基于关键点的车道线检测
以下是提供的几种方案思路，并不代表具体的任务方案。
示例一
设计一个关键点监督的网络，可以采用基于卷积神经网络（CNN）的方法，通过对车道线数据集进行训练来实现车道线点的分类和关键点坐标的回归。
关键点的生成可以采用以下的方法：
- 对于每个车道线点，将其坐标作为关键点坐标
- 对于每个像素点，如果其为车道线点，将其坐标作为关键点坐标
- 对于每个车道线点，将其周围的几个像素点（如8个）作为关键点坐标
这里采用第二种方法。
代码示例如下：
python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LaneNet(nn.Module):
def __init__(self, num_classes=7):
super(LaneNet, self).__init__()
# encoder
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
self.conv4 = nn.Conv2d(256, 512, 3, padding=1)
# decoder
self.deconv1 = nn.ConvTranspose2d(512, 256, 2, stride=2)
self.deconv2 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.deconv3 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.classifier = nn.Conv2d(64, num_classes, 1)
# key points offset regression
self.offset_conv1 = nn.Conv2d(256, 256, 3, padding=1)
self.offset_conv2 = nn.Conv2d(256, 256, 3, padding=1)
self.offset_conv3 = nn.Conv2d(256, 256, 3, padding=1)
self.offset_conv4 = nn.Conv2d(256, 256, 3, padding=1)
self.offset_output = nn.Conv2d(256, 2, 1)
# initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, input):
# encoder
conv1 = F.relu(self.conv1(input))
conv2 = F.relu(self.conv2(conv1))
conv3 = F.relu(self.conv3(conv2))
conv4 = F.relu(self.conv4(conv3))
# key points offset regression
offset1 = F.relu(self.offset_conv1(conv3))
offset2 = F.relu(self.offset_conv2(offset1))
offset3 = F.relu(self.offset_conv3(offset2))
offset4 = F.relu(self.offset_conv4(offset3))
offset_output = self.offset_output(offset4)
# decoder
deconv1 = F.relu(self.deconv1(conv4))
deconv2 = F.relu(self.deconv2(deconv1 + conv3))
deconv3 = F.relu(self.deconv3(deconv2 + conv2))
score = self.classifier(deconv3 + conv1)
return score, offset_output上述代码中的LaneNet网络采用encoder-decoder结构，其中encoder部分由4个卷积层组成，decoder部分由3个反卷积层组成，同时也添加了关键点偏移量回归的网络结构。
其中，score为分类结果，offset_output为关键点偏移量回归结果，可以通过下面的函数计算关键点坐标：
python
def generate_keypoints(score_map, offset_map):
"""
Generate key points from score map and offset map
"""
# get positions where score > 0
mask = score_map > 0
idx = torch.nonzero(mask)
# get offsets for each position
offsets = offset_map[0, :, idx[:, 0], idx[:, 1]]
offsets = offsets.transpose(0, 1).contiguous()
# calculate key points
keypoints = idx.float() + offsets
keypoints = torch.cat((keypoints, score_map[idx[:, 0], idx[:, 1], None]), dim=1)
return keypoints关键点坐标的回归可以通过采用坐标差的方式来计算loss，即对于每个关键点，计算其预测值与真实值的坐标差，然后取平均值作为loss的值。
loss函数的代码实现如下：
python
class LaneLoss(nn.Module):
def __init__(self, delta_v=0.1, delta_d=3.0):
super(LaneLoss, self).__init__()
self.delta_v = delta_v
self.delta_d = delta_d
def forward(self, score_map, offset_map, gt):
# generate key points
keypoints = generate_keypoints(score_map, offset_map)
# get gt keypoints
gt_keypoints = []
for i in range(gt.shape[0]):
idx = (gt[i] < 255)
gt_keypoints.append(torch.nonzero(idx.float()))
# calculate loss
loss = 0
count = 0
for i in range(len(keypoints)):
if gt_keypoints[i].shape[0] > 0:
v_diff = keypoints[i, 0] - gt_keypoints[i][:, 1:2].float()
d_diff = keypoints[i, 1] - gt_keypoints[i][:, 0:1].float()
mask = (torch.abs(v_diff) < self.delta_v) & (torch.abs(d_diff) < self.delta_d)
if torch.sum(mask) > 0:
loss += torch.mean(torch.sqrt(torch.sum(torch.pow(v_diff[mask], 2) + torch.pow(d_diff[mask], 2))))
count += 1
if count > 0:
loss /= count
return loss在训练过程中，可以同时计算分类结果和关键点回归结果的loss，然后将两者loss加起来作为总的loss，用于反向传播更新模型参数。
python
# define model and loss function
model = LaneNet(num_classes=7)
criterion = nn.CrossEntropyLoss()
lane_loss = LaneLoss(delta_v=0.1, delta_d=3.0)
# training loop
for epoch in range(num_epochs):
# train model
for batch_idx, (image, label) in enumerate(train_loader):
optimizer.zero_grad()
# forward pass
score, offset = model(image)
loss_cls = criterion(score, label)
loss_offset = lane_loss(score, offset, label)
loss = loss_cls + loss_offset
# backward pass
loss.backward()
optimizer.step()
# log
if (batch_idx+1) % log_interval == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.8f}, Loss_cls: {:.8f}, Loss_offset: {:.8f}'
.format(epoch+1, num_epochs, batch_idx+1, len(train_loader), loss.item(), loss_cls.item(), loss_offset.item()))示例二
关键点监督的网络可以使用U-Net或者FCN等语义分割模型。这里假设使用U-Net模型。
关键点生成的思路是将每个车道线点都视为一个关键点，对应的label值就是该点的类别。因为每个点都具有唯一的坐标，所以我们将坐标作为该点的关键点。
loss函数可以分为两部分：分类损失和关键点坐标回归损失。分类损失使用交叉熵损失函数，关键点坐标回归损失使用均方根误差（RMSE）损失函数。
以下是使用Pytorch实现的代码示例：
python
import torch
import torch.nn as nn
class LaneDetectionModel(nn.Module):
def __init__(self):
super(LaneDetectionModel, self).__init__()
self.inc = DoubleConv(3, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 512)
self.up1 = Up(1024, 256)
self.up2 = Up(512, 128)
self.up3 = Up(256, 64)
self.up4 = Up(128, 64)
self.outc = nn.Conv2d(64, 6, 1) # 6 classes including background
self.keypoints = nn.Conv2d(64, 2, 1) # 2 channels for x, y coordinates of keypoints
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
out = torch.softmax(logits, dim=1)
keypoints = self.keypoints(x)
return out, keypoints
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class Down(nn.Module):
def __init__(self, in_channels, out_channels):
super(Down, self).__init__()
self.mpconv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
x = self.mpconv(x)
return x
class Up(nn.Module):
def __init__(self, in_channels, out_channels):
super(Up, self).__init__()
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = nn.functional.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
def lane_detection_loss(out, keypoints, labels, coords):
# out: output of the model with shape (batch_size, num_classes, height, width)
# keypoints: output of the model with shape (batch_size, 2, height, width)
# labels: input label with shape (batch_size, height, width)
# coords: coordinates of keypoints with shape (batch_size, num_keypoints, 2)
batch_size, num_classes, height, width = out.shape
num_keypoints = coords.shape[1]
# classification loss
criterion_cls = nn.CrossEntropyLoss()
loss_cls = criterion_cls(out, labels)
# keypoint regression loss
keypoints = keypoints.permute(0, 2, 3, 1).view(batch_size, height * width, 2)
coords = coords.view(batch_size, num_keypoints, 2)
loss_kp = torch.sqrt(torch.mean(torch.sum((keypoints - coords) ** 2, dim=2)))
return loss_cls + loss_kp
# example usage
model = LaneDetectionModel()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = lane_detection_loss
for epoch in range(num_epochs):
for batch in dataloader:
images, labels, keypoints, coords = batch
optimizer.zero_grad()
out, kps = model(images)
loss = criterion(out, kps, labels, coords)
loss.backward()
optimizer.step()示例三
关键点监督的网络可以使用卷积神经网络（CNN）进行设计。具体实现步骤如下：
-
将输入的点云数据集转换为图像数据集，例如使用卷积操作将点云转换为144*144的图像数据。这样每个像素点对应原点云中的点，可以很好地描述车道线的位置和形状。
-
使用卷积神经网络对图像进行学习和分类。可以使用经典的CNN网络结构，例如VGG、ResNet等等。
-
生成关键点。设计一个关键点检测模块，在网络的中间层或之后添加一些卷积和池化操作，使得输出的特征图具有更高的空间分辨率。然后使用一些回归算法，例如基于均值方差的回归算法或者基于滑动窗口的回归算法，去预测每个车道线的关键点。
-
损失函数设计。因为需要同时进行分类和关键点检测，所以可以设计一个综合分类和回归的损失函数。损失函数一般包含两部分，一部分是分类损失，使用交叉熵损失函数；另一部分是关键点回归损失，可以使用均方误差损失函数。两部分损失函数加权求和，得到最终的损失函数。
下面是使用PyTorch实现的代码示例：
python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LaneNet(nn.Module):
def __init__(self):
super(LaneNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
self.conv4 = nn.Conv2d(256, 512, 3, padding=1)
self.conv5 = nn.Conv2d(512, 256, 3, padding=1)
self.conv6 = nn.Conv2d(256, 128, 3, padding=1)
self.conv7 = nn.Conv2d(128, 32, 3, padding=1)
self.conv8 = nn.Conv2d(32, 6, 3, padding=1)
self.pool = nn.MaxPool2d(2, stride=2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = self.pool(F.relu(self.conv4(x)))
x = self.pool(F.relu(self.conv5(x)))
x = F.relu(self.conv6(x))
x = F.upsample(x, scale_factor=2, mode="bilinear")
x = F.relu(self.conv7(x))
x = F.upsample(x, scale_factor=2, mode="bilinear")
out1 = F.softmax(self.conv8(x), dim=1) # 分类输出
out2 = self.conv7(x) # 关键点回归输出
return out1, out2
class LaneLoss(nn.Module):
def __init__(self, alpha=1, beta=2):
super(LaneLoss, self).__init__()
self.alpha = alpha
self.beta = beta
def forward(self, y_pred, y_true):
# 分类损失
loss_cls = -torch.mean(y_true * torch.log(y_pred + 1e-6)) # 交叉熵损失
# 关键点回归损失
mask = (y_true > 0).float() # 生成掩码
loss_reg = torch.mean(torch.sum(mask * (y_pred - y_true)**2, dim=[1, 2])) # 均方误差损失
# 加权求和
loss = self.alpha * loss_cls + self.beta * loss_reg
return loss在训练过程中，每个车道线的关键点可以使用如下方式生成：
python
import cv2
import numpy as np
def generate_keypoints(label):
keypoints = []
for i in range(1, 7):
mask = (label == i).astype(np.uint8)
if np.sum(mask) == 0: # 没有检测到该车道线
keypoints.append([-100, -100]) # 标记为负数
else:
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
keypoints.append([cx, cy]) # 保存中心点坐标
return keypoints在训练过程中，计算损失函数可以使用以下方式：
python
model = LaneNet()
criterion = LaneLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
for inputs, labels in data_loader: # 迭代数据集
optimizer.zero_grad()
outputs = model(inputs) # 前向传播
y_pred = outputs[0]
y_true = F.one_hot(labels, num_classes=7).permute(0, 3, 1, 2).float() # 转换为one-hot编码
loss = criterion(y_pred, y_true) # 计算损失函数
loss.backward() # 反向传播
optimizer.step()
# 打印损失函数值和准确率
print("Epoch: {}, Loss: {:.4f}".format(epoch, loss.item()))需要注意的是，在测试或实际应用中，可以使用预测结果和关键点回归模块生成关键点，方法与训练过程中相同。
示例四
关键点监督的网络可以是一个基于ViT的Encoder-Decoder模型，Encoder部分用ViT提取特征，Decoder部分用于进行分类及关键点坐标回归。关键点生成可以采用以下方法：
-
针对每个车道线类别，提取其对应的所有点坐标，并计算其中心点坐标作为关键点坐标。
-
针对每个车道线类别，提取其对应的所有点坐标，将其视为一组二维点云，通过聚类算法（如K-Means）将其分为若干簇，每个簇的中心点作为关键点坐标。
-
针对每个车道线类别，提取其对应的所有点坐标，将其视为一个二维曲线，通过曲线拟合算法（如Bezier曲线拟合）得到拟合曲线的关键点坐标。
Loss函数可以采用多任务学习的方式，同时优化分类及回归任务的损失函数，如下所示：
python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LaneDetection(nn.Module):
def __init__(self, num_classes=6, num_keypoints=12):
super().__init__()
self.num_classes = num_classes
self.num_keypoints = num_keypoints
self.vit = ... # ViT backbone
self.classifier = nn.Linear(in_features=..., out_features=num_classes)
self.keypoints = nn.Linear(in_features=..., out_features=num_keypoints * 2) # 每个关键点有两个坐标，因此输出维度为num_keypoints * 2
def forward(self, x):
feat_map = self.vit(x)
logits = self.classifier(feat_map) # 输出分类结果
keypoints = self.keypoints(feat_map) # 输出关键点坐标
return logits, keypoints
criterion_cls = nn.CrossEntropyLoss() # 分类任务损失函数
criterion_kp = nn.MSELoss() # 回归任务损失函数
model = LaneDetection()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
for epoch in range(num_epochs):
for imgs, labels in dataloader:
imgs, labels = imgs.to(device), labels.to(device)
optimizer.zero_grad()
logits, keypoints = model(imgs)
loss_cls = criterion_cls(logits, labels)
loss_kp = criterion_kp(keypoints, generate_keypoints(labels)) # 生成关键点坐标的方法根据自己选择的方法进行调用
loss = loss_cls + loss_kp
loss.backward()
optimizer.step()模型的输出shape为一个二元组 (B, num_classes) 和 (B, num_keypoints * 2)，分别表示分类结果和关键点坐标。其中，B 表示批量大小。
示例五（heatmap)
设计关键点监督的网络：
- 输入层：输入一个固定大小的点云，可以通过将点云投影到一个平面上，形成144144像素的RGB图像，或者用PointNet++或PointCNN等网络进行特征提取，并将特征映射到一个144144的二维图像上作为输入。
- 骨干网络：使用ViT作为骨干网络，将输入的图像映射到一个固定大小的特征向量。
- 关键点生成层：在ViT输出的特征向量上添加一个关键点生成层，该层将输出一个关键点坐标的热力图，其中每个像素表示车道线的一个关键点。关键点坐标的热力图可以使用高斯分布表示，使得关键点周围的像素得到更高的值，而离关键点更远的像素则得到较低的值。
- 分类层：在ViT输出的特征向量上添加一个分类层，该层将输出一个144*144的矩阵，其中每个像素表示车道线的类别（1-6）或背景（255）。
关键点生成与Loss设计：
生成关键点热力图的方式：
对于每一个车道线的关键点，以该点为中心，在热力图上生成一个高斯分布，使得热力图上离该点越近的像素越接近于1，离该点越远的像素越接近于0。对于每一个像素，将其对应的高斯分布相加作为该像素的最终值。
Loss函数：
关键点坐标的回归损失（L2损失）和分类损失（交叉熵损失）分别计算，并将两个损失加权相加，得到总的Loss。
代码示例：
python
import torch
import torch.nn as nn
class LaneDetectionNetwork(nn.Module):
def __init__(self):
super(LaneDetectionNetwork, self).__init__()
# 定义骨干网络
self.vit = ...
# 定义关键点生成层和分类层
self.keypoints = nn.Sequential(
nn.Conv2d(...),
nn.ReLU(),
nn.Conv2d(...),
nn.Sigmoid()
)
self.classification = nn.Sequential(
nn.Conv2d(...),
nn.ReLU(),
nn.Conv2d(...)
)
def forward(self, x):
# 骨干网络
features = self.vit(x)
# 关键点生成层
keypoints = self.keypoints(features)
# 分类层
classification = self.classification(features)
return keypoints, classification
# 定义Loss函数
def loss_function(keypoints_pred, keypoints_gt, classification_pred, classification_gt):
# 计算关键点回归损失
keypoints_loss = nn.MSELoss()(keypoints_pred, keypoints_gt)
# 计算分类损失
classification_loss = nn.CrossEntropyLoss()(classification_pred, classification_gt)
# 加权平均得到总的Loss
loss = keypoints_loss + 0.1 * classification_loss
return loss
# 训练网络
net = LaneDetectionNetwork()
optimizer = torch.optim.Adam(net.parameters())
keypoints_gt = ... # 真实的关键点坐标
classification_gt = ... # 真实的分类标签
for i in range(num_epochs):
optimizer.zero_grad()
keypoints_pred, classification_pred = net(x)
loss = loss_function(keypoints_pred, keypoints_gt, classification_pred, classification_gt)
loss.backward()
optimizer.step()输出的shape:
对于一个图像，输出的shape为：
- keypoints: tensor of shape [1, 6, 144, 144]，即每个像素对应的6个关键点
- classification: tensor of shape [1, 7, 144, 144]，即每个像素对应的7个分类标签（1-6对应车道线，7对应背景）
示例六（voxel思路）
设计关键点监督的网络
- 首先，我们可以将原始点云编码成多通道的伪图像，使用类似于Pillar层的方法。对于每个点，我们将其位置转换为像素坐标，然后将其分配到相应的像素位置。然后，我们可以在每个像素位置上使用几个特征值，比如点的高度、离地面高度、反射率等，以增加图像的信息量。
- 将伪图像输入到ViT模型中作为骨干网，得到一个高维特征图。
- 我们可以在高维特征图上添加一个全局池化层，以获得整个图像的特征表示。
- 使用这个全局特征向量作为输入，构建一个神经网络，以进行分类和关键点回归。网络的输出应该包括分类结果以及关键点的坐标。
生成关键点
我们可以将车道线的点作为关键点。在训练集中，我们可以先将车道线的点从label中提取出来，并计算其在伪图像中的像素坐标。然后，我们可以将这些像素坐标标记为关键点。在测试集中，我们可以使用我们的模型来预测车道线的点，并从中提取出关键点。
Loss函数
对于分类任务，我们可以使用交叉熵损失函数。对于回归任务，我们可以使用均方误差损失函数。另外，我们需要为分类和回归任务分别设置权重，以便平衡两个任务的影响。
输出结果的shape
输出结果应该包括分类结果和关键点的坐标。因此，输出的shape应该是一个元组，包括分类结果和关键点坐标的shape。比如，假设我们有6个车道线点需要分类和回归，那么输出的shape应该是(6, 7)，其中第一维是点的数量，第二维是分类结果和关键点坐标的数量加一。
PyTorch代码示例
以下是可能实现该模型的PyTorch代码示例：
python
import torch
import torch.nn as nn
import torch.optim as optim
class LaneDetectionModel(nn.Module):
def __init__(self, num_classes, num_keypoints):
super(LaneDetectionModel, self).__init__()
self.encoder = nn.Sequential(
# define your encoder layers here
)
self.pooling = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Linear(in_features=..., out_features=num_classes)
self.keypoint_regressor = nn.Linear(in_features=..., out_features=num_keypoints)
def forward(self, x):
x = self.encoder(x)
x = self.pooling(x)
x = x.flatten(start_dim=1)
classifications = self.classifier(x)
keypoint_coordinates = self.keypoint_regressor(x)
return classifications, keypoint_coordinates
# define your training loop here示例七（transformer）
网络的设计：
python
import torch
import torch.nn as nn
from timm.models.vision_transformer import Block, Mlp
from timm.models.registry import register_model
class ViT_Line(nn.Module):
def __init__(self, in_channels=5, num_classes=7):
super(ViT_Line, self).__init__()
self.patch_embed = nn.Sequential(
nn.Conv2d(in_channels, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.GELU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.GELU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.GELU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.GELU()
)
self.blocks = nn.Sequential(
Block(
dim=128,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm
) for i in range(4)
)
self.head = nn.Sequential(
nn.Linear(128 * 9 * 9, 512),
nn.ReLU(inplace=True),
nn.Linear(512, num_classes)
)
self.reg_head = nn.Sequential(
nn.Linear(128 * 9 * 9, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 2 * num_classes)
)
def forward(self, x, coords):
x = self.patch_embed(x)
for blk in self.blocks:
x = blk(x)
x = x.flatten(1)
cls_pred = self.head(x)
reg_pred = self.reg_head(x)
keypoint_pred = []
for i in range(reg_pred.shape[0]):
keypoints = []
for j in range(reg_pred.shape[1] // 2):
coord = coords[j]
x, y = coord[0], coord[1]
kpt_x = reg_pred[i][2 * j] + x
kpt_y = reg_pred[i][2 * j + 1] + y
keypoints.append(torch.stack([kpt_x, kpt_y]))
keypoint_pred.append(torch.stack(keypoints))
keypoint_pred = torch.stack(keypoint_pred)
return cls_pred, keypoint_pred其中，ViT_Line模型包括三个部分：
patch_embed：pillar前处理编码，将原始的点云数据转换为图像格式；blocks：ViT的骨干网络，用于对图像进行分类；head：分类头，对图像进行分类，输出类别概率；reg_head：回归头，用于关键点的坐标回归。
生成关键点：
关键点生成的基本思路是根据label的类别信息，将每个类别的点取出，计算出类别对应的点的中心坐标，即可生成关键点。
下面是具体的实现代码：
python
import numpy as np
def generate_keypoints(label, stride=8):
keypoints = []
for i in range(1, 7):
indices = np.argwhere(label == i)
if len(indices) > 0:
indices = np.mean(indices, axis=0).astype(int)
x, y = indices[1] * stride, indices[0] * stride
keypoints.append([x, y])
return np.array(keypoints)其中，generate_keypoints函数接收一个label，输出一个关键点的列表。该函数先通过np.argwhere函数找到所有类别为i的点，然后求出这些点的均值，即为该类别的中心点坐标。最后返回的是一个坐标形式为[x, y]的列表。
loss函数的设计：
分类loss采用交叉熵，关键点坐标回归采用L1 loss。
下面是具体的实现代码：
python
class ViTLoss(nn.Module):
def __init__(self):
super(ViTLoss, self).__init__()
self.cls_loss = nn.CrossEntropyLoss()
self.reg_loss = nn.L1Loss()
def forward(self, cls_pred, keypoint_pred, cls_label, keypoint_label):
cls_loss = self.cls_loss(cls_pred, cls_label)
reg_loss = self.reg_loss(keypoint_pred, keypoint_label)
loss = cls_loss + reg_loss
return loss其中，ViTLoss模型通过CrossEntropyLoss计算分类loss，通过L1Loss计算关键点坐标回归的loss。
pytorch代码示例：
下面是一个完整的pytorch示例代码：
python
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import functional as F
import numpy as np
from PIL import Image
import random
from tqdm import tqdm
class LaneDetectionDataset(Dataset):
def __init__(self, data_path):
self.data_path = data_path
self.data = np.load(data_path, allow_pickle=True)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
point_cloud, label = self.data[idx]
point_cloud = point_cloud.astype(np.float32)
label = label.astype(np.int64)
keypoints = generate_keypoints(label)
point_cloud, label, keypoints = random_crop(point_cloud, label, keypoints)
point_cloud, label = normalize(point_cloud, label)
point_cloud = point_cloud.transpose(2, 0, 1) # [C, H, W]
point_cloud = torch.from_numpy(point_cloud)
label = torch.from_numpy(label)
keypoints = torch.from_numpy(keypoints)
return point_cloud, label, keypoints
class RandomCrop:
def __init__(self, crop_size):
self.crop_size = crop_size
def __call__(self, point_cloud, label, keypoints):
H, W, C = point_cloud.shape
y_start = random.randint(0, H - self.crop_size[0])
x_start = random.randint(0, W - self.crop_size[1])
point_cloud = point_cloud[y_start:y_start+self.crop_size[0], x_start:x_start+self.crop_size[1], :]
label = label[y_start:y_start+self.crop_size[0], x_start:x_start+self.crop_size[1]]
keypoints[:, 0] = keypoints[:, 0] - x_start
keypoints[:, 1] = keypoints[:, 1] - y_start
return point_cloud, label, keypoints
class Normalize:
def __call__(self, point_cloud, label):
mean = np.mean(point_cloud, axis=(0, 1))
std = np.std(point_cloud, axis=(0, 1))
point_cloud = (point_cloud - mean) / std
return point_cloud, label
def collate_fn(batch_data):
batch_size = len(batch_data)
pcs = []
labels = []
keypoints = []
for i in range(batch_size):
pc, label, kpts = batch_data[i]
pcs.append(pc)
labels.append(label)
keypoints.append(kpts)
pcs = torch.stack(pcs, dim=0)
labels = torch.stack(labels, dim=0)
keypoints = torch.stack(keypoints, dim=0)
return pcs, labels, keypoints
def generate_keypoints(label, stride=8):
keypoints = []
for i in range(1, 7):
indices = np.argwhere(label == i)
if len(indices) > 0:
indices = np.mean(indices, axis=0).astype(int)
x, y = indices[1] * stride, indices[0] * stride
keypoints.append([x, y])
return np.array(keypoints)
class ViT_Line(nn.Module):
def __init__(self, in_channels=5, num_classes=7):
super(ViT_Line, self).__init__()
self.patch_embed = nn.Sequential(
nn.Conv2d(in_channels, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.GELU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.GELU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.GELU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.GELU()
)
self.blocks = nn.Sequential(
Block(
dim=128,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm
) for i in range(4)
)
self.head = nn.Sequential(
nn.Linear(128 * 9 * 9, 512),
nn.ReLU(inplace=True),
nn.Linear(512, num_classes)
)
self.reg_head = nn.Sequential(
nn.Linear(128 * 9 * 9, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 2 * num_classes)
)
def forward(self, x, coords):
x = self.patch_embed(x)
for blk in self.blocks:
x = blk(x)
x = x.flatten(1)
cls_pred = self.head(x)
reg_pred = self.reg_head(x)
keypoint_pred = []
for i in range(reg_pred.shape[0]):
keypoints = []
for j in range(reg_pred.shape[1] // 2):
coord = coords[j]
x, y = coord[0], coord[1]
kpt_x = reg_pred[i][2 * j] + x
kpt_y = reg_pred[i][2 * j + 1] + y
keypoints.append(torch.stack([kpt_x, kpt_y]))
keypoint_pred.append(torch.stack(keypoints))
keypoint_pred = torch.stack(keypoint_pred)
return cls_pred, keypoint_pred
class ViTLoss(nn.Module):
def __init__(self):
super(ViTLoss, self).__init__()
self.cls_loss = nn.CrossEntropyLoss()
self.reg_loss = nn.L1Loss()
def forward(self, cls_pred, keypoint_pred, cls_label, keypoint_label):
cls_loss = self.cls_loss(cls_pred, cls_label)
reg_loss = self.reg_loss(keypoint_pred, keypoint_label)
loss = cls_loss + reg_loss
return loss
# 数据集
data_path = "data.npy"
dataset = LaneDetectionDataset(data_path)
# 数据加载器
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)
# 模型
model = ViT_Line()
model = model.cuda()
# 优化器
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
# loss函数
criterion = ViTLoss()
# 训练
num_epochs = 50
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for i, (points, labels, keypoints) in enumerate(tqdm(data_loader)):
points = points.cuda()
labels = labels.cuda()
keypoints = keypoints.cuda()
cls_pred, keypoint_pred = model(points, keypoints)
loss = criterion(cls_pred, keypoint_pred, labels, keypoints)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
print("Epoch [{}/{}], Loss: {:.4f}".format(epoch+1, num_epochs, running_loss / len(data_loader)))
# 测试
model.eval()
with torch.no_grad():
points, labels, keypoints = dataset[0]
points = points.unsqueeze(0)
labels = labels.unsqueeze(0)
keypoints = keypoints.unsqueeze(0)
points = points.cuda()
labels = labels.cuda()
keypoints = keypoints.cuda()
cls_pred, keypoint_pred = model(points, keypoints)
print("Classification output shape:", cls_pred.shape)
print("Keypoint output shape:", keypoint_pred.shape)其中，通过LaneDetectionDataset类读取数据集，然后使用DataLoader加载数据。
在训练过程中，先将输入数据转移到GPU，使用模型计算得到分类概率和关键点，然后计算loss并进行反向传播和优化。
在测试时，构造一个形状为[1, 5, 144, 144]的输入点云数据，使用模型进行推断，得到分类输出和关键点输出的形状。
示例八
首先，我们需要将原始点云转化成伪图像形式。这里可以使用Pillar-based方法（参考论文：PointPillars）。Pillar-based方法的思路是将点云分割成小的体素（pillar），每个pillar内部的点云信息再转化为一个 f × f f \times f f×f的矩阵，其中 f f f为特征图分辨率。
接下来，我们可以使用一个基于ViT（Vision Transformer）的骨干网络来提取特征。ViT是一种非常优秀的图像分类模型，可以将输入的图像转化为一组向量。如果我们将伪图像看做是一张图像，那么我们同样可以使用ViT来提取相应的特征。在这里，我们可以使用开源的PyTorch实现：pytorch-image-models。
在特征提取完毕后，我们需要使用检测头网络来对车道线点进行分类和关键点坐标回归。这里可以使用一种常见的检测头设计：Anchor-based方法（参考论文：Faster R-CNN）。Anchor-based方法的核心思想是在特征图上放置一组预定义的anchor框，对于每个anchor框，我们可以计算它和每个车道线点的IoU（Intersection over Union），并将其分配给IoU最大的车道线点。然后，我们可以根据分配的车道线点来计算分类损失和回归损失，从而优化整个网络。
具体实现上，可以使用PyTorch中提供的torchvision.models.detection来构建检测头网络。关键点的生成可以在预处理时进行，通过标注的车道线点在伪图像中的位置来生成。其中，关键点坐标的回归可以通过预测出的anchor框坐标和偏移量来计算得到。
最后，对于loss函数的设计，可以使用交叉熵损失和Smooth L1损失来计算分类损失和回归损失。最终的loss可以由这两部分损失加权求和。关于权重的选择，可以通过调节各部分损失的系数来进行实验。具体实现可以参考以下代码：
python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from torchvision.models.detection import anchor_utils
from torchvision.models.detection.transform import GeneralizedRCNNTransform
class LaneDetection(nn.Module):
def __init__(self, num_classes=6, num_keypoints=2):
super().__init__()
# define pillar-based encoder
# ...
# define ViT backbone
self.backbone = models.vit_base_patch16_224(pretrained=True)
# modify ViT output layer
self.backbone.head = nn.Sequential(
nn.LayerNorm(768),
nn.Linear(768, num_classes + 4 * num_keypoints)
)
# define detection head
self.anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
self.aspect_ratios = ((0.5, 1.0, 2.0),) * len(self.anchor_sizes)
self.num_anchors = len(self.aspect_ratios[0]) * len(self.anchor_sizes)
self.transform = GeneralizedRCNNTransform(min_size=224, max_size=224)
self.anchor_generator = anchor_utils.AnchorGenerator(
sizes=self.anchor_sizes,
aspect_ratios=self.aspect_ratios
)
self.rpn_head = nn.Conv2d(768, self.num_anchors * 2, kernel_size=1)
self.rpn_bbox = nn.Conv2d(768, self.num_anchors * 4, kernel_size=1)
self.classification_loss = nn.CrossEntropyLoss()
self.regression_loss = nn.SmoothL1Loss(beta=1.0)
def forward(self, x, labels=None):
# encode input point cloud into pillar-based representation
# ...
# generate pseudo image and resize to 224x224
x = F.interpolate(x, size=224, mode='bilinear')
# pass through ViT backbone
features = self.backbone(x)
# split output into classification and regression logits
classification, regression = features[..., :6], features[..., 6:]
# compute anchor boxes and apply regression
image_sizes = [x.shape[-2:]] * x.shape[0]
anchors = self.anchor_generator(image_sizes)
anchors = anchors.to(x.device)
# compute classification and regression losses
if self.training and labels is not None:
# generate ground-truth anchor targets
targets = self.transform.get_ground_truth(image_sizes, labels)
targets = [{k: v.to(x.device) for k, v in t.items()} for t in targets]
# compute RPN losses
classification_loss, regression_loss = self.compute_rpn_loss(
classification, regression, anchors, targets)
loss = classification_loss + regression_loss
return loss
else:
# decode anchor boxes and apply nms
boxes = anchor_utils.decode(anchors, regression)
scores = F.softmax(classification, dim=-1)
boxes, scores = anchor_utils.clip_boxes_to_image(boxes, scores, image_sizes)
boxes, scores = anchor_utils.remove_small_boxes(boxes, scores)
# select top-k boxes per image
pred_boxes = []
pred_scores = []
for b, s in zip(boxes, scores):
topk_idx = torch.topk(s[:, :-1], k=2)[1].flatten()
b = b[topk_idx]
s = s[topk_idx]
pred_boxes.append(b)
pred_scores.append(s)
return torch.stack(pred_boxes), torch.stack(pred_scores)
def compute_rpn_loss(self, classification, regression, anchors, targets):
classification_loss = 0
regression_loss = 0
for c, r, a, t in zip(classification, regression, anchors, targets):
# compute classification loss
c = c.permute(0, 2, 3, 1).flatten(end_dim=-2)
c_targets = anchor_utils.label_to_targets(t['labels'], self.num_anchors)
classification_loss += self.classification_loss(c, c_targets)
# compute regression loss
r = r.permute(0, 2, 3, 1).reshape(-1, 4)
a = a.reshape(-1, 4)
r_targets = anchor_utils.get_regression_targets(a, t['boxes'], self.aspect_ratios)
regression_loss += self.regression_loss(r, r_targets)
return classification_loss, regression_loss
# example usage
model = LaneDetection()
inputs = torch.randn(2, 144, 144)
labels = torch.randint(1, 7, (2, 144, 144))
loss = model(inputs, labels) # train the model with this loss
outputs = model(inputs) # inference mode, output shape: (2, 2, 4)示例九
以下是如何设计该网络的建议：
-
首先，需要对原始点云进行 pillar 化处理，将其转换为三维稀疏张量。这里可以使用 PointPillars 等方法，将点云分割成不同的 pillar，并利用编码器将每个 pillar 编码成一个向量。这些编码向量将作为模型输入。
-
接下来，可以使用 ViT 等模型作为骨干网络，用于将 pillar 编码图像化。首先，将 pillar 编码向量扩展为类似图像的形状，然后将它们输入到 ViT 中，以获得图像特征。
-
接下来，可以使用检测头网络进行车道线点的分类和关键点坐标回归。这里建议使用 FCOS 等目标检测方法，对各级特征图进行预测。每个预测包括类别分数和关键点坐标。
-
针对这一任务，也可以使用关键点监督方法进行训练。可以通过将每个车道线点的标签转换为其对应的关键点标签，来生成关键点。然后可以使用 MSE 损失或者 Smooth L1 损失函数来优化关键点坐标的回归，同时使用交叉熵损失函数来优化分类。
-
最后，输出结果应该包括分类结果和关键点坐标。分类结果的 shape 为 N×H×W×C（N 为 batch 大小，H 和 W 分别为特征图的高和宽，C 为类别数加背景类别数），关键点坐标的 shape 为 N×H×W×K×2（K 为关键点数，这里为 6）。
以下是一份用 PyTorch 实现的汽车车道线关键点监督网络的示例代码：
python
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class PointPillar(nn.Module):
def __init__(self, num_pillars, encoder_size, num_input_features):
super(PointPillar, self).__init__()
self.num_pillars = num_pillars
self.encoder_size = encoder_size
self.num_input_features = num_input_features
self.encoder = nn.Sequential(
nn.Linear(num_input_features, encoder_size),
nn.ReLU(),
nn.Linear(encoder_size, encoder_size),
nn.ReLU(),
nn.Linear(encoder_size, encoder_size),
nn.ReLU(),
nn.Linear(encoder_size, encoder_size))
def forward(self, x):
x = x.contiguous().view(-1, self.num_input_features)
x = self.encoder(x)
return x.view(-1, self.num_pillars, self.encoder_size)
class ViT(nn.Module):
def __init__(self, image_size, patch_size, num_classes, num_input_channels, dim, depth, heads, mlp_dim, dropout):
super(ViT, self).__init__()
self.image_size = image_size
self.patch_size = patch_size
self.num_classes = num_classes
self.num_input_channels = num_input_channels
self.dim = dim
self.depth = depth
self.heads = heads
self.mlp_dim = mlp_dim
self.dropout = dropout
num_patches = (image_size // patch_size) ** 2
patch_dim = num_input_channels * patch_size ** 2
self.to_patch_embedding = nn.Sequential(
nn.Conv2d(num_input_channels, num_input_channels, kernel_size=patch_size, stride=patch_size),
nn.Flatten(),
nn.Linear(patch_dim, dim),
)
self.pos_embedding = Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = Parameter(torch.randn(1, 1, dim))
self.dropout_layer = nn.Dropout(dropout)
self.transformer = nn.ModuleList([
nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim, dropout=dropout)
for _ in range(depth)
])
self.fc = nn.Linear(dim, num_classes)
def forward(self, x):
B, C, H, W = x.size()
assert H == W == self.image_size
x = self.to_patch_embedding(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(H // self.patch_size) ** 2 + 1]
x = self.dropout_layer(x)
for transformer in self.transformer:
x = transformer(x)
x = x[:, 0]
x = self.fc(x)
return x
class FCOS(nn.Module):
def __init__(self, num_classes, num_keypoints, feature_maps, strides, base_size):
super(FCOS, self).__init__()
self.num_classes = num_classes
self.feature_maps = feature_maps
self.strides = strides
self.base_size = base_size
self.num_keypoints = num_keypoints
self.cls_heads = nn.ModuleList([
nn.Conv2d(base_size, num_classes, kernel_size=3, stride=1, padding=1)
for _ in range(len(feature_maps))
])
self.reg_heads = nn.ModuleList([
nn.Conv2d(base_size, 4, kernel_size=3, stride=1, padding=1)
for _ in range(len(feature_maps))
])
self.kps_heads = nn.ModuleList([
nn.Conv2d(base_size, 2 * num_keypoints, kernel_size=3, stride=1, padding=1)
for _ in range(len(feature_maps))
])
def forward(self, features):
cls_preds = []
reg_preds = []
kps_preds = []
for idx, feature in enumerate(features):
cls_pred = self.cls_heads[idx](feature)
reg_pred = self.reg_heads[idx](feature)
kps_pred = self.kps_heads[idx](feature)
cls_preds.append(cls_pred)
reg_preds.append(reg_pred)
kps_preds.append(kps_pred)
return cls_preds, reg_preds, kps_preds
class LaneDetector(nn.Module):
def __init__(self, num_classes, num_keypoints, pillar_encoder_size, backbone_dim, backbone_depth,
backbone_heads, backbone_mlp_dim, backbone_dropout,
feature_maps, strides, num_input_channels, base_size):
super(LaneDetector, self).__init__()
self.pillar_encoder = PointPillar(num_pillars=144, encoder_size=pillar_encoder_size, num_input_features=num_input_channels)
self.vit_backbone = ViT(image_size=128, patch_size=32, num_classes=num_classes, num_input_channels=backbone_dim, dim=backbone_dim,
depth=backbone_depth, heads=backbone_heads, mlp_dim=backbone_mlp_dim, dropout=backbone_dropout)
self.fcos_head = FCOS(num_classes=num_classes, num_keypoints=num_keypoints, feature_maps=feature_maps,
strides=strides, base_size=base_size)
def forward(self, x):
pillar_features = self.pillar_encoder(x)
image_features = self.vit_backbone(pillar_features.view(-1, 6, 24, 24))
cls_preds, reg_preds, kps_preds = self.fcos_head(image_features)
return cls_preds, reg_preds, kps_preds在示例代码中，PointPillar 模型用于将点云数据编码为伪图像，ViT模型用于将伪图像获取图像特征，FCOS模型用于对图像进行分类和关键点回归。在 LaneDetector 模型中，将这些模型串联在一起，构成完整的车道线关键点监督网络。