ResNet-50 目标识别

进入 Radxa Model-zoo ResNet 目录
```
cd Radxa-Model-Zoo/sample/ResNet
```

下载模型，可选 F32/F16/INT8 量化模型

# F32
wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/resnet50_fp32_1b.bmodel

# F16
wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/resnet50_fp16_1b.bmodel

# INT8
wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/resnet50_int8_1b.bmodel

下载测试图片到数据文件夹

mkdir images && cd images
wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/grace_hopper.bmp

创建虚拟环境

必须创建虚拟环境，否则可能会影响其他应用的正常运行，虚拟环境使用请参考这里
```
python3 -m virtualenv .venv
source .venv/bin/activate
```

安装 python 依赖包

pip3 install --upgrade pip
pip3 install numpy
pip3 install https://github.com/radxa-edge/TPU-Edge-AI/releases/download/v0.1.0/sophon_arm-3.7.0-py3-none-any.whl

运行 python 示例

python 目录下提供了一系列Python例程，具体情况如下：

序号	Python例程	说明
1	resnet_opencv.py	使用OpenCV解码、OpenCV前处理、SAIL推理
2	resnet_bmcv.py	使用SAIL解码、BMCV前处理、SAIL推理

运行 resnet_opencv.py

export LD_LIBRARY_PATH=/opt/sophon/libsophon-current/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$PYTHONPATH:/opt/sophon/sophon-opencv-latest/opencv-python/
python3 python/resnet_opencv.py --input ./images --bmodel ./resnet50_int8_1b.bmodel

参数说明

resnet_opencv.py [--input IMG_PATH] [--bmodel BMODEL]

--input: 推理图片路径，可输入整个图片文件夹的路径

--bmodel: 用于推理的bmodel路径

(.venv) linaro@bm1684:/data/ssd/docs_check/Radxa-Model-Zoo/sample/ResNet$ python3 python/resnet_opencv.py --input ./images --bmodel ./resnet50_int8_1b.bmodel
[BMRT][bmcpu_setup:406] INFO:cpu_lib 'libcpuop.so' is loaded.
bmcpu init: skip cpu_user_defined
open usercpu.so, init user_cpu_init
[BMRT][load_bmodel:1084] INFO:Loading bmodel from [./resnet50_int8_1b.bmodel]. Thanks for your patience...
[BMRT][load_bmodel:1023] INFO:pre net num: 0, load net num: 1
INFO:root:filename: ./images/grace_hopper.bmp, res: (457, 0.12794505059719086)
INFO:root:result saved in ./results/resnet50_int8_1b.bmodel_dataset_opencv_python_result.json
INFO:root:------------------ Inference Time Info ----------------------
INFO:root:decode_time(ms): 2.94
INFO:root:preprocess_time(ms): 17.86
INFO:root:inference_time(ms): 4.36
INFO:root:postprocess_time(ms): 0.35

运行结果会保存在 ./results/resnet50_int8_1b.bmodel_dataset_opencv_python_result.json

[
  {
    "filename": "./dataset/grace_hopper.bmp",
    "prediction": 457,
    "score": 0.12794505059719086
  }
]

运行 resnet_bmcv.py

export LD_LIBRARY_PATH=/opt/sophon/libsophon-current/lib:$LD_LIBRARY_PATH
export PYTHONPATH=$PYTHONPATH:/opt/sophon/sophon-opencv-latest/opencv-python/
python3 python/resnet_bmcv.py  --input ./images --bmodel ./resnet50_int8_1b.bmodel

参数说明

resnet_bmcv.py [--input IMG_PATH] [--bmodel BMODEL]

--input: 推理图片路径，可输入整个图片文件夹的路径

--bmodel: 用于推理的bmodel路径

(.venv) linaro@bm1684:/data/ssd/docs_check/Radxa-Model-Zoo/sample/ResNet$ python3 python/resnet_bmcv.py  --input ./images --bmodel ./resnet50_int8_1b.bmodel
[BMRT][bmcpu_setup:406] INFO:cpu_lib 'libcpuop.so' is loaded.
bmcpu init: skip cpu_user_defined
open usercpu.so, init user_cpu_init
[BMRT][load_bmodel:1084] INFO:Loading bmodel from [./resnet50_int8_1b.bmodel]. Thanks for your patience...
[BMRT][load_bmodel:1023] INFO:pre net num: 0, load net num: 1
INFO:root:filename: ./images/grace_hopper.bmp, res: (457, 0.14356361329555511)
INFO:root:result saved in ./results/resnet50_int8_1b.bmodel_images_bmcv_python_result.json
INFO:root:------------------ Inference Time Info ----------------------
INFO:root:decode_time(ms): 4.01
INFO:root:preprocess_time(ms): 5.56
INFO:root:inference_time(ms): 1.86
INFO:root:postprocess_time(ms): 0.30

运行结果会保存在 ./results/resnet50_int8_1b.bmodel_images_bmcv_python_result.json

[
  {
    "filename": "./images/grace_hopper.bmp",
    "prediction": 457,
    "score": 0.14356361329555511
  }
]

FAQ

更多有关模型转换，模型量化细节, 模型精度测试，请参考

模型转换与 Radxa Model-Zoo ResNet

ResNet-50 目标识别

FAQ​

FAQ