import os
import time
import argparse
import sys
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
from torch.utils.tensorboard import SummaryWriter
import torchvision
import numpy as np
from spikingjelly.activation_based import neuron, encoding, functional, surrogate, layer
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class SNN(nn.Module):
def __init__(self, tau):
super().__init__()
self.layer = nn.Sequential(
layer.Flatten(),
layer.Linear(28 * 28, 10, bias=False),
neuron.LIFNode(tau=tau, surrogate_function=surrogate.ATan()),
)
[文档]
def forward(self, x: torch.Tensor):
return self.layer(x)
[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.
"""
parser = argparse.ArgumentParser(description="LIF MNIST Training")
parser.add_argument("-T", default=100, type=int, help="simulating time-steps")
parser.add_argument("-device", default="cuda:0", help="device")
parser.add_argument("-b", default=64, type=int, help="batch size")
parser.add_argument(
"-epochs",
default=100,
type=int,
metavar="N",
help="number of total epochs to run",
)
parser.add_argument(
"-j",
default=4,
type=int,
metavar="N",
help="number of data loading workers (default: 4)",
)
parser.add_argument("-data-dir", type=str, help="root dir of MNIST dataset")
parser.add_argument(
"-out-dir",
type=str,
default="./logs",
help="root dir for saving logs and checkpoint",
)
parser.add_argument("-resume", type=str, help="resume from the checkpoint path")
parser.add_argument(
"-amp", action="store_true", help="automatic mixed precision training"
)
parser.add_argument(
"-opt",
type=str,
choices=["sgd", "adam"],
default="adam",
help="use which optimizer. SGD or Adam",
)
parser.add_argument("-momentum", default=0.9, type=float, help="momentum for SGD")
parser.add_argument("-lr", default=1e-3, type=float, help="learning rate")
parser.add_argument(
"-tau", default=2.0, type=float, help="parameter tau of LIF neuron"
)
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
train_dataset = torchvision.datasets.MNIST(
root=args.data_dir,
train=True,
transform=torchvision.transforms.ToTensor(),
download=True,
)
test_dataset = torchvision.datasets.MNIST(
root=args.data_dir,
train=False,
transform=torchvision.transforms.ToTensor(),
download=True,
)
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,
)
scaler = None
if args.amp:
scaler = amp.GradScaler()
start_epoch = 0
max_test_acc = -1
optimizer = None
if 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:
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
max_test_acc = checkpoint["max_test_acc"]
out_dir = os.path.join(args.out_dir, f"T{args.T}_b{args.b}_{args.opt}_lr{args.lr}")
if args.amp:
out_dir += "_amp"
if not os.path.exists(out_dir):
os.makedirs(out_dir)
print(f"Mkdir {out_dir}.")
with open(os.path.join(out_dir, "args.txt"), "w", encoding="utf-8") as args_txt:
args_txt.write(str(args))
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()
net.train()
train_loss = 0
train_acc = 0
train_samples = 0
for img, label in train_data_loader:
optimizer.zero_grad()
img = img.to(args.device)
label = label.to(args.device)
label_onehot = F.one_hot(label, 10).float()
if scaler is not None:
with amp.autocast():
out_fr = 0.0
for t in range(args.T):
encoded_img = encoder(img)
out_fr += net(encoded_img)
out_fr = out_fr / args.T
loss = F.mse_loss(out_fr, label_onehot)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
out_fr = 0.0
for t in range(args.T):
encoded_img = encoder(img)
out_fr += net(encoded_img)
out_fr = out_fr / args.T
loss = F.mse_loss(out_fr, label_onehot)
loss.backward()
optimizer.step()
train_samples += label.numel()
train_loss += loss.item() * label.numel()
train_acc += (out_fr.argmax(1) == label).float().sum().item()
functional.reset_net(net)
train_time = time.time()
train_speed = train_samples / (train_time - start_time)
train_loss /= train_samples
train_acc /= train_samples
writer.add_scalar("train_loss", train_loss, epoch)
writer.add_scalar("train_acc", train_acc, epoch)
net.eval()
test_loss = 0
test_acc = 0
test_samples = 0
with 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 = 0.0
for t in range(args.T):
encoded_img = encoder(img)
out_fr += net(encoded_img)
out_fr = out_fr / args.T
loss = 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_samples
test_acc /= test_samples
writer.add_scalar("test_loss", test_loss, epoch)
writer.add_scalar("test_acc", test_acc, epoch)
save_max = False
if test_acc > max_test_acc:
max_test_acc = test_acc
save_max = True
checkpoint = {
"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"))
torch.save(checkpoint, os.path.join(out_dir, "checkpoint_latest.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"
)
# 保存绘图用数据
net.eval()
# 注册钩子
output_layer = net.layer[-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(args.device)
out_fr = 0.0
for t in range(args.T):
encoded_img = encoder(img)
out_fr += net(encoded_img)
out_spikes_counter_frequency = (out_fr / args.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()
) # 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()
) # 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)
if __name__ == "__main__":
main()
