# ===================================================================
# FULL CODE: Lane Detection with Multi-Task Learning on TuSimple
# Model: ModifiedResNet50 + FPN + MultiTaskHead
# Loss: Focal + Regress + Distance + Variance
# Dataset: TuSimple
# Config: epoch=50, batch_size=4, lr=0.001, Adam
# ===================================================================
import os
import json
import cv2
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as T
import torchvision.models as models
# ======================================
# 1. 可变形卷积模块（简化版 DCN）
# ======================================
class DeformableConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False):
super().__init__()
self.offset_conv = nn.Conv2d(in_channels, 2 * kernel_size * kernel_size, kernel_size, stride, padding,
bias=bias)
self.dcn = torch.ops.torchvision.deform_conv2d
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.bilinear_offset = nn.Upsample(scale_factor=1, mode='bilinear', align_corners=True)
def forward(self, x):
offset = self.offset_conv(x)
# deform_conv only supports certain configurations
return torch.ops.torchvision.deform_conv2d(
x, offset,
weight=torch.randn_like(torch.empty(out_channels, in_channels, self.kernel_size, self.kernel_size),
device=x.device),
padding=self.padding, stride=self.stride
) # This is a placeholder; we'll use standard conv for now to avoid C++ extension issues
# In practice, install `deform_conv` or use grid_sample based implementation
# Use regular Conv if DCN not available
class SimpleDeformConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=False)
self.norm = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu(self.norm(self.conv(x)))
# ======================================
# 2. Bottleneck with Optional DCN
# ======================================
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, use_dcn=False):
super(Bottleneck, self).__init__()
self.use_dcn = use_dcn
conv_layer = SimpleDeformConv if use_dcn else nn.Conv2d
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
padding = dilation
if use_dcn:
self.conv2 = SimpleDeformConv(planes, planes, kernel_size=3, stride=stride, padding=padding)
else:
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
if not self.use_dcn:
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
# ======================================
# 3. 构建修改后的 ResNet50
# ======================================
def make_res_layer(block, inplanes, planes, blocks, stride=1, dilation=1, use_dcn=False):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
first_dilation = 1 if dilation == 1 else dilation // 2
layers.append(block(inplanes, planes, stride, downsample, dilation=first_dilation, use_dcn=False))
for _ in range(1, blocks):
layers.append(block(planes * block.expansion, planes, dilation=dilation, use_dcn=use_dcn))
return nn.Sequential(*layers)
class ModifiedResNet50(nn.Module):
def __init__(self, pretrained=True):
super(ModifiedResNet50, self).__init__()
original = models.resnet50(pretrained=pretrained)
self.inplanes = 64
self.conv1 = original.conv1
self.bn1 = original.bn1
self.relu = original.relu
self.maxpool = original.maxpool
self.layer1 = original.layer1 # res2
self.layer2 = original.layer2 # res3
self.layer3 = make_res_layer(Bottleneck, 512, 128, 23, stride=1, dilation=2, use_dcn=True) # res4
self.layer4 = make_res_layer(Bottleneck, 1024, 256, 3, stride=1, dilation=4, use_dcn=True) # res5
def forward(self, x):
c1 = self.relu(self.bn1(self.conv1(x)))
c1 = self.maxpool(c1)
c2 = self.layer1(c1) # 256, H/4, W/4
c3 = self.layer2(c2) # 512, H/8, W/8
c4 = self.layer3(c3) # 1024, H/8, W/8 (dilation=2)
c5 = self.layer4(c4) # 2048, H/8, W/8 (dilation=4)
return c3, c4, c5
# ======================================
# 4. FPN Neck
# ======================================
class FPN(nn.Module):
def __init__(self, in_channels_list=[512, 1024, 2048], out_channels=256):
super(FPN, self).__init__()
self.lateral_convs = nn.ModuleList([
nn.Conv2d(in_c, out_channels, 1) for in_c in in_channels_list
])
self.fpn_convs = nn.ModuleList([
nn.Conv2d(out_channels, out_channels, 3, padding=1) for _ in range(len(in_channels_list))
])
def forward(self, inputs):
c3, c4, c5 = inputs
p5 = self.lateral_convs[2](c5)
p4 = self.lateral_convs[1](c4) + F.interpolate(p5, scale_factor=2, mode='nearest')
p3 = self.lateral_convs[0](c3) + F.interpolate(p4, scale_factor=2, mode='nearest')
p5 = self.fpn_convs[2](p5)
p4 = self.fpn_convs[1](p4)
p3 = self.fpn_convs[0](p3)
out = F.interpolate(p3, scale_factor=4, mode='bilinear', align_corners=False) + \
F.interpolate(p4, scale_factor=2, mode='bilinear', align_corners=False) + \
p5
return out
# ======================================
# 5. 多任务 Head
# ======================================
class MultiTaskHead(nn.Module):
def __init__(self, in_channels, num_classes=5):
super(MultiTaskHead, self).__init__()
self.cls = nn.Conv2d(in_channels, num_classes, kernel_size=1) # 分类
self.offset = nn.Conv2d(in_channels, 2, kernel_size=1) # 偏移 dx, dy
self.distance = nn.Conv2d(in_channels, 1, kernel_size=1) # 距离场
self.variance = nn.Conv2d(in_channels, 2, kernel_size=1) # 方差（对应 offset）
def forward(self, x):
size = x.shape[2:]
return {
'cls': F.interpolate(self.cls(x), size=size, mode='bilinear', align_corners=False),
'offset': F.interpolate(self.offset(x), size=size, mode='bilinear', align_corners=False),
'distance': F.interpolate(self.distance(x), size=size, mode='bilinear', align_corners=False),
'variance': F.interpolate(self.variance(x), size=size, mode='bilinear', align_corners=False)
}
# ======================================
# 6. 主模型
# ======================================
class LaneSegNet_MultiTask(nn.Module):
def __init__(self, num_classes=5):
super(LaneSegNet_MultiTask, self).__init__()
self.backbone = ModifiedResNet50(pretrained=True)
self.fpn = FPN(out_channels=256)
self.head = MultiTaskHead(256, num_classes)
def forward(self, x):
feats = self.backbone(x)
fpn_out = self.fpn(feats)
return self.head(fpn_out)
# ======================================
# 7. TuSimple Dataset
# ======================================
class TuSimpleDataset(Dataset):
def __init__(self, root, split='train', img_size=(384, 640), transform=None):
self.root = root
self.img_size = img_size
self.transform = transform or T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.image_paths = []
self.lanes_data = []
label_files = [
os.path.join(root, 'label_data_0313.json'),
os.path.join(root, 'label_data_0601.json')
] if split == 'train' else [os.path.join(root, 'test_label.json')]
for file in label_files:
if not os.path.exists(file):
raise FileNotFoundError(f"{file} not found.")
with open(file, 'r') as f:
lines = f.readlines()
for line in lines:
try:
data = json.loads(line.strip())
raw_file = data['raw_file']
img_path = os.path.join(root, raw_file)
if os.path.exists(img_path):
self.image_paths.append(img_path)
self.lanes_data.append(data)
except:
continue
if split != 'test':
valid_indices = [i for i, d in enumerate(self.lanes_data) if 'lanes' in d and 'h_samples' in d]
self.image_paths = [self.image_paths[i] for i in valid_indices]
self.lanes_data = [self.lanes_data[i] for i in valid_indices]
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
img_path = self.image_paths[idx]
data = self.lanes_data[idx]
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
orig_h, orig_w = image.shape[:2]
image_pil = Image.fromarray(image)
image_resized = image_pil.resize((self.img_size[1], self.img_size[0]), Image.BILINEAR)
cls_map = np.zeros((self.img_size[0], self.img_size[1]), dtype=np.int64)
offset_map = np.zeros((2, self.img_size[0], self.img_size[1]), dtype=np.float32)
distance_map = np.full((1, self.img_size[0], self.img_size[1]), 1e6, dtype=np.float32)
ratio_h, ratio_w = self.img_size[0] / orig_h, self.img_size[1] / orig_w
lanes = data.get('lanes', [])
h_samples = data.get('h_samples', [])
all_lane_points = []
for lane_id, xs in enumerate(lanes):
points = []
for x, y in zip(xs, h_samples):
if x >= 0:
px = int(x * ratio_w)
py = int(y * ratio_h)
if 0 <= px < self.img_size[1] and 0 <= py < self.img_size[0]:
points.append([px, py])
cls_map[py, px] = lane_id + 1 # background=0
if len(points) > 1:
all_lane_points.append(np.array(points))
if len(all_lane_points) == 0:
cls_map[:] = 0
offset_map[:] = 0
distance_map[:] = 1.0
else:
yy, xx = np.mgrid[0:self.img_size[0], 0:self.img_size[1]]
coords = np.stack([xx, yy], axis=-1).astype(np.float32) # [H, W, 2]
for points in all_lane_points:
dists = np.linalg.norm(coords[:, :, None] - points[None, None, :, :], axis=-1) # [H, W, N]
min_dists = dists.min(axis=-1) # [H, W]
nearest_idx = dists.argmin(axis=-1)
nearest_pts = points[nearest_idx] # [H, W, 2]
offsets = nearest_pts - coords # [H, W, 2] -> dx, dy
update_mask = min_dists < distance_map[0]
distance_map[0][update_mask] = min_dists[update_mask]
offset_map[:, update_mask] = offsets[update_mask].transpose(2, 0, 1)
# Normalize offset
offset_map /= 16.0
distance_map = np.clip(distance_map, 0, 100) / 50.0 # normalize to ~[0, 2]
image_tensor = self.transform(image_resized)
label_tensor = torch.from_numpy(cls_map).long()
offset_tensor = torch.from_numpy(offset_map).float()
distance_tensor = torch.from_numpy(distance_map).float()
return {
'image': image_tensor,
'label': label_tensor,
'offset': offset_tensor,
'distance': distance_tensor
}
# ======================================
# 8. 损失函数
# ======================================
class FocalLoss(nn.Module):
def __init__(self, alpha=1, gamma=2):
super().__init__()
self.alpha = alpha
self.gamma = gamma
def forward(self, pred, target):
ce_loss = F.cross_entropy(pred, target, ignore_index=0, reduction='none')
pt = torch.exp(-ce_loss)
focal_loss = self.alpha * (1 - pt) ** self.gamma * ce_loss
return focal_loss.mean()
class RegressLoss(nn.Module):
def __init__(self):
super().__init__()
self.criterion = nn.SmoothL1Loss(reduction='mean')
def forward(self, pred, target, mask):
if mask.sum() == 0:
return pred.new_zeros([])
return self.criterion(pred[mask], target[mask])
class DistanceLoss(nn.Module):
def __init__(self):
super().__init__()
self.criterion = nn.MSELoss()
def forward(self, pred, target):
return self.criterion(pred, target)
class VarianceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred_mean, pred_logvar, target):
precision = torch.exp(-pred_logvar)
loss = precision * (target - pred_mean) ** 2 + pred_logvar
return loss.mean()
# ======================================
# 9. 主函数
# ======================================
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")
# 修改为你本地的 TuSimple 路径
TUSIMPLE_ROOT = "/your/path/to/tusimple"
# Transform
transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Dataset & Dataloader
dataset = TuSimpleDataset(root=TUSIMPLE_ROOT, split='train', img_size=(384, 640), transform=transform)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4, pin_memory=True)
# Model
model = LaneSegNet_MultiTask(num_classes=5).to(device)
# Optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Losses
criterion_focal = FocalLoss().to(device)
criterion_regress = RegressLoss().to(device)
criterion_distance = DistanceLoss().to(device)
criterion_variance = VarianceLoss().to(device)
# Weights
w_cls, w_reg, w_dist, w_var = 1.0, 1.0, 0.5, 0.3
# Training Loop
epochs = 50
for epoch in range(epochs):
model.train()
total_loss = 0.0
for i, data in enumerate(dataloader):
images = data['image'].to(device)
labels = data['label'].to(device)
offsets = data['offset'].to(device)
distances = data['distance'].to(device)
masks = (labels > 0).unsqueeze(1).expand_as(offsets)
optimizer.zero_grad()
outputs = model(images)
loss_cls = criterion_focal(outputs['cls'], labels)
loss_reg = criterion_regress(outputs['offset'], offsets, masks)
loss_dist = criterion_distance(outputs['distance'], distances)
loss_var = criterion_variance(outputs['offset'], outputs['variance'], offsets)
loss = w_cls * loss_cls + w_reg * loss_reg + w_dist * loss_dist + w_var * loss_var
loss.backward()
optimizer.step()
total_loss += loss.item()
if (i + 1) % 20 == 0:
print(f"Epoch [{epoch + 1}/50], Step [{i + 1}/{len(dataloader)}], Loss: {loss.item():.4f}")
avg_loss = total_loss / len(dataloader)
print(f"Epoch [{epoch + 1}/50] Average Loss: {avg_loss:.4f}")
# Save Model
save_path = "tusimple_lane_model.pth"
torch.save(model.state_dict(), save_path)
print(f"Training completed. Model saved to {save_path}")
if __name__ == "__main__":
main()
最新发布
在使用TensorFlow的`tf.data.Dataset.map`进行数据预处理时，遇到了一个错误，该错误涉及到图像的通道格式问题。在尝试将图片模糊并连接清晰图片通道时，由于CPU不支持NCHW格式，导致`Conv2D`操作失败。代码中通过`tf.image.decode_jpeg`读取并解码图片，然后进行模糊处理和通道连接。目前的问题可能是由于在增加batch维度时，系统误将该维度理解为了通道维度。解决方案可能包括确保数据格式为NHWC，或者在GPU上运行整个数据处理流程。
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