torch.no_grad():
    y_nn = functional.seq_to_ann_forward(x_seq, layer_nn)
    y_sl = lava_exchange.NXT_to_TNX(layer_sl(lava_exchange.TNX_to_NXT(x_seq)))
    print('max error:', (y_nn - y_sl).abs().max())

spikingjelly.activation_based.lava_exchange.conv2d_to_lava_synapse_conv(conv2d_nn: Conv2d)

参数:

conv2d_nn (nn.Conv2d) – a pytorch conv2d layer without bias

返回:

a lava slayer conv synapse

返回类型:

slayer.synapse.Conv

Codes example:

T = 4
N = 2
layer_nn = nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1, bias=False)
layer_sl = lava_exchange.conv2d_to_lava_synapse_conv(layer_nn)
x_seq = torch.rand([T, N, 3, 28, 28])
with torch.no_grad():
    y_nn = functional.seq_to_ann_forward(x_seq, layer_nn)
    y_sl = lava_exchange.NXT_to_TNX(layer_sl(lava_exchange.TNX_to_NXT(x_seq)))
    print('max error:', (y_nn - y_sl).abs().max())

spikingjelly.activation_based.lava_exchange.avgpool2d_to_lava_synapse_pool(pool2d_nn: AvgPool2d)

参数:

pool2d_nn (nn.AvgPool2d) – a pytorch AvgPool2d layer

返回:

a lava slayer pool layer

返回类型:

slayer.synapse.Pool

Warning

The lava slayer pool layer applies sum pooling, rather than average pooling.

T = 4
N = 2
layer_nn = nn.AvgPool2d(kernel_size=2, stride=2)
layer_sl = lava_exchange.avgpool2d_to_lava_synapse_pool(layer_nn)
x_seq = torch.rand([T, N, 3, 28, 28])
with torch.no_grad():
    y_nn = functional.seq_to_ann_forward(x_seq, layer_nn)
    y_sl = lava_exchange.NXT_to_TNX(layer_sl(lava_exchange.TNX_to_NXT(x_seq))) / 4.
    print('max error:', (y_nn - y_sl).abs().max())

spikingjelly.activation_based.lava_exchange.to_lava_block_dense(fc: Linear, sj_ms_neuron: Module, quantize_to_8bit: bool = True)

spikingjelly.activation_based.lava_exchange.to_lava_block_conv(conv2d_nn: Conv2d, sj_ms_neuron: Module, quantize_to_8bit: bool = True)

spikingjelly.activation_based.lava_exchange.to_lava_block_pool(pool2d_nn: AvgPool2d, sj_ms_neuron: Module, quantize_to_8bit: bool = True)

spikingjelly.activation_based.lava_exchange.to_lava_block_flatten(flatten_nn: Flatten)

spikingjelly.activation_based.lava_exchange.to_lava_blocks(net: list)

Supported layer types input : {shape, type} flatten: {shape, type} average: {shape, type} concat : {shape, type, layers} dense : {shape, type, neuron, inFeatures, outFeatures, weight, delay(if available)} pool : {shape, type, neuron, kernelSize, stride, padding, dilation, weight} conv : {shape, type, neuron, inChannels, outChannels, kernelSize, stride,

padding, dilation, groups, weight, delay(if available)}

|-> this is the description of the compartment parameters |-> {iDecay, vDecay, vThMant, refDelay, … (other additional params)}

class spikingjelly.activation_based.lava_exchange.SumPool2d(kernel_size, stride=None, padding=0, dilation=1)

基类：Module

x = torch.rand([4, 2, 4, 16, 16])

with torch.no_grad():
    sp_sj = SumPool2d(kernel_size=2, stride=2)
    y_sj = functional.seq_to_ann_forward(x, sp_sj)

    sp_la = slayer.synapse.Pool(kernel_size=2, stride=2)
    y_la = lava_exchange.NXT_to_TNX(sp_la(lava_exchange.TNX_to_NXT(x)))
    print((y_sj - y_la).abs().sum())

forward(x: Tensor)

training: bool

class spikingjelly.activation_based.lava_exchange.BlockContainer(synapse: Conv2d, neu: CubaLIFNode, step_mode: str = 's')

基类：Module, StepModule

training: bool

property step_mode

forward(x: Tensor)

to_lava_block()