Hi,

can i use the models under "bdd100k-models/det/" to make predictions on other images ?

When i followed the "Usage"-Section, it seems that the models can only be used to evaluate the Test/Val Images.

When I am running Deeplabv3+ model by using: python ./test.py configs/drivable/deeplabv3plus_r50-d8_512x1024_40k_drivable_bdd100k.py --format-only --format-dir output It just stuck in around 1490 step I have tried several different configs, they all have the same issue.

Hello,

I'm generating semantic segmentation using the following command.

python ./test.py ~/config.py --show-dir ~/Documents/bdd100k-models/data/bdd100k/labels/seg_track_20/val --opacity 1

This generates the colormaps for the images, however, the output produced is in .jpg format which results in blur within the labels (as shown below.) How can I update the script so that it generates the labels in png format. My input images are from the MOTS 2020 Images dataset, which are in jpg format.

Error when running Sem_seg model inference Command Run: python ./test.py ./configs/sem_seg/deeplabv3+_r50-d8_512x1024_40k_sem_seg_bdd100k.py --format-only --format-dir ./outputs

ERROR:

workers per gpu=2
/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/mmseg/models/losses/cross_entropy_loss.py:235: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
  warnings.warn(
load checkpoint from http path: https://dl.cv.ethz.ch/bdd100k/sem_seg/models/deeplabv3+_r50-d8_512x1024_40k_sem_seg_bdd100k.pth
'CLASSES' not found in meta, use dataset.CLASSES instead
'PALETTE' not found in meta, use dataset.PALETTE instead
[                                                  ] 0/1000, elapsed: 0s, ETA:ERROR: Unexpected segmentation fault encountered in worker.
ERROR: Unexpected segmentation fault encountered in worker.
ERROR: Unexpected segmentation fault encountered in worker.
Traceback (most recent call last):
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1011, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/queue.py", line 179, in get
    self.not_empty.wait(remaining)
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/threading.py", line 306, in wait
    gotit = waiter.acquire(True, timeout)
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 15796) is killed by signal: Segmentation fault. 

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "./test.py", line 174, in <module>
    main()
  File "./test.py", line 150, in main
    outputs = single_gpu_test(
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/mmseg/apis/test.py", line 89, in single_gpu_test
    for batch_indices, data in zip(loader_indices, data_loader):
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    data = self._next_data()
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1207, in _next_data
    idx, data = self._get_data()
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1163, in _get_data
    success, data = self._try_get_data()
  File "/home/lunet/codsn/.conda/envs/bdd100k-mmseg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1024, in _try_get_data
    raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
RuntimeError: DataLoader worker (pid(s) 15796) exited unexpectedly

Hello, thanks for your marvelous contribution.I would like to know that the category of red traffic lights is not available on bdd, have you re-labeled it on the bdd dataset?

hi hi, thanks for your marvelous contribution. I am very impressed. Now I want to apply this pretrain model（tagging road type and weather） on my own dataset, do you have any codebase for finetuning?

https://github.com/open-mmlab/mmsegmentation/blob/master/docs/model_zoo.md#common-settings The above link is not working

I would like to know the settings under which the segmentation models are trained , so that i can replicate the results . thank you.

Hello,

I am trying to convert the bdd100k instance segmentation using this command: python3 -m bdd100k.label.to_coco -m ins_seg --only-mask -i ./bdd100k/labels/ins_seg/bitmasks/val -o ./ins_seg_val_cocofmt_v2.json

Also, tried this: python3 -m bdd100k.label.to_coco -m ins_seg -i ./bdd100k/labels/ins_seg/polygons/ins_seg_val.json -o ./ins_seg_val_cocofmt_v3.json -mb ./bdd100k/labels/ins_seg/bitmasks/val

The conversion is successful in both cases and the annotation looks like this

** that's not how coco annotations are.

Now, if you see the segmentation field above there's string encoding of the masks. Now, I am unsure if that's expected or not.

Further, assuming it's correct, I tried to load the annotations using loader from DETR https://github.com/facebookresearch/detr/blob/091a817eca74b8b97e35e4531c1c39f89fbe38eb/datasets/coco.py#L36

The line I have mentioned above is supposed to do the conversion but I am getting an error from the pycocotools that it's not expecting a string in the mask.

So, I am unsure where the problem is? Is the conversion correct to coco then the loader should work? Note: I tried to convert the detections and they worked fine.

Thank you for any help you can provide.

Hello! thank you for your work~~but i wonder if i could train these models on my own gpu? i wonder if there are som instructions or usages? plz ,thank u!