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Test on embedded device

Here are the test conclusions of our edge devices. You can directly obtain the results of your own environment with model profiling.

Software and hardware environment

  • host OS ubuntu 18.04

  • backend SNPE-1.59

  • device Mi11 (qcom 888)

mmcls

model dataset spatial fp32 top-1 (%) snpe gpu hybrid fp32 top-1 (%) latency (ms)
ShuffleNetV2 ImageNet-1k 224x224 69.55 69.83* 20±7
MobilenetV2 ImageNet-1k 224x224 71.86 72.14* 15±6

tips:

  1. The ImageNet-1k dataset is too large to test, only part of the dataset is used (8000/50000)

  2. The heating of device will downgrade the frequency, so the time consumption will actually fluctuate. Here are the stable values after running for a period of time. This result is closer to the actual demand.

mmocr detection

model dataset spatial fp32 hmean snpe gpu hybrid hmean latency(ms)
PANet ICDAR2015 1312x736 0.795 0.785 @thr=0.9 3100±100

mmpose

model dataset spatial snpe hybrid AR@IoU=0.50 snpe hybrid AP@IoU=0.50 latency(ms)
pose_hrnet_w32 Animalpose 256x256 0.997 0.989 630±50

tips:

  • Test pose_hrnet using AnimalPose’s test dataset instead of val dataset.

mmseg

model dataset spatial mIoU latency(ms)
fcn Cityscapes 512x1024 71.11 4915±500

tips:

  • fcn works fine with 512x1024 size. Cityscapes dataset uses 1024x2048 resolution which causes device to reboot.

Notes

  • We needs to manually split the mmdet model into two parts. Because

    • In snpe source code, onnx_to_ir.py can only parse onnx input while ir_to_dlc.py does not support topk operator

    • UDO (User Defined Operator) does not work with snpe-onnx-to-dlc

  • mmedit model

    • srcnn requires cubic resize which snpe does not support

    • esrgan converts fine, but loading the model causes the device to reboot

  • mmrotate depends on e2cnn and needs to be installed manually its Python3.6 compatible branch

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