tusen-ai/RangeDet

error when run tools/train.py

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#6 opened on Nov 23, 2021

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good first issue

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Description

[Warning] Your mxnet does not support ProposalTarget [Warning] Your mxnet does not support DecodeBBox [Warning] Your mxnet does not support BBoxNorm [Warning] Your mxnet does not support FocalLoss Traceback (most recent call last): File "tools/train.py", line 412, in <module> train_net(parse_args()) File "tools/train.py", line 407, in parse_args config = importlib.import_module(args.config.replace('.py', '').replace('/', '.')) File "/mmdetection3d/RangeDet/config/rangedet/rangedet_wo_aug_4_18e.py", line 22, in <module> from rangedet.symbol.head.builder import RangeRCNN as Detector File "/mmdetection3d/RangeDet/rangedet/symbol/head/builder.py", line 4, in <module> from operator_py import \ File "/mmdetection3d/RangeDet/operator_py/batch_rotated_iou.py", line 3, in <module> from mxnet.ndarray.contrib import RotatedIOU ImportError: cannot import name 'RotatedIOU' from 'mxnet.ndarray.contrib' (/usr/local/lib/python3.7/dist-packages/mxnet/ndarray/contrib.py)

the link to download MxNet is invalid, can you provide a new link?

this is provided by the SimpleDet, but it is invalid https://1dv.aflat.top/mxnet_cu101-1.6.0b20191214-py2.py3-none-manylinux1_x86_64.whl

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Research direction
Check the MXNet version and install the custom operator package. The error indicates RotatedIOU is missing from mxnet.ndarray.contrib. Look at the project's installation instructions and ensure the correct MXNet build (with custom operators) is used.
Tech stack
python
Domain
backendmachine learningai
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonMXNet
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75