RKNN Toolkit lite2工具详解与工程应用

ttkwzyttk2026-01-28 12:33

一、RKNN Toolkit lite2介绍

在之前的博客中，有对rknn-toolkit lite2工具进行简要介绍，rknn-toolkit lite2在嵌入式平台上进行模型推理，它主要用来部署已经转换好的rknn模型。使用python接口对模型进行调用，实现模型推理，瑞芯微提供了两种模型部署到嵌入式平台上的工具，一种是RKNPU使用C/C++接口，另一种就是rknn toolkit lite2使用python接口。RKNN-Toolkit Lite2为用户提供板端模型推理的Python接口,方便用户使用Python语言进行AI应用开发。本博文中将详细对rknn toolkit lite2进行介绍

rknn-toolkit lite2工具的具体代码在瑞芯微AI github仓库中https://github.com/airockchip/rknn-toolkit2，可以通过clone的方式或者直接下载zip压缩包文件

文件夹名称 作用
CHANGELOG.TXT 主要记录了工具的版本更新内容
example 存放了rknn-toolkit lite2的示例工程文件
packages 存放了rknn-toolkit lite2的python安装whl文件以及环境依赖文件

二、环境搭建

rknn-toolkit lite2的环境搭建与rkknn-toolkit2工具的搭建流程类似只不过是平台换成了嵌入式平台，主要分为两个部分，第一就是python环境的搭建包括python的安装还有对应依赖模块的板状，第二就是rkknn-toolkit lite2 whl文件的安装，这里的使用的是rk3576，烧录的系统为debain系统，注意如果使用buildroot可能会缺少一些系统依赖导致后续出现奇奇怪怪的问题，这里博主没有深入研究buildroot系统下的情况。

2.1 rknn-toolkit lite2环境的搭建

1、miniconda安装

从anaconda官网上下载miniconda，需要注意的是现在是需要将miniconda安装在rk3576上，所以下载aarch64版本，也就是arm64版

将下载好的miniconda.sh脚本上传到嵌入式平台上，进行安装，之后的安装流程与在X86上安装miniconda的步骤相同，这里就不赘述了，可以参考我之前的rknn toolkit2的博客

2、rknn-toolkit lite2 whl文件安装

在SDK中找到官方提供的rknn-toolkit lite2的whl软件包，这个文件主要用来安装rknn-toolkit lite2 的python模块，具体路径在rknn-toolkit2-master/rknn-toolkit-lite2/packages/下

在安装并配置好的conda环境中，具体conda环境配置可以参考之前的rknn toolkit2的博客，执行如下命令

shell 复制代码 
pip install rknn_toolkit_lite2-2.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

安装完成之后，如下图所示

执行以下命令查看rknn-toolkit lite2 模块是否安装成功

shell 复制代码 
# 先在conda环境中执行python3
python3

# 在python3环境中输入以下命令
from rknnlite.api import RKNNLite
RKNNLite(verbose=True)

如果成功打印处rknn-toolkit-lite2的版本号，说明环境已经安装完成，接下来就可以直接部署python接口的模型推理程序了

三、rknn-toolkit-lite2工具使用与RKNN模型部署

注意使用rknn-toolkit-lite2时，部分API接口与rknn-toolkit2不同，编写应用程序时，需要注意以下两点

1、导入模块不同

python 复制代码 
import numpy as np  
import cv2  
# rknn-toolkit-lite2导入的模块是RKNNLite
from rknnlite.api import RKNNLite

# 将详细的日志信息输出到屏幕,并写到inference.log文件中
rknn_lite = RKNNLite(verbose=True, verbose_file='./inference.log')
# 只在屏幕打印详细的日志信息
rknn_lite = RKNNLite(verbose=True)

rknn_lite.release()

2、init_runtime函数接口不同

在使用rknn-toolkit-lite2时，init_runtime没有target参数，只需要使用core_mask指定运行的NPU模式就行

python 复制代码 
# 初始化运行时环境
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO)
if ret != 0:
print('Init runtime environment failed')
exit(ret)

示例代码如下

python 复制代码 
import numpy as np  
import cv2  
from rknnlite.api import RKNNLite  
import os  
def show_outputs(output):  
    index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)  
    fp = open('./labels.txt', 'r')  
    labels = fp.readlines()  
    top5_str = 'resnet18\n-----TOP 5-----\n'  
    for i in range(5):  
        value = output[index[i]]  
        if value > 0:  
            topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1])  
        else:  
            topi = '[ -1]: 0.0\n'  
        top5_str += topi  
    print(top5_str.strip())  
  
def show_perfs(perfs):  
    perfs = 'perfs: {}\n'.format(perfs)  
    print(perfs)  
  
def softmax(x):  
    return np.exp(x)/sum(np.exp(x))  
  
if __name__ == '__main__':  
  
    # Create RKNN object  
    rknn = RKNNLite(verbose=True)  
  
    rknn.load_rknn(path='./resnet_18.rknn')  
    # Set inputs  
    img = cv2.imread('./space_shuttle_224.jpg')  
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  
    img = np.expand_dims(img, 0)  
  
    # Init runtime environment  
    print('--> Init runtime environment')  
    ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO)  
    if ret != 0:  
        print('Init runtime environment failed!')  
        exit(ret)  
    print('done')  
  
    # Inference  
    print('--> Running model')  
    outputs = rknn.inference(inputs=[img])  
    show_outputs(softmax(np.array(outputs[0][0])))  
    print('done')  
  
    rknn.release()

通过ADB将例程程序运行需要的文件以及模型上传到嵌入式板卡中，激活conda环境，使用以下命令运行程序

shell 复制代码 
wzy@wzy-JiaoLong:~/rknn/rknn-toolkit2-master/rknn-toolkit2/examples/pytorch/resnet18$ adb shell
root@linaro-alip:/# cd rknn/  
root@linaro-alip:/rknn# conda activate rknn  
(rknn) root@linaro-alip:/rknn# ls  
labels.txt  resnet_18.rknn  space_shuttle_224.jpg  test_rknn_lite2.py  
(rknn) root@linaro-alip:/rknn# python3 test_rknn_lite2.py

附上rk3576嵌入式平台上官方例程的运行结果

shell 复制代码 
wzy@wzy-JiaoLong:~/rknn/rknn-toolkit2-master/rknn-toolkit2/examples/pytorch/resnet18$ adb shell
root@linaro-alip:/# cd rknn/  
root@linaro-alip:/rknn# conda activate rknn  
(rknn) root@linaro-alip:/rknn# ls  
labels.txt  resnet_18.rknn  space_shuttle_224.jpg  test_rknn_lite2.py  
(rknn) root@linaro-alip:/rknn# python3 test_rknn_lite2.py
W rknn-toolkit-lite2 version: 2.3.2
W Verbose file path is invalid, debug info will not dump to file.  
--> Init runtime environment  
D target set by user is: None  
D Starting ntp or adb, target soc is RK3576, device id is: None  
I RKNN: [03:36:13.388] RKNN Runtime Information, librknnrt version: 2.3.2 (429f97ae6b@2025-04-09T09:09:27)  
I RKNN: [03:36:13.388] RKNN Driver Information, version: 0.9.8  
I RKNN: [03:36:13.388] RKNN Model Information, version: 6, toolkit version: 2.3.2(compiler version: 2.3.2 (e045de294f@2025-04-07T19:48:25)), target: RKNPU f2, target platform: rk3576, fram  
ework name: PyTorch, framework layout: NCHW, model inference type: static_shape  
D RKNN: [03:36:13.389] allocated memory, name: task, virt addr: 0x7fb7ea5000, dma addr: 0xfffff000, obj addr: 0xffffff80c921a000, size: 4080, aligned size: 4096, fd: 4, handle: 1, flags: 0  
x40b, gem name: 1, iommu domain id: 0  
D RKNN: [03:36:13.406] allocated memory, name: weight, virt addr: 0x7f9a0a6000, dma addr: 0xff495000, obj addr: 0xffffff80c9218400, size: 11968000, aligned size: 11968512, fd: 5, handle: 2  
, flags: 0x403, gem name: 2, iommu domain id: 0  
D RKNN: [03:36:13.421] subgraph0 regcmdbuffer size: 110464, taskbuffer size: 4080  
D RKNN: [03:36:13.423] allocated memory, name: internal, virt addr: 0x7fb420b000, dma addr: 0xff37b000, obj addr: 0xffffff80c921ec00, size: 1154048, aligned size: 1155072, fd: 6, handle: 3  
, flags: 0x403, gem name: 3, iommu domain id: 0  
D RKNN: [03:36:13.424] -------------------------------------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.424]                                           Feature Tensor Information Table                                  
D RKNN: [03:36:13.424] ---------------------------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] ID  User           Tensor             DataType  DataFormat   OrigShape      NativeShape      |     [Start       End)       Size  
D RKNN: [03:36:13.424] ---------------------------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] 1   ConvRelu       x.3                INT8      NC1HWC2      (1,3,224,224)  (1,1,224,224,3)  | 0xff37b000 0xff39fc00 0x00024c00  
D RKNN: [03:36:13.424] 2   MaxPool        82                 INT8      NC1HWC2      (1,64,112,112) (1,4,112,112,16) | 0xff39fc00 0xff463c00 0x000c4000  
D RKNN: [03:36:13.424] 3   ConvRelu       input.13           INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff463c00*0xff494c00 0x00031000  
D RKNN: [03:36:13.424] 4   ConvAddRelu    120                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff37b000 0xff3ac000 0x00031000  
D RKNN: [03:36:13.424] 4   ConvAddRelu    input.13           INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff463c00*0xff494c00 0x00031000  
D RKNN: [03:36:13.424] 5   ConvRelu       142                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff3ac000 0xff3dd000 0x00031000  
D RKNN: [03:36:13.424] 6   ConvAddRelu    169                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff37b000 0xff3ac000 0x00031000  
D RKNN: [03:36:13.424] 6   ConvAddRelu    142                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff3ac000 0xff3dd000 0x00031000  
D RKNN: [03:36:13.424] 7   ConvRelu       191                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff3dd000 0xff40e000 0x00031000  
D RKNN: [03:36:13.424] 8   Conv           222                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff37b000 0xff393800 0x00018800  
D RKNN: [03:36:13.424] 9   ConvAddRelu    191                INT8      NC1HWC2      (1,64,56,56)   (1,4,56,56,16)   | 0xff3dd000 0xff40e000 0x00031000  
D RKNN: [03:36:13.424] 9   ConvAddRelu    out.7              INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff393800 0xff3ac000 0x00018800  
D RKNN: [03:36:13.424] 10  ConvRelu       267                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff37b000 0xff393800 0x00018800  
D RKNN: [03:36:13.424] 11  ConvAddRelu    294                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff393800 0xff3ac000 0x00018800  
D RKNN: [03:36:13.424] 11  ConvAddRelu    267                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff37b000 0xff393800 0x00018800  
D RKNN: [03:36:13.424] 12  ConvRelu       316                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff3ac000 0xff3c4800 0x00018800  
D RKNN: [03:36:13.424] 13  Conv           347                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff37b000 0xff387400 0x0000c400  
D RKNN: [03:36:13.424] 14  ConvAddRelu    316                INT8      NC1HWC2      (1,128,28,28)  (1,8,28,28,16)   | 0xff3ac000 0xff3c4800 0x00018800  
D RKNN: [03:36:13.424] 14  ConvAddRelu    out.11             INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff387400 0xff393800 0x0000c400  
D RKNN: [03:36:13.424] 15  ConvRelu       392                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff37b000 0xff387400 0x0000c400  
D RKNN: [03:36:13.424] 16  ConvAddRelu    419                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff387400 0xff393800 0x0000c400  
D RKNN: [03:36:13.424] 16  ConvAddRelu    392                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff37b000 0xff387400 0x0000c400  
D RKNN: [03:36:13.424] 17  ConvRelu       441                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff393800 0xff39fc00 0x0000c400  
D RKNN: [03:36:13.424] 18  Conv           472                INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff37b000 0xff381800 0x00006800  
D RKNN: [03:36:13.424] 19  ConvAddRelu    441                INT8      NC1HWC2      (1,256,14,14)  (1,16,14,14,16)  | 0xff393800 0xff39fc00 0x0000c400  
D RKNN: [03:36:13.424] 19  ConvAddRelu    out.2              INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff381800 0xff388000 0x00006800  
D RKNN: [03:36:13.424] 20  ConvRelu       517                INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff37b000 0xff381800 0x00006800  
D RKNN: [03:36:13.424] 21  ConvAddRelu    544                INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff381800 0xff388000 0x00006800  
D RKNN: [03:36:13.424] 21  ConvAddRelu    517                INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff37b000 0xff381800 0x00006800  
D RKNN: [03:36:13.424] 22  Conv           566                INT8      NC1HWC2      (1,512,7,7)    (1,33,7,7,16)    | 0xff388000 0xff38e800 0x00006800  
D RKNN: [03:36:13.424] 23  Conv           x.1                INT8      NC1HWC2      (1,512,1,1)    (1,32,1,1,16)    | 0xff37b000 0xff37b200 0x00000200  
D RKNN: [03:36:13.424] 24  Reshape        572_mm             INT8      NC1HWC2      (1,1000,1,1)   (1,64,1,1,16)    | 0xff37b200 0xff37b600 0x00000400  
D RKNN: [03:36:13.424] 25  OutputOperator 572                INT8      UNDEFINED    (1,1000)       (1,1000)         | 0xff37b600 0xff37ba00 0x00000400  
D RKNN: [03:36:13.424] ---------------------------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] -----------------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.424]                                    Const Tensor Information Table                          
D RKNN: [03:36:13.424] -------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] ID  User        Tensor                          DataType  OrigShape      |     [Start       End)       Size  
D RKNN: [03:36:13.424] -------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] 1   ConvRelu    weight.12                       INT8      (64,3,7,7)     | 0xfffb8000 0xfffbf000 0x00007000  
D RKNN: [03:36:13.424] 1   ConvRelu    bias.8_1_bias                   INT32     (64)           | 0xfffbf000 0xfffbf200 0x00000200  
D RKNN: [03:36:13.424] 3   ConvRelu    weight.20                       INT8      (64,64,3,3)    | 0xfffaf000 0xfffb8000 0x00009000  
D RKNN: [03:36:13.424] 3   ConvRelu    bias.6_5_bias                   INT32     (64)           | 0xfffbf200 0xfffbf400 0x00000200  
D RKNN: [03:36:13.424] 4   ConvAddRelu weight.16                       INT8      (64,64,3,3)    | 0xfffa6000 0xfffaf000 0x00009000  
D RKNN: [03:36:13.424] 4   ConvAddRelu bias.10_8_bias                  INT32     (64)           | 0xfffbf400 0xfffbf600 0x00000200  
D RKNN: [03:36:13.424] 5   ConvRelu    weight.24                       INT8      (64,64,3,3)    | 0xfff9d000 0xfffa6000 0x00009000  
D RKNN: [03:36:13.424] 5   ConvRelu    bias.12_12_bias                 INT32     (64)           | 0xfffbf600 0xfffbf800 0x00000200  
D RKNN: [03:36:13.424] 6   ConvAddRelu weight.28                       INT8      (64,64,3,3)    | 0xfff94000 0xfff9d000 0x00009000  
D RKNN: [03:36:13.424] 6   ConvAddRelu bias.14_15_bias                 INT32     (64)           | 0xfffbf800 0xfffbfa00 0x00000200  
D RKNN: [03:36:13.424] 7   ConvRelu    weight.32                       INT8      (128,64,3,3)   | 0xfff82000 0xfff94000 0x00012000  
D RKNN: [03:36:13.424] 7   ConvRelu    bias.16_19_bias                 INT32     (128)          | 0xfffbfa00 0xfffbfe00 0x00000400  
D RKNN: [03:36:13.424] 8   Conv        weight.36                       INT8      (128,128,3,3)  | 0xfff5e000 0xfff82000 0x00024000  
D RKNN: [03:36:13.424] 8   Conv        bias.18_22_bias                 INT32     (128)          | 0xfffbfe00 0xfffc0200 0x00000400  
D RKNN: [03:36:13.424] 9   ConvAddRelu weight.40                       INT8      (128,64,1,1)   | 0xfff5c000 0xfff5e000 0x00002000  
D RKNN: [03:36:13.424] 9   ConvAddRelu bias.20_24_bias                 INT32     (128)          | 0xfffc0200 0xfffc0600 0x00000400  
D RKNN: [03:36:13.424] 10  ConvRelu    weight.44                       INT8      (128,128,3,3)  | 0xfff38000 0xfff5c000 0x00024000  
D RKNN: [03:36:13.424] 10  ConvRelu    bias.22_28_bias                 INT32     (128)          | 0xfffc0600 0xfffc0a00 0x00000400  
D RKNN: [03:36:13.424] 11  ConvAddRelu weight.48                       INT8      (128,128,3,3)  | 0xfff14000 0xfff38000 0x00024000  
D RKNN: [03:36:13.424] 11  ConvAddRelu bias.24_31_bias                 INT32     (128)          | 0xfffc0a00 0xfffc0e00 0x00000400  
D RKNN: [03:36:13.424] 12  ConvRelu    weight.52                       INT8      (256,128,3,3)  | 0xffecc000 0xfff14000 0x00048000  
D RKNN: [03:36:13.424] 12  ConvRelu    bias.26_35_bias                 INT32     (256)          | 0xfffc0e00 0xfffc1600 0x00000800  
D RKNN: [03:36:13.424] 13  Conv        weight.56                       INT8      (256,256,3,3)  | 0xffe3c000 0xffecc000 0x00090000  
D RKNN: [03:36:13.424] 13  Conv        bias.28_38_bias                 INT32     (256)          | 0xfffc1600 0xfffc1e00 0x00000800  
D RKNN: [03:36:13.424] 14  ConvAddRelu weight.60                       INT8      (256,128,1,1)  | 0xffe34000 0xffe3c000 0x00008000  
D RKNN: [03:36:13.424] 14  ConvAddRelu bias.30_40_bias                 INT32     (256)          | 0xfffc1e00 0xfffc2600 0x00000800  
D RKNN: [03:36:13.424] 15  ConvRelu    weight.64                       INT8      (256,256,3,3)  | 0xffda4000 0xffe34000 0x00090000  
D RKNN: [03:36:13.424] 15  ConvRelu    bias.32_44_bias                 INT32     (256)          | 0xfffc2600 0xfffc2e00 0x00000800  
D RKNN: [03:36:13.424] 16  ConvAddRelu weight.68                       INT8      (256,256,3,3)  | 0xffd14000 0xffda4000 0x00090000  
D RKNN: [03:36:13.424] 16  ConvAddRelu bias.34_47_bias                 INT32     (256)          | 0xfffc2e00 0xfffc3600 0x00000800  
D RKNN: [03:36:13.424] 17  ConvRelu    weight.8                        INT8      (512,256,3,3)  | 0xffbf4000 0xffd14000 0x00120000  
D RKNN: [03:36:13.424] 17  ConvRelu    bias.3_51_bias                  INT32     (512)          | 0xfffc3600 0xfffc4600 0x00001000  
D RKNN: [03:36:13.424] 18  Conv        weight.7                        INT8      (512,512,3,3)  | 0xff9b4000 0xffbf4000 0x00240000  
D RKNN: [03:36:13.424] 18  Conv        bias.4_54_bias                  INT32     (512)          | 0xfffc4600 0xfffc5600 0x00001000  
D RKNN: [03:36:13.424] 19  ConvAddRelu weight.3                        INT8      (512,256,1,1)  | 0xff994000 0xff9b4000 0x00020000  
D RKNN: [03:36:13.424] 19  ConvAddRelu bias.5_56_bias                  INT32     (512)          | 0xfffc5600 0xfffc6600 0x00001000  
D RKNN: [03:36:13.424] 20  ConvRelu    weight.2                        INT8      (512,512,3,3)  | 0xff754000 0xff994000 0x00240000  
D RKNN: [03:36:13.424] 20  ConvRelu    bias.2_60_bias                  INT32     (512)          | 0xfffc6600 0xfffc7600 0x00001000  
D RKNN: [03:36:13.424] 21  ConvAddRelu weight.6                        INT8      (512,512,3,3)  | 0xff514000 0xff754000 0x00240000  
D RKNN: [03:36:13.424] 21  ConvAddRelu bias.36_63_bias                 INT32     (512)          | 0xfffc7600 0xfffc8600 0x00001000  
D RKNN: [03:36:13.424] 22  Conv        x.1_GlobalAveragePool_2conv0_i1 INT8      (1,512,7,7)    | 0xfffc8600 0xfffd4a00 0x0000c400  
D RKNN: [03:36:13.424] 22  Conv        x.1_GlobalAveragePool_2conv0_i2 INT32     (512)          | 0xfffd4a00 0xfffd5600 0x00000c00  
D RKNN: [03:36:13.424] 23  Conv        weight.1                        INT8      (1000,512,1,1) | 0xff497000 0xff514000 0x0007d000  
D RKNN: [03:36:13.424] 23  Conv        bias.1                          INT32     (1000)         | 0xff495000 0xff497000 0x00002000  
D RKNN: [03:36:13.424] 24  Reshape     572_mm_tp_rs_i1                 INT64     (2)            | 0xfffd5600 0xfffd5610 0x00000010  
D RKNN: [03:36:13.424] -------------------------------------------------------------------------+---------------------------------  
D RKNN: [03:36:13.424] ----------------------------------------  
D RKNN: [03:36:13.424] Total Internal Memory Size: 1127KB  
D RKNN: [03:36:13.424] Total Weight Memory Size: 11550.8KB  
D RKNN: [03:36:13.425] ----------------------------------------  
D RKNN: [03:36:13.425] The RKNN_FLAG_EXECUTE_FALLBACK_PRIOR_DEVICE_GPU is not set and without GPU op in Graphs, OpenCL will not be initialized  
W RKNN: [03:36:13.426] query RKNN_QUERY_INPUT_DYNAMIC_RANGE error, rknn model is static shape type, please export rknn with dynamic_shapes  
W Query dynamic range failed. Ret code: RKNN_ERR_MODEL_INVALID. (If it is a static shape RKNN model, please ignore the above warning message.)  
done  
--> Running model  
D RKNN: [03:36:13.428] allocated memory, name: x.3, virt addr: 0x7fb41e6000, dma addr: 0xff356000, obj addr: 0xffffff80c9228800, size: 150528, aligned size: 151552, fd: 7, handle: 4, flags  
: 0x403, gem name: 4, iommu domain id: 0  
D RKNN: [03:36:13.428] allocated memory, name: 572, virt addr: 0x7fb7ea4000, dma addr: 0xff355000, obj addr: 0xffffff80c922c400, size: 1024, aligned size: 4096, fd: 8, handle: 5, flags: 0x  
403, gem name: 5, iommu domain id: 0  
D RKNN: [03:36:13.428] enable argb mode, dtype: UINT8, channel: 3  
D RKNN: [03:36:13.428] normalize target: NPU  
D RKNN: [03:36:13.428] update cvt sign: 0  
D RKNN: [03:36:13.429] Get NPU frequency: 950MHz  
D RKNN: [03:36:13.429] Get DDR frequency: 528MHz  
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------  
---------------------------------------------------------------------  
D RKNN: [03:36:13.439]                                                                               Network Layer Information Table                                                          
                                
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------  
---------------------------------------------------------------------  
D RKNN: [03:36:13.439] ID   OpType             DataType Target InputShape                               OutputShape            Cycles(DDR/NPU/Total)    Time(us)     MacUsage(%)          Wo  
rkLoad(0/1)        SparseRatio  RW(KB)       FullName           
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------  
---------------------------------------------------------------------  
D RKNN: [03:36:13.439] 1    InputOperator      UINT8    CPU    \                                        (1,3,224,224)          0/0/0                    12                                0.  
0%/0.0%                         0            InputOperator:x.3  
D RKNN: [03:36:13.439] 2    ConvRelu           UINT8    NPU    (1,3,224,224),(64,3,7,7),(64)            (1,64,112,112)         569350/1229312/1229312   418          29.02/0.00           10  
0.0%/0.0%          0%           946          Conv:input.21_Conv  
D RKNN: [03:36:13.439] 3    MaxPool            INT8     NPU    (1,64,112,112)                           (1,64,56,56)           0/0/0                    454                               10  
0.0%/0.0%                       980          MaxPool:input.13_MaxPool  
D RKNN: [03:36:13.439] 4    ConvRelu           INT8     NPU    (1,64,56,56),(64,64,3,3),(64)            (1,64,56,56)           205825/112896/205825     315          37.73/0.00           10  
0.0%/0.0%          0%           429          Conv:input.17_Conv  
D RKNN: [03:36:13.439] 5    ConvAddRelu        INT8     NPU    (1,64,56,56),(64,64,3,3),(64),...        (1,64,56,56)           270306/112896/270306     255          46.60/0.00           10  
0.0%/0.0%          0%           625          Conv:input.23_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 6    ConvRelu           INT8     NPU    (1,64,56,56),(64,64,3,3),(64)            (1,64,56,56)           205825/112896/205825     254          46.79/0.00           10  
0.0%/0.0%          0%           429          Conv:input.27_Conv  
D RKNN: [03:36:13.439] 7    ConvAddRelu        INT8     NPU    (1,64,56,56),(64,64,3,3),(64),...        (1,64,56,56)           270306/112896/270306     268          44.34/0.00           10  
0.0%/0.0%          0%           625          Conv:input.31_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 8    ConvRelu           INT8     NPU    (1,64,56,56),(128,64,3,3),(128)          (1,128,28,28)          153352/56448/153352      274          21.69/0.00           10  
0.0%/0.0%          0%           368          Conv:input.35_Conv  
D RKNN: [03:36:13.439] 9    Conv               INT8     NPU    (1,128,28,28),(128,128,3,3),(128)        (1,128,28,28)          144799/112896/144799     305          38.96/0.00           10  
0.0%/0.0%          0%           342          Conv:input.39_Conv  
D RKNN: [03:36:13.439] 10   ConvAddRelu        INT8     NPU    (1,64,56,56),(128,64,1,1),(128),...      (1,128,28,28)          162255/12544/162255      187          3.53/0.00            10  
0.0%/0.0%          0%           395          Conv:input.41_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 11   ConvRelu           INT8     NPU    (1,128,28,28),(128,128,3,3),(128)        (1,128,28,28)          144799/112896/144799     246          48.31/0.00           10  
0.0%/0.0%          0%           342          Conv:input.45_Conv  
D RKNN: [03:36:13.439] 12   ConvAddRelu        INT8     NPU    (1,128,28,28),(128,128,3,3),(128),...    (1,128,28,28)          177039/112896/177039     249          47.73/0.00           10  
0.0%/0.0%          0%           440          Conv:input.49_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 13   ConvRelu           INT8     NPU    (1,128,28,28),(256,128,3,3),(256)        (1,256,14,14)          160261/59904/160261      195          30.47/0.00           10  
0.0%/0.0%          0%           438          Conv:input.53_Conv  
D RKNN: [03:36:13.439] 14   Conv               INT8     NPU    (1,256,14,14),(256,256,3,3),(256)        (1,256,14,14)          238888/119808/238888     318          37.37/0.00           10  
0.0%/0.0%          0%           677          Conv:input.57_Conv  
D RKNN: [03:36:13.439] 15   ConvAddRelu        INT8     NPU    (1,128,28,28),(256,128,1,1),(256),...    (1,256,14,14)          89900/6656/89900         138          4.78/0.00            10  
0.0%/0.0%          0%           224          Conv:input.59_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 16   ConvRelu           INT8     NPU    (1,256,14,14),(256,256,3,3),(256)        (1,256,14,14)          238888/119808/238888     259          45.88/0.00           10  
0.0%/0.0%          0%           677          Conv:input.63_Conv  
D RKNN: [03:36:13.439] 17   ConvAddRelu        INT8     NPU    (1,256,14,14),(256,256,3,3),(256),...    (1,256,14,14)          255008/119808/255008     330          36.01/0.00           10  
0.0%/0.0%          0%           726          Conv:input.67_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 18   ConvRelu           INT8     NPU    (1,256,14,14),(512,256,3,3),(512)        (1,512,7,7)            412919/73728/412919      532          11.17/0.00           10  
0.0%/0.0%          0%           1230         Conv:input.6_Conv  
D RKNN: [03:36:13.439] 19   Conv               INT8     NPU    (1,512,7,7),(512,512,3,3),(512)          (1,512,7,7)            783846/147456/783846     915          12.99/0.00           10  
0.0%/0.0%          0%           2358         Conv:input.9_Conv  
D RKNN: [03:36:13.439] 20   ConvAddRelu        INT8     NPU    (1,256,14,14),(512,256,1,1),(512),...    (1,512,7,7)            81881/8192/81881         229          2.88/0.00            10  
0.0%/0.0%          0%           224          Conv:input.10_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 21   ConvRelu           INT8     NPU    (1,512,7,7),(512,512,3,3),(512)          (1,512,7,7)            783846/147456/783846     894          13.29/0.00           10  
0.0%/0.0%          0%           2358         Conv:input.5_Conv  
D RKNN: [03:36:13.439] 22   ConvAddRelu        INT8     NPU    (1,512,7,7),(512,512,3,3),(512),...      (1,512,7,7)            791907/147456/791907     888          13.38/0.00           10  
0.0%/0.0%          0%           2382         Conv:input.1_Conv_ConvAddRelu  
D RKNN: [03:36:13.439] 23   Conv               INT8     NPU    (1,512,7,7),(1,512,7,7),(512)            (1,512,1,1)            25872/6272/25872         170          0.02/0.00            10  
0.0%/0.0%          0%           78           Conv:x.1_GlobalAveragePool_2conv0  
D RKNN: [03:36:13.439] 24   Conv               INT8     NPU    (1,512,1,1),(1000,512,1,1),(1000)        (1,1000,1,1)           168249/8192/168249       278          0.19/0.00            10  
0.0%/0.0%          0%           510          Conv:572_Gemm#2    
D RKNN: [03:36:13.439] 25   Reshape            INT8     NPU    (1,1000,1,1),(2)                         (1,1000)               0/0/0                    82                                10  
0.0%/0.0%                       2            Reshape:572_mm_tp_rs  
D RKNN: [03:36:13.439] 26   OutputOperator     INT8     CPU    (1,1000)                                 \                      0/0/0                    11                                0.  
0%/0.0%                         0            OutputOperator:572  
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------  
---------------------------------------------------------------------  
D RKNN: [03:36:13.439] Total Operator Elapsed Per Frame Time(us): 8476  
D RKNN: [03:36:13.439] Total Internal Memory Read/Write Per Frame Size(KB): 6354.34  
D RKNN: [03:36:13.439] Total Weight Memory Read/Write Per Frame Size(KB): 11459.00  
D RKNN: [03:36:13.439] Total Memory Read/Write Per Frame Size(KB): 17813.34  
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------------------------------------------------------------------------  
---------------------------------------------------------------------  
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.439]                                  Operator Time Consuming Ranking Table               
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.439] OpType             CallNumber   CPUTime(us)  GPUTime(us)  NPUTime(us)  TotalTime(us)  TimeRatio(%)     
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.439] ConvRelu           9            0            0            3387         3387           39.96%           
D RKNN: [03:36:13.439] ConvAddRelu        8            0            0            2544         2544           30.01%           
D RKNN: [03:36:13.439] Conv               5            0            0            1986         1986           23.43%           
D RKNN: [03:36:13.439] MaxPool            1            0            0            454          454            5.36%            
D RKNN: [03:36:13.439] Reshape            1            0            0            82           82             0.97%            
D RKNN: [03:36:13.439] InputOperator      1            12           0            0            12             0.14%            
D RKNN: [03:36:13.439] OutputOperator     1            11           0            0            11             0.13%            
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------  
D RKNN: [03:36:13.439] Total                           23           0            8453         8476             
D RKNN: [03:36:13.439] ---------------------------------------------------------------------------------------------------  
resnet18  
-----TOP 5-----  
[812] score:0.999638 class:"space shuttle"  
[404] score:0.000281 class:"airliner"  
[657] score:0.000014 class:"missile"  
[466] score:0.000010 class:"bullet train, bullet"  
[833] score:0.000010 class:"submarine, pigboat, sub, U-boat"  
done  
D RKNN: [03:36:13.444] free memory, name: internal, virt addr: 0x7fb420b000, dma addr: 0xff37b000, obj addr: 0xffffff80c921ec00, size: 1154048, aligned size: 1155072, fd: 6, handle: 3, fla  
gs: 0x403, gem name: 3, iommu domain id: 0  
D RKNN: [03:36:13.445] free memory, name: weight, virt addr: 0x7f9a0a6000, dma addr: 0xff495000, obj addr: 0xffffff80c9218400, size: 11968000, aligned size: 11968512, fd: 5, handle: 2, fla  
gs: 0x403, gem name: 2, iommu domain id: 0  
D RKNN: [03:36:13.452] free memory, name: task, virt addr: 0x7fb7ea5000, dma addr: 0xfffff000, obj addr: 0xffffff80c921a000, size: 4080, aligned size: 4096, fd: 4, handle: 1, flags: 0x40b,  
gem name: 1, iommu domain id: 0  
D RKNN: [03:36:13.453] free memory, name: x.3, virt addr: 0x7fb41e6000, dma addr: 0xff356000, obj addr: 0xffffff80c9228800, size: 150528, aligned size: 151552, fd: 7, handle: 4, flags: 0x4  
03, gem name: 4, iommu domain id: 0  
D RKNN: [03:36:13.453] free memory, name: 572, virt addr: 0x7fb7ea4000, dma addr: 0xff355000, obj addr: 0xffffff80c922c400, size: 1024, aligned size: 4096, fd: 8, handle: 5, flags: 0x403,  
gem name: 5, iommu domain id: 0  
(rknn) root@linaro-alip:/rknn#

可以看见，加载的resnet18 rknn模型正确将space shuttle图片分类出来，到这里，rknn-toolkit-lite2工具的使用，环境搭建以及rknn模型在嵌入式平台python接口部署就介绍完成了

四、总结

如果后续需要在嵌入式平台上使用python接口来部署rknn模型，一般的流程为

1、先在x86平台上使用rknn-toolkit2工具将模型调试好

2、在同一python文件内修改模块名称为rknn-toolkit-lite2，修改一些接口环境，就转换成了rknn-toolkit-lite2相应程序

3、拷贝所需依赖资源与文件到嵌入式开发平台上，之后直接运行rknn-toolkit-lite2对应的程序就可以直接在开发板上使用了

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