L4 级自动驾驶汽车发展综述

1. 网络结构
   
2. 代码

# CIFAR 10
'''
完整的模型训练套路：

'''
import torch.optim
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

from model import *

# 1. 准备数据集
train_data = torchvision.datasets.CIFAR10('data',train=True,
                                       transform=torchvision.transforms.ToTensor(),
                                       download=True)
test_data = torchvision.datasets.CIFAR10('data',train=False,
                                         transform=torchvision.transforms.ToTensor(),
                                         download=True)
# 数据集大小
train_data_size = len(train_data)
test_data_size = len(test_data)
print('训练数据集的长度为{}'.format(train_data_size))
print('测试数据集的长度为{}'.format(test_data_size))

# 2 利用DataLoader加载数据集
train_dataloader = DataLoader(train_data,batch_size=64)
test_dataloader = DataLoader(test_data,batch_size=64)

# 3 搭建神经网络
# 4 创建网络模型
tudui = Tudui()

# 5 损失函数
loss_fn = nn.CrossEntropyLoss()

# 6 优化器 1e-2=1x10^(-2)
learning_rate = 0.01
optimizer = torch.optim.SGD(tudui.parameters(),lr=learning_rate)

# 7 设置训练网络的一些参数
total_train_step = 0 # 记录训练次数
total_test_step = 0 # 记录测试次数
epoch = 10 #训练轮数
# 添加tensorboard
writer = SummaryWriter('logs_model')

for i in range(epoch):
    print('-----------第{}轮训练开始-----------'.format(i+1))
    # 训练开始
    # 训练步骤开始 dropout batchNorm仅对某些层次有作用
    tudui.train()
    for data in train_dataloader:
        imgs, targets = data
        output = tudui(imgs) #训练模型的预测输出
        loss = loss_fn(output,targets)
        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print('训练次数是{}时，loss是{}'.format(total_train_step,loss.item()))# 加了item() tensor变成了数字
            writer.add_scalar('train_loss',loss.item(),total_train_step)

    # 训练完一轮，看是否训练好,有没有达到想要的需求，测试数据集中跑一篇看准确率或者损失
    # 测试步骤开始
    tudui.eval()
    total_test_loss = 0
    total_accuracy = 0
    # 测试不需要对梯度进行调整
    with torch.no_grad():
        for data in test_dataloader:
            imgs,targets = data
            outputs = tudui(imgs)
            loss = loss_fn(outputs,targets)
            total_test_loss += loss.item()
            # accuracy 正确预测的样本数量
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy += accuracy
    print('整体测试集上的loss是{}'.format(total_test_loss))
    print('整体测试集上的正确率是{}'.format(total_accuracy/test_data_size))
    writer.add_scalar('test_loss',total_test_loss,total_test_step)
    writer.add_scalar('test_accuracy', total_accuracy, total_test_step)
    total_test_step+=1

    torch.save(tudui,'tudui_{}.pth'.format(i))
    print('模型已保存')

writer.close()

# model.py
import torch
from torch import nn

# 3 搭建神经网络
class Tudui(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3,32,5,1,2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024,64),
            nn.Linear(64, 10)
        )
    def forward(self,x):
        x = self.model(x)
        return x

if __name__ == '__main__':
    tudui = Tudui()
    # 验证一下输入输出尺寸
    input = torch.ones((64,3,32,32))
    output = tudui(input)
    print(output.shape)

运行结果：