Running hailomz optimize reports layer yolov8n/conv41 doesn't have one output layer

Community Projects
, ,
9

Hi,
The issue stems from the fact that your custom yolov8n model must have different end nodes (right before the NMS) than the original yolov8n model in the Hailo model zoo. You may have passed one of these to the parser whereas there should be 6 of them.
These end modes are supplied to the parser so it can cut the model right before the NMS processing. Later on, the optimizer will “attach” the NMS post-processing to the parser model. The NMS post-processing doesn’t necessarily run on the Hailo-8 and might run on the host processor, depending on the model. For yolov8, it will run on the host processor.

How do you find the name of the end nodes of any model ?

You can use the CLI command

hailo parser onnx -y

TIP: if you need help with what arguments the command accepts, try to run both “hailo parser --help” and “hailo parser onnx --help”

For example, if I apply this command to the yolov8n.onnx downloaded from the model zoo: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.13.0/hailo8/yolov8n.hef
You’ll find these lines amongst the output text displayed:

[info] NMS structure of yolov8 (or equivalent architecture) was detected.
[info] In order to use HailoRT post-processing capabilities, these end node names should be used: /model.22/cv2.0/cv2.0.2/Conv /model.22/cv3.0/cv3.0.2/Conv /model.22/cv2.1/cv2.1.2/Conv /model.22/cv3.1/cv3.1.2/Conv /model.22/cv2.2/cv2.2.2/Conv /model.22/cv3.2/cv3.2.2/Conv.

Now that you have the names of the end nodes, how do you pass them to the “hailomz parse” command ?

There are two options:

  1. Directly as arguments to the “hailomz parse” command. If you run “hailomz parse --help”, you’ll see that it accepts --start-node-names and --end-node-names as arguments.
    So, if I apply the end node names found earlier to this command, it would look like this:

hailomz parse yolov8n --end-node-names /model.22/cv2.0/cv2.0.2/Conv /model.22/cv3.0/cv3.0.2/Conv /model.22/cv2.1/cv2.1.2/Conv /model.22/cv3.1/cv3.1.2/Conv /model.22/cv2.2/cv2.2.2/Conv /model.22/cv3.2/cv3.2.2/Conv

You may be wondering now, if you don’t pass any end-node-names argument to “hailomz parse”, what node names would it use ? In this case, it will use the end node names written in the default yaml file corresponding to yolo8n. This lead to the second option of how you can pass the end node names to the parser.

  1. When you invoke “hailomz parse yolov8n”, it will use meta-information stored in the file yolov8n.yaml file: hailo_model_zoo/hailo_model_zoo/cfg/networks/yolov8n.yaml at master · hailo-ai/hailo_model_zoo · GitHub .
    You will notice both the start and the end nodes listed there:

parser:
nodes:

  • null
    • /model.22/cv2.0/cv2.0.2/Conv
    • /model.22/cv3.0/cv3.0.2/Conv
    • /model.22/cv2.1/cv2.1.2/Conv
    • /model.22/cv3.1/cv3.1.2/Conv
    • /model.22/cv2.2/cv2.2.2/Conv
    • /model.22/cv3.2/cv3.2.2/Conv

Therefore, one way to specify new end nodes is to make a copy of this yaml file, modify the end nodes field only and pass this new yaml file as argument to “hailomz parse yolov8n” as follow:

hailomz parse yolov8n --yaml <PATH_TO_NEW_YAML_FILE>

Hopefully this resolves your issue. You should delete all the yolob8n HAR files that were created and run the correct sequence.
Note that running “hailomz optimize yolov8n” didn’t cause any errors because it used the default yolov8n model, not your custom trained model.

Last note, for conveniency, Hailo provides a yolov8 retraining docker as well: hailo_model_zoo/training/yolov8 at master · hailo-ai/hailo_model_zoo · GitHub
You don’t have to use it but the README.rst can provide useful information as well.