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I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
master branch https://github.com/open-mmlab/mmdetection
Python 3.8, PyTorch 1.13 and mmdet 2.26.0
python tools/test.py configs/sodad-benchmarks/autoassign_r50_1x.py work_dirs/autoassign_r50_1x/epoch_1.pth
[>>>>>>>>>>>>>>>>>>>>>>>] 109126/109126, 8.8 task/s, elapsed: 12385s, ETA: 0s
Merge detected results of patch for whole image evaluating...
当使用RetinaNet和Faster R-CNN这种anchor-based时,能够正常推理。但是使用FCOS或autoassign这种anchor-free检测器时,就会卡在这个合并步骤,无法下一步推理。
No response
The text was updated successfully, but these errors were encountered:
检查下子图有没有正常预测的检测框
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将12轮训练后的ckpt进行测试可视化是有正常预测的检测框和分类。
No branches or pull requests
Prerequisite
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Branch
master branch https://github.com/open-mmlab/mmdetection
Environment
Python 3.8, PyTorch 1.13 and mmdet 2.26.0
Reproduces the problem - code sample
python tools/test.py configs/sodad-benchmarks/autoassign_r50_1x.py work_dirs/autoassign_r50_1x/epoch_1.pth
Reproduces the problem - command or script
python tools/test.py configs/sodad-benchmarks/autoassign_r50_1x.py work_dirs/autoassign_r50_1x/epoch_1.pth
Reproduces the problem - error message
[>>>>>>>>>>>>>>>>>>>>>>>] 109126/109126, 8.8 task/s, elapsed: 12385s, ETA: 0s
当使用RetinaNet和Faster R-CNN这种anchor-based时,能够正常推理。但是使用FCOS或autoassign这种anchor-free检测器时,就会卡在这个合并步骤,无法下一步推理。
Additional information
No response
The text was updated successfully, but these errors were encountered: