Test your model
Introduction
Before developing an application to run your model on a device, it's important to test the model to ensure it meets your requirements and is suitable for your use case. Testing the model allows you to evaluate its performance and make any necessary adjustments before proceeding with application development.
Test your model on the device
To test your model on the device, you can use the larod-client tool. Follow these steps:
- Connect to your device using ssh (see instructions).
- Run the following command:
larod-client -a
This command will provide you with a list of all the interfaces that larod can use to run your model.
$ larod-client -a
> 2023-12-05T10:56:58.019 Connecting to larod...
> 2023-12-05T10:56:58.036 Connected
> 2023-12-05T10:56:58.036 Devices:
> ___________________________________________
> Name Instance
> ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
> cpu-tflite 0
> axis-ace-proc 0
> cpu-proc 0
> axis-a8-dlpu-tflite 0
> axis-a8-dlpu-native 0
> axis-a8-dlpu-proc 0
> axis-a8-gpu-proc 0
> ___________________________________________
To test the performance of your model on the DLPU, you can use the larod-client command with the following options:
larod-client -g <model_file> -c axis-a8-dlpu-tflite -R 100 -w 5 -i ''
This command runs the specified model on the ARTPEC-8 DLPU and performs 100 inferences. The -i '' option indicates that no input is required, as you are only testing the speed.
Before running this command, make sure that the model is loaded on the device.
When you run the command, the output will be displayed in the console. It will show the connection status, model loading status, setup of input tensors, and the execution time for the job. Here is an example of the output:
$ larod-client -g ./mobilenet_v2_1.0_224_quant.tflite -c axis-a8-dlpu-tflite -R 100 -i ''
> 2023-12-05T15:47:31.748 Connecting to larod...
> 2023-12-05T15:47:31.765 Connected
> 2023-12-05T15:47:57.308 Model mobilenet_v2_1.0_224_quant.tflite loaded
> 2023-12-05T15:47:57.308 Setting up randomly generated input tensors using random seed 1701787677
> 2023-12-05T15:47:57.321 Running job for 100 round(s)...
> 2023-12-05T15:47:57.881 Output written to:
> 2023-12-05T15:47:57.882 1701787677.out0
> 2023-12-05T15:47:57.882 Mean execution time for job: 5.60 ms
The output provides information about the connection status, model loading status, setup of input tensors, and the mean execution time for the job.
The output of the inference is saved in a file, such as 1701787677.out0 in the example above. Since the input was random, the output is not relevant. However, the mean execution time for the 100 inferences is shown, which in this case is 5.60 ms.
Input and output with the model
If you are interested in providing an input and obtaining an output from the model, you can use the -i option to specify the input file for the model, and the -o option to specify the output file.
To specify the input and output files when using the larod-client command, use the following syntax:
larod-client -g <model_file> -c axis-a8-dlpu-tflite -R 1 -o <output_file> -i <input_file>
The <input_file> should be a binary file containing the input for the model. There is also a major difference in the input format between ARTPEC-7/8 and CV25, for ARTPEC the input is expected to be rgb-interleaved, while for CV25 the input is expected to be rgb-planar.
You can use the following Python code snippets to convert an image to the required binary format for an ARTPEC device:
import numpy as np
import cv2
img = cv2.imread('image.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img.tofile('input.bin')
For CV25, the input is expected to be rgb-planar, so you need to transpose the image before saving it to the binary file:
import numpy as np
import cv2
img = cv2.imread('image.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.transpose(2, 0, 1)
img.tofile('input.bin')
Running the above command will produce the same results as before, but the output will be saved in the file specified by the -o option. The output file will be a binary file containing the output of the model. You will need to parse it according to your model output format.
Here is an example of running the command with input and output files specified:
$ larod-client -g ./mobilenet_v2_1.0_224_quant.tflite -c axis-a8-dlpu-tflite -R 1 -i ./input.bin -o output.bin
> 2023-12-05T15:58:34.521 Connecting to larod...
> 2023-12-05T15:58:34.538 Connected
> 2023-12-05T15:58:35.492 Model mobilenet_v2_1.0_224_quant.tflite loaded
> 2023-12-05T15:58:35.492 Running job for 100 round(s)...
> 2023-12-05T15:58:36.049 Output written to:
> 2023-12-05T15:58:36.049 output.bin
> 2023-12-05T15:58:36.049 Mean execution time for job: 5.57 ms
Retrieve device logs
If you encounter any errors with the commands mentioned above, you can retrieve the device logs by using the following command:
journalctl -u larod
This command will provide you with the logs from the larod service, which can help you identify and understand the issue you are experiencing. Common messages you may encounter indicate that certain layers in your network are not supported. To ensure proper functionality, ensure that your network only includes supported layers. For more information, refer to supported frameworks.