calculate_ap fails when nothing is predicted. Can't train from scratch.

[Abdul-Mukit]

Describe the bug

Started training using the command:
python yolo/lazy.py task=train model=v9-s dataset=mock task.epoch=1 cpu_num=0 device=cpu weight=False
Note weight=False. I was trying to do a sanity check by training the model from scratch on a few images.

Ran into the following error:

Traceback (most recent call last):
  File "/home/abdul/projects/YOLO/yolo/lazy.py", line 39, in main
    solver.solve(dataloader)
  File "/home/abdul/projects/YOLO/yolo/tools/solver.py", line 149, in solve
    mAPs = self.validator.solve(self.validation_dataloader, epoch_idx=epoch_idx)
  File "/home/abdul/projects/YOLO/yolo/tools/solver.py", line 264, in solve
    result = calculate_ap(self.coco_gt, predict_json)
  File "/home/abdul/projects/YOLO/yolo/utils/solver_utils.py", line 12, in calculate_ap
    coco_dt = coco_gt.loadRes(pd_path)
  File "/home/abdul/projects/YOLO/.venv/lib/python3.10/site-packages/pycocotools/coco.py", line 329, in loadRes
    if 'caption' in anns[0]:
IndexError: list index out of range

Reason:
As the model is being trained from scratch, there were not detections by the model. predictions were empty.
As a result, calculate_ap throws an exception.

Proposed solution:
Please consider adopting MeanAveragePrecision from pytorch lightning. Or consider implementing something like that.
https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html
The current implementation of calculate_ap is quite buggy. See this MR #79. The bug, stems from the WIP state of calculate_ap.
If calculate_ap was like MeanAveragePrecision, then the training dataloader would never need to return the image_id/image_name. Thus MR #79 would have been necessary too.

Expected behavior

Behavior could be like implementation in PyTorch-Lightning.
https://lightning.ai/docs/torchmetrics/stable/detection/mean_average_precision.html

[bherbruck]

That scam bot...
Can somebody report those posts?

[Abdul-Mukit]

@henrytsui000 please take a look at the comments made by spambot accounts. I think these are harmful.
Someone might fall into the traps.
I tried reporting one of the accounts. Can others do too, please?

[Abdul-Mukit]

@henrytsui000 what is your opinion about torchmetrics. It has Apache License like OpenCV.
Do you think a PR replacing calculate_ap with a mAP class from torchmetrics will be helpful?

[henrytsui000]

@henrytsui000 what is your opinion about torchmetrics. It has Apache License like OpenCV. Do you think a PR replacing calculate_ap with a mAP class from torchmetrics will be helpful?

Calling torchmetrics is acceptable because it doesn't involve any copy-pasting or modifications—it's limited to installing and calling the API.
If there's a better way to accumulate mAP during inference (per batch) and then obtain the total mAP after inference without rerunning the process, that would be greatly appreciated!

Best regards,
Henry Tsui

[Abdul-Mukit]

@henrytsui000
I am bit confused.

If there's a better way to accumulate mAP during inference (per batch) and then obtain the total mAP after inference without rerunning the process, that would be greatly appreciated!

Do you prefer having a class like torchmetric's MeanAveragePrecision but not have to install the whole library?
Or a PR that directly uses torchmetrics?

For now, I'll try sending a PR that will use torchmetrics to calculate mAP.
I'll try testing it too to see how reliable it is. Let me know whether you would just prefer a standalone class MeanAveragePrecision instead. I can try rewriting it and send a PR.
Thank you.