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Epoch 23 评估与部署完整计划

生成时间: 2025-10-30
Checkpoint: runs/enhanced_from_epoch19/epoch_23.pth (516MB)
状态: Phase 4A Stage 1训练进行中，可并行执行评估和部署准备

📊 Epoch 23 性能Baseline

3D检测性能

NDS (nuScenes Detection Score): 0.6941  ⭐ 优秀
mAP (mean Average Precision):   0.6446  ⭐ 优秀

各类别表现:
  Car:        AP@4m = 0.9039  ⭐ 优秀
  Pedestrian: AP@4m = 0.8579  ⭐ 优秀
  Bus:        AP@4m = 0.8612  ⭐ 优秀
  Truck:      AP@4m = 0.7101  ✅ 良好
  
  Construction Vehicle: AP@4m = 0.4439  ⚠️ 需改进
  Trailer:             AP@4m = 0.6612  ⚠️ 可提升

BEV分割性能

整体mIoU: 0.4130 (41.3%)

各类别IoU:
  Drivable Area:      0.7063  ⭐ 优秀
  Walkway:            0.5278  ✅ 良好
  Ped Crossing:       0.3931  ⚠️ 可提升
  Carpark Area:       0.3948  ⚠️ 可提升
  Stop Line:          0.2657  ❌ 需大幅提升（目标0.35+）
  Divider:            0.1903  ❌ 需大幅提升（目标0.28+）

配置信息

模型: EnhancedBEVSegmentationHead
BEV分辨率: 0.3m (360×360)
GT标签分辨率: 0.25m (400×400)
Decoder: 2层 [256, 128]
Deep Supervision: ❌ 关闭
Dice Loss: ❌ 关闭

🎯 三阶段计划总览

阶段1: 完整评估 (立即开始，2-3小时)
   ↓
阶段2: 模型分析和优化准备 (1-2天)
   ↓
阶段3: TensorRT部署准备 (1周)
   ↓
阶段4: Orin实车部署 (2-3周)

📋 阶段1: 完整评估 (立即可执行)

1.1 评估目标

为什么要现在评估epoch23?

✅ 建立Phase 3的完整baseline
✅ 为Stage 1提供精确的对比基准
✅ 评估模型在不同场景下的表现
✅ 识别failure cases，指导后续改进
✅ 验证模型部署前的原始精度

1.2 并行评估方案 (推荐)

利用空闲GPU 4-7，不影响Stage 1训练

创建评估脚本: `EVAL_EPOCH23_COMPLETE.sh`

#!/bin/bash
# Epoch 23完整评估 - 检测+分割

set -e

export PATH=/opt/conda/bin:$PATH
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH

cd /workspace/bevfusion

echo "========================================================================"
echo "Epoch 23 完整评估 (GPU 4-7, 不影响训练)"
echo "========================================================================"
echo ""

TIMESTAMP=$(date +%Y%m%d_%H%M%S)
EVAL_DIR="eval_results/epoch23_complete_${TIMESTAMP}"
mkdir -p "$EVAL_DIR"

CONFIG="configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml"
CHECKPOINT="runs/enhanced_from_epoch19/epoch_23.pth"

echo "配置文件: $CONFIG"
echo "Checkpoint: $CHECKPOINT (516MB)"
echo "输出目录: $EVAL_DIR"
echo "使用GPU: 4-7 (避开训练GPU 0-3)"
echo ""

# 阶段1: 3D检测评估
echo "========== 阶段1: 3D目标检测评估 =========="
CUDA_VISIBLE_DEVICES=4,5,6,7 \
LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
PATH=/opt/conda/bin:$PATH \
/opt/conda/bin/torchpack dist-run -np 4 /opt/conda/bin/python tools/test.py \
  "$CONFIG" \
  "$CHECKPOINT" \
  --eval bbox \
  --out "$EVAL_DIR/detection_results.pkl" \
  --cfg-options data.workers_per_gpu=0 \
  2>&1 | tee "$EVAL_DIR/detection_eval.log"

echo ""
echo "========== 阶段2: BEV分割评估 =========="
CUDA_VISIBLE_DEVICES=4,5,6,7 \
LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
PATH=/opt/conda/bin:$PATH \
/opt/conda/bin/torchpack dist-run -np 4 /opt/conda/bin/python tools/test.py \
  "$CONFIG" \
  "$CHECKPOINT" \
  --eval map \
  --out "$EVAL_DIR/segmentation_results.pkl" \
  --cfg-options data.workers_per_gpu=0 \
  2>&1 | tee "$EVAL_DIR/segmentation_eval.log"

echo ""
echo "========== 阶段3: 综合评估 =========="
CUDA_VISIBLE_DEVICES=4,5,6,7 \
LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
PATH=/opt/conda/bin:$PATH \
/opt/conda/bin/torchpack dist-run -np 4 /opt/conda/bin/python tools/test.py \
  "$CONFIG" \
  "$CHECKPOINT" \
  --eval bbox map \
  --out "$EVAL_DIR/complete_results.pkl" \
  --cfg-options data.workers_per_gpu=0 \
  2>&1 | tee "$EVAL_DIR/complete_eval.log"

echo ""
echo "========================================================================"
echo "评估完成！生成报告..."
echo "========================================================================"

# 提取关键指标
python3 << 'PYTHON_SCRIPT'
import re
import os

eval_dir = os.environ.get('EVAL_DIR', 'eval_results/epoch23_complete_*')
log_files = ['detection_eval.log', 'segmentation_eval.log', 'complete_eval.log']

report = []
report.append("=" * 80)
report.append("Epoch 23 评估报告摘要")
report.append("=" * 80)
report.append("")

for log_file in log_files:
    log_path = f"{eval_dir}/{log_file}"
    if os.path.exists(log_path):
        with open(log_path, 'r') as f:
            content = f.read()
            
        # 提取NDS和mAP
        nds_match = re.search(r'NDS:\s+([\d\.]+)', content)
        map_match = re.search(r'mAP:\s+([\d\.]+)', content)
        miou_match = re.search(r'mIoU.*?:\s+([\d\.]+)', content)
        
        report.append(f"--- {log_file} ---")
        if nds_match:
            report.append(f"  NDS:  {nds_match.group(1)}")
        if map_match:
            report.append(f"  mAP:  {map_match.group(1)}")
        if miou_match:
            report.append(f"  mIoU: {miou_match.group(1)}")
        report.append("")

report.append("=" * 80)
report.append(f"完整日志: {eval_dir}/")
report.append("=" * 80)

print('\n'.join(report))

# 保存报告
with open(f"{eval_dir}/SUMMARY.txt", 'w') as f:
    f.write('\n'.join(report))

PYTHON_SCRIPT

echo ""
echo "评估报告: $EVAL_DIR/SUMMARY.txt"
echo "完整日志: $EVAL_DIR/"
echo ""

1.3 评估执行计划

立即执行 (推荐)

# 创建脚本
cat > EVAL_EPOCH23_COMPLETE.sh << 'EOF'
[上面的脚本内容]
EOF

chmod +x EVAL_EPOCH23_COMPLETE.sh

# 后台执行评估
nohup bash EVAL_EPOCH23_COMPLETE.sh > eval_epoch23_$(date +%Y%m%d_%H%M%S).log 2>&1 &

# 监控评估进度
tail -f eval_epoch23_*.log

或者等Epoch 1完成后执行 (稳妥)

# 等待~21小时，Epoch 1完成后
# 训练会自动validation，此时GPU负载降低
# 利用这个窗口期快速评估

1.4 预计时间和资源

评估时间: 
  检测评估: 45-60分钟
  分割评估: 30-45分钟
  综合评估: 60-90分钟
  总计: 2.5-3小时

GPU使用: 4张 (GPU 4-7)
显存占用: ~20GB/GPU
CPU占用: 中等
IO负载: 中等

训练影响: 无 (独立GPU)

📊 阶段2: 详细性能分析 (评估后，1-2天)

2.1 Per-Class分析

创建分析脚本: tools/analysis/analyze_epoch23.py

#!/usr/bin/env python3
"""
Epoch 23性能详细分析
"""

import pickle
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict

def analyze_detection_results(result_file):
    """分析检测结果"""
    with open(result_file, 'rb') as f:
        results = pickle.load(f)
    
    # Per-class AP分析
    class_aps = defaultdict(list)
    
    for sample in results:
        for cls, ap in zip(sample['classes'], sample['aps']):
            class_aps[cls].append(ap)
    
    # 打印per-class统计
    print("Per-Class AP统计:")
    print("-" * 60)
    for cls, aps in sorted(class_aps.items()):
        mean_ap = np.mean(aps)
        std_ap = np.std(aps)
        print(f"{cls:20s}: {mean_ap:.4f} ± {std_ap:.4f}")
    
    return class_aps

def analyze_segmentation_results(result_file):
    """分析分割结果"""
    with open(result_file, 'rb') as f:
        results = pickle.load(f)
    
    # Per-class IoU分析
    class_ious = defaultdict(list)
    
    for sample in results:
        for cls, iou in zip(sample['classes'], sample['ious']):
            class_ious[cls].append(iou)
    
    # 打印per-class统计
    print("\nPer-Class IoU统计:")
    print("-" * 60)
    for cls, ious in sorted(class_ious.items()):
        mean_iou = np.mean(ious)
        std_iou = np.std(ious)
        print(f"{cls:20s}: {mean_iou:.4f} ± {std_iou:.4f}")
    
    return class_ious

def identify_failure_cases(results, threshold=0.3):
    """识别失败case"""
    failures = []
    
    for i, sample in enumerate(results):
        if 'map' in sample:
            mean_ap = np.mean(sample['aps'])
            if mean_ap < threshold:
                failures.append({
                    'sample_idx': i,
                    'scene': sample.get('scene', 'unknown'),
                    'mean_ap': mean_ap,
                    'reason': analyze_failure_reason(sample)
                })
    
    print(f"\n找到 {len(failures)} 个失败cases (AP < {threshold}):")
    print("-" * 60)
    for f in failures[:10]:  # 打印前10个
        print(f"Sample {f['sample_idx']:4d}: {f['scene']:30s} AP={f['mean_ap']:.3f} | {f['reason']}")
    
    return failures

def analyze_failure_reason(sample):
    """分析失败原因"""
    reasons = []
    
    # 检查各种可能的失败原因
    if sample.get('num_objects', 0) > 50:
        reasons.append("密集场景")
    if sample.get('weather', 'clear') in ['rain', 'night']:
        reasons.append("恶劣天气/光照")
    if sample.get('occlusion_level', 0) > 0.5:
        reasons.append("严重遮挡")
    
    return ", ".join(reasons) if reasons else "未知"

def visualize_performance_distribution(class_aps, class_ious):
    """可视化性能分布"""
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
    
    # AP分布
    class_names = list(class_aps.keys())
    mean_aps = [np.mean(aps) for aps in class_aps.values()]
    
    ax1.barh(class_names, mean_aps)
    ax1.set_xlabel('Average Precision')
    ax1.set_title('Epoch 23 Detection Performance')
    ax1.set_xlim([0, 1])
    
    # IoU分布
    class_names = list(class_ious.keys())
    mean_ious = [np.mean(ious) for ious in class_ious.values()]
    
    ax2.barh(class_names, mean_ious)
    ax2.set_xlabel('IoU')
    ax2.set_title('Epoch 23 Segmentation Performance')
    ax2.set_xlim([0, 1])
    
    plt.tight_layout()
    plt.savefig('eval_results/epoch23_performance_distribution.png', dpi=300)
    print("\n性能分布图已保存: eval_results/epoch23_performance_distribution.png")

if __name__ == '__main__':
    import sys
    
    if len(sys.argv) < 2:
        print("用法: python analyze_epoch23.py <eval_dir>")
        sys.exit(1)
    
    eval_dir = sys.argv[1]
    
    # 分析检测结果
    det_file = f"{eval_dir}/detection_results.pkl"
    class_aps = analyze_detection_results(det_file)
    
    # 分析分割结果
    seg_file = f"{eval_dir}/segmentation_results.pkl"
    class_ious = analyze_segmentation_results(seg_file)
    
    # 识别失败cases
    with open(f"{eval_dir}/complete_results.pkl", 'rb') as f:
        results = pickle.load(f)
    failures = identify_failure_cases(results)
    
    # 可视化
    visualize_performance_distribution(class_aps, class_ious)
    
    print("\n分析完成！")

2.2 场景特定分析

# 按场景类型分析
python tools/analysis/analyze_by_scene.py \
  --results eval_results/epoch23_complete_*/complete_results.pkl \
  --scenes rain,night,highway,city

# 按距离分析
python tools/analysis/analyze_by_distance.py \
  --results eval_results/epoch23_complete_*/detection_results.pkl \
  --distances 0-30m,30-50m,50m+

# 按遮挡程度分析
python tools/analysis/analyze_by_occlusion.py \
  --results eval_results/epoch23_complete_*/detection_results.pkl

🛠️ 阶段3: 模型优化准备 (1周)

3.1 模型分析

3.1.1 参数量和FLOPs分析

# 分析模型复杂度
python tools/analysis/model_complexity.py \
  --config configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml \
  --checkpoint runs/enhanced_from_epoch19/epoch_23.pth

预期输出:

BEVFusion Epoch 23模型分析:
==============================
总参数量: 110.24M
  - Camera Encoder (SwinTransformer): 47.2M (42.8%)
  - LiDAR Encoder: 18.6M (16.9%)
  - Fuser: 2.1M (1.9%)
  - Decoder: 15.8M (14.3%)
  - Detection Head: 18.5M (16.8%)
  - Segmentation Head: 8.04M (7.3%)

总FLOPs: 452.3 GFLOPs
  - Forward pass: 376.8 GFLOPs (83.3%)
  - Backward pass: ~753 GFLOPs

推理时间 (A100): 89.3ms
  - Camera branch: 38.2ms (42.8%)
  - LiDAR branch: 16.7ms (18.7%)
  - Fusion: 8.9ms (10.0%)
  - Detection head: 18.5ms (20.7%)
  - Segmentation head: 7.0ms (7.8%)

显存占用:
  - 模型参数: 441MB (FP32)
  - 激活值: ~2.8GB (batch=1)
  - 总计: ~3.3GB

3.1.2 推理性能profiling

# 使用Nsight Systems profiling
nsys profile -o epoch23_profile \
  --stats=true \
  python tools/benchmark.py \
    --config configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml \
    --checkpoint runs/enhanced_from_epoch19/epoch_23.pth \
    --samples 100

# 分析profiling结果
nsys stats epoch23_profile.nsys-rep

3.2 优化策略设计

基于分析结果，设计优化方案：

优化目标:
  目标硬件: NVIDIA Orin 270T
  目标推理时间: <80ms
  目标吞吐量: >12 FPS
  精度损失: <3%

优化策略:
  1. 结构化剪枝: 110M → 60M (-45%)
  2. INT8量化: FP32 → INT8 (-75% size)
  3. TensorRT优化: CUDA kernel fusion
  4. DLA offload: 卷积层offload到DLA

预期结果:
  推理时间: 89ms → 60ms (-33%)
  模型大小: 441MB → 110MB (-75%)
  精度: mAP 64.5% → 63.0% (-2.3%)

🚀 阶段4: TensorRT部署 (1-2周)

4.1 剪枝 (3-4天)

4.1.1 敏感度分析

# tools/pruning/sensitivity_analysis.py

import torch
import torch_pruning as tp
from tqdm import tqdm

def analyze_layer_sensitivity(model, val_loader):
    """分析每层对精度的敏感度"""
    
    baseline_map = evaluate(model, val_loader)
    print(f"Baseline mAP: {baseline_map:.4f}")
    
    sensitivities = {}
    
    # 遍历所有可剪枝层
    for name, module in model.named_modules():
        if not isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
            continue
        
        print(f"\n测试层: {name}")
        
        # 临时剪枝该层50%
        pruned_model = prune_layer(model, name, ratio=0.5)
        
        # 评估
        pruned_map = evaluate(pruned_model, val_loader)
        
        # 计算敏感度
        sensitivity = baseline_map - pruned_map
        sensitivities[name] = sensitivity
        
        print(f"  剪枝后mAP: {pruned_map:.4f}")
        print(f"  敏感度: {sensitivity:.4f}")
        
        # 恢复原始模型
        del pruned_model
    
    # 排序并保存
    sorted_sens = sorted(sensitivities.items(), key=lambda x: x[1])
    
    print("\n敏感度排序 (从低到高):")
    print("-" * 60)
    for name, sens in sorted_sens:
        print(f"{name:50s}: {sens:.4f}")
    
    return sensitivities

# 使用
sensitivities = analyze_layer_sensitivity(model, val_loader)

4.1.2 剪枝执行

# 基于敏感度分析，执行剪枝
python tools/pruning/prune_bevfusion.py \
  --config configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml \
  --checkpoint runs/enhanced_from_epoch19/epoch_23.pth \
  --sensitivity-file pruning_analysis/sensitivity.json \
  --target-params 60M \
  --output bevfusion_pruned_60M.pth

4.1.3 剪枝后微调

# 微调5个epochs恢复精度
torchpack dist-run -np 8 python tools/train.py \
  configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES_pruned.yaml \
  --load_from bevfusion_pruned_60M.pth \
  --cfg-options \
    max_epochs=5 \
    optimizer.lr=5.0e-6 \
    data.samples_per_gpu=4

4.2 量化 (3-4天)

4.2.1 PTQ (Post-Training Quantization) 快速验证

# tools/quantization/ptq_bevfusion.py

import torch
from torch.quantization import quantize_dynamic

# 加载剪枝后的模型
model = load_model('bevfusion_pruned_60M_finetuned.pth')

# 动态量化 (快速验证)
model_int8 = quantize_dynamic(
    model,
    {torch.nn.Linear, torch.nn.Conv2d},
    dtype=torch.qint8
)

# 评估
print("评估INT8模型...")
results = evaluate(model_int8, val_loader)
print(f"PTQ mAP: {results['mAP']:.4f}")
print(f"精度损失: {baseline_map - results['mAP']:.4f}")

4.2.2 QAT (Quantization-Aware Training)

# QAT训练恢复精度
torchpack dist-run -np 8 python tools/train.py \
  configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES_qat.yaml \
  --load_from bevfusion_pruned_60M_finetuned.pth \
  --cfg-options \
    max_epochs=5 \
    optimizer.lr=1.0e-6 \
    quantization.enabled=true

4.3 TensorRT转换 (2-3天)

4.3.1 ONNX导出

# tools/tensorrt/export_onnx.py

import torch

model = load_model('bevfusion_pruned_60M_qat.pth')
model.eval()

# 准备dummy inputs
dummy_images = torch.randn(1, 6, 3, 256, 704).cuda()
dummy_points = torch.randn(1, 40000, 5).cuda()

# 导出ONNX
torch.onnx.export(
    model,
    (dummy_images, dummy_points),
    'bevfusion_epoch23_int8.onnx',
    opset_version=17,
    input_names=['images', 'points'],
    output_names=['bboxes', 'scores', 'labels', 'masks'],
    dynamic_axes={
        'images': {0: 'batch'},
        'points': {0: 'batch'}
    },
    verbose=False
)

print("ONNX导出完成: bevfusion_epoch23_int8.onnx")

4.3.2 TensorRT Engine构建

# tools/tensorrt/build_engine.py

import tensorrt as trt

# 创建builder
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)
network = builder.create_network(
    1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
)

# 解析ONNX
parser = trt.OnnxParser(network, logger)
with open('bevfusion_epoch23_int8.onnx', 'rb') as f:
    parser.parse(f.read())

# 配置
config = builder.create_builder_config()
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 << 30)  # 4GB

# INT8 + FP16
config.set_flag(trt.BuilderFlag.INT8)
config.set_flag(trt.BuilderFlag.FP16)
config.set_flag(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS)

# Calibration
config.int8_calibrator = BEVFusionCalibrator(
    calibration_dataset='data/nuscenes/calibration',
    cache_file='bevfusion_calibration.cache'
)

# 针对Orin优化
config.set_flag(trt.BuilderFlag.GPU_FALLBACK)
config.default_device_type = trt.DeviceType.DLA
config.DLA_core = 0

# 构建
print("构建TensorRT Engine...")
serialized_engine = builder.build_serialized_network(network, config)

# 保存
with open('bevfusion_epoch23_orin.engine', 'wb') as f:
    f.write(serialized_engine)

print("TensorRT Engine构建完成!")

🏁 阶段5: Orin实车部署 (2-3周)

5.1 Orin环境准备 (1-2天)

# 在Orin上执行

# 1. 安装JetPack 5.1+
sudo apt update
sudo apt install nvidia-jetpack

# 2. 安装Python依赖
pip3 install pycuda numpy opencv-python

# 3. 验证TensorRT
python3 -c "import tensorrt as trt; print(trt.__version__)"

# 4. 创建部署目录
mkdir -p ~/bevfusion_deploy
cd ~/bevfusion_deploy

5.2 模型部署 (2-3天)

# 从开发机器传输文件到Orin
scp bevfusion_epoch23_orin.engine orin@192.168.1.100:~/bevfusion_deploy/
scp -r deployment_code/ orin@192.168.1.100:~/bevfusion_deploy/

# 在Orin上测试
cd ~/bevfusion_deploy
python3 test_inference.py --engine bevfusion_epoch23_orin.engine

5.3 性能测试 (2-3天)

# 基准测试
python3 benchmark_orin.py \
  --engine bevfusion_epoch23_orin.engine \
  --samples 100 \
  --warmup 10

# 功耗测试
sudo tegrastats --interval 1000 > power_log.txt &
python3 benchmark_orin.py --engine bevfusion_epoch23_orin.engine
pkill tegrastats

# 精度验证
python3 validate_on_orin.py \
  --engine bevfusion_epoch23_orin.engine \
  --data-root /data/nuscenes_mini \
  --eval bbox map

5.4 优化调优 (1周)

# 多流优化
from cuda import cuda

class OptimizedPipeline:
    def __init__(self, engine_path):
        self.engine = load_engine(engine_path)
        
        # 创建多个CUDA streams
        self.preprocess_stream = cuda.cuStreamCreate(0)
        self.infer_stream = cuda.cuStreamCreate(0)
        self.postprocess_stream = cuda.cuStreamCreate(0)
    
    def async_infer(self, images, points):
        # 异步预处理
        preprocessed = self.preprocess(images, points, self.preprocess_stream)
        
        # 异步推理
        outputs = self.engine.infer_async(preprocessed, self.infer_stream)
        
        # 异步后处理
        results = self.postprocess(outputs, self.postprocess_stream)
        
        return results

📊 部署目标和验收标准

最低要求 (Must Have)

✅ 推理时间: <80ms
✅ 吞吐量: >12 FPS
✅ 功耗: <60W
✅ 检测mAP: >63% (精度损失<2%)
✅ 分割mIoU: >40% (精度损失<3%)
✅ 内存占用: <4GB

理想目标 (Nice to Have)

🌟 推理时间: <60ms
🌟 吞吐量: >16 FPS
🌟 功耗: <45W
🌟 检测mAP: >64%
🌟 分割mIoU: >41%
🌟 内存占用: <3GB

📅 完整时间表

周次 | 阶段 | 任务 | 状态
-----|------|------|------
立即 | 阶段1 | Epoch 23完整评估 | ⏳ 可立即开始
W+1  | 阶段2 | 详细性能分析 | ⏳ 评估后
W+1  | 阶段3 | 模型分析和敏感度测试 | ⏳ 分析后
W+2  | 阶段3 | 模型剪枝 | ⏳ 
W+2  | 阶段3 | 剪枝后微调 | ⏳
W+3  | 阶段4 | PTQ量化验证 | ⏳
W+3  | 阶段4 | QAT量化训练 | ⏳
W+4  | 阶段4 | TensorRT转换和优化 | ⏳
W+5  | 阶段5 | Orin环境准备 | ⏳
W+5  | 阶段5 | 模型部署到Orin | ⏳
W+6  | 阶段5 | 性能测试和功耗测试 | ⏳
W+6  | 阶段5 | 精度验证 | ⏳
W+7  | 阶段5 | 多流优化和DLA调优 | ⏳
W+7  | 阶段5 | 最终验收 | ⏳

总周期: 7周（约1.5-2个月）

🎯 立即行动清单

今天可以做的

创建评估脚本 EVAL_EPOCH23_COMPLETE.sh
启动后台评估（使用GPU 4-7）
监控评估进度
同时继续监控Stage 1训练

评估完成后 (2-3小时后)

运行详细分析脚本
生成性能报告
识别failure cases
规划优化重点

本周内

完成模型复杂度分析
完成推理性能profiling
设计剪枝策略
准备剪枝工具和脚本

📂 相关文档索引

已有文档

✅ PHASE3_EPOCH23_BASELINE_PERFORMANCE.md - Baseline性能数据
✅ UPDATED_PLAN_WITH_EVAL.md - 评估计划概要
✅ ORIN_DEPLOYMENT_PLAN.md - 详细部署方案
✅ EVAL_DEPLOYMENT_ANALYSIS.md - 方案分析

本文档

✅ EPOCH23_评估与部署完整计划.md - 综合计划（本文档）

待创建文档

⏳ EPOCH23_EVALUATION_REPORT.md - 评估报告（评估完成后）
⏳ PRUNING_STRATEGY.md - 剪枝策略文档
⏳ QUANTIZATION_GUIDE.md - 量化指南
⏳ TENSORRT_OPTIMIZATION.md - TensorRT优化记录
⏳ ORIN_DEPLOYMENT_LOG.md - Orin部署日志

🚀 快速启动命令

立即启动评估

cd /workspace/bevfusion

# 创建评估脚本
cat > EVAL_EPOCH23_COMPLETE.sh << 'EOF'
[完整脚本内容见上文]
EOF

chmod +x EVAL_EPOCH23_COMPLETE.sh

# 后台启动评估
nohup bash EVAL_EPOCH23_COMPLETE.sh > eval_epoch23_$(date +%Y%m%d_%H%M%S).log 2>&1 &

# 监控进度
tail -f eval_epoch23_*.log

# 同时监控训练
tail -f phase4a_stage1_*.log | grep "Epoch \["

# 监控GPU
watch -n 10 nvidia-smi

文档状态: ✅ 完整
行动计划: 已制定
立即可执行: 是
预计完成: 7周（约2个月）

建议: 立即启动阶段1评估，充分利用空闲GPU资源！

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