--freeze option doen't work as expected

[bilzard]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Training

Bug

The change made in #6019 currently not working when I specified single integer.
When specified single integer value, the freeze value is created as a list of single integer: e.g. [10]. Therefore, the train.py code only freezes the 11th layer, remaining other former layers 0, .., 9 are ignored.

The expected behavior is:

python train.py --freeze 10 # freeze up to 10.

Environment

No response

Minimal Reproducible Example

python train.py --freeze 10

the output:

freezing model.10.conv.weight
freezing model.10.bn.weight
freezing model.10.bn.bias

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @bilzard, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[bilzard]

I read the initial issue.
#6001 (comment)

I think meeting both specifications 1, 2 is difficult:

specification. 1: python train.py --freeze 10  # freeze up to 10
specification. 2: python train.py --freeze 5 6 7 8 9 10  # freeze layers 5-10

Because when specifying single integer, the program can't distinguish the two cases: 1) freeze only layer 10 and 2) freeze layer from 0 to 9.

I think there are two solutions:

a) give up specification 1 or 2
b) separate option for specification 1 to 2. e.g: --freeze-up-to 10 and --freeze 0 1 2

[bilzard]

Personally, I prefer keeping specification 1 and give up 2 because specification 1 is simple.
But I don't know if there are the cases when freezing only middle layers or latter layers.
Since specification 1 is just a syntax sugar, keeping spec 2 might be better.

[glenn-jocher]

@bilzard thanks for the bug report! I'm able to reproduce. Will push a fix.

[glenn-jocher]

@bilzard good news 😃! Your original issue may now be fixed ✅ in PR #6044. To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[bilzard]

@glenn-jocher Thank you for the quick reply.
I checked the PR. It works most of the cases, but I think this solution is insufficient.

How do the user specify to freeze only one layer? See, layer 0 only?