garbage_picture

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This sample provides reference for you to learn the Ascend AI Software Stack and cannot be used for commercial purposes.

This sample applies to CANN 3.0.0 and later versions, and supports Atlas 200 DK and Atlas 300 (AI1s).

This readme file provides only guidance for running the sample in command line (CLI) mode.

Garbage Sorting Sample

Function: classifies input images by using the MobileNetV2 model.

Input: JPG images to be inferred

Output: JPG images after inference

For details about the training, see Waste Sorting with MobileNetV2.

Prerequisites

Before deploying this sample, ensure that:

• The environment has been set up by referring to Environment Preparation and Dependency Installation.

• The development environment and operating environment of the corresponding product have been installed.

Software Preparation

1. Obtain the source code package.

   You can download the source code in either of the following ways:

   • Command line (The download takes a long time, but the procedure is simple.)

     In the development environment, run the following commands as a non-root user to download the source code repository:

     cd $HOME

     git clone https://github.com/Ascend/samples.git

   • Compressed package (The download takes a short time, but the procedure is complex.)

     1. Click Clone or download in the upper right corner of the samples repository and click Download ZIP.

     2. Upload the .zip package to the home directory of a common user in the development environment, for example, $HOME/ascend-samples-master.zip.

     3. In the development environment, run the following commands to unzip the package:

        cd $HOME

        unzip ascend-samples-master.zip

2. Obtain the source model required by the application.

   Obtain the original model and its weight files used in the application by referring to the following table and save them to any directory of a common user in the development environment, for example, $HOME/models/garbage_picture.

   Model Name	Description	How to Obtain
   mobilenetV2	Image classification inference model. It is a MobileNetV2 model based on MindSpore.	Download the model and weight files by referring to the README.md file in https://github.com/Ascend/modelzoo/tree/master/contrib/TensorFlow/Research/cv/garbage_classification/ATC_mobilenetv2_mindspore_AE.

   NOTE

   • The converted OM model provided by ModelZoo does not match the current sample. Therefore, you need to download the original model and weight files, and convert the model by yourself.

3. Convert the original model to a Da Vinci model.

   Note: Ensure that the environment variables have been configured in Environment Preparation and Dependency Installation.

   1. Set the LD_LIBRARY_PATH environment variable.

      The LD_LIBRARY_PATH environment variable conflicts with the sample when the ATC tool is used. Therefore, you need to set this environment variable separately in the CLI to facilitate modification.

      export install_path=$HOME/Ascend/ascend-toolkit/latest

      export LD_LIBRARY_PATH=\${install_path}/atc/lib64

   2. Run the following commands to download the AIPP configuration file and convert the model:

      cd $HOME/models/googlenet_imagenet_picture

      https://c7xcode.obs.cn-north-4.myhuaweicloud.com/models/garbage_picture/insert_op_yuv.cfg

      atc --model=./mobilenetv2.air --framework=1 --output=garbage_yuv --soc_version=Ascend310 --insert_op_conf=./insert_op_yuv.cfg --input_shape="data:1,3,224,224" --input_format=NCHW

   3. Run the following command to copy the converted model to the model folder of the sample:

      cp ./garbage_yuv.om $HOME/samples/python/contrib/garbage_picture/model/

4. Obtain the test images required by the sample.

   Run the following commands to go to the data folder of the sample and download the corresponding test images:

   cd $HOME/samples/python/contrib/garbage_picture/data

   https://c7xcode.obs.cn-north-4.myhuaweicloud.com/models/garbage_picture/newspaper.jpg

   https://c7xcode.obs.cn-north-4.myhuaweicloud.com/models/garbage_picture/bottle.jpg

   https://c7xcode.obs.cn-north-4.myhuaweicloud.com/models/garbage_picture/dirtycloth.jpg

Sample Running

Note: If the development environment and operating environment are set up on the same server, skip step 1 and go to step 2 directly.

1. Run the following commands to upload the garbage_picture directory in the development environment to any directory in the operating environment, for example, /home/HwHiAiUser, and log in to the operating environment (host) as the HwHiAiUser user:

   scp -r $HOME/samples/python/contrib/garbage_picture HwHiAiUser@xxx.xxx.xxx.xxx:/home/HwHiAiUser

   ssh HwHiAiUser@xxx.xxx.xxx.xxx

   NOTE

   • In the following information, xxx.xxx.xxx.xxx is the IP address of the operating environment. The IP address of Atlas 200 DK is 192.168.1.2 when it is connected over the USB port, and that of Atlas 300 (AI1s) is the corresponding public IP address.

2. Run the executable file.

   • If the development environment and operating environment are set up on the same server, run the following commands to set the operating environment variables and switch the directory:

     export LD_LIBRARY_PATH=

     source ~/.bashrc

     cd $HOME/samples/python/contrib/garbage_picture/

   • If the development environment and operating environment are set up on separate servers, run the following command to switch the directory:

     cd $HOME/garbage_picture/

   Run the following command to run the sample:

   python3.6 src/classify_test.py ./data/

Result Checking

After the execution is complete, find the JPG images with inference results in the outputs directory.