import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import numpy as np
from spikingjelly.clock_driven import neuron, encoding, functional
from torch.utils.tensorboard import SummaryWriter
import sys
if sys.platform != 'win32':
import readline
from tqdm import tqdm
[文档]def main():
'''
* :ref:`API in English <lif_fc_mnist.main-en>`
.. _lif_fc_mnist.main-cn:
:return: None
使用全连接-LIF-全连接-LIF的网络结构，进行MNIST识别。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-FC-LIF structure for classifying MNIST. This function initials the network, starts training
and shows accuracy on test dataset.
'''
device = input('输入运行的设备，例如“cpu”或“cuda:0”\n input device, e.g., "cpu" or "cuda:0": ')
dataset_dir = input('输入保存MNIST数据集的位置，例如“./”\n input root directory for saving MNIST dataset, e.g., "./": ')
batch_size = int(input('输入batch_size，例如“64”\n input batch_size, e.g., "64": '))
learning_rate = float(input('输入学习率，例如“1e-3”\n input learning rate, e.g., "1e-3": '))
T = int(input('输入仿真时长，例如“100”\n input simulating steps, e.g., "100": '))
tau = float(input('输入LIF神经元的时间常数tau，例如“100.0”\n input membrane time constant, tau, for LIF neurons, e.g., "100.0": '))
train_epoch = int(input('输入训练轮数，即遍历训练集的次数，例如“100”\n input training epochs, e.g., "100": '))
log_dir = input('输入保存tensorboard日志文件的位置，例如“./”\n input root directory for saving tensorboard logs, e.g., "./": ')
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 = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True)
test_data_loader = torch.utils.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=learning_rate)
# 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):
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
accuracy = (out_spikes_counter_frequency.max(1)[1] == label.to(device)).float().mean().item()
writer.add_scalar('train_accuracy', accuracy, train_times)
train_accs.append(accuracy)
train_times += 1
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_sum = 0
correct_sum = 0
for img, label in 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())
correct_sum += (out_spikes_counter.max(1)[1] == label.to(device)).float().sum().item()
test_sum += label.numel()
functional.reset_net(net)
test_accuracy = 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(f'Epoch {epoch}: device={device}, dataset_dir={dataset_dir}, batch_size={batch_size}, learning_rate={learning_rate}, T={T}, log_dir={log_dir}, max_test_accuracy={max_test_accuracy}, train_times={train_times}')
# 保存绘图用数据
net.eval()
functional.set_monitor(net, True)
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 = net[-1] # 输出层
v_t_array = np.asarray(output_layer.monitor['v']).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 = np.asarray(output_layer.monitor['s']).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()