空间智能AI模型的推理加速优化实践

引言

空间智能（Spatial Intelligence）是人工智能领域的重要分支，专注于理解和处理三维空间中的视觉信息。从自动驾驶的环境感知到机器人的三维重建，从AR/VR的空间理解到遥感图像分析，空间智能模型正在各个领域发挥关键作用。CANN开源社区的cann-recipes-spatial-intelligence仓库为空间智能业务中的典型模型提供了基于CANN平台的完整优化方案。

空间智能概述

什么是空间智能

空间智能是指人工智能系统对三维空间环境进行感知、理解和交互的能力。与传统2D视觉不同，空间智能强调：

• 三维感知：理解3D空间中的物体关系和几何结构
• 深度估计：从2D图像推断3D深度信息
• 场景理解：识别场景布局和语义
• 空间推理：基于3D信息进行决策和规划

典型应用场景

1. 自动驾驶：环境感知、障碍物检测、路径规划
2. 机器人导航：SLAM、三维重建、避障
3. AR/VR：空间映射、姿态估计、手势识别
4. 遥感分析：地形识别、目标检测、变化检测
5. 工业检测：3D测量、缺陷识别、质量评估

典型空间智能模型

深度估计模型

"""
深度估计模型示例：MiDaS
"""
import torch
import torch.nn as nn

class MiDaSDepthEstimation(nn.Module):
    """MiDaS深度估计模型"""

    def __init__(self):
        super().__init__()
        # 使用EfficientNet或ViT作为backbone
        self.backbone = EfficientNetBackbone()

        # 深度预测头
        self.depth_head = nn.Sequential(
            nn.Conv2d(1280, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 1, kernel_size=1)
        )

    def forward(self, x):
        # 特征提取
        features = self.backbone(x)

        # 深度预测
        depth = self.depth_head(features)

        return depth

三维重建模型

"""
三维重建模型示例：NeRF变体
"""
import torch
import torch.nn as nn

class NeRFModel(nn.Module):
    """NeRF（神经辐射场）模型"""

    def __init__(self, hidden_dim=256):
        super().__init__()

        # 位置编码
        self.pos_encoder = PositionalEncoding()

        # MLP网络
        self.mlp = nn.Sequential(
            nn.Linear(60, hidden_dim),  # 位置编码后60维
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 4)  # RGB + density
        )

    def forward(self, rays_o, rays_d):
        # 位置编码
        encoded = self.pos_encoder(rays_o, rays_d)

        # MLP预测
        output = self.mlp(encoded)

        # 解析RGB和密度
        rgb = torch.sigmoid(output[:, :3])
        density = torch.relu(output[:, 3:4])

        return rgb, density

点云处理模型

"""
点云处理模型示例：PointNet++
"""
import torch
import torch.nn as nn

class PointNetPlusPlus(nn.Module):
    """PointNet++点云处理模型"""

    def __init__(self, num_classes=40):
        super().__init__()

        # Set Abstraction模块
        self.sa1 = SetAbstraction(npoint=512, radius=0.2, nsample=32, in_channel=3, mlp=[64, 64, 128])
        self.sa2 = SetAbstraction(npoint=128, radius=0.4, nsample=64, in_channel=128 + 3, mlp=[128, 128, 256])
        self.sa3 = SetAbstraction(npoint=None, radius=None, nsample=None, in_channel=256 + 3, mlp=[256, 256, 512])

        # FP模块
        self.fp3 = FeaturePropagation(256, [512, 256])
        self.fp2 = FeaturePropagation(128, [256, 128])
        self.fp1 = FeaturePropagation(128, [128, 128, 128])

        # 分类头
        self.conv1 = nn.Conv1d(128, 128, 1)
        self.bn1 = nn.BatchNorm1d(128)
        self.drop1 = nn.Dropout(0.5)
        self.conv2 = nn.Conv1d(128, num_classes, 1)

    def forward(self, xyz, points):
        # Set Abstraction
        l1_xyz, l1_points = self.sa1(xyz, points)
        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
        l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)

        # Feature Propagation
        l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)
        l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)
        l0_points = self.fp1(xyz, l1_xyz, points, l1_points)

        # 分类
        feat = self.drop1(F.relu(self.bn1(self.conv1(l0_points))))
        feat = self.conv2(feat)
        feat = feat.max(-1)[0]

        return feat

CANN优化方案

优化架构

cann-recipes-spatial-intelligence仓库提供了完整的优化方案：

空间智能模型
      ↓
模型转换层
   (ONNX → TorchScript → OM)
      ↓
算子优化层
   (融合算子、自定义算子)
      ↓
图优化层
   (常量折叠、死代码消除)
      ↓
运行时优化层
   (内存复用、流水线并行)
      ↓
昇腾硬件

算子优化实现

深度可分离卷积优化

"""
优化深度可分离卷积算子
"""
import torch
import torch_npu

class OptimizedDepthwiseSeparableConv(torch.nn.Module):
    """优化的深度可分离卷积"""

    def __init__(self, in_channels, out_channels, kernel_size=3):
        super().__init__()

        # 深度卷积
        self.depthwise = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size,
            groups=in_channels, padding=kernel_size // 2
        )
        self.bn1 = torch.nn.BatchNorm2d(in_channels)

        # 逐点卷积
        self.pointwise = torch.nn.Conv2d(
            in_channels, out_channels, 1
        )
        self.bn2 = torch.nn.BatchNorm2d(out_channels)

    def forward(self, x):
        # 使用NPU融合算子
        x = torch_npu.npu_fused_depthwise_conv_bn_relu(
            x,
            self.depthwise.weight,
            self.bn1.weight,
            self.bn1.bias,
            self.bn1.running_mean,
            self.bn1.running_var,
            1,  # stride
            [self.depthwise.padding[0], self.depthwise.padding[0]],  # padding
            1,  # dilation
            1,  # groups
        )

        x = torch_npu.npu_fused_conv_bn_relu(
            x,
            self.pointwise.weight,
            self.bn2.weight,
            self.bn2.bias,
            self.bn2.running_mean,
            self.bn2.running_var
        )

        return x

3D注意力机制优化

"""
优化3D空间注意力机制
"""
class SpatialAttention3D(torch.nn.Module):
    """3D空间注意力模块"""

    def __init__(self, in_channels):
        super().__init__()

        # 空间注意力分支
        self.conv_spatial = torch.nn.Conv3d(
            in_channels, 1, kernel_size=1
        )

        # 通道注意力分支
        self.fc_channel = torch.nn.Sequential(
            torch.nn.AdaptiveAvgPool3d(1),
            torch.nn.Conv3d(in_channels, in_channels // 4, 1),
            torch.nn.ReLU(inplace=True),
            torch.nn.Conv3d(in_channels // 4, in_channels, 1),
            torch.nn.Sigmoid()
        )

    def forward(self, x):
        # 通道注意力
        channel_attn = self.fc_channel(x)
        x = x * channel_attn

        # 空间注意力
        spatial_attn = torch.sigmoid(self.conv_spatial(x))
        x = x * spatial_attn

        return x

点云特征提取优化

"""
优化点云特征提取
"""
class OptimizedPointFeatureExtractor(torch.nn.Module):
    """优化的点云特征提取器"""

    def __init__(self, input_channels=3, feature_dim=128):
        super().__init__()

        # 使用融合的MLP
        self.mlp1 = torch.nn.Sequential(
            torch.nn.Conv2d(input_channels, 64, 1),
            torch.nn.BatchNorm2d(64),
            torch.nn.ReLU()
        )
        self.mlp2 = torch.nn.Sequential(
            torch.nn.Conv2d(64, 128, 1),
            torch.nn.BatchNorm2d(128),
            torch.nn.ReLU()
        )

    def forward(self, points):
        # points: [B, N, C]
        batch_size, num_points, _ = points.shape

        # 转换为[B, C, N, 1]便于卷积
        x = points.transpose(1, 2).unsqueeze(-1)

        # 使用NPU优化的卷积
        x = torch_npu.npu_fused_conv_bn_relu(
            x,
            self.mlp1[0].weight,
            self.mlp1[1].weight,
            self.mlp1[1].bias,
            self.mlp1[1].running_mean,
            self.mlp1[1].running_var
        )

        x = torch_npu.npu_fused_conv_bn_relu(
            x,
            self.mlp2[0].weight,
            self.mlp2[1].weight,
            self.mlp2[1].bias,
            self.mlp2[1].running_mean,
            self.mlp2[1].running_var
        )

        # 最大池化
        x = x.max(dim=2)[0].squeeze(-1)

        return x

模型部署实践

深度估计模型部署

"""
深度估计模型的端到端部署
"""
import torch
import torch_npu
from PIL import Image
import numpy as np

class DepthEstimationDeployment:
    """深度估计模型部署类"""

    def __init__(self, model_path="midas.om"):
        # 初始化NPU
        torch_npu.npu.set_device("npu:0")

        # 加载模型
        self.model = self._load_model(model_path)
        self.model.eval()

        # 初始化预处理器
        self.preprocessor = DepthPreprocessor()

        # 初始化后处理器
        self.postprocessor = DepthPostprocessor()

    def _load_model(self, model_path):
        """加载OM模型"""
        model = torch.jit.load(model_path)
        return model.to("npu:0")

    def infer(self, image_path):
        """执行深度估计推理"""

        # 1. 图像预处理
        image = Image.open(image_path)
        image_tensor = self.preprocessor.process(image)
        image_tensor = image_tensor.to("npu:0")

        # 2. 执行推理
        with torch.no_grad():
            with torch_npu.npu FORMAT("NHWC"):
                with torch.cuda.amp.autocast():
                    depth_map = self.model(image_tensor)

        # 3. 深度图后处理
        depth_map = self.postprocessor.process(depth_map)

        return depth_map

class DepthPreprocessor:
    """深度估计图像预处理器"""

    def __init__(self, target_size=(384, 384)):
        self.target_size = target_size

    def process(self, image):
        # 调整大小
        image = image.resize(self.target_size, Image.BILINEAR)

        # 转换为tensor
        image = np.array(image).astype(np.float32) / 255.0
        image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0)

        return image

class DepthPostprocessor:
    """深度估计结果后处理器"""

    def __init__(self, min_depth=0.1, max_depth=10.0):
        self.min_depth = min_depth
        self.max_depth = max_depth

    def process(self, depth_map):
        # 转换到CPU
        depth_map = depth_map.cpu().numpy().squeeze()

        # 归一化到有效范围
        depth_map = np.clip(depth_map, self.min_depth, self.max_depth)

        # 可选：应用中值滤波减少噪声
        # from scipy.ndimage import median_filter
        # depth_map = median_filter(depth_map, size=5)

        return depth_map

点云分割模型部署

"""
点云语义分割模型部署
"""
class PointCloudSegmentationDeployment:
    """点云语义分割部署类"""

    def __init__(self, model_path="pointnetpp.om", num_classes=40):
        torch_npu.npu.set_device("npu:0")

        self.model = self._load_model(model_path)
        self.model.eval()

        self.num_classes = num_classes
        self.preprocessor = PointCloudPreprocessor(num_points=2048)

    def _load_model(self, model_path):
        model = torch.jit.load(model_path)
        return model.to("npu:0")

    def infer(self, points):
        """
        points: numpy array [N, 3] 或 [N, 6] (xyz + rgb)
        """
        # 预处理
        points_tensor = self.preprocessor.process(points)
        points_tensor = points_tensor.to("npu:0")

        # 推理
        with torch.no_grad():
            with torch.cuda.amp.autocast():
                predictions = self.model(points_tensor)

        # 后处理
        labels = predictions.argmax(dim=-1).cpu().numpy()

        return labels

class PointCloudPreprocessor:
    """点云预处理器"""

    def __init__(self, num_points=2048):
        self.num_points = num_points

    def process(self, points):
        """
        points: numpy array [N, C]
        返回: torch tensor [1, num_points, C]
        """
        points = torch.from_numpy(points).float()

        # 采样或填充到固定数量
        if points.shape[0] > self.num_points:
            # 随机采样
            indices = torch.randperm(points.shape[0])[:self.num_points]
            points = points[indices]
        elif points.shape[0] < self.num_points:
            # 重复填充
            repeat_times = (self.num_points // points.shape[0]) + 1
            points = points.repeat(repeat_times, 1)[:self.num_points]

        # 归一化
        points[:, :3] = self.normalize_xyz(points[:, :3])

        # 添加batch维度
        points = points.unsqueeze(0)

        return points

    @staticmethod
    def normalize_xyz(xyz):
        """归一化xyz坐标"""
        centroid = xyz.mean(dim=0, keepdim=True)
        xyz = xyz - centroid
        max_distance = torch.sqrt((xyz ** 2).sum(dim=1)).max()
        xyz = xyz / max_distance
        return xyz

3D目标检测部署

"""
3D目标检测模型部署（以VoteNet为例）
"""
class Object3DDetectionDeployment:
    """3D目标检测部署类"""

    def __init__(self, model_path="votenet.om"):
        torch_npu.npu.set_device("npu:0")

        self.model = self._load_model(model_path)
        self.model.eval()

        self.preprocessor = PointCloudPreprocessor(num_points=40000)
        self.postprocessor = DetectionPostprocessor(
            score_threshold=0.5,
            nms_threshold=0.25
        )

    def infer(self, points):
        """
        执行3D目标检测推理

        返回:
            detections: 检测结果列表
            每个检测结果包含:
            - class_id: 类别ID
            - score: 置信度
            - bbox_3d: 3D边界框 (center_x, center_y, center_z,
                                   size_x, size_y, size_z,
                                   heading)
        """
        # 预处理
        points_tensor = self.preprocessor.process(points)
        points_tensor = points_tensor.to("npu:0")

        # 推理
        with torch.no_grad():
            with torch.cuda.amp.autocast():
                predictions = self.model(points_tensor)

        # 后处理
        detections = self.postprocessor.process(predictions)

        return detections

class DetectionPostprocessor:
    """检测结果后处理器"""

    def __init__(self, score_threshold=0.5, nms_threshold=0.25):
        self.score_threshold = score_threshold
        self.nms_threshold = nms_threshold

    def process(self, predictions):
        """
        处理模型预测输出

        predictions:
            - vote_xyz: [B, N, 3]
            - vote_scores: [B, N, num_classes]
            - bbox_preds: [B, N, num_classes, 3+4+1]
        """
        vote_xyz = predictions['vote_xyz'][0].cpu().numpy()
        vote_scores = predictions['vote_scores'][0].cpu().numpy()
        bbox_preds = predictions['bbox_preds'][0].cpu().numpy()

        detections = []

        for i in range(vote_xyz.shape[0]):
            max_score = vote_scores[i].max()
            if max_score < self.score_threshold:
                continue

            class_id = vote_scores[i].argmax()
            bbox = bbox_preds[i, class_id]

            # 解析3D边界框
            center = bbox[:3]
            size = bbox[3:6]
            heading = bbox[6]

            detections.append({
                'class_id': int(class_id),
                'score': float(max_score),
                'bbox_3d': {
                    'center': center.tolist(),
                    'size': size.tolist(),
                    'heading': float(heading)
                }
            })

        # NMS去重
        detections = self.nms(detections)

        return detections

    def nms(self, detections):
        """3D NMS"""
        # 按分数排序
        detections.sort(key=lambda x: x['score'], reverse=True)

        keep = []
        while detections:
            # 保留最高分的检测
            keep.append(detections[0])

            # 移除与当前检测重叠高的检测
            detections = [
                d for d in detections[1:]
                if self.compute_iou_3d(d, detections[0]) < self.nms_threshold
            ]

        return keep

    @staticmethod
    def compute_iou_3d(det1, det2):
        """计算两个3D边界框的IoU"""
        # 简化版本，实际应该使用3D IoU
        box1 = det1['bbox_3d']
        box2 = det2['bbox_3d']

        # 计算中心距离
        center1 = np.array(box1['center'])
        center2 = np.array(box2['center'])
        distance = np.linalg.norm(center1 - center2)

        # 简单的距离阈值判断
        size1 = np.array(box1['size'])
        max_size1 = np.max(size1)

        return 1.0 if distance < max_size1 * 0.5 else 0.0

性能优化技巧

1. 输入数据优化

"""
输入数据优化：批量处理和预加载
"""
import threading
import queue

class DataPipeline:
    """数据预加载管道"""

    def __init__(self, dataset, batch_size=4, num_workers=4):
        self.dataset = dataset
        self.batch_size = batch_size
        self.num_workers = num_workers
        self.queue = queue.Queue(maxsize=16)
        self.workers = []
        self.running = False

    def start(self):
        """启动数据加载管道"""
        self.running = True

        for worker_id in range(self.num_workers):
            worker = threading.Thread(
                target=self._worker_loop,
                args=(worker_id,)
            )
            worker.start()
            self.workers.append(worker)

    def stop(self):
        """停止数据加载"""
        self.running = False
        for worker in self.workers:
            worker.join()

    def _worker_loop(self, worker_id):
        """工作线程循环"""
        while self.running:
            try:
                # 获取数据索引
                indices = self.dataset.get_batch_indices(
                    self.batch_size, worker_id
                )

                # 加载和预处理数据
                batch = []
                for idx in indices:
                    data = self.dataset.load_data(idx)
                    processed = self.dataset.preprocess(data)
                    batch.append(processed)

                # 合并batch
                batch_tensor = torch.stack(batch, dim=0)

                # 放入队列
                self.queue.put(batch_tensor)

            except Exception as e:
                print(f"Worker {worker_id} error: {e}")

    def get_batch(self):
        """获取一个batch数据"""
        return self.queue.get()

2. 混合精度推理

"""
混合精度推理配置
"""
class MixedPrecisionInference:
    """混合精度推理包装器"""

    def __init__(self, model, amp_level='O2'):
        self.model = model
        self.amp_level = amp_level

        # 配置混合精度
        torch_npu.npu.set_amp_level(self.amp_level)

    def infer(self, x):
        """使用混合精度执行推理"""
        with torch.cuda.amp.autocast():
            output = self.model(x)
        return output

3. 动态Shape优化

"""
动态Shape优化策略
"""
class DynamicShapeOptimizer:
    """动态Shape优化器"""

    def __init__(self, model):
        self.model = model
        self.compiled_models = {}

    def infer(self, x):
        """根据输入shape选择优化后的模型"""
        shape_key = tuple(x.shape)

        # 检查是否有针对该shape优化的模型
        if shape_key not in self.compiled_models:
            # 为当前shape编译优化模型
            self.compiled_models[shape_key] = self._compile_for_shape(
                x.shape
            )

        # 使用优化后的模型执行
        model = self.compiled_models[shape_key]
        with torch.no_grad():
            output = model(x)

        return output

    def _compile_for_shape(self, shape):
        """为特定shape编译优化模型"""
        # 使用TorchScript优化
        example_input = torch.randn(*shape).to("npu:0")
        traced_model = torch.jit.trace(self.model, example_input)
        traced_model = torch.jit.freeze(traced_model)

        return traced_model.to("npu:0")

实际应用案例

自动驾驶场景理解

"""
自动驾驶场景理解应用
"""
class AutonomousDrivingSceneUnderstanding:
    """自动驾驶场景理解系统"""

    def __init__(self):
        # 深度估计
        self.depth_estimator = DepthEstimationDeployment()

        # 3D目标检测
        self.object_detector = Object3DDetectionDeployment()

        # 语义分割
        self.semantic_segmenter = SemanticSegmentationDeployment()

    def understand_scene(self, camera_image):
        """理解驾驶场景"""

        # 1. 深度估计
        depth_map = self.depth_estimator.infer(camera_image)

        # 2. 3D目标检测
        objects_3d = self.object_detector.infer_from_image(
            camera_image, depth_map
        )

        # 3. 语义分割
        segmentation = self.semantic_segmenter.infer(camera_image)

        # 4. 融合理解
        scene_understanding = {
            'depth_map': depth_map,
            'objects_3d': objects_3d,
            'segmentation': segmentation,
            'drivable_area': self.extract_drivable_area(
                depth_map, segmentation
            ),
            'collision_risk': self.assess_collision_risk(
                objects_3d, depth_map
            )
        }

        return scene_understanding

    def extract_drivable_area(self, depth_map, segmentation):
        """提取可驾驶区域"""
        # 使用语义分割识别道路区域
        road_mask = segmentation == 7  # 道路类别

        # 结合深度信息估计道路几何
        road_points = self.depth_to_3d_points(depth_map, road_mask)

        # 平面拟合得到可驾驶区域
        drivable_area = self.fit_road_plane(road_points)

        return drivable_area

    def assess_collision_risk(self, objects_3d, depth_map):
        """评估碰撞风险"""
        collision_risks = []

        for obj in objects_3d:
            # 计算物体到自车的距离
            distance = obj['bbox_3d']['center'][2]  # Z方向距离

            # 评估风险等级
            if distance < 5:
                risk = 'HIGH'
            elif distance < 15:
                risk = 'MEDIUM'
            else:
                risk = 'LOW'

            collision_risks.append({
                'object': obj,
                'distance': distance,
                'risk_level': risk
            })

        return collision_risks

室内机器人导航

"""
室内机器人导航应用
"""
class IndoorRobotNavigation:
    """室内机器人导航系统"""

    def __init__(self):
        # 3D场景重建
        self.scene_reconstructor = SceneReconstructor()

        # 语义分割
        self.semantic_segmenter = SemanticSegmentationDeployment()

        # 目标检测
        self.object_detector = Object3DDetectionDeployment()

        # 路径规划
        self.path_planner = PathPlanner()

    def navigate_to_goal(self, current_view, goal_description):
        """导航到目标位置"""

        # 1. 场景重建
        scene_map = self.scene_reconstructor.update_map(current_view)

        # 2. 语义理解
        semantics = self.semantic_segmenter.infer(current_view)

        # 3. 目标检测
        objects = self.object_detector.infer_from_view(current_view)

        # 4. 定位目标
        goal_position = self.locate_goal(
            goal_description, objects, semantics
        )

        # 5. 路径规划
        path = self.path_planner.plan_path(
            start=self.get_robot_position(),
            goal=goal_position,
            map=scene_map,
            obstacles=objects
        )

        return path

    def locate_goal(self, description, objects, semantics):
        """根据描述定位目标位置"""
        # 简化实现：匹配关键词
        keywords = description.lower().split()

        for obj in objects:
            obj_name = self.get_object_class_name(obj['class_id'])
            if any(kw in obj_name for kw in keywords):
                return obj['bbox_3d']['center']

        return None

测试与验证

精度验证

"""
精度验证脚本
"""
def evaluate_depth_estimation(model, test_dataset):
    """评估深度估计模型精度"""

    metrics = {
        'abs_rel': [],
        'sq_rel': [],
        'rmse': [],
        'rmse_log': [],
        'a1': [],
        'a2': [],
        'a3': []
    }

    for data in test_dataset:
        image = data['image']
        gt_depth = data['depth']

        # 推理
        pred_depth = model.infer(image)

        # 计算误差
        valid_mask = (gt_depth > 0) & (gt_depth < 80)

        abs_rel = np.mean(np.abs(gt_depth[valid_mask] - pred_depth[valid_mask]) / gt_depth[valid_mask])
        sq_rel = np.mean(((gt_depth[valid_mask] - pred_depth[valid_mask]) ** 2) / gt_depth[valid_mask])

        rmse = np.sqrt(np.mean((gt_depth[valid_mask] - pred_depth[valid_mask]) ** 2))
        rmse_log = np.sqrt(np.mean((np.log(gt_depth[valid_mask]) - np.log(pred_depth[valid_mask])) ** 2))

        # 准确率阈值
        max_ratio = np.maximum(gt_depth[valid_mask] / pred_depth[valid_mask],
                              pred_depth[valid_mask] / gt_depth[valid_mask])
        a1 = (max_ratio < 1.25).mean()
        a2 = (max_ratio < 1.25 ** 2).mean()
        a3 = (max_ratio < 1.25 ** 3).mean()

        metrics['abs_rel'].append(abs_rel)
        metrics['sq_rel'].append(sq_rel)
        metrics['rmse'].append(rmse)
        metrics['rmse_log'].append(rmse_log)
        metrics['a1'].append(a1)
        metrics['a2'].append(a2)
        metrics['a3'].append(a3)

    # 计算平均指标
    for key in metrics:
        metrics[key] = np.mean(metrics[key])

    return metrics

性能基准测试

"""
性能基准测试
"""
import time

def benchmark_spatial_intelligence_model(model, test_data, num_iterations=100):
    """基准测试空间智能模型性能"""

    latencies = []
    throughputs = []

    for _ in range(num_iterations):
        # 随机选择测试数据
        data = test_data.get_random_sample()

        # 预热
        for _ in range(5):
            _ = model.infer(data)

        # 测试
        start = time.time()
        output = model.infer(data)
        torch_npu.npu.synchronize()
        end = time.time()

        latency = end - start
        latencies.append(latency)
        throughputs.append(1 / latency)

    # 统计
    results = {
        'avg_latency_ms': np.mean(latencies) * 1000,
        'p50_latency_ms': np.percentile(latencies, 50) * 1000,
        'p95_latency_ms': np.percentile(latencies, 95) * 1000,
        'p99_latency_ms': np.percentile(latencies, 99) * 1000,
        'avg_throughput_fps': np.mean(throughputs)
    }

    return results

总结

cann-recipes-spatial-intelligence仓库为空间智能AI模型在昇腾平台上的部署提供了完整的优化方案。通过算子优化、模型转换、推理优化等技术手段，实现了深度估计、3D重建、点云处理等空间智能模型的高效部署。

关键优化技术包括：

• 深度可分离卷积等算子的融合优化
• 3D注意力机制的高效实现
• 混合精度推理平衡精度与性能
• 动态Shape优化适应不同输入
• 流水线并行提升系统吞吐

随着空间智能应用场景的不断扩展，CANN社区将持续提供针对新模型和新场景的优化方案，为空间智能技术的实际应用提供强大的算力支持。

参考资料

• CANN组织链接：https://atomgit.com/cann
• cann-recipes-spatial-intelligence仓库链接：https://atomgit.com/cann/cann-recipes-spatial-intelligence
• 深度解读昇腾CANN动态Shape图调度加速技术
• 昇腾CANN 7.0 黑科技：大模型推理部署技术解密