#!/usr/bin/env python
"""
解释BEV特征1440×1440尺寸的由来
"""

def explain_bev_resolution():
    print("="*80)
    print("🎯 BEV特征尺寸1440×1440详细解释")
    print("="*80)

    # 1. 基本参数
    print("\n📊 1. 基本参数")
    print("-" * 50)

    bev_range = 54 - (-54)  # 108米
    voxel_size = 0.075  # 米/像素
    sparse_shape = [1440, 1440, 41]

    print("LiDAR配置参数:")
    print(f"├── 检测范围: [-54m, +54m] = {bev_range}m × {bev_range}m")
    print(f"├── 体素尺寸: {voxel_size}m × {voxel_size}m × 0.2m")
    print(f"├── 稀疏形状: {sparse_shape}")

    # 2. 尺寸计算过程
    print("\n🔢 2. 尺寸计算过程")
    print("-" * 50)

    print("BEV像素计算:")
    print(f"├── 空间范围: {bev_range}米")
    print(f"├── 像素分辨率: {voxel_size}米/像素")
    print(f"├── 像素数量: {bev_range} ÷ {voxel_size} = {bev_range / voxel_size}")
    print("└── 实际尺寸: 1440 × 1440 像素 ✅")

    # 3. 验证计算
    print("\n✅ 3. 计算验证")
    print("-" * 50)

    calculated_pixels = int(bev_range / voxel_size)
    print(f"理论计算: {bev_range} / {voxel_size} = {calculated_pixels}")
    print(f"配置指定: {sparse_shape[0]} × {sparse_shape[1]}")
    print(f"匹配程度: {'✅ 完全匹配' if calculated_pixels == sparse_shape[0] else '❌ 不匹配'}")

    # 4. 实际空间分辨率
    print("\n📏 4. 实际空间分辨率")
    print("-" * 50)

    pixel_count = sparse_shape[0]
    actual_resolution = bev_range / pixel_count

    print("分辨率分析:")
    print(f"├── BEV覆盖范围: {bev_range}m")
    print(f"├── 像素数量: {pixel_count}")
    print(f"├── 实际分辨率: {actual_resolution:.4f}m/像素")
    print(f"├── 配置分辨率: {voxel_size:.4f}m/像素")
    print(f"└── 精度: {'✅ 高精度' if actual_resolution <= voxel_size else '❌ 精度不足'}")

    # 5. 为什么需要这么高分辨率？
    print("\n🎯 5. 为什么需要1440×1440高分辨率？")
    print("-" * 50)

    reasons = [
        ("精确几何重建", "LiDAR点云密度高，需要高分辨率保持几何细节"),
        ("多尺度特征", "为分割头提供丰富的多尺度上下文信息"),
        ("安全冗余", "自动驾驶需要厘米级精度，0.075m≈7.5cm足够"),
        ("融合效率", "与Camera BEV (541×541) 的分辨率差异需要中间层过渡"),
        ("分割精度", "道路元素分割需要细粒度特征表示")
    ]

    for i, (title, explanation) in enumerate(reasons, 1):
        print(f"{i}. {title}:")
        print(f"   {explanation}")

    # 6. 与其他分辨率的对比
    print("\n📊 6. 分辨率对比")
    print("-" * 50)

    resolutions = {
        "Camera BEV": {"size": "541×541", "resolution": "0.2m/px", "purpose": "图像投影BEV"},
        "LiDAR BEV": {"size": "1440×1440", "resolution": "0.075m/px", "purpose": "点云稠密BEV"},
        "分割输出": {"size": "598×598", "resolution": "0.167m/px", "purpose": "最终分割图"}
    }

    print("各阶段分辨率对比:")
    print("阶段".ljust(12), "尺寸".ljust(12), "分辨率".ljust(12), "用途")
    print("-" * 60)
    for stage, info in resolutions.items():
        print(f"{stage:<12} {info['size']:<12} {info['resolution']:<12} {info['purpose']}")

    # 7. 内存影响
    print("\n💾 7. 内存占用影响")
    print("-" * 50)

    channels = 256  # SECONDFPN输出通道数
    batch_size = 1
    bytes_per_float32 = 4

    bev_memory = sparse_shape[0] * sparse_shape[1] * channels * bytes_per_float32
    bev_memory_mb = bev_memory / (1024 * 1024)

    print("内存计算 (单batch, float32):")
    print(f"├── BEV尺寸: {sparse_shape[0]}×{sparse_shape[1]}×{channels}ch")
    print(f"├── 像素总数: {sparse_shape[0] * sparse_shape[1]:,}")
    print(f"├── 参数总数: {sparse_shape[0] * sparse_shape[1] * channels:,}")
    print(f"├── 内存占用: {bev_memory_mb:.1f} MB")
    print(f"└── GPU需求: 需要至少8GB VRAM可用空间")

    # 8. 技术权衡
    print("\n⚖️ 8. 技术权衡分析")
    print("-" * 50)

    tradeoffs = {
        "优点": [
            "极高几何精度 (7.5cm)",
            "丰富的上下文信息",
            "支持精细分割任务",
            "LiDAR数据充分利用"
        ],
        "挑战": [
            "巨大内存占用 (2GB+)",
            "计算复杂度高",
            "训练时间长",
            "存储空间需求大"
        ],
        "优化策略": [
            "分层特征提取",
            "稀疏计算优化",
            "混合精度训练",
            "数据并行训练"
        ]
    }

    for category, items in tradeoffs.items():
        print(f"\n{category}:")
        for item in items:
            print(f"├── {item}")

    print("\n" + "="*80)
    print("🏁 BEV分辨率解释完成！1440×1440是精确几何重建的必然选择！")
    print("="*80)

if __name__ == '__main__':
    explain_bev_resolution()