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brian2跑大型 snn 网络的效率属实是一言难尽。最近在学习 spikingjelly，希望一切顺利🙏。

spikingjelly 建议在官方文档学习使用单层全连接SNN识别MNIST — spikingjelly alpha 文档

补：有一点大家要注意，windows 的朋友们要把 device 改成 cpu 或者 cuda！

import os.path
import sys
import time
import datetime
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.cuda import amp
import argparseimport torchvision.datasets
from spikingjelly.activation_based import monitor, neuron, functional, layer, surrogate, encoding
from torch.utils.checkpoint import checkpoint
from torch.utils.tensorboard import SummaryWriter
from urllib3.filepost import writer
import numpy as npclass SNN(nn.Module):def __init__(self,tau):super().__init__()# surrogate_function是替代函数self.layer = nn.Sequential(layer.Flatten(),layer.Linear(28*28,10,bias=False),neuron.LIFNode(tau=tau,surrogate_function=surrogate.ATan()))def forward(self,x:torch.Tensor):return self.layer(x)def main():parser = argparse.ArgumentParser(description='LIF MNIST Training')parser.add_argument('-T',default=100,type=int,help='步长')parser.add_argument('-device',default='mps',help='设备')parser.add_argument('-b', default=64, type=int, help='batch size')parser.add_argument('-epochs', default=100, type=int, metavar='N',help='训练轮数')parser.add_argument('-j', default=4, type=int, metavar='N',help='number of data loading workers (default: 4)')# /Users/Shared/开发/数据集,MNIST数据读取器只用到上一级目录parser.add_argument('-data-dir', type=str, help=' MNIST 路径')parser.add_argument('-out-dir', type=str, default='./logs', help='checkpoints 和 logs 输出路径')parser.add_argument('-resume', type=str, help='从检查点恢复路径')parser.add_argument('-opt', type=str, choices=['sgd', 'adam'], default='adam',help='使用 sgd 或adam 优化器')parser.add_argument('-momentum', default=0.9, type=float, help='设置 sgd 的动量参数')parser.add_argument('-lr', default=1e-3, type=float, help='学习率')parser.add_argument('-tau', default=2.0, type=float, help=' tau')args = parser.parse_args()print(args)net = SNN(tau=args.tau)print(net)net.to(args.device)# 初始化数据加载器# transform后像素值从 0-255归一化到 0-1train_dataset = torchvision.datasets.MNIST(root=args.data_dir,train=True,transform=torchvision.transforms.ToTensor(),download=False)test_dataset = torchvision.datasets.MNIST(root=args.data_dir,train=False,transform=torchvision.transforms.ToTensor(),download=False)# pin_memory=True将数据加载到固定内存（页锁定内存）中，这# 样可以加快数据从 CPU 到 GPU 的传输速度，适用于使用 GPU 进行训练的情况。train_data_loader = data.DataLoader(dataset=train_dataset,batch_size=args.b,shuffle=True,drop_last=True,num_workers=args.j,pin_memory=True)test_data_loader = data.DataLoader(dataset=test_dataset,batch_size=args.b,shuffle=False,drop_last=False,num_workers=args.j,pin_memory=True)print('数据读取成功')start_epoch = 0max_test_acc = -1optimizer = Noneif args.opt == 'sgd':optimizer = torch.optim.SGD(net.parameters(),lr=args.lr,momentum=args.momentum)elif args.opt == 'adam':optimizer = torch.optim.Adam(net.parameters(),lr=args.lr)else:raise NotImplementedError(args.opt)if args.resume:# map_location='cpu'指定将加载的数据映射到哪个设备上checkpoint = torch.load(args.resume,map_location='cpu')net.load_state_dict(checkpoint['net'])optimizer.load_state_dict(checkpoint['optimizer'])start_epoch = checkpoint['epoch']+1# 从检查点文件中加载的之前达到的最高测试准确率max_text_acc = checkpoint['max_test_acc']out_dir = os.path.join(args.out_dir,f'T{args.T}_b{args.b}_lr{args.lr}')if not os.path.exists(out_dir):os.makedirs(out_dir)print(f'Mkdir{out_dir}')# SummaryWriter：是 torch.utils.tensorboard.SummaryWriter 类的实例，# 用于将训练过程中的各种信息（如损失值、准确率等）写入 TensorBoard 日志文件，方便后续可视化分析。writer = SummaryWriter(out_dir,purge_step=start_epoch)with open(os.path.join(out_dir,'args.txt'),'w',encoding='utf-8') as args_txt:args_txt.write(str(args))args_txt.write('\n')args_txt.write(' '.join(sys.argv))encoder = encoding.PoissonEncoder()for epoch in range(start_epoch,args.epochs):start_time = time.time()# 将网络设置为训练模式net.train()train_loss = 0train_acc = 0train_samples = 0for img,label in train_data_loader:optimizer.zero_grad()img = img.to(args.device)label = label.to(args.device)# 将标签转为 ont-hot 编码，方便后续计算损失label_onehot = F.one_hot(label,10).float()# 计算损失并进行反向传播out_fr = 0.for t in range(args.T):encoded_img = encoder(img)out_fr += net(encoded_img)out_fr = out_fr/args.Tloss = F.mse_loss(out_fr,label_onehot)loss.backward()optimizer.step()# label.numel()是当前批次的样本数量# loss.item() 是当前批次的损失值，将其乘以样本数量并累加到 train_loss 中。# (out_fr.argmax(1) == label).float().sum().item()# 计算当前批次的正确预测数量，将其累加到 train_acc 中。train_samples += label.numel()train_loss += loss.item() * label.numel()train_acc += (out_fr.argmax(1) == label).float().sum().item()# 重置模型状态functional.reset_net(net)train_time = time.time()train_speed = train_samples / (train_time - start_time)train_loss /= train_samplestrain_acc /= train_sampleswriter.add_scalar('train_loss', train_loss, epoch)writer.add_scalar('train_acc', train_acc, epoch)net.eval()test_loss = 0test_acc = 0test_samples = 0with torch.no_grad():for img, label in test_data_loader:img = img.to(args.device)label = label.to(args.device)label_onehot = F.one_hot(label, 10).float()out_fr = 0.for t in range(args.T):encoded_img = encoder(img)out_fr += net(encoded_img)out_fr = out_fr / args.Tloss = F.mse_loss(out_fr, label_onehot)test_samples += label.numel()test_loss += loss.item() * label.numel()test_acc += (out_fr.argmax(1) == label).float().sum().item()functional.reset_net(net)test_time = time.time()test_speed = test_samples / (test_time - train_time)test_loss /= test_samplestest_acc /= test_sampleswriter.add_scalar('test_loss', test_loss, epoch)writer.add_scalar('test_acc', test_acc, epoch)save_max = Falseif test_acc > max_test_acc:max_test_acc = test_accsave_max = Truecheckpoint = {'net': net.state_dict(),'optimizer': optimizer.state_dict(),'epoch': epoch,'max_test_acc': max_test_acc}if save_max:torch.save(checkpoint, os.path.join(out_dir, 'checkpoint_max.pth'))print(args)print(out_dir)print(f'epoch ={epoch}, train_loss ={train_loss: .4f}, train_acc ={train_acc: .4f}, test_loss ={test_loss: .4f}, test_acc ={test_acc: .4f}, max_test_acc ={max_test_acc: .4f}')print(f'train speed ={train_speed: .4f} images/s, test speed ={test_speed: .4f} images/s')print(f'escape time = {(datetime.datetime.now() + datetime.timedelta(seconds=(time.time() - start_time) * (args.epochs - epoch))).strftime("%Y-%m-%d %H:%M:%S")}\n')if __name__ == '__main__':main()