AttributeError: module 'numpy.linalg._umath_linalg' has no attribute '_ilp64'
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Description
Search before asking
- I have searched the Roboflow Notebooks issues and found no similar bug report.
Notebook name
how-to-detect-and-count-objects-in-polygon-zone.ipynb
Bug
Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-23-d827f2069f82>](https://localhost:8080/#) in <cell line: 3>()
1 import torch
2
----> 3 model = torch.hub.load('ultralytics/yolov5', 'yolov5x6')
12 frames
[/usr/local/lib/python3.10/dist-packages/numpy/testing/_private/utils.py](https://localhost:8080/#) in <module>
55 IS_PYSTON = hasattr(sys, "pyston_version_info")
56 HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON
---> 57 HAS_LAPACK64 = numpy.linalg._umath_linalg._ilp64
58
59 _OLD_PROMOTION = lambda: np._get_promotion_state() == 'legacy'
AttributeError: module 'numpy.linalg._umath_linalg' has no attribute '_ilp64'
Environment
-Google colab
https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb#scrollTo=0EL4o_eOQmej
Advanced YOLOv5 Market Square Example
Minimal Reproducible Example
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5x6')
Additional
just running the official demo in this link
Are you willing to submit a PR?
- Yes I'd like to help by submitting a PR!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb and the cell containing torch.hub.load('ultralytics/yolov5', 'yolov5x6'); run the minimal example in Google Colab to reproduce the NumPy traceback. Trace the notebook's dependency environment and verify that the official demo runs without the AttributeError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, numpy, python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100