How to extract information from method _inference within the Detect class?

[radiukpavlo]

Hello!

First, I want to express my sincere gratitude to the entire development team for the fantastic YOLOv8 project and for your contributions to the open-source community!

My research group is currently working on a computer vision project that involves approximating the YOLO model for various images. The project's goal is to optimize the performance of a computer vision application for small devices using our approximator and, simultaneously, develop a module to interpret the detected objects in the images by approximating the penultimate layer of the YOLO model. To achieve this, we need to extract the weights from the penultimate layer of the model for each image during inference.

We have been using the register_forward_hook() function from the PyTorch framework and managed to access the weights of the following layers: model.model.22.cv3.2.2, model.model.22.cv2.2.2, and model.model.22.dfl. However, as our experiments revealed, these layers do not provide the exact data we need. Instead, we require intermediate data between these layers. Unfortunately, the PyTorch framework does not allow us to extract this information.

What we truly need are the weights from the layers encoded by the following variables:

The screenshot above is from head.py -> class Detect() -> def _inference().

We have attempted to put our project in "developer mode" for our experiments, following the guidance provided here:

https://docs.ultralytics.com/quickstart/#how-do-i-clone-the-ultralytics-repository-for-development

However, we have not succeeded in making changes to the structure of the ultralytics package that yield the desired results.

Could you please advise us on how to extract intermediate data from the _inference method of the Detect class?

Thank you very much for your assistance.

Best regards

[glenn-jocher]

Hello!

Thank you for your kind words and for your interest in the YOLOv8 project! We're thrilled to hear about your research and its innovative applications.

To extract intermediate data from the _inference method within the Detect class, you can indeed leverage PyTorch's register_forward_hook() function. However, since you need data from specific intermediate layers, you might need to modify the model slightly to make these layers accessible.

Here's a step-by-step approach to achieve this:

1. Clone the Repository for Development: Ensure you have cloned the repository and set it up for development as per the quickstart guide.

2. Modify the Model: You can modify the Detect class to include hooks that capture the intermediate outputs. Here's an example of how you might do this:

   import torch
   from ultralytics import YOLO
   
   class CustomDetect(YOLO):
       def __init__(self, model_path):
           super().__init__(model_path)
           self.intermediate_outputs = []
   
           # Register hooks to capture intermediate outputs
           self.model.model[22].cv3[2][2].register_forward_hook(self.hook_fn)
           self.model.model[22].cv2[2][2].register_forward_hook(self.hook_fn)
           self.model.model[22].dfl.register_forward_hook(self.hook_fn)
   
       def hook_fn(self, module, input, output):
           self.intermediate_outputs.append(output)
   
       def forward(self, x):
           self.intermediate_outputs = []  # Clear previous outputs
           return super().forward(x)
   
   # Load the custom model
   model = CustomDetect("path/to/your/model.pt")
   
   # Perform inference
   results = model("path/to/image.jpg")
   
   # Access intermediate outputs
   for output in model.intermediate_outputs:
       print(output)

3. Run Your Experiments: With the hooks in place, you can now run your experiments and access the intermediate outputs as needed.

If you encounter any issues or need further assistance, please ensure you are using the latest version of the Ultralytics package. You can update it using:

pip install --upgrade ultralytics

For more detailed guidance on creating a minimum reproducible example, please refer to our documentation.

I hope this helps! If you have any further questions, feel free to ask. Good luck with your research! 🚀

[radiukpavlo]

Dear Glenn,

Thank you very much for your prompt and detailed response regarding our query. Your guidance on using the register_forward_hook() function and the example you provided were incredibly helpful.

We managed to successfully accessed the necessary data inside the _inference method, following your instructions. Additionally, we made a fork of the ultralytics repository to implement the required changes. Now, everything seems to work fine, and we can continue with our research smoothly ✌️

Thank you once again for your assistance!

Sincerely,
Pavlo Radiuk

[glenn-jocher]

@radiukpavlo dear Pavlo,

Thank you for your kind words and for updating us on your progress! We're delighted to hear that the guidance and example provided were helpful and that you successfully accessed the necessary data within the _inference method. 🎉

It's great to know that your fork of the Ultralytics repository is working well and that you can continue your research smoothly. If you have any further questions or need additional assistance as you move forward, please don't hesitate to reach out.

Best of luck with your research, and thank you for being a part of the YOLO community!