import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import torchvision
import numpy as np
from spikingjelly.clock_driven import neuron, encoding, functional
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
parser = argparse.ArgumentParser(description='spikingjelly LIF MNIST Training')
parser.add_argument('--device', default='cuda:0', help='运行的设备，例如“cpu”或“cuda:0”\n Device, e.g., "cpu" or "cuda:0"')
parser.add_argument('--dataset-dir', default='./', help='保存MNIST数据集的位置，例如“./”\n Root directory for saving MNIST dataset, e.g., "./"')
parser.add_argument('--log-dir', default='./', help='保存tensorboard日志文件的位置，例如“./”\n Root directory for saving tensorboard logs, e.g., "./"')
parser.add_argument('--model-output-dir', default='./', help='模型保存路径，例如“./”\n Model directory for saving, e.g., "./"')
parser.add_argument('-b', '--batch-size', default=64, type=int, help='Batch 大小，例如“64”\n Batch size, e.g., "64"')
parser.add_argument('-T', '--timesteps', default=100, type=int, dest='T', help='仿真时长，例如“100”\n Simulating timesteps, e.g., "100"')
parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, metavar='LR', help='学习率，例如“1e-3”\n Learning rate, e.g., "1e-3": ', dest='lr')
parser.add_argument('--tau', default=2.0, type=float, help='LIF神经元的时间常数tau，例如“100.0”\n Membrane time constant, tau, for LIF neurons, e.g., "100.0"')
parser.add_argument('-N', '--epoch', default=100, type=int, help='训练epoch，例如“100”\n Training epoch, e.g., "100"')
[文档]def main():
'''
:return: None
* :ref:`API in English <lif_fc_mnist.main-en>`
.. _lif_fc_mnist.main-cn:
使用全连接-LIF的网络结构，进行MNIST识别。\n
这个函数会初始化网络进行训练，并显示训练过程中在测试集的正确率。
* :ref:`中文API <lif_fc_mnist.main-cn>`
.. _lif_fc_mnist.main-en:
The network with FC-LIF structure for classifying MNIST.\n
This function initials the network, starts trainingand shows accuracy on test dataset.
'''
args = parser.parse_args()
print("########## Configurations ##########")
print('\n'.join(f'{k}={v}' for k, v in vars(args).items()))
print("####################################")
device = args.device
dataset_dir = args.dataset_dir
log_dir = args.log_dir
model_output_dir = args.model_output_dir
batch_size = args.batch_size
lr = args.lr
T = args.T
tau = args.tau
train_epoch = args.epoch
writer = SummaryWriter(log_dir)
# 初始化数据加载器
train_dataset = torchvision.datasets.MNIST(
root=dataset_dir,
train=True,
transform=torchvision.transforms.ToTensor(),
download=True
)
test_dataset = torchvision.datasets.MNIST(
root=dataset_dir,
train=False,
transform=torchvision.transforms.ToTensor(),
download=True
)
train_data_loader = data.DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True
)
test_data_loader = data.DataLoader(
dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False
)
# 定义并初始化网络
net = nn.Sequential(
nn.Flatten(),
nn.Linear(28 * 28, 10, bias=False),
neuron.LIFNode(tau=tau)
)
net = net.to(device)
# 使用Adam优化器
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
# 使用泊松编码器
encoder = encoding.PoissonEncoder()
train_times = 0
max_test_accuracy = 0
test_accs = []
train_accs = []
for epoch in range(train_epoch):
print("Epoch {}:".format(epoch))
print("Training...")
train_correct_sum = 0
train_sum = 0
net.train()
for img, label in tqdm(train_data_loader):
img = img.to(device)
label = label.to(device)
label_one_hot = F.one_hot(label, 10).float()
optimizer.zero_grad()
# 运行T个时长，out_spikes_counter是shape=[batch_size, 10]的tensor
# 记录整个仿真时长内，输出层的10个神经元的脉冲发放次数
for t in range(T):
if t == 0:
out_spikes_counter = net(encoder(img).float())
else:
out_spikes_counter += net(encoder(img).float())
# out_spikes_counter / T 得到输出层10个神经元在仿真时长内的脉冲发放频率
out_spikes_counter_frequency = out_spikes_counter / T
# 损失函数为输出层神经元的脉冲发放频率，与真实类别的MSE
# 这样的损失函数会使，当类别i输入时，输出层中第i个神经元的脉冲发放频率趋近1，而其他神经元的脉冲发放频率趋近0
loss = F.mse_loss(out_spikes_counter_frequency, label_one_hot)
loss.backward()
optimizer.step()
# 优化一次参数后，需要重置网络的状态，因为SNN的神经元是有“记忆”的
functional.reset_net(net)
# 正确率的计算方法如下。认为输出层中脉冲发放频率最大的神经元的下标i是分类结果
train_correct_sum += (out_spikes_counter_frequency.max(1)[1] == label.to(device)).float().sum().item()
train_sum += label.numel()
train_batch_accuracy = (out_spikes_counter_frequency.max(1)[1] == label.to(device)).float().mean().item()
writer.add_scalar('train_batch_accuracy', train_batch_accuracy, train_times)
train_accs.append(train_batch_accuracy)
train_times += 1
train_accuracy = train_correct_sum / train_sum
print("Testing...")
net.eval()
with torch.no_grad():
# 每遍历一次全部数据集，就在测试集上测试一次
test_correct_sum = 0
test_sum = 0
for img, label in tqdm(test_data_loader):
img = img.to(device)
for t in range(T):
if t == 0:
out_spikes_counter = net(encoder(img).float())
else:
out_spikes_counter += net(encoder(img).float())
test_correct_sum += (out_spikes_counter.max(1)[1] == label.to(device)).float().sum().item()
test_sum += label.numel()
functional.reset_net(net)
test_accuracy = test_correct_sum / test_sum
writer.add_scalar('test_accuracy', test_accuracy, epoch)
test_accs.append(test_accuracy)
max_test_accuracy = max(max_test_accuracy, test_accuracy)
print("Epoch {}: train_acc = {}, test_acc={}, max_test_acc={}, train_times={}".format(epoch, train_accuracy, test_accuracy, max_test_accuracy, train_times))
print()
# 保存模型
torch.save(net, model_output_dir + "/lif_snn_mnist.ckpt")
# 读取模型
# net = torch.load(model_output_dir + "/lif_snn_mnist.ckpt")
# 保存绘图用数据
net.eval()
# 注册钩子
output_layer = net[-1] # 输出层
output_layer.v_seq = []
output_layer.s_seq = []
def save_hook(m, x, y):
m.v_seq.append(m.v.unsqueeze(0))
m.s_seq.append(y.unsqueeze(0))
output_layer.register_forward_hook(save_hook)
with torch.no_grad():
img, label = test_dataset[0]
img = img.to(device)
for t in range(T):
if t == 0:
out_spikes_counter = net(encoder(img).float())
else:
out_spikes_counter += net(encoder(img).float())
out_spikes_counter_frequency = (out_spikes_counter / T).cpu().numpy()
print(f'Firing rate: {out_spikes_counter_frequency}')
output_layer.v_seq = torch.cat(output_layer.v_seq)
output_layer.s_seq = torch.cat(output_layer.s_seq)
v_t_array = output_layer.v_seq.cpu().numpy().squeeze().T # v_t_array[i][j]表示神经元i在j时刻的电压值
np.save("v_t_array.npy",v_t_array)
s_t_array = output_layer.s_seq.cpu().numpy().squeeze().T # s_t_array[i][j]表示神经元i在j时刻释放的脉冲，为0或1
np.save("s_t_array.npy",s_t_array)
train_accs = np.array(train_accs)
np.save('train_accs.npy', train_accs)
test_accs = np.array(test_accs)
np.save('test_accs.npy', test_accs)
if __name__ == '__main__':
main()