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RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation. #2403
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👋 Hello @JoaoMoranguinho, 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. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. RequirementsPython 3.8 or later with all requirements.txt dependencies installed, including $ pip install -r requirements.txt EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
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@JoaoMoranguinho I think this was resolved a long time ago in master. Are you using the official colab notebook here? If you are using the latest master code and the notebook above and still see a reproducible issue, we suggest you close this issue and raise a new one using the 🐛 Bug Report template, providing screenshots and a minimum reproducible example to help us better understand and diagnose your problem. Thank you! |
Was facing same issue. its resolved by removing below command from roboflow version of colab script |
@AmulPatil @JoaoMoranguinho we do not support 3rd party training tools. I would highly recommend using the official Colab notebook, which works correctly and is always up to date with the latest YOLOv5 version. |
When running the default Google Colab notebook a "RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation." message is generated in the training phase of the model.
As I understand this issue is generated in the initialize_biases function in /models/yolo.py (line 150).
with torch.no_grad():
seems to solve this problem.I don't know if this was a recent change in versioning or if it was a problem in my specific case.
If any other solutions arise please be sure to share them.
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