pulling out model's layer intermediates

LindyZh commented 4 months ago

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Question

I'm trying to adapt the model architecture (append attention head, etc) of the existing structure but this requires me to pull out the layer intermediate (output from the 8th layer) in my custom pytorch class.

I understand we could use our pretrain weights like follows with a tensor input model = torch.hub.load('./yolov5', 'custom', path='best.pt', source='local') results = model(torch.zeros(16,3,320,640))

but how can I use the intermediate layer result instead of letting the input running through the entire data?

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github-actions[bot] commented 4 months ago

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glenn-jocher commented 4 months ago

@LindyZh hi there! It's great to see your interest in customizing the YOLOv5 architecture. To extract intermediate layer outputs, you can modify the forward method of the YOLOv5 model. Here's a concise way to achieve this:

Load the Model: First, load your model as usual.

import torch
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', source='local')

Modify the Forward Method: You can create a custom forward method to extract the output from the 8th layer. Here’s an example:

from copy import deepcopy

class CustomYOLOv5(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model.model  # access the model's layers

    def forward(self, x):
        for i, layer in enumerate(self.model):
            x = layer(x)
            if i == 7:  # 8th layer (0-indexed)
                intermediate_output = deepcopy(x)
                break
        return intermediate_output

# Instantiate custom model
custom_model = CustomYOLOv5(model)

Run Inference: Now you can run inference and get the intermediate output:

input_tensor = torch.zeros(16, 3, 320, 640)
intermediate_output = custom_model(input_tensor)
print(intermediate_output.shape)

This approach allows you to extract the output from any specific layer. If you need to append additional layers or heads, you can further modify the CustomYOLOv5 class to include those layers and adjust the forward method accordingly.

If you encounter any issues or need further assistance, please ensure you are using the latest version of YOLOv5 and PyTorch. Feel free to reach out with more questions. Happy coding! 🚀

For more detailed information, you can refer to the PyTorch Hub Model Loading tutorial.

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