蚂蚁灵波LingBot-Vision深度解析：空间原生视觉基模如何用11亿参数逆袭70亿DINOv3，Boundary Forcing让机器人真正"看懂"世界

class RandomMaskingAnalyzer:
    """
    DINOv3随机掩码策略分析
    
    核心问题：随机掩码对几何结构的学习是间接的、低效的
    """
    
    def __init__(self):
        # 定义图像patch的"信息类型"
        self.patch_types = {
            "flat": {"info_density": 0.1, "geometric_value": 0.0, "ratio": 0.6},
            "edge": {"info_density": 0.9, "geometric_value": 1.0, "ratio": 0.1},
            "texture": {"info_density": 0.5, "geometric_value": 0.3, "ratio": 0.2},
            "corner": {"info_density": 0.8, "geometric_value": 0.8, "ratio": 0.1},
        }
    
    def analyze_random_mask_efficiency(self, mask_ratio: float = 0.3) -> dict:
        """
        分析随机掩码策略的几何学习效率
        
        假设图像中有大量平坦区域
        随机掩码大概率选中平坦区域而非边界
        """
        total_patches = 100
        flat_patches = int(total_patches * self.patch_types["flat"]["ratio"])
        edge_patches = int(total_patches * self.patch_types["edge"]["ratio"])
        texture_patches = int(total_patches * self.patch_types["texture"]["ratio"])
        corner_patches = total_patches - flat_patches - edge_patches - texture_patches
        
        # 随机掩码选中各类patch的概率
        masked_count = int(total_patches * mask_ratio)
        
        expected_flat = flat_patches * mask_ratio
        expected_edge = edge_patches * mask_ratio
        expected_texture = texture_patches * mask_ratio
        expected_corner = corner_patches * mask_ratio
        
        # 几何信息获取量
        geometric_gain = (
            expected_edge * self.patch_types["edge"]["geometric_value"] +
            expected_texture * self.patch_types["texture"]["geometric_value"] +
            expected_corner * self.patch_types["corner"]["geometric_value"]
        )
        
        # 对比：如果强制只遮挡边界patch
        boundary_forced_gain = (
            edge_patches * self.patch_types["edge"]["geometric_value"] +
            corner_patches * self.patch_types["corner"]["geometric_value"]
        )
        
        efficiency = geometric_gain / boundary_forced_gain * 100 if boundary_forced_gain > 0 else 0
        
        return {
            "expected_masked_flat": expected_flat,
            "expected_masked_edge": expected_edge,
            "expected_masked_texture": expected_texture,
            "expected_masked_corner": expected_corner,
            "random_mask_geometric_gain": geometric_gain,
            "boundary_forced_geometric_gain": boundary_forced_gain,
            "geometric_learning_efficiency_pct": efficiency,
        }

analyzer = RandomMaskingAnalyzer()
result = analyzer.analyze_random_mask_efficiency(0.3)
print(f"随机掩码 - 几何信息获取量: {result['random_mask_geometric_gain']:.1f}")
print(f"边界强制掩码 - 几何信息获取量: {result['boundary_forced_geometric_gain']:.1f}")
print(f"几何学习效率: {result['geometric_learning_efficiency_pct']:.1f}%")

package boundary

import (
	"math"
)

// BoundaryField 边界场表示
// 将连续几何值离散化为分类分布，避免回归坍缩
type BoundaryField struct {
	// 每个像素的边界场编码为 K × D 分类分布
	// K = 24（距离分类数），D = 12（方向分类数）
	DistanceClasses    int  // 24 个离散距离类别
	DirectionClasses   int  // 12 个离散方向类别
	
	// 分类分布概率（softmax输出）
	DistanceLogits     [][]float64 // [H][W][24]
	DirectionLogits    [][]float64 // [H][W][12]
	
	// 解码后的连续边界场（推理时使用）
	DecodedDistances   [][]float64 // [H][W] 距离值
	DecodedDirections  [][]float64 // [H][W] 方向值（弧度）
}

// NewBoundaryField 创建边界场
func NewBoundaryField(height, width int) *BoundaryField {
	bf := &BoundaryField{
		DistanceClasses:  24,
		DirectionClasses: 12,
	}
	
	bf.DistanceLogits = make([][]float64, height)
	bf.DirectionLogits = make([][]float64, height)
	bf.DecodedDistances = make([][]float64, height)
	bf.DecodedDirections = make([][]float64, height)
	
	for i := 0; i < height; i++ {
		bf.DistanceLogits[i] = make([]float64, width*24)
		bf.DirectionLogits[i] = make([]float64, width*12)
		bf.DecodedDistances[i] = make([]float64, width)
		bf.DecodedDirections[i] = make([]float64, width)
	}
	
	return bf
}

// DecodeBoundary 从分类分布解码连续边界场
func (bf *BoundaryField) DecodeBoundary(height, width int) {
	for y := 0; y < height; y++ {
		for x := 0; x < width; x++ {
			dStart := x * bf.DistanceClasses
			dEnd := dStart + bf.DistanceClasses
			dProbs := softmax(bf.DistanceLogits[y][dStart:dEnd])
			
			// 期望距离（加权平均）
			expectedDist := 0.0
			for k := 0; k < bf.DistanceClasses; k++ {
				// 距离类别映射到0-1范围
				distance := float64(k) / float64(bf.DistanceClasses-1)
				expectedDist += dProbs[k] * distance
			}
			bf.DecodedDistances[y][x] = expectedDist
			
			dirStart := x * bf.DirectionClasses
			dirEnd := dirStart + bf.DirectionClasses
			dirProbs := softmax(bf.DirectionLogits[y][dirStart:dirEnd])
			
			// 期望方向（角度，考虑周期性）
			sinSum, cosSum := 0.0, 0.0
			for d := 0; d < bf.DirectionClasses; d++ {
				angle := 2.0 * math.Pi * float64(d) / float64(bf.DirectionClasses)
				sinSum += dirProbs[d] * math.Sin(angle)
				cosSum += dirProbs[d] * math.Cos(angle)
			}
			bf.DecodedDirections[y][x] = math.Atan2(sinSum, cosSum)
		}
	}
}

// BoundaryForcingLoss 边界强制损失计算
// 包含语义自蒸馏损失 + 边界场分类损失
type BoundaryForcingLoss struct {
	SemanticWeight  float64 // α
	BoundaryWeight  float64 // β
}

// Compute 计算联合损失
func (l *BoundaryForcingLoss) Compute(
	studentFeatures [][]float64,
	teacherFeatures [][]float64,
	predictedBoundary *BoundaryField,
	teacherBoundary *BoundaryField,
	maskedTokens []int,
	height, width int,
) float64 {
	// L_semantic：被masked token的余弦相似度
	semanticLoss := 0.0
	for _, tokenIdx := range maskedTokens {
		y := tokenIdx / width
		x := tokenIdx % width
		semanticLoss += cosineSimilarity(
			studentFeatures[y][x*768:(x+1)*768],
			teacherFeatures[y][x*768:(x+1)*768],
		)
	}
	semanticLoss /= float64(len(maskedTokens))
	
	// L_boundary：边界token的交叉熵
	boundaryLoss := 0.0
	boundaryTokenCount := 0
	for y := 0; y < height; y++ {
		for x := 0; x < width; x++ {
			if teacherBoundary.DecodedDistances[y][x] > 0.1 {
				// 距离 > 0.1 视为边界token
				dStart := x * predictedBoundary.DistanceClasses
				dEnd := dStart + predictedBoundary.DistanceClasses
				dLoss := crossEntropy(
					predictedBoundary.DistanceLogits[y][dStart:dEnd],
					teacherBoundary.DistanceLogits[y][dStart:dEnd],
				)
				
				dirStart := x * predictedBoundary.DirectionClasses
				dirEnd := dirStart + predictedBoundary.DirectionClasses
				dirLoss := crossEntropy(
					predictedBoundary.DirectionLogits[y][dirStart:dirEnd],
					teacherBoundary.DirectionLogits[y][dirStart:dirEnd],
				)
				
				boundaryLoss += dLoss + dirLoss
				boundaryTokenCount++
			}
		}
	}
	boundaryLoss /= float64(boundaryTokenCount)
	
	return l.SemanticWeight*semanticLoss + l.BoundaryWeight*boundaryLoss
}

func softmax(logits []float64) []float64 {
	maxVal := logits[0]
	for _, v := range logits {
		if v > maxVal {
			maxVal = v
		}
	}
	
	sum := 0.0
	result := make([]float64, len(logits))
	for i, v := range logits {
		result[i] = math.Exp(v - maxVal)
		sum += result[i]
	}
	for i := range result {
		result[i] /= sum
	}
	return result
}

func cosineSimilarity(a, b []float64) float64 {
	dot, normA, normB := 0.0, 0.0, 0.0
	for i := range a {
		dot += a[i] * b[i]
		normA += a[i] * a[i]
		normB += b[i] * b[i]
	}
	if normA == 0 || normB == 0 {
		return 0
	}
	return dot / (math.Sqrt(normA) * math.Sqrt(normB))
}

func crossEntropy(pred, target []float64) float64 {
	loss := 0.0
	for i := range pred {
		targetProb := math.Exp(target[i])
		loss -= targetProb * math.Log(pred[i]+1e-10)
	}
	return loss
}

class AContrarioValidator:
    """
    A-contrario检验：自动过滤伪边界
    
    核心思想：
    1. 定义零假设H0：观察到的线段是由随机噪声产生的
    2. 计算NFA（Number of False Alarms）= E[在随机噪声下出现该线段次数]
    3. NFA < ε（通常ε=1）：拒绝H0，认定为真实边界
    """
    
    def __init__(self, epsilon: float = 1.0):
        self.epsilon = epsilon  # 显著性阈值
        self.nfa_cache = {}
    
    def validate_boundary(self, 
                        segment_pixels: list,
                        image_shape: tuple,
                        boundary_strength: float) -> dict:
        """
        验证边界段是否显著
        
        Args:
            segment_pixels: 边界段的像素坐标列表
            image_shape: 图像尺寸 (H, W)
            boundary_strength: 边界强度（0-1）
            
        Returns:
            is_significant: 是否通过检验
            nfa: Number of False Alarms
            eps: 精度（子像素级）
        """
        H, W = image_shape
        N = len(segment_pixels)  # 线段像素数
        N_test = H * W  # 总测试次数
        
        # 1. 计算线段的对齐精度
        # 子像素级精度的衡量
        eps = self._compute_alignment_precision(segment_pixels)
        
        # 2. 在随机噪声中出现该线段的概率
        # P = (π * eps²)^N / N! 泊松近似
        p = self._random_occurrence_probability(N, eps)
        
        # 3. NFA = N_test * P
        nfa = N_test * p
        
        # 4. 是否通过检验
        is_significant = nfa < self.epsilon and boundary_strength > 0.3
        
        return {
            "is_significant": is_significant,
            "nfa": nfa,
            "precision_pixels": eps,
            "segment_length": N,
            "boundary_strength": boundary_strength,
        }
    
    def _compute_alignment_precision(self, pixels: list) -> float:
        """计算线段拟合精度（子像素级）"""
        if len(pixels) < 3:
            return 1.0
        
        # 最小二乘直线拟合
        xs = [p[0] for p in pixels]
        ys = [p[1] for p in pixels]
        n = len(xs)
        
        # 线性回归求残差
        sx = sum(xs)
        sy = sum(ys)
        sxx = sum(x * x for x in xs)
        sxy = sum(xs[i] * ys[i] for i in range(n))
        
        denom = n * sxx - sx * sx
        if denom == 0:
            return 1.0
        
        a = (n * sxy - sx * sy) / denom
        b = (sy - a * sx) / n
        
        # 均方残差（子像素精度）
        residuals = [abs(ys[i] - (a * xs[i] + b)) for i in range(n)]
        rmse = (sum(r * r for r in residuals) / n) ** 0.5
        
        return rmse
    
    def _random_occurrence_probability(self, n: int, eps: float) -> float:
        """计算线段在随机噪声中出现的概率（泊松近似）"""
        import math
        # 线段由n个点构成，每个点落在eps范围内
        # 概率 ≈ (π * eps²)^n / n!
        area = math.pi * eps * eps
        log_prob = n * math.log(area) - sum(math.log(i) for i in range(1, n+1))
        return math.exp(log_prob)
    
    def batch_validate(self, 
                      all_segments: list,
                      image_shape: tuple) -> list:
        """
        批量验证并过滤，只保留显著边界
        
        Returns: 通过检验的边界段列表
        """
        validated = []
        rejected = []
        
        for segment in all_segments:
            result = self.validate_boundary(
                segment["pixels"],
                image_shape,
                segment["strength"],
            )
            
            if result["is_significant"]:
                validated.append({
                    **segment,
                    "nfa": result["nfa"],
                    "precision": result["precision_pixels"],
                })
            else:
                rejected.append(segment)
        
        return validated, rejected

# 模拟训练过程中的边界验证
validator = AContrarioValidator(epsilon=1.0)

# 模拟一条真实边界（20个像素，高对齐精度）
true_boundary = {
    "pixels": [(x, 2*x + 3 + 0.1*(x%2)) for x in range(20)],
    "strength": 0.85,
}
result = validator.validate_boundary(
    true_boundary["pixels"], (224, 224), true_boundary["strength"]
)
print(f"真实边界 - 通过: {result['is_significant']}, NFA={result['nfa']:.6f}")

# 模拟一条噪声（20个像素，随机位置）
import random
noise_boundary = {
    "pixels": [(random.randint(0, 223), random.randint(0, 223)) for _ in range(20)],
    "strength": 0.15,
}
result2 = validator.validate_boundary(
    noise_boundary["pixels"], (224, 224), noise_boundary["strength"]
)
print(f"噪声边界 - 通过: {result2['is_significant']}, NFA={result2['nfa']:.2f}")

# 模拟自举过程：初始阶段教师模型只能预测角点
initial_segments = []
for _ in range(100):
    initial_segments.append({
        "pixels": [(random.randint(0, 223), random.randint(0, 223)) for _ in range(5)],
        "strength": random.uniform(0.05, 0.3),
    })
validated, rejected = validator.batch_validate(initial_segments, (224, 224))
print(f"初始阶段 - {len(validated)}条通过, {len(rejected)}条被过滤")

class CornerBootstrapAnalyzer:
    """
    角点自举分析
    
    即使边界场是随机噪声，只要角点锚定了解码过程，
    解码出的线段依然连贯一致
    """
    
    def __init__(self):
        self.corners = []  # 角点列表
    
    def generate_random_boundary_field(self, 
                                      height: int,
                                      width: int,
                                      num_corners: int = 5) -> list:
        """生成随机边界场，但角点固定"""
        import random
        
        # 随机生成角点
        self.corners = [
            (random.randint(0, width-1), random.randint(0, height-1))
            for _ in range(num_corners)
        ]
        
        # 基于角点生成边界场
        # 每个像素的边界值由最近角点决定
        field = []
        for y in range(height):
            row = []
            for x in range(width):
                # 到最近角点的距离
                min_dist = min(
                    ((x - cx)**2 + (y - cy)**2)**0.5
                    for cx, cy in self.corners
                )
                # 边界场值（随机 + 距离衰减）
                value = random.uniform(0, 0.3) + math.exp(-min_dist/20)
                row.append(value)
            field.append(row)
        
        return field
    
    def decode_line_segments(self, 
                            field: list,
                            threshold: float = 0.5) -> list:
        """
        从边界场解码线段
        使用Canny-like边缘追踪
        """
        from collections import defaultdict
        
        height = len(field)
        width = len(field[0])
        
        # 非极大值抑制
        suppressed = [[0.0] * width for _ in range(height)]
        for y in range(1, height-1):
            for x in range(1, width-1):
                if field[y][x] > threshold:
                    # 检查是否为局部最大值
                    neighbors = [
                        field[y-1][x-1], field[y-1][x], field[y-1][x+1],
                        field[y][x-1],                   field[y][x+1],
                        field[y+1][x-1], field[y+1][x], field[y+1][x+1],
                    ]
                    if field[y][x] >= max(neighbors):
                        suppressed[y][x] = field[y][x]
        
        # 收集线段
        segments = []
        visited = set()
        
        for y in range(height):
            for x in range(width):
                if suppressed[y][x] > 0 and (x, y) not in visited:
                    # BFS追踪线段
                    segment = []
                    stack = [(x, y)]
                    while stack:
                        cx, cy = stack.pop()
                        if (cx, cy) in visited:
                            continue
                        visited.add((cx, cy))
                        segment.append((cx, cy))
                        
                        # 检查8邻域
                        for dy in [-1, 0, 1]:
                            for dx in [-1, 0, 1]:
                                nx, ny = cx+dx, cy+dy
                                if (0 <= nx < width and 0 <= ny < height and
                                    suppressed[ny][nx] > 0 and
                                    (nx, ny) not in visited):
                                    stack.append((nx, ny))
                    
                    if len(segment) > 5:  # 足够长的线段
                        segments.append(segment)
        
        return segments
    
    def measure_consistency(self, 
                          trials: int = 5,
                          height: int = 100,
                          width: int = 100) -> float:
        """
        测量多次随机边界场的解码一致性
        
        如果角点足够锚定，即使边界场随机，
        解码结果也应高度一致
        """
        all_segments = []
        
        for _ in range(trials):
            field = self.generate_random_boundary_field(height, width)
            segments = self.decode_line_segments(field)
            all_segments.append(segments)
        
        # 计算一致性分数
        # 如果解码的线段在多次试验中位置相近→一致性高
        consistency_scores = []
        
        for i in range(trials):
            for j in range(i+1, trials):
                score = self._segment_overlap(
                    all_segments[i], all_segments[j], height, width
                )
                consistency_scores.append(score)
        
        return sum(consistency_scores) / len(consistency_scores) if consistency_scores else 0.0
    
    def _segment_overlap(self, 
                        segs1: list, segs2: list,
                        height: int, width: int) -> float:
        """计算两组线段的IoU"""
        mask1 = set()
        mask2 = set()
        
        for seg in segs1:
            for p in seg:
                mask1.add(p)
        for seg in segs2:
            for p in seg:
                mask2.add(p)
        
        if not mask1 and not mask2:
            return 1.0
        if not mask1 or not mask2:
            return 0.0
        
        intersection = len(mask1 & mask2)
        union = len(mask1 | mask2)
        return intersection / union

import math
bootstrap = CornerBootstrapAnalyzer()
consistency = bootstrap.measure_consistency(trials=5)
print(f"5次随机边界场解码一致性: {consistency:.3f}")

package training

import (
	"fmt"
	"math"
	"sync"
)

// LingBotVisionTrainer LingBot-Vision训练器
// 实现Masked Boundary Modeling训练循环
type LingBotVisionTrainer struct {
	// 模型
	Teacher *VisionTransformer  // 教师模型（动量更新）
	Student *VisionTransformer  // 学生模型（梯度更新）
	
	// 边界模块
	BoundaryDecoder *BoundaryFieldDecoder
	BoundaryTeacher *BoundaryFieldDecoder // 教师边界场
	
	// 校验器
	Validator *AContrarioValidator
	
	// 训练配置
	Config *TrainingConfig
}

// TrainingConfig 训练配置
type TrainingConfig struct {
	BatchSize          int    // 1024
	LearningRate       float64 // 1e-4
	MomentumTeacher    float64 // 0.996
	MaskRatio          float64 // 0.3
	BoundaryMaskRatio  float64 // 0.15
	SemanticWeight     float64 // α = 1.0
	BoundaryWeight     float64 // β = 0.5
	ImageSize          int    // 224
	PatchSize          int    // 16
	NumEpochs          int    // 100
	NumWorkers         int    // 8
}

// VisionTransformer 简化的ViT结构
type VisionTransformer struct {
	EmbedDim    int      // 1536 (ViT-g)
	NumHeads    int      // 24
	NumLayers   int      // 40
	MLPRatio    int      // 4
	
	PatchEmbed  *PatchEmbedding
	Transformer *TransformerEncoder
	Norm        *LayerNorm
}

// PatchEmbedding patch嵌入层
type PatchEmbedding struct {
	KernelSize  int
	Stride      int
	InChannels  int
	OutChannels int
	Weights     [][]float64
}

// TransformerEncoder 简化Transformer编码器
type TransformerEncoder struct {
	Layers []*TransformerLayer
}

// TransformerLayer Transformer层
type TransformerLayer struct {
	SelfAttn *MultiHeadAttention
	FFN      *FeedForward
	Norm1    *LayerNorm
	Norm2    *LayerNorm
}

// MultiHeadAttention 多头自注意力
type MultiHeadAttention struct {
	NumHeads    int
	HeadDim     int
	QProj, KProj, VProj, OProj [][]float64
}

// FeedForward FFN层
type FeedForward struct {
	W1, W2 [][]float64
}

// LayerNorm 层归一化
type LayerNorm struct {
	Gamma, Beta []float64
	Eps         float64
}

// TrainStep 单步训练
func (t *LingBotVisionTrainer) TrainStep(images [][][]float32) *TrainingStepResult {
	batchSize := len(images)
	
	// 1. 前向传播（学生）
	studentFeatures := make([][][]float64, batchSize)
	for i, img := range images {
		studentFeatures[i] = t.Student.Forward(img)
	}
	
	// 2. 边界场预测（教师）
	boundaryFields := make([]*BoundaryField, batchSize)
	for i, img := range images {
		bf := t.BoundaryTeacher.Predict(img)
		// A-contrario检验过滤
		t.Validator.ValidateBatch(bf)
		boundaryFields[i] = bf
	}
	
	// 3. 边界强制掩码
	maskedIndices := make([][]int, batchSize)
	for i, bf := range boundaryFields {
		maskedIndices[i] = t.generateBoundaryMask(bf, batchSize)
	}
	
	// 4. 前向传播（教师，完整图像）
	teacherFeatures := make([][][]float64, batchSize)
	for i, img := range images {
		teacherFeatures[i] = t.Teacher.Forward(img)
	}
	
	// 5. 计算损失
	totalLoss := 0.0
	for i := range images {
		// 语义自蒸馏损失
		semanticLoss := t.computeSemanticLoss(
			studentFeatures[i], teacherFeatures[i], maskedIndices[i],
		)
		
		// 边界场分类损失
		boundaryLoss := t.computeBoundaryLoss(
			studentFeatures[i], boundaryFields[i], maskedIndices[i],
		)
		
		totalLoss += t.Config.SemanticWeight*semanticLoss +
			t.Config.BoundaryWeight*boundaryLoss
	}
	totalLoss /= float64(batchSize)
	
	// 6. 动量更新教师模型
	t.momentumUpdateTeacher()
	
	return &TrainingStepResult{
		Loss:         totalLoss,
		StudentFeats: studentFeatures,
		TeacherFeats: teacherFeatures,
	}
}

// generateBoundaryMask 生成边界强制掩码
func (t *LingBotVisionTrainer) generateBoundaryMask(
	bf *BoundaryField, batchSize int,
) []int {
	numPatches := (t.Config.ImageSize / t.Config.PatchSize) *
		(t.Config.ImageSize / t.Config.PatchSize)
	
	// 计算每个patch的边界分数
	patchBoundaryScores := make([]float64, numPatches)
	patchesPerRow := t.Config.ImageSize / t.Config.PatchSize
	
	for y := 0; y < t.Config.ImageSize; y++ {
		for x := 0; x < t.Config.ImageSize; x++ {
			if bf.DecodedDistances[y][x] > 0.1 {
				// 该像素属于边界
				py := y / t.Config.PatchSize
				px := x / t.Config.PatchSize
				idx := py*patchesPerRow + px
				patchBoundaryScores[idx] += bf.DecodedDistances[y][x]
			}
		}
	}
	
	// 选择边界分数最高的patch作为强制mask
	totalMasked := int(float64(numPatches) * t.Config.BoundaryMaskRatio)
	
	// 排序选top-k
	type scoredIdx struct {
		idx   int
		score float64
	}
	scored := make([]scoredIdx, numPatches)
	for i := 0; i < numPatches; i++ {
		scored[i] = scoredIdx{i, patchBoundaryScores[i]}
	}
	
	// 简单选择排序top-k
	for i := 0; i < totalMasked && i < numPatches; i++ {
		maxIdx := i
		for j := i + 1; j < numPatches; j++ {
			if scored[j].score > scored[maxIdx].score {
				maxIdx = j
			}
		}
		scored[i], scored[maxIdx] = scored[maxIdx], scored[i]
	}
	
	result := make([]int, totalMasked)
	for i := 0; i < totalMasked; i++ {
		result[i] = scored[i].idx
	}
	return result
}

// momentumUpdateTeacher 动量更新教师模型参数
func (t *LingBotVisionTrainer) momentumUpdateTeacher() {
	m := t.Config.MomentumTeacher
	
	// 逐层动量更新（简化实现）
	t.Teacher.PatchEmbed.Weights = t.momentumUpdateMatrix(
		t.Teacher.PatchEmbed.Weights,
		t.Student.PatchEmbed.Weights, m,
	)
	
	// 实际实现中需要递归所有参数
	// 这里仅示意
	_ = m
}

func (t *LingBotVisionTrainer) momentumUpdateMatrix(
	teacher, student [][]float64, m float64,
) [][]float64 {
	h := len(teacher)
	w := len(teacher[0])
	result := make([][]float64, h)
	for i := range result {
		result[i] = make([]float64, w)
		for j := range result[i] {
			result[i][j] = m*teacher[i][j] + (1-m)*student[i][j]
		}
	}
	return result
}

// TrainingStepResult 训练步结果
type TrainingStepResult struct {
	Loss         float64
	StudentFeats [][][]float64
	TeacherFeats [][][]float64
}

// TrainEpoch 训练一个epoch
func (t *LingBotVisionTrainer) TrainEpoch(
	dataset []ImageBatch,
) float64 {
	var wg sync.WaitGroup
	losses := make([]float64, len(dataset))
	
	for i, batch := range dataset {
		wg.Add(1)
		go func(idx int, imgs [][][]float32) {
			defer wg.Done()
			result := t.TrainStep(imgs)
			losses[idx] = result.Loss
		}(i, batch.Images)
	}
	
	wg.Wait()
	
	totalLoss := 0.0
	for _, l := range losses {
		totalLoss += l
	}
	return totalLoss / float64(len(losses))
}

class LingBotDepth2:
    """
    LingBot-Depth 2.0深度补全Pipeline
    
    基于LingBot-Vision编码器 + Sensor-Validity Masking
    """
    
    def __init__(self, vision_encoder, model_size: str = "ViT-g"):
        self.encoder = vision_encoder  # LingBot-Vision ViT编码器
        self.depth_decoder = DepthDecoder()
        self.sensor_validity = SensorValidityMask()
        
        self.config = {
            "ViT-g": {"params": 1.1e9, "embed_dim": 1536},
            "ViT-L": {"params": 0.3e9, "embed_dim": 1024},
            "ViT-B": {"params": 86e6, "embed_dim": 768},
            "ViT-S": {"params": 21e6, "embed_dim": 384},
        }[model_size]
    
    def depth_completion(self, 
                        rgb_image: np.ndarray,
                        sparse_depth: np.ndarray,
                        sensor_mask: np.ndarray = None) -> np.ndarray:
        """
        深度补全
        
        Args:
            rgb_image: RGB图像 (H, W, 3)
            sparse_depth: 稀疏深度图 (H, W) - 传感器可直接测量的深度
            sensor_mask: 传感器有效性掩码 (H, W) - 哪些像素有有效深度
            
        Returns:
            dense_depth: 补全后的密集深度图 (H, W)
        """
        # 1. 编码RGB图像
        features = self.encoder.encode(rgb_image)  # (H/14, W/14, D)
        
        # 2. 融合稀疏深度
        depth_features = self._fuse_sparse_depth(sparse_depth, features)
        
        # 3. Sensor-Validity Masking
        if sensor_mask is None:
            sensor_mask = self.sensor_validity.estimate_validity(
                rgb_image, sparse_depth
            )
        
        # 4. 解码密集深度
        dense_depth = self.depth_decoder.decode(
            depth_features, sensor_mask
        )
        
        # 5. 后处理：边界锐化
        dense_depth = self._boundary_sharpen(dense_depth, rgb_image)
        
        return dense_depth
    
    def _fuse_sparse_depth(self, 
                          sparse_depth: np.ndarray,
                          features: np.ndarray) -> np.ndarray:
        """
        融合稀疏深度与视觉特征
        
        使用可变形注意力融合
        """
        H, W, D = features.shape
        
        # 将稀疏深度映射到特征空间
        depth_encoding = np.zeros((H, W, 64), dtype=np.float32)
        for y in range(H):
            for x in range(W):
                # 映射到原始图像坐标
                py = int(y * 14 + 7)
                px = int(x * 14 + 7)
                if py < sparse_depth.shape[0] and px < sparse_depth.shape[1]:
                    d = sparse_depth[py, px]
                    if d > 0:  # 有有效深度
                        depth_encoding[y, x, :32] = d
                        depth_encoding[y, x, 32:] = 1.0  # validity indicator
        
        # 特征拼接 + 投影
        fused = np.concatenate([features, depth_encoding], axis=-1)
        projection = np.random.randn(D + 64, D).astype(np.float32) * 0.01
        
        return fused @ projection
    
    def _boundary_sharpen(self, 
                         depth: np.ndarray,
                         rgb: np.ndarray) -> np.ndarray:
        """
        基于LingBot-Vision边界场进行深度图锐化
        
        在检测到边界的区域，强制深度在边界两侧保持跳变
        """
        # 获取边界场
        boundary_field = self.encoder.get_boundary_field(rgb)
        
        H, W = depth.shape
        result = depth.copy()
        
        # 在边界区域应用双边滤波
        for y in range(1, H-1):
            for x in range(1, W-1):
                if boundary_field[y, x] > 0.3:
                    # 边界区域：保留跳变，不做平滑
                    continue
                else:
                    # 非边界区域：局部平滑
                    neighborhood = depth[y-1:y+2, x-1:x+2]
                    result[y, x] = np.median(neighborhood)
        
        return result


class SensorValidityMask:
    """
    Sensor-Validity Masking
    
    核心创新：用真实传感器失效模式代替随机mask
    训练时模型学习的是真实的深度缺失分布
    """
    
    def estimate_validity(self, 
                         rgb: np.ndarray,
                         depth: np.ndarray) -> np.ndarray:
        """
        估计传感器每个像素的深度有效性
        
        典型失效模式：
        - 高反光表面：深度噪点/空洞
        - 透明表面：光穿透过导致无回波
        - 纹理缺乏区域：深度匹配失败
        - 远距离：信噪比下降
        """
        H, W = depth.shape
        validity = np.ones((H, W), dtype=np.float32)
        
        # 1. 深度为零或NaN -> 无效
        validity[depth <= 0] = 0.0
        validity[np.isnan(depth)] = 0.0
        
        # 2. 高亮度区域 -> 可能反光
        gray = np.mean(rgb, axis=-1)
        validity[gray > 240] *= 0.3  # 高置信度反光
        
        # 3. 低纹理区域 -> ToF可能失效
        from scipy.ndimage import variance
        texture = variance(gray, size=5)
        validity[texture < 10] *= 0.5
        
        # 4. 远距离 -> 置信度衰减
        distance_weight = np.exp(-np.arange(H) / (H * 0.3))
        validity *= distance_weight[:, np.newaxis]
        
        return validity


class DepthDecoder:
    """深度解码器"""
    
    def __init__(self):
        # 简化的上采样解码器
        self.upsample_layers = 4
        self.channels = [1536, 768, 384, 192, 1]
    
    def decode(self, 
              features: np.ndarray,
              sensor_mask: np.ndarray) -> np.ndarray:
        """解码密集深度图"""
        x = features
        
        # 逐层上采样
        for i in range(self.upsample_layers):
            # 2x最近邻上采样
            x = np.repeat(np.repeat(x, 2, axis=0), 2, axis=1)
            
            # 简化的卷积投影
            in_c = self.channels[i]
            out_c = self.channels[i+1]
            proj = np.random.randn(in_c, out_c).astype(np.float32) * 0.01
            x = x @ proj
        
        # 最终深度输出
        depth = x.squeeze(-1)
        
        # 应用sensor_mask：保留原传感器深度，补全缺失区域
        depth = depth * (1 - sensor_mask) + sensor_mask
        
        return depth


# 模拟深度补全
import numpy as np
encoder = type('obj', (object,), {
    'encode': lambda self, x: np.random.randn(16, 16, 1536).astype(np.float32),
    'get_boundary_field': lambda self, x: np.random.rand(224, 224),
})()
depth_model = LingBotDepth2(encoder, "ViT-g")

rgb_demo = np.random.randn(224, 224, 3).astype(np.float32)
sparse_demo = np.random.rand(224, 224).astype(np.float32)
sparse_demo[sparse_demo < 0.3] = 0  # 70%缺失

result_depth = depth_model.depth_completion(rgb_demo, sparse_demo)
print(f"输入稀疏深度非零像素: {(sparse_demo > 0).sum()} / {224*224}")
print(f"输出密集深度像素: {result_depth.shape}")
print(f"补全区域: {(result_depth > 0).sum()} / {224*224}")

package depth

// TransparentObjectCompletion 透明物体深度补全
// 利用LingBot-Vision的边界场先验
type TransparentObjectCompletion struct {
	BoundaryField   *BoundaryField
	DepthPrior      *DepthPrior
}

// DepthPrior 深度先验模型
// 基于"边界内深度连续"的几何先验
type DepthPrior struct {
	SmoothnessWeight   float64
	BoundaryWeight     float64
	DataWeight         float64
}

// CompleteTransparent 补全透明物体区域深度
func (toc *TransparentObjectCompletion) CompleteTransparent(
	sparseDepth [][]float64,
	sensorValidity [][]bool,
	height, width int,
) [][]float64 {
	// 1. 获取LingBot-Vision的边界场
	boundaryField := toc.BoundaryField.DecodedDistances
	
	// 2. 标记透明物体区域
	transparentMask := make([][]bool, height)
	for y := 0; y < height; y++ {
		transparentMask[y] = make([]bool, width)
		for x := 0; x < width; x++ {
			// 传感器无效 + 边界场检测到轮廓 = 透明物体
			transparentMask[y][x] = !sensorValidity[y][x] &&
				boundaryField[y][x] > 0.2
		}
	}
	
	// 3. 构建拉普拉斯矩阵求解深度
	// 优化目标：min ||L·d||² + λ_b·||B·d - b·||² + λ_d·||d - d_sensor||²
	result := make([][]float64, height)
	for y := 0; y < height; y++ {
		result[y] = make([]float64, width)
		copy(result[y], sparseDepth[y])
	}
	
	// 迭代求解（简化的Jacobi迭代）
	for iter := 0; iter < 100; iter++ {
		newResult := make([][]float64, height)
		for y := 0; y < height; y++ {
			newResult[y] = make([]float64, width)
			for x := 0; x < width; x++ {
				if transparentMask[y][x] {
					// 透明区域：用邻居插值 + 边界约束
					sumNeighbors := 0.0
					count := 0
					
					for dy := -1; dy <= 1; dy++ {
						for dx := -1; dx <= 1; dx++ {
							ny, nx := y+dy, x+dx
							if ny >= 0 && ny < height && nx >= 0 && nx < width {
								if !transparentMask[ny][nx] {
									// 非透明邻居有有效深度
									sumNeighbors += result[ny][nx]
									count++
								} else if iter > 0 {
									// 透明邻居用上次迭代值
									sumNeighbors += result[ny][nx]
									count++
								}
							}
						}
					}
					
					if count > 0 {
						newResult[y][x] = sumNeighbors / float64(count)
					} else {
						newResult[y][x] = result[y][x]
					}
				} else if sensorValidity[y][x] {
					newResult[y][x] = sparseDepth[y][x]
				} else {
					// 非透明缺失区域：平滑插值
					newResult[y][x] = result[y][x]
				}
			}
		}
		result = newResult
	}
	
	return result
}

class OrbbecIntegration:
    """
    奥比中光硬件 + LingBot-Depth 2.0 软硬一体化方案
    """
    
    def __init__(self, camera_model: str = "Gemini330"):
        self.camera_model = camera_model
        self.camera_params = {
            "Gemini330": {
                "resolution": (1280, 800),
                "fps": 30,
                "depth_range_m": (0.3, 8.0),
                "chipset": "MX6800",
            }
        }[camera_model]
    
    def edge_deployment(self, model_size: str = "ViT-L") -> dict:
        """端侧部署配置"""
        edge_config = {
            "ViT-S": {"ram_mb": 128, "flops_g": 12, "fps": 30},
            "ViT-B": {"ram_mb": 256, "flops_g": 48, "fps": 15},
            "ViT-L": {"ram_mb": 1024, "flops_g": 196, "fps": 8},
        }[model_size]
        
        return {
            "camera": self.camera_model,
            "model": f"LingBot-Vision-{model_size}",
            "edge_requirements": edge_config,
            "sdk_available": True,
            "integrated_camera_eta": "2026年底",
        }

integration = OrbbecIntegration("Gemini330")
config = integration.edge_deployment("ViT-L")
print(f"摄像头: {config['camera']}")
print(f"模型: {config['model']}")
print(f"端侧需求: {config['edge_requirements']}")
print(f"SDK可用: {config['sdk_available']}")
print(f"一体化相机预计: {config['integrated_camera_eta']}")