bev-project/docker/start-ssh.sh

#!/bin/bash
# SSH启动脚本 - 确保密码和SSH服务正确配置

# 设置root密码
echo 'root:Aa@123456' | chpasswd

# 确保SSH目录存在
mkdir -p /var/run/sshd

# 如果SSH主机密钥不存在，生成它们
if [ ! -f /etc/ssh/ssh_host_rsa_key ]; then
    ssh-keygen -A
fi

# 启动SSH服务
echo "Starting SSH daemon..."
exec /usr/sbin/sshd -D
-												Complete project state snapshot: Phase 4B RMT-PPAD Integration

🎯 Training Status:
- Current Epoch: 2/10 (13.3% complete)
- Segmentation Dice: 0.9594
- Detection IoU: 0.5742
- Training stable with 8 GPUs

🔧 Technical Achievements:
- ✅ RMT-PPAD Transformer segmentation decoder integrated
- ✅ Task-specific GCA architecture optimized
- ✅ Multi-scale feature fusion (180×180, 360×360, 600×600)
- ✅ Adaptive scale weight learning implemented
- ✅ BEVFusion multi-task framework enhanced

📊 Performance Highlights:
- Divider segmentation: 0.9793 Dice (excellent)
- Pedestrian crossing: 0.9812 Dice (excellent)
- Stop line: 0.9812 Dice (excellent)
- Carpark area: 0.9802 Dice (excellent)
- Walkway: 0.9401 Dice (good)
- Drivable area: 0.8959 Dice (good)

🛠️ Code Changes Included:
- Enhanced BEVFusion model (bevfusion.py)
- RMT-PPAD integration modules (rmtppad_integration.py)
- Transformer segmentation head (enhanced_transformer.py)
- GCA module optimizations (gca.py)
- Configuration updates (Phase 4B configs)
- Training scripts and automation tools
- Comprehensive documentation and analysis reports

📅 Snapshot Date: Fri Nov 14 09:06:09 UTC 2025
📍 Environment: Docker container
🎯 Phase: RMT-PPAD Integration Complete

											
										
										
											2025-11-14 17:06:09 +08:00
+								#!/bin/bash
 								# SSH启动脚本 - 确保密码和SSH服务正确配置
 								# 设置root密码
 								echo 'root:Aa@123456' | chpasswd
 								# 确保SSH目录存在
 								mkdir -p /var/run/sshd
 								# 如果SSH主机密钥不存在，生成它们
 								if [ ! -f /etc/ssh/ssh_host_rsa_key ]; then
 								    ssh-keygen -A
 								fi
 								# 启动SSH服务
 								echo "Starting SSH daemon..."
 								exec /usr/sbin/sshd -D