EfficientNet-B0 Example

This document describes using the QAI AppBuilder Python API to perform inference with the EfficientNet-B0 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_b0/efficientnet_b0.py

  $ python3 efficientnet_b0/efficientnet_b0.py
  Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/efficientnet_b0
       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
  
     122.3ms [WARNING] Time: Read model file to memory. 5.33
  
       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
  
     143.3ms [WARNING] Time: contextCreateFromBinary. 20.87
  
     143.3ms [WARNING] Time: UnmapViewOfFile. 0.00
  
     143.7ms [WARNING] Time: model_initialize efficientnet_b0 143.64
  
     214.6ms [WARNING] Time: model_inference efficientnet_b0 6.08
  
  Top 5 predictions for image:
  
  Samoyed 0.6965298057
  Pomeranian 0.1052966565
  keeshond 0.0131986532
  Eskimo dog 0.0075131184
  Arctic fox 0.0056676473
       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
     235.9ms [WARNING] Time: model_destroy efficientnet_b0 18.66

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