Image Object Classification Network Application (Python)

This application can run on the Atlas 200 DK to classify objects in images using the GoogLeNet network and output the top five classes with the highest confidence scores.

Software Preparation

Before running this sample, obtain the source code package.

1. Obtain the source code package.
   mkdir -p $HOME/AscendProjects

   cd $HOME/AscendProjects

   git clone https://github.com/Atlas200dk/sample_image_classification_c73_python.git

   OR

   wget https://github.com/Atlas200dk/sample_image_classification_c73_python.git

   Then, unzip the downloaded file:

   unzip sample_image_classification_c73_python.zip

2. Obtain the original model required by the application.

   Obtain the original model file and weight file by referring to Table 1 Model for an image classification application and store them to any directory of the Ubuntu server where Mind Studio is installed.

   Table 1 Model for an image classification application

   Model name	Model description	Model download link
   googlenet	Image classification model It is a GoogLeNet model based on Caffe.	Download the original model file and weight file by referring to README_en.md at https://github.com/Ascend-Huawei/models/blob/master/computer_vision/classification/googlenet/README_en.md.

3. Convert the original model to an offline model adapted to the Ascend AI Processor.

   1. Choose Tools > Model Converter from the menu bar of Mind Studio.
   2. Configure the model conversion settings in the displayed Model Converter dialog box.
   3. Set the parameters by referring to the following figure.
      • Select the model file downloaded in Step 2 in Model File, and Weight File is automatically filled in.
      • Set the model name to googlenet.
      • Set Input Type to FP16 and set the value of N in Input Node: data to 1.
      • Disable Data Preprocessing

   
   
   

4. Save the converted model to the model directory in the project file.

   mkdir ~/AscendProjects/sample_image_classification_c73_python/model/

   cp ~/modelzoo/googlenet/device/googlenet.om ~/AscendProjects/sample_image_classification_c73_python/model/

Environment Deployment

1. Copy the application code to the developer board.

   Go to the root directory of the classification network application code (Python) as the Mind Studio installation user, for example, AscendProjects/sample_image_classification_c73_python, and run the following command to copy the application code to the developer board:

   scp -r ~/AscendProjects/sample_image_classification_c73_python HwHiAiUser@192.168.1.2:/home/HwHiAiUser/HIAI_PROJECTS

   Enter the password of the developer board as prompted. The default password is Mind@123

2. Log in to the developer board and add environment variables.

   ssh HwHiAiUser@192.168.1.2
   vim ~/.bashrc
   Append the following two lines:
   export LD_LIBRARY_PATH=/home/HwHiAiUser/Ascend/acllib/lib64
   export PYTHONPATH=/home/HwHiAiUser/Ascend/:${PYTHONPATH}
   
   Run the following command for the environment variables to take effect:
   source ~/.bashrc

Run

1. Log in to the developer board, go to the project directory, and run the following command to run the application:

   cd ~/HIAI_PROJECTS/sample_image_classification_c73_python/
   python3 main.py

2. View the inference result on the terminal window.

   

3. View the result images.

   The result images are stored in the outputs folder and can be uploaded to the home directory of the Mind Studio installation user.

   Run the following from Mind Studio installation server.

   scp -r username@host_ip:/home/username/HIAI_PROJECTS/sample_image_classification_c73_python/outputs ~

   • username: user name of the developer board, default to HwHiAiUser.
   • host_ip: IP address of the developer board. Generally, for USB connection, the IP address is 192.168.1.2. For network cable connection, the IP address if 192.168.0.2.

   Example:
   scp -r HwHiAiUser@192.168.1.2:/home/HwHiAiUser/HIAI_PROJECTS/sample_image_classification_c73_python/outputs ~

   Then, you will find the Result.jpg in ~/outputs/