EfficientNet-V2-s Example

This document describes using the QAI AppBuilder Python API to perform inference with the EfficientNet-V2-s target recognition model using Qualcomm® Hexagon™ Processor (NPU).

Supported Devices

Device	SoC
Dragon Q6A	QCS6490
Dragon Q8B	SC8280XP

Install QAI AppBuilder

tip

1. Please install QAI AppBuilder according to QAI AppBuilder Installation Guide.

2. Please configure ADSP environment variables according to Create ADSP Environment Variables.

Run Example

Install Dependencies

Device

pip3 install requests tqdm qai-hub py3_wget opencv-python torch torchvision

Run Script

• Navigate to the example directory

  QCS6490

  Device

  cd ai-engine-direct-helper/samples/linux/python

  SC8280XP

  Device

  cd ai-engine-direct-helper/samples/linux/python

• Prepare input image, using the following image as input example

  

  input image

• Execute inference

  Device

  python3 efficientnet_v2_s/efficientnet_v2_s.py

  $ python3 efficientnet_v2_s/efficientnet_v2_s.py
  Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/efficientnet_v2_s
       0.0ms [WARNING]  <W> Initializing HtpProvider
  
  /prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:38:dummy call to rpcmem_init, rpcmem APIs will be used from libxdsprpc
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
     129.0ms [WARNING] Time: Read model file to memory. 11.18
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
     158.9ms [WARNING] Time: contextCreateFromBinary. 29.89
  
     159.0ms [WARNING] Time: UnmapViewOfFile. 0.00
  
     159.4ms [WARNING] Time: model_initialize efficientnet_v2_s 159.34
  
     227.4ms [WARNING] Time: model_inference efficientnet_v2_s 10.06
  
  Top 5 predictions for image:
  
  Samoyed 0.7607400417
  keeshond 0.0230328161
  Pomeranian 0.0204458293
  white wolf 0.0193288065
  Arctic fox 0.0062915389
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
       0.0ms [WARNING]  <W> This META does not have Alloc2 Support
  
  /prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:42:dummy call to rpcmem_deinit, rpcmem APIs will be used from libxdsprpc
     239.8ms [WARNING] Time: model_destroy efficientnet_v2_s 9.40

  The printed results show that Samoyed has the highest confidence, which matches the input image content.