Carla 使用神经网络训练自动驾驶车辆—模型搭建及训练

上一节已经搭建好了一个carla仿真环境，并且进行了数据采集

现在需要使用采集到的摄像头图片和转角数据进行模型训练

创建dataset类

import numpy as np
import config
from torch.utils.data import Dataset
class TrainDataSet(Dataset):
def __init__(self):
self.x=np.load(config.DATASET_PATH+"x_train.npy")[:config.TRAIN_DATASET_SIZES]
self.y=np.load(config.DATASET_PATH+"y_train.npy")[:config.TRAIN_DATASET_SIZES]
def __len__(self):
return len(self.x)
def __getitem__(self,index):
return (self.x[index],self.y[index])
class TestDataSet(Dataset):
def __init__(self):
self.x = np.load(config.DATASET_PATH+'x_train.npy')[config.TRAIN_DATASET_SIZES:]
self.y = np.load(config.DATASET_PATH+'y_train.npy')[config.TRAIN_DATASET_SIZES:]
def __len__(self):
return len(self.x)
def __getitem__(self, index):
return (self.x[index], self.y[index])

模型部分

import torch
import torch.nn as nn
from torchsummary import summary
class Block(nn.Module):
def __init__(self, in_channels, out_channels, maxpool=False):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
self.relu = nn.ReLU()
self.maxpool = maxpool
self.max_pool = nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.conv(x)
x = self.relu(x)
if self.maxpool:
x = self.max_pool(x)
return x
class Model(nn.Module):
def __init__(self, in_channels=3, out_size=1):
super().__init__()
self.conv_block = nn.Sequential(
Block(in_channels, 16),
Block(16, 16, True),
Block(16, 32),
Block(32, 32, True),
Block(32, 64),
Block(64, 64, True),
Block(64, 128),
Block(128, 128, True),
Block(128, 128),
Block(128, 128, True),
)
self.fc_block = nn.Sequential(
nn.Linear(1536, 100),
nn.ReLU(),
nn.Linear(100, 20),
nn.ReLU(),
nn.Linear(20, out_size)
)
def forward(self, x):
x = self.conv_block(x)
x = x.view(x.size(0), -1)
x = self.fc_block(x)
return x
if __name__ == '__main__':
model = Model()
summary(model, input_size=(3, 66, 200), device='cpu')
data = torch.ones(1, 3, 66, 200)
out = model(data)
print(out.shape)

训练部分：

import torch
import torch.nn as nn
import keyboard
import matplotlib.pyplot as plt
import config
from torch.utils.data import DataLoader
from dataset import TrainDataSet
from model import Model
class Trainer:
def __init__(self, model, train_dataset, model_state=None):
self.model = model
self.model_state = model_state
self.train_dataset = train_dataset
self.loss_list = []
self.main()
def main(self):
torch.manual_seed(config.SEED)
torch.cuda.manual_seed(config.SEED)
model = self.model
loss_list = self.loss_list
model.to(config.DEVICE)
if self.model_state:
state = torch.load(self.model_state)
model.load_state_dict(state)
model.train()
dataloader = DataLoader(self.train_dataset, batch_size=config.BATCH_SIZE, shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE)
criterion = nn.MSELoss()
for epoch in range(config.NUM_EPOCH):
for x, y in dataloader:
x, y = x.to(config.DEVICE).float(), y.to(config.DEVICE).float()
x = x.reshape(x.size(0), 3, 66, 200)
optimizer.zero_grad()
out = model(x)
loss = criterion(out, y)
loss.backward()
optimizer.step()
loss_list.append(loss.item())
self._show_loss(loss_list, '3')
print('epoc[%i/%i] loss=%.5f' % (epoch, config.NUM_EPOCH, loss.item()))
torch.save(model.state_dict(), config.MODEL_PATH+'model_state.pth')
def _show_loss(self, loss_list, key='3'):
if keyboard.is_pressed(key):
plt.plot(loss_list)
plt.ylim(0, 0.1)
plt.show()
if __name__ == '__main__':
# Trainer(Model(), TrainDataSet(), model_state='model_state.pth')
Trainer(Model(), TrainDataSet())

这样就会得到神经网络的权重model_state.pth，下一节使用训练好的神经网络在carla环境中进行自动驾驶测试

最后

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