How can I process the features during inference?

[Yangchen-nudt]

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So much thank if developers can see my question and chat with me :) I use yolov5 project with ByteTrack(which is a two stage method: detect, then associate) to achieve multi-object tracking. But I found that there existing some missed detection: As shown in the pic, the car in the Bottom Right side cannot be detected (maybe due to the shadow cast on the car) However, i can inform the yolov5 algorithm the probable position of the undetected car, because it's detected in the previous tracking. So i think maybe i can enhance the three feature maps before the detect head. Specifically speaking, I generate one Gaussian distribution heatmap(the probable position is the peak point), and element-wise multiply the heatmap with the feature map. In this case, I want to let the yolov5 pay more attention to the probable position. Then when it comes to the pratical coding, I meet some problems cause I'm not that familiar with pytorch. I don't know how to extract the features before the Detect Head during inference, process them and them feed them back to the final Detect Head. I notice before the non_max_suppression, the detected result is given by: # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False if model.xml and im.shape[0] > 1: pred = None for image in ims: if pred is None: pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) else: pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) pred = [pred, None] else: pred = model(im, augment=augment, visualize=visualize) and the model is loaded with my trained weight. What should I do if i want to extract the feature map and then feed it back to the final Detect head?

I'll appreciate it for any instructions given to me. Long for your reply

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[glenn-jocher]

@Yangchen-nudt hello,

Thank you for your detailed question and for providing context on your use case with ByteTrack and YOLOv5. Enhancing feature maps during inference is an interesting approach to address missed detections.

To achieve this, you will need to modify the YOLOv5 model to extract and manipulate the feature maps before they are passed to the detection head. Here’s a step-by-step guide to help you get started:

1. Modify the YOLOv5 Model: You will need to modify the model.py file to extract the feature maps. Specifically, you can hook into the forward pass of the model to access the intermediate feature maps.

2. Extract Feature Maps: You can use PyTorch hooks to extract the feature maps. Here’s an example of how you can do this:

   import torch
   from models.yolo import Model
   
   # Load your model
   model = Model('path/to/your/yolov5.yaml', ch=3, nc=80)
   model.load_state_dict(torch.load('path/to/your/weights.pt')['model'])
   
   # Register hooks to extract feature maps
   feature_maps = []
   
   def hook_fn(module, input, output):
      feature_maps.append(output)
   
   hooks = []
   for layer in model.model:
      if isinstance(layer, torch.nn.Conv2d):
          hooks.append(layer.register_forward_hook(hook_fn))
   
   # Perform inference
   img = torch.randn(1, 3, 640, 640)  # Example input
   with torch.no_grad():
      pred = model(img)
   
   # Remove hooks
   for hook in hooks:
      hook.remove()
   
   # Now feature_maps contains the intermediate feature maps

3. Enhance Feature Maps: Once you have the feature maps, you can enhance them using your Gaussian heatmap. Here’s an example of how you might do this:

   import torch.nn.functional as F
   
   # Generate Gaussian heatmap
   heatmap = torch.zeros_like(feature_maps[0])
   center = (320, 320)  # Example center
   sigma = 10
   for i in range(heatmap.shape[2]):
      for j in range(heatmap.shape[3]):
          heatmap[0, 0, i, j] = torch.exp(-((i - center[0]) ** 2 + (j - center[1]) ** 2) / (2 * sigma ** 2))
   
   # Enhance feature maps
   enhanced_feature_maps = [fm * heatmap for fm in feature_maps]

4. Feed Enhanced Feature Maps to Detection Head: Finally, you need to modify the forward pass of the model to use the enhanced feature maps. This will require deeper changes to the model’s code to ensure the enhanced feature maps are used in the detection head.

Please ensure you are using the latest versions of torch and https://github.com/ultralytics/yolov5 to avoid any compatibility issues. If you encounter any specific errors or need further assistance, please provide a minimum reproducible code example as outlined in our documentation.

I hope this helps! If you have any further questions, feel free to ask.