super_resolution_dynamic

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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 README file provides only guidance for running the sample in command line (CLI) mode. For details about how to run the sample in MindStudio, see Running Image Samples in MindStudio.

Super Resolution Sample

Function: Use FSRCNN to perform super resolution processing on the input image. The resolutions in the sample are 256, 256, 512, 512, 288, and 288. Load the .om file in the application, select the resolution for inference by setting the input parameters, and save the inference result to a file.

Input: source BMP image.

Output: PNG image with inference results.

Prerequisites

Check whether the following requirements are met. If not, perform operations according to the remarks. If the CANN version is upgraded, check whether the third-party dependencies need to be reinstalled. (The third-party dependencies for 5.0.4 and later versions are different from those for earlier versions.)

Item	Requirement	Remarks
CANN version	≥ 5.0.4	Install the CANN by referring to Sample Deployment in the About Ascend Samples Repository. If the CANN version is earlier than the required version, switch to the samples repository specific to the CANN version. See Release Notes.
Hardware	Atlas 200 DK/Atlas 300 (AI1s)	Currently, the Atlas 200 DK and Atlas 300 have passed the test. For details about the product description, see Hardware Platform. For other products, adaptation may be required.
Third-party dependencies	presentagent, ffmpeg+acllite	For details, see Third-Party Dependency Installation Guide (C++ Sample).

Sample Preparation

1. Obtain the source 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 repository:   
     cd ${HOME}     
     git clone https://github.com/Ascend/samples.git
     

     To switch to another tag (for example, v0.5.0), run the following command:

     git checkout v0.5.0
     

   • Compressed package (The download takes a short time, but the procedure is complex.)
     Note: If you want to download the code of another version, switch the branch of the samples repository according to the prerequisites.

      # 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. Convert the model.

   Model	Description	How to Obtain
   FSRCNN	Super resolution inference model.	Download the model file by referring to the links in README.md in the ATC_FSRCNN_caffe_AE directory of the ModelZoo repository.

   # To facilitate download, the commands for downloading the original model and converting the model are provided here. You can directly copy and run the commands. You can also refer to the above table to download the model from ModelZoo and manually convert it.    
   
   cd ${HOME}/samples/cplusplus/level2_simple_inference/6_other/super_resolution_dynamic/model   
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/super_resolution/FSRCNN/FSRCNN.caffemodel
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/super_resolution/FSRCNN/FSRCNN.prototxt
   atc --model=./FSRCNN.prototxt --weight=./FSRCNN.caffemodel --framework=0 --input_format=NCHW --input_shape="data: 1, 1, -1, -1" --dynamic_image_size="256,256;512,512;288,288" --output=./FSRCNN_x3 --soc_version=Ascend310 --output_type=FP32
   

Sample Deployment

Run the following commands to execute the compilation script to start sample compilation:

cd ${HOME}/samples/cplusplus/level2_simple_inference/6_other/super_resolution_dynamic/scripts    
bash sample_build.sh

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 super_resolution_dynamic 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 running user (HwHiAiUser):

   # 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.
   scp -r ${HOME}/samples/cplusplus/level2_simple_inference/6_other/super_resolution_dynamic HwHiAiUser@xxx.xxx.xxx.xxx:/home/HwHiAiUser    
   ssh HwHiAiUser@xxx.xxx.xxx.xxx     
   cd ${HOME}/super_resolution_dynamic/scripts
   

2. Execute the script to run the sample.

   bash sample_run.sh
   

   Note: You can also run the following commands to select the resolution. The first and second input parameters of the executable program must be replaced with the actual height and width, respectively. The resolution must be one of the resolutions specified by the --dynamic_image_size parameter during model conversion.

    ./main ../data 256 256     
    ./main ../data 512 512     
    ./main ../data 288 288     
   

Result Viewing

After the execution is complete, an inferred image is generated in the /out/output directory of the sample project.

Common Errors

For details about how to rectify the errors, see Troubleshooting. If an error is not included in Wiki, submit an issue to the samples repository.