CANN端侧部署实践

CANN组织链接：https://atomgit.com/cann
CANN community仓库链接：https://atomgit.com/cann/community

一、端侧部署概述

1.1 端侧AI应用场景

端侧AI是指将AI模型部署在终端设备上，如手机、相机、IoT设备等。CANN为端侧部署提供了完整的解决方案。

1.1.1 主要应用场景

• 智能摄像头：人脸识别、行为分析
• 智能手机：图像增强、语音助手
• 自动驾驶：环境感知、决策控制
• 工业检测：缺陷检测、质量判断
• 医疗设备：影像分析、辅助诊断

1.2 端侧部署挑战

1.2.1 资源限制

• 计算能力有限
• 内存容量较小
• 功耗限制严格
• 存储空间有限

1.2.2 性能要求

• 实时性要求高
• 低延迟响应
• 高吞吐量需求

1.3 CANN端侧部署优势

• 轻量级运行时
• 高效推理引擎
• 完善的工具链
• 多硬件平台支持

二、端侧环境准备

2.1 硬件平台

2.1.1 Atlas 200 DK开发者套件

Atlas 200 DK是华为推出的端侧AI开发套件，基于Ascend 310芯片。

# 查看设备信息
npu-smi info

# 预期输出
# +----------------------+---------------+-------------------------------------------------------+
# | NPU     Name         Health        Power(mW)        Temp(C)         Hugepages-Usage(page)  |
# +----------------------+---------------+-------------------------------------------------------+
# | 0       310          OK            15000            42              0   / 0                  |
# +----------------------+---------------+-------------------------------------------------------+

2.1.2 设备初始化

# 设置设备性能模式
npu-smi set -t performance -i 0

# 查看设备属性
npu-smi info -t product -i 0

2.2 软件环境

2.2.1 安装CANN运行时

# 下载CANN运行时包
wget https://repo.huaweicloud.com/kunpeng/pool/yuming/Ascend/ascend-repo/latest/ascend-repo.tar.gz

# 解压安装
tar -zxvf ascend-repo.tar.gz
cd ascend-repo

# 安装运行时
sudo apt-get install ascend-runtime_<version>_ubuntu18.04.aarch64.rpm

# 配置环境变量
source /usr/local/Ascend/ascend-toolkit/set_env.sh

2.2.2 交叉编译环境

# 安装交叉编译工具链
sudo apt-get install gcc-aarch64-linux-gnu g++-aarch64-linux-gnu

# 设置交叉编译环境
export CC=aarch64-linux-gnu-gcc
export CXX=aarch64-linux-gnu-g++

三、模型转换与优化

3.1 端侧模型转换

3.1.1 转换为端侧格式

# 转换为Atlas 200 DK格式
atc --framework=5 \
    --model=model.onnx \
    --output=model_310 \
    --soc_version=Ascend310 \
    --input_format=NCHW \
    --input_shape="input:1,3,224,224" \
    --enable_small_channel=1 \
    --opt_level=2

3.1.2 端侧优化选项

# 启用端侧优化
atc --framework=5 \
    --model=model.onnx \
    --output=model_optimized \
    --soc_version=Ascend310 \
    --input_format=NCHW \
    --enable_small_channel=1 \
    --op_type_map="conv2d:fp16,add:fp16" \
    --enable_compress_weight=1 \
    --compress_weight_config=weight_compress.ini

3.2 模型压缩

3.2.1 权重压缩配置

# weight_compress.ini
[weight_compress]
compress_mode=4
enable_mid_result_compress=1
compress_mid_result_num=10

3.2.2 量化模型

import torch
import torch.quantization as quant

# 动态量化
def quantize_model(model):
    """量化模型"""
    # 配置量化
    model.qconfig = quant.get_default_qconfig('fbgemm')

    # 准备量化
    quant.prepare(model, inplace=True)

    # 校准（使用代表性数据）
    # ... 校准代码 ...

    # 转换为量化模型
    quant.convert(model, inplace=True)

    return model

# 使用示例
quantized_model = quantize_model(model)

四、端侧推理应用开发

4.1 基础推理应用

4.1.1 C语言实现

#include <stdio.h>
#include <stdlib.h>
#include "acl/acl.h"

#define ACL_RETCODE_SUCCESS 0
#define MODEL_PATH "model/resnet50_310.om"

// 全局变量
aclrtContext g_context = NULL;
uint32_t g_modelId = 0;
aclmdlDesc *g_modelDesc = NULL;

// 初始化资源
int InitResource() {
    const char *aclConfigPath = "../data/config/acl.json";
    aclError ret = aclInit(aclConfigPath);
    if (ret != ACL_SUCCESS) {
        printf("acl init failed, ret = %d\n", ret);
        return -1;
    }

    ret = aclrtSetDevice(0);
    if (ret != ACL_SUCCESS) {
        printf("set device failed, ret = %d\n", ret);
        return -1;
    }

    ret = aclrtCreateContext(&g_context, 0);
    if (ret != ACL_SUCCESS) {
        printf("create context failed, ret = %d\n", ret);
        return -1;
    }

    // 加载模型
    ret = aclmdlLoadFromFile(MODEL_PATH, &g_modelId);
    if (ret != ACL_SUCCESS) {
        printf("load model failed, ret = %d\n", ret);
        return -1;
    }

    // 创建模型描述
    g_modelDesc = aclmdlCreateDesc();
    ret = aclmdlGetDesc(g_modelDesc, g_modelId);
    if (ret != ACL_SUCCESS) {
        printf("get model desc failed, ret = %d\n", ret);
        return -1;
    }

    printf("init resource success\n");
    return 0;
}

// 执行推理
int ExecuteInference(const char *imagePath) {
    // 读取并预处理图像
    // ... 图像读取代码 ...

    // 准备输入
    size_t inputSize = aclmdlGetInputSizeByIndex(g_modelDesc, 0);
    void *inputBuffer = NULL;
    aclError ret = aclrtMalloc(&inputBuffer, inputSize, ACL_MEM_MALLOC_HUGE_FIRST);
    if (ret != ACL_SUCCESS) {
        printf("malloc input buffer failed, ret = %d\n", ret);
        return -1;
    }

    // 拷贝数据
    ret = aclrtMemcpy(inputBuffer, inputSize, imageData, inputSize, ACL_MEMCPY_HOST_TO_DEVICE);
    if (ret != ACL_SUCCESS) {
        printf("memcpy input failed, ret = %d\n", ret);
        aclrtFree(inputBuffer);
        return -1;
    }

    // 创建数据集
    aclmdlDataset *inputDataset = aclmdlCreateDataset();
    aclDataBuffer *inputDataBuffer = aclCreateDataBuffer(inputBuffer, inputSize);
    aclmdlAddDatasetBuffer(inputDataset, inputDataBuffer);

    // 准备输出
    size_t outputSize = aclmdlGetOutputSizeByIndex(g_modelDesc, 0);
    void *outputBuffer = NULL;
    ret = aclrtMalloc(&outputBuffer, outputSize, ACL_MEM_MALLOC_HUGE_FIRST);
    if (ret != ACL_SUCCESS) {
        printf("malloc output buffer failed, ret = %d\n", ret);
        aclrtFree(inputBuffer);
        return -1;
    }

    aclmdlDataset *outputDataset = aclmdlCreateDataset();
    aclDataBuffer *outputDataBuffer = aclCreateDataBuffer(outputBuffer, outputSize);
    aclmdlAddDatasetBuffer(outputDataset, outputDataBuffer);

    // 执行推理
    ret = aclmdlExecute(g_modelId, inputDataset, outputDataset);
    if (ret != ACL_SUCCESS) {
        printf("execute inference failed, ret = %d\n", ret);
        aclrtFree(inputBuffer);
        aclrtFree(outputBuffer);
        return -1;
    }

    // 获取输出
    void *outputData = NULL;
    ret = aclrtMallocHost(&outputData, outputSize);
    if (ret != ACL_SUCCESS) {
        printf("malloc host output failed, ret = %d\n", ret);
        aclrtFree(inputBuffer);
        aclrtFree(outputBuffer);
        return -1;
    }

    ret = aclrtMemcpy(outputData, outputSize, outputBuffer, outputSize, ACL_MEMCPY_DEVICE_TO_HOST);
    if (ret != ACL_SUCCESS) {
        printf("memcpy output failed, ret = %d\n", ret);
        aclrtFree(inputBuffer);
        aclrtFree(outputBuffer);
        aclrtFreeHost(outputData);
        return -1;
    }

    // 处理输出结果
    // ... 后处理代码 ...

    // 清理资源
    aclrtFree(inputBuffer);
    aclrtFree(outputBuffer);
    aclrtFreeHost(outputData);
    aclmdlDestroyDataset(inputDataset);
    aclmdlDestroyDataset(outputDataset);

    return 0;
}

// 释放资源
void ReleaseResource() {
    if (g_modelDesc) {
        aclmdlDestroyDesc(g_modelDesc);
    }
    if (g_modelId) {
        aclmdlUnload(g_modelId);
    }
    if (g_context) {
        aclrtDestroyContext(g_context);
    }
    aclrtResetDevice(0);
    aclFinalize();
}

int main(int argc, char *argv[]) {
    if (argc < 2) {
        printf("Usage: %s <image_path>\n", argv[0]);
        return -1;
    }

    // 初始化
    if (InitResource() != 0) {
        return -1;
    }

    // 执行推理
    if (ExecuteInference(argv[1]) != 0) {
        ReleaseResource();
        return -1;
    }

    // 释放资源
    ReleaseResource();

    return 0;
}

4.1.2 Python实现

import acl
import numpy as np
import cv2

class EdgeInference:
    def __init__(self, model_path, device_id=0):
        """端侧推理类"""
        self.model_path = model_path
        self.device_id = device_id
        self._init_resource()
        self._load_model()

    def _init_resource(self):
        """初始化ACL资源"""
        # 初始化ACL
        ret = acl.init()
        if ret != 0:
            raise Exception(f"acl init failed, ret = {ret}")

        # 设置设备
        ret = acl.rt.set_device(self.device_id)
        if (ret != 0):
            raise Exception(f"set device failed, ret = {ret}")

        # 创建上下文
        self.context, ret = acl.rt.create_context(self.device_id)
        if ret != 0:
            raise Exception(f"create context failed, ret = {ret}")

    def _load_model(self):
        """加载模型"""
        self.model_id, ret = acl.mdl.load_from_file(self.model_path)
        if ret != 0:
            raise Exception(f"load model failed, ret = {ret}")

        self.model_desc = acl.mdl.create_desc()
        ret = acl.mdl.get_desc(self.model_desc, self.model_id)
        if ret != 0:
            raise Exception(f"get model desc failed, ret = {ret}")

    def preprocess(self, image_path):
        """预处理图像"""
        image = cv2.imread(image_path)
        image = cv2.resize(image, (224, 224))
        image = image.astype(np.float32) / 255.0
        image = np.transpose(image, (2, 0, 1))
        image = np.expand_dims(image, axis=0)
        return image.astype(np.float32)

    def inference(self, image_path):
        """执行推理"""
        # 预处理
        input_data = self.preprocess(image_path)

        # 准备输入
        input_size = acl.mdl.get_input_size_by_index(self.model_desc, 0)
        input_buffer, ret = acl.rt.malloc(input_size, 0)

        bytes_data = input_data.tobytes()
        ret = acl.rt.memcpy(input_buffer, input_size, bytes_data, input_size, 1)

        input_dataset = acl.mdl.create_dataset()
        data_buffer = acl.create_data_buffer(input_buffer, input_size)
        acl.mdl.add_dataset_buffer(input_dataset, data_buffer)

        # 准备输出
        output_size = acl.mdl.get_output_size_by_index(self.model_desc, 0)
        output_buffer, ret = acl.rt.malloc(output_size, 0)
        output_dataset = acl.mdl.create_dataset()
        output_data_buffer = acl.create_data_buffer(output_buffer, output_size)
        acl.mdl.add_dataset_buffer(output_dataset, output_data_buffer)

        # 执行推理
        ret = acl.mdl.execute(self.model_id, input_dataset, output_dataset)

        # 获取输出
        output_ptr = acl.get_data_buffer_addr(output_data_buffer)
        output_data = bytearray(output_size)
        ret = acl.rt.memcpy(output_data, output_size, output_ptr, output_size, 0)

        output_array = np.frombuffer(output_data, dtype=np.float32)

        # 清理
        acl.rt.free(input_buffer)
        acl.rt.free(output_buffer)
        acl.mdl.destroy_dataset(input_dataset)
        acl.mdl.destroy_dataset(output_dataset)

        return output_array

    def __del__(self):
        """释放资源"""
        if hasattr(self, 'model_desc'):
            acl.mdl.destroy_desc(self.model_desc)
        if hasattr(self, 'model_id'):
            acl.mdl.unload(self.model_id)
        if hasattr(self, 'context'):
            acl.rt.destroy_context(self.context)
        acl.rt.reset_device(self.device_id)
        acl.finalize()

# 使用示例
if __name__ == "__main__":
    inferencer = EdgeInference("model/resnet50_310.om")
    result = inferencer.inference("test_image.jpg")
    print(f"Prediction: {result.argmax()}")

4.2 视频流处理应用

import cv2
import acl
import time

class VideoStreamProcessor:
    def __init__(self, model_path, device_id=0):
        """视频流处理器"""
        self.inference = EdgeInference(model_path, device_id)
        self.running = False

    def process_stream(self, camera_id=0):
        """处理视频流"""
        cap = cv2.VideoCapture(camera_id)
        self.running = True

        frame_count = 0
        total_time = 0

        while self.running:
            ret, frame = cap.read()
            if not ret:
                break

            start_time = time.time()

            # 保存临时帧
            temp_path = f"temp_frame_{frame_count}.jpg"
            cv2.imwrite(temp_path, frame)

            # 推理
            result = self.inference.inference(temp_path)

            # 后处理并显示
            # ... 后处理代码 ...
            cv2.imshow("Edge AI", frame)

            # 计算FPS
            elapsed = time.time() - start_time
            total_time += elapsed
            frame_count += 1

            fps = frame_count / total_time if total_time > 0 else 0
            print(f"FPS: {fps:.2f}")

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break

        cap.release()
        cv2.destroyAllWindows()

    def stop(self):
        """停止处理"""
        self.running = False

# 使用示例
if __name__ == "__main__":
    processor = VideoStreamProcessor("model/resnet50_310.om")
    processor.process_stream(0)

五、端侧性能优化

5.1 内存优化

5.1.1 内存池管理

class EdgeMemoryPool:
    def __init__(self, input_size, output_size, pool_size=5):
        """端侧内存池"""
        self.input_size = input_size
        self.output_size = output_size
        self.pool_size = pool_size

        # 预分配内存
        self.input_pool = []
        self.output_pool = []

        for _ in range(pool_size):
            input_buf, ret = acl.rt.malloc(input_size, 0)
            output_buf, ret = acl.rt.malloc(output_size, 0)
            self.input_pool.append(input_buf)
            self.output_pool.append(output_buf)

        self.index = 0

    def get_buffer(self):
        """获取缓冲区"""
        idx = self.index % self.pool_size
        self.index += 1
        return self.input_pool[idx], self.output_pool[idx]

    def release(self):
        """释放所有内存"""
        for buf in self.input_pool + self.output_pool:
            acl.rt.free(buf)
        self.input_pool.clear()
        self.output_pool.clear()

5.1.2 数据复用

def optimize_memory_usage():
    """优化内存使用"""
    # 复用输入输出缓冲区
    input_buffer, output_buffer = memory_pool.get_buffer()

    # 拷贝数据
    acl.rt.memcpy(input_buffer, input_size, data, input_size, 1)

    # 推理
    acl.mdl.execute(model_id, input_dataset, output_dataset)

    # 获取结果后立即释放
    # ... 处理结果 ...

    # 缓冲区自动返回池中

5.2 功耗优化

class PowerManager:
    def __init__(self, device_id=0):
        """功耗管理器"""
        self.device_id = device_id

    def set_performance_mode(self, mode="performance"):
        """设置性能模式"""
        if mode == "performance":
            # 高性能模式
            acl.rt.set_device(self.device_id)
        elif mode == "power_save":
            # 节能模式
            # 降低频率等
            pass

    def get_power_info(self):
        """获取功耗信息"""
        # 使用npu-smi获取功耗信息
        import subprocess
        result = subprocess.run(
            ["npu-smi", "info", "-t", "power", "-i", str(self.device_id)],
            capture_output=True, text=True
        )
        return result.stdout

5.3 实时性优化

class RealtimeProcessor:
    def __init__(self, model_path):
        """实时处理器"""
        self.model_path = model_path
        self.inference = EdgeInference(model_path)
        self.frame_queue = queue.Queue(maxsize=2)

    def capture_thread(self, camera_id):
        """图像采集线程"""
        cap = cv2.VideoCapture(camera_id)

        while True:
            ret, frame = cap.read()
            if not ret:
                break

            # 如果队列满，丢弃最旧的帧
            if self.frame_queue.full():
                self.frame_queue.get_nowait()

            self.frame_queue.put(frame)

        cap.release()

    def inference_thread(self):
        """推理线程"""
        while True:
            frame = self.frame_queue.get()

            # 推理
            temp_path = "temp_frame.jpg"
            cv2.imwrite(temp_path, frame)
            result = self.inference.inference(temp_path)

            # 显示结果
            # ... 显示代码 ...

    def start(self, camera_id=0):
        """启动处理"""
        import threading

        capture_t = threading.Thread(target=self.capture_thread, args=(camera_id,))
        inference_t = threading.Thread(target=self.inference_thread)

        capture_t.start()
        inference_t.start()

        capture_t.join()
        inference_t.join()

六、实际应用案例

6.1 智能摄像头应用

class SmartCamera:
    def __init__(self, detector_model, classifier_model):
        """智能摄像头"""
        self.detector = EdgeInference(detector_model)
        self.classifier = EdgeInference(classifier_model)

    def process_frame(self, frame):
        """处理帧"""
        # 检测人员
        detections = self.detector.inference(frame)

        # 识别每个检测到的人
        results = []
        for det in detections:
            # 裁剪
            crop = self._crop_person(frame, det)
            # 识别
            identity = self.classifier.inference(crop)
            results.append({
                "bbox": det,
                "identity": identity
            })

        return results

    def _crop_person(self, frame, detection):
        """裁剪人员区域"""
        x1, y1, x2, y2 = detection["bbox"]
        return frame[y1:y2, x1:x2]

6.2 工业检测应用

class IndustrialInspector:
    def __init__(self, defect_model):
        """工业检测器"""
        self.model = EdgeInference(defect_model)
        self.threshold = 0.8

    def inspect(self, image_path):
        """检测缺陷"""
        result = self.model.inference(image_path)

        # 判断是否有缺陷
        has_defect = result.max() > self.threshold

        if has_defect:
            # 定位缺陷位置
            defect_location = self._locate_defect(result)

            return {
                "has_defect": True,
                "location": defect_location,
                "confidence": float(result.max())
            }
        else:
            return {
                "has_defect": False,
                "confidence": float(result.max())
            }

    def _locate_defect(self, result):
        """定位缺陷"""
        # 实现缺陷定位逻辑
        return {"x": 100, "y": 200, "w": 50, "h": 50}

七、部署与发布

7.1 打包应用

# 创建部署包
mkdir -p deployment/
cp model/resnet50_310.om deployment/
cp edge_inference deployment/
cp -r lib deployment/

# 打包
tar -czf edge_ai_app.tar.gz deployment/

7.2 远程部署

# 传输到目标设备
scp edge_ai_app.tar.gz root@<device_ip>:/home/

# 在目标设备上解压
ssh root@<device_ip>
cd /home/
tar -xzf edge_ai_app.tar.gz

# 运行应用
cd deployment/
./edge_inference

八、总结

CANN端侧部署提供了完整的解决方案，从模型转换优化到应用开发部署。通过合理的设计和优化，可以在资源受限的端侧设备上实现高效的AI推理。

端侧AI应用正在快速发展，CANN作为昇腾AI处理器的软件栈，为端侧AI应用开发提供了强大的支持。