Read before creating a new issue

Users who want to use SpikingJelly should first be familiar with the usage of PyTorch.
If you do not know much about PyTorch, we recommend that the user can learn the basic tutorials of PyTorch.
Do not ask for help with the basic conception of PyTorch/Machine Learning but not related to SpikingJelly. For these questions, please refer to Google or PyTorch Forums.

For faster response

You can @ the corresponding developers for your issue. Here is the division:

Features	Developers
Neurons and Surrogate Functions	fangwei123456 Yanqi-Chen
CUDA Acceleration	fangwei123456 Yanqi-Chen
Reinforcement Learning	lucifer2859
ANN to SNN Conversion	DingJianhao Lyu6PosHao
Biological Learning (e.g., STDP)	AllenYolk
Others	Grasshlw lucifer2859 AllenYolk Lyu6PosHao DingJianhao Yanqi-Chen fangwei123456

We are glad to add new developers who are volunteering to help solve issues to the above table.

Issue type

[ ] Bug Report
[ ] Feature Request
[ ] Help wanted
[x] Other

SpikingJelly version

0.0.0.0.2

Description

...RuntimeError: adaptive_avg_pool2d_backward_cuda does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True)'. You can turn off determinism just for this operation, or you can use the 'warn_only=True' option, if that's acceptable for your application. You can also file an issue at https://github.com/pytorch/pytorch/issues to help us prioritize adding deterministic support for this operation.

在我尝试用spiking-vgg11 训练cifar10的时候出现了如下报错我不知道这里如何修改torch.use_deterministic_algorithms

Minimal code to reproduce the error/bug

import torch
import sys
from spikingjelly.activation_based import surrogate, neuron, functional
from spikingjelly.activation_based.model import spiking_resnet, train_classify, spiking_vgg, parametric_lif_net
from spikingjelly.activation_based.monitor import GPUMonitor
from datetime import datetime

class SResNetTrainer(train_classify.Trainer):
    def preprocess_train_sample(self, args, x: torch.Tensor):
        # define how to process train sample before send it to model
        return x.unsqueeze(0).repeat(args.T, 1, 1, 1, 1)  # [N, C, H, W] -> [T, N, C, H, W]

    def preprocess_test_sample(self, args, x: torch.Tensor):
        # define how to process test sample before send it to model
        return x.unsqueeze(0).repeat(args.T, 1, 1, 1, 1)  # [N, C, H, W] -> [T, N, C, H, W]

    def process_model_output(self, args, y: torch.Tensor):
        return y.mean(0)  # return firing rate

    def get_args_parser(self, add_help=True):
        parser = super().get_args_parser()
        parser.add_argument('--T', type=int, help="total time-steps")
        parser.add_argument('--cupy', action="store_true", help="set the neurons to use cupy backend")
        return parser

    def get_tb_logdir_name(self, args):
        now_time = datetime.now()
        return super().get_tb_logdir_name(args) + f'_T{args.T}' + '_' + str(now_time.day) + '_' + str(
            now_time.hour) + '_' + str(now_time.minute)

    def load_data(self, args):
        return self.load_CIFAR10(args)

    def load_model(self, args, num_classes):
        if args.model in spiking_vgg.__all__:
            model = spiking_vgg.__dict__[args.model](pretrained=args.pretrained, spiking_neuron=neuron.LIFNode,
                                                 surrogate_function=surrogate.ATan(), detach_reset=True,
                                                 num_classes=num_classes)
            functional.set_step_mode(model, step_mode='m')
            if args.cupy:
                functional.set_backend(model, 'cupy', neuron.LIFNode)
            return model
        else:
            raise ValueError(f"args.model should be one of {spiking_vgg.__all__}")

if __name__ == "__main__":
    trainer = SResNetTrainer()
    args = trainer.get_args_parser().parse_args()
    trainer.main(args)

fangwei123456 / spikingjelly

使用spikingvgg11识别cifar10的时候，adaptive_avg_pool2d_backward_cuda报错 #554

Read before creating a new issue