[技术]

【米尔MYD-JX8MPQ开发板试用体验】之机器学习测试

jf_1137202360

2022-4-14 23:02:10 18603 米尔开发板试用

e693246a70ebb666921fb949b890719f

参考

https://www.toradex.cn/blog/nxp-imx8ji-yueiq-kuang-jia-ce-shi-machine-learning

IMX-MACHINE-LEARNING-UG.pdf

CPU和NPU图像分类

cd /usr/bin/tensorflow-lite-2.4.0/examples

CPU运行

./label_image -m mobilenet_v1_1.0_224_quant.tflite -i grace_hopper.bmp -l labels.txt

INFO: Loaded model mobilenet_v1_1.0_224_quant.tflite

INFO: resolved reporter

INFO: invoked

INFO: average time: 50.66 ms

INFO: 0.780392: 653 military uniform

INFO: 0.105882: 907 Windsor tie

INFO: 0.0156863: 458 bow tie

INFO: 0.0117647: 466 bulletproof vest

INFO: 0.00784314: 835 suit

GPU/NPU加速运行

./label_image -m mobilenet_v1_1.0_224_quant.tflite -i grace_hopper.bmp -l labels.txt -a 1

INFO: Loaded model mobilenet_v1_1.0_224_quant.tflite

INFO: resolved reporter

INFO: Created TensorFlow Lite delegate for NNAPI.

INFO: Applied NNAPI delegate.

INFO: invoked

INFO: average time: 2.775 ms

INFO: 0.768627: 653 military uniform

INFO: 0.105882: 907 Windsor tie

INFO: 0.0196078: 458 bow tie

INFO: 0.0117647: 466 bulletproof vest

INFO: 0.00784314: 835 suit

USE_GPU_INFERENCE=0 ./label_image -m mobilenet_v1_1.0_224_quant.tflite -i grace_hopper.bmp -l labels.txt --external_delegate_path=/usr/lib/libvx_delegate.so

Python运行

python3 label_image.py

INFO: Created TensorFlow Lite delegate for NNAPI.

Applied NNAPI delegate.

WARM-up time: 6628.5 ms

Inference time: 2.9 ms

0.870588: military uniform

0.031373: Windsor tie

0.011765: mortarboard

0.007843: bow tie

0.007843: bulletproof vest

基准测试CPU单核运行

./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite

STARTING!

Log parameter values verbosely: [0]

Graph: [mobilenet_v1_1.0_224_quant.tflite]

Loaded model mobilenet_v1_1.0_224_quant.tflite

The input model file size (MB): 4.27635

Initialized session in 15.076ms.

Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.

count=4 first=166743 curr=161124 min=161054 max=166743 avg=162728 std=2347

Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.

count=50 first=161039 curr=161030 min=160877 max=161292 avg=161039 std=94

Inference timings in us: Init: 15076, First inference: 166743, Warmup (avg): 162728, Inference (avg): 161039

Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.

Peak memory footprint (MB): init=2.65234 overall=9.00391

CPU多核运行

./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite --num_threads=4

4核--num_threads设置为4性能最好

STARTING!

Log parameter values verbosely: [0]

Num threads: [4]

Graph: [mobilenet_v1_1.0_224_quant.tflite]

#threads used for CPU inference: [4]

Loaded model mobilenet_v1_1.0_224_quant.tflite

The input model file size (MB): 4.27635

Initialized session in 2.536ms.

Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.

count=11 first=48722 curr=44756 min=44597 max=49397 avg=45518.9 std=1679

Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.

count=50 first=44678 curr=44591 min=44590 max=50798 avg=44965.2 std=1170

Inference timings in us: Init: 2536, First inference: 48722, Warmup (avg): 45518.9, Inference (avg): 44965.2

Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.

Peak memory footprint (MB): init=1.38281 overall=8.69922

GPU/NPU加速

./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite --num_threads=4 --use_nnapi=true

STARTING!

Log parameter values verbosely: [0]

Num threads: [4]

Graph: [mobilenet_v1_1.0_224_quant.tflite]

#threads used for CPU inference: [4]

Use NNAPI: [1]

NNAPI accelerators available: [vsi-npu]

Loaded model mobilenet_v1_1.0_224_quant.tflite

INFO: Created TensorFlow Lite delegate for NNAPI.

Explicitly applied NNAPI delegate, and the model graph will be completely executed by the delegate.

The input model file size (MB): 4.27635

Initialized session in 3.968ms.

Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.

count=1 curr=6611085

Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.

count=369 first=2715 curr=2623 min=2572 max=2776 avg=2634.2 std=20

Inference timings in us: Init: 3968, First inference: 6611085, Warmup (avg): 6.61108e+06, Inference (avg): 2634.2

Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.

Peak memory footprint (MB): init=2.42188 overall=28.4062

结果对比

	CPU运行	CPU多核多线程	NPU加速
图像分类	50.66 ms		2.775 ms
基准测试	161039uS	44965.2uS	2634.2uS

OpenCV DNN

cd /usr/share/OpenCV/samples/bin

./example_dnn_classification --input=dog416.png --zoo=models.yml squeezenet

下载模型

cd /usr/share/opencv4/testdata/dnn/

python3 download_models_basic.py

图像分类

cd /usr/share/OpenCV/samples/bin

./example_dnn_classification --input=dog416.png --zoo=models.yml squeezenet

文件浏览器地址栏输入

ftp://ftp.toradex.cn/Linux/i.MX8/eIQ/OpenCV/Image_Classification.zip

下载文件

解压得到文件models.yml和squeezenet_v1.1.caffemodel

cd /usr/share/OpenCV/samples/bin

将文件导入到开发板的/usr/share/OpenCV/samples/bin目录下

$ cp /usr/share/opencv4/testdata/dnn/dog416.png /usr/share/OpenCV/samples/bin/
$ cp /usr/share/opencv4/testdata/dnn/squeezenet_v1.1.prototxt /usr/share/OpenCV/samples/bin/
$ cp /usr/share/OpenCV/samples/data/dnn/classification_classes_ILSVRC2012.txt /usr/share/OpenCV/samples/bin/
$ cd /usr/share/OpenCV/samples/bin/

图片输入

./example_dnn_classification --input=dog416.png --zoo=models.yml squeezenet

报错

root@myd-jx8mp:/usr/share/OpenCV/samples/bin# ./example_dnn_classification --input=dog416.png --zoo=model.yml squeezenet

ERRORS:

Missing parameter: 'mean'

Missing parameter: 'rgb'

加入参数--rgb 和 --mean=1

还是报错加入参数--mode

root@myd-jx8mp:/usr/share/OpenCV/samples/bin# ./example_dnn_classification --rgb --mean=1 --input=dog416.png --zoo=models.yml squeezenet

[ WARN:0] global /usr/src/debug/opencv/4.4.0.imx-r0/git/modules/videoio/src/cap_gstreamer.cpp (898) open OpenCV | GStreamer warning: unable to query duration of stream

[ WARN:0] global /usr/src/debug/opencv/4.4.0.imx-r0/git/modules/videoio/src/cap_gstreamer.cpp (935) open OpenCV | GStreamer warning: Cannot query video position: status=1, value=0, duration=-1

root@myd-jx8mp:/usr/share/OpenCV/samples/bin# ./example_dnn_classification --rgb --mean=1 --input=dog416.png --zoo=models.yml squeezenet --mode

[ WARN:0] global /usr/src/debug/opencv/4.4.0.imx-r0/git/modules/videoio/src/cap_gstreamer.cpp (898) open OpenCV | GStreamer warning: unable to query duration of stream

[ WARN:0] global /usr/src/debug/opencv/4.4.0.imx-r0/git/modules/videoio/src/cap_gstreamer.cpp (935) open OpenCV | GStreamer warning: Cannot query video position: status=1, value=0, duration=-1

视频输入

./example_dnn_classification --device=2 --zoo=models.yml squeezenet

问题

如果testdata目录下没有文件,则查找下

lhj@DESKTOP-BINN7F8:~/myd-jx8mp-yocto$ find . -name "dog416.png"

./build-xwayland/tmp/work/cortexa53-crypto-mx8mp-poky-linux/opencv/4.4.0.imx-r0/extra/testdata/dnn/dog416.png

再将相应的文件复制到开发板

cd ./build-xwayland/tmp/work/cortexa53-crypto-mx8mp-poky-linux/opencv/4.4.0.imx-r0/extra/testdata/

tar -cvf /mnt/e/dnn.tar ./dnn/

cd /usr/share/opencv4/testdata 目录不存在则先创建

rz导入dnn.tar

解压 tar -xvf dnn.tar

terminate called after throwing an instance of 'cv::Exception'

what(): OpenCV(4.4.0) /usr/src/debug/opencv/4.4.0.imx-r0/git/samples/dnn/classification.cpp:81: error: (-215:Assertion failed) !model.empty() in function 'main'

Aborted

lhj@DESKTOP-BINN7F8:~/myd-jx8mp-yocto/build-xwayland$ find . -name classification.cpp

lhj@DESKTOP-BINN7F8:~/myd-jx8mp-yocto/build-xwayland$ cp ./tmp/work/cortexa53-crypto-mx8mp-poky-linux/opencv/4.4.0.imx-r0/packages-split/opencv-src/usr/src/debug/opencv/4.4.0.imx-r0/git/samples/dnn/classification.cpp /mnt/e

lhj@DESKTOP-BINN7F8:~/myd-jx8mp-yocto/build-xwayland$

YOLO对象检测

cd /usr/share/OpenCV/samples/bin

./example_dnn_object_detection --width=1024 --height=1024 --scale=0.00392 --input=dog416.png --rgb --zoo=models.yml yolo

https://pjreddie.com/darknet/yolo/下下载cfg和weights文件

cd /usr/share/OpenCV/samples/bin/

导入上面下载的文件

cp /usr/share/OpenCV/samples/data/dnn/object_detection_classes_yolov3.txt /usr/share/OpenCV/samples/bin/

cp /usr/share/opencv4/testdata/dnn/yolov3.cfg /usr/share/OpenCV/samples/bin/

./example_dnn_object_detection --width=1024 --height=1024 --scale=0.00392 --input=dog416.png --rgb --zoo=models.yml yolo

OpenCV经典机器学

cd /usr/share/OpenCV/samples/bin