SpikingLSTM中前后时刻的隐状态接收问题

[Seazoned]

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Neurons and Surrogate Functions	fangwei123456 Yanqi-Chen
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ANN to SNN Conversion	DingJianhao Lyu6PosHao
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Issue type

• Bug Report
• Feature Request
• Help wanted
• Other

SpikingJelly version

0.0.0.0.14

Description

在处理序列信息时需要将前一时刻snnLSTM的隐状态值作为下一时刻的snnLSTM的隐状态初始值，但是这样做之后会报错。
代码如下。

Minimal code to reproduce the error/bug

import torch
import torch.nn as nn
from spikingjelly.activation_based import rnn, neuron, layer, surrogate

T = 6
h_dim = 32
batch = 5

x = torch.randn([T, batch, h_dim])
lstm = rnn.SpikingLSTM(32, 32, 1)
states = None
for t in range(8):
    out, states = lstm(x, states)

以下是报错信息

Traceback (most recent call last):
File "E:\SNN\SNN_LSTM\SNN_LSTM\encoder.py", line 80, in
out, states = lstm(x, states)
File "E:\anaconda3\envs\social\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "E:\anaconda3\envs\social\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "E:\anaconda3\envs\social\lib\site-packages\spikingjelly\activation_based\rnn.py", line 473, in forward
new_states_list[:, 0] = torch.stack(self.cells[0](x[t], states_list[:, 0]))
File "E:\anaconda3\envs\social\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "E:\anaconda3\envs\social\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "E:\anaconda3\envs\social\lib\site-packages\spikingjelly\activation_based\rnn.py", line 685, in forward
i, f, g, o = torch.split(self.surrogate_function1(self.linear_ih(x) + self.linear_hh(h)),
ValueError: not enough values to unpack (expected 4, got 1)

[Seazoned]

也许我应该换一种描述方式：
pytorch的lstm输入张量的size为(序列长度，batch_size， 特征长度)
SpikingJelly中的lstm输入张量的维度是(T, batch_size, 特征长度)，其中T按照我的理解是脉冲序列的时间步长，也就是说snnlstm并没有原来的“序列长度”这个维度，因此在输入一定长度的序列时，我才想通过循环获得一个通过lstm的隐状态。
我的理解有误吗？恳请赐教