WideResNet50 Example

This document describes using the QAI AppBuilder Python API to perform inference with the WideResNet50 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 wideresnet50/wideresnet50.py

  $ python3 wideresnet50/wideresnet50.py
  Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/wideresnet50
       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
  
     146.7ms [WARNING] Time: Read model file to memory. 30.54
  
       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
  
     179.0ms [WARNING] Time: contextCreateFromBinary. 32.18
  
     179.0ms [WARNING] Time: UnmapViewOfFile. 0.00
  
     179.5ms [WARNING] Time: model_initialize wideresnet50 179.49
  
     243.2ms [WARNING] Time: model_inference wideresnet50 9.66
  
  Top 5 predictions for image:
  
  Samoyed 0.8442133665
  keeshond 0.0415370725
  Pomeranian 0.0302517358
  white wolf 0.0258170888
  chow 0.0072637959
       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
     258.1ms [WARNING] Time: model_destroy wideresnet50 11.75

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