Closed shenxiaowrj closed 1 year ago
It is a small updating bug of the ProgressBar, which does not influence the evaluation performance. We have fixed it, please pull the latest code.
It is a small updating bug of the ProgressBar, which does not influence the evaluation performance. We have fixed it, please pull the latest code.
Thanks! The previous issue was solved. But the testing phase still didn't go smoothly. It encountered the following issues.
6019/6019, 0.8 task/s, elapsed: 7264s, ETA: 0sTraceback (most recent call last):
File "./tools/test.py", line 278, in
The problem is fixed, it is caused by repeated evaluation. You don't have to re-eval your model, as the last print info in the figure is your final performance. To avoid similar errors, please pull the latest code.
Thanks!
It is a small updating bug of the ProgressBar, which does not influence the evaluation performance. We have fixed it, please pull the latest code.
Thanks! The previous issue was solved. But the testing phase still didn't go smoothly. It encountered the following issues.
6019/6019, 0.8 task/s, elapsed: 7264s, ETA: 0sTraceback (most recent call last): File "./tools/test.py", line 278, in main() File "./tools/test.py", line 275, in main print(dataset.evaluate(outputs, **eval_kwargs)) File "/media/re/2384a6b4-4dae-400d-ad72-9b7044491b55/data/OpenOccupancy/projects/occ_plugin/datasets/nuscenes_occ_dataset.py", line 130, in evaluate ious = cm_to_ious(evaluation_semantic) File "/media/re/2384a6b4-4dae-400d-ad72-9b7044491b55/data/OpenOccupancy/projects/occ_plugin/utils/formating.py", line 8, in cm_to_ious tp = cm[i, i] IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
Hi,I encountered the same error as you, have you solved it?
I regenerated the ground_truth with the latest version of the code. And i use the python ./tools/test.py ./projects/configs/baselines/CAM-R50_img1600_128x128x10.py ./work_dirs/CAM-R50_img1600_128x128x10/latest.pth --deterministic --eval bbox to test the c-baseline model. The weights I used were obtained after training two epochs. The test exceeded the set maximum test dataset length.
How can I solve this problem?
Thanks.