fangwei123456 / parallel-spiking-neuron Goto Github PK

您好！
我尝试运行CIFAR10DVS数据集中的代码。我已经安装好相关依赖
当我运行
python train_vgg.py -b 32 --epochs 200 -method PSN -TET -T 10
时，会报以下错误：
Traceback (most recent call last):
File "train_vgg.py", line 145, in
train_dataset, val_dataset = build_dvscifar()
File "train_vgg.py", line 138, in build_dvscifar
train_set = CIFAR10DVS(root='../dataset', train=True, data_type='frame', frames_number=args.T, split_by='number', transform=transform_train)

请问该如何解决呢？
谢谢！

您好，您的工作十分有趣，我在试图复现您的程序。
我在配置好依赖以及使用move_data.py文件与处理好数据后，运行
python train_vgg.py -b 32 --epochs 200 -method PSN -TET -T 10
命令。
发现在每个batch中，输出的mean_out值是一样的。
并且训练后的正确率是10%左右。
请问是我的操作在哪里有问题吗，您那里是否会显示这样的特性呢。
谢谢！

SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!

    ********************************************************************************
    Please avoid running ``setup.py`` directly.
    Instead, use pypa/build, pypa/installer or other
    standards-based tools.

    See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
    ********************************************************************************

!!

Hi, I tried PSN ResNet-20 on cifar10 with t=8. Pytorch official implementation but with ReLU substituted into PSN. 200 epochs. Cosine scheduler. But, the classification result is ~85%. Is it normal? Any advice on hyperparameters settings both on PSN or training would be appreciated.

Except for the most effective improvement in parallel computing and SNN performance, I also noticed PSN's representation space is larger than GLIF if the resetting mechanism is ignored. GLIF should be a subclass neuronal model of PSN. What an elegant parametric method! But, I wonder whether such a computation-efficient neuronal model would be accepted in the field of neurocomputing. Is it possible for the SNN researchers to fully embrace PSN to accelerate the research, and apply PSN to any circumstances where the traditional LIF is used?

If PSN can fully replace LIFs, I think this field could be more active because experiments can be done faster, and more ideas can show up with the emergence of PSN. I am happy to see but not certain about this view. If PSN can not fully replace LIFs, which means more SNN researchers are concerned, what obstacles would it be?

I am very curious about the author's view. Maybe we can talk on WeChat? or not?

Thanks for the novel and contributive work. I am expecting your reply.