Hi Tom,

That's great work!
I would love to play around with the data and models :)

Do you plan to release anytime soon the following?

1. the dataset-creation pipeline from ArgoVerse and NuScenes
2. the created dataset (ground-truth maps etc.)
3. your networks and models.

I think all of the above would be of great value to the community.
Are you planning to do that soon? Or is it dependent on the paper first being published?

Thanks a lot!
Z.

torch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:3550.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
==> Loading NuScenes dataset...

Loading NuScenes tables for version v1.0-mini...
23 category,
8 attribute,
4 visibility,
911 instance,
12 sensor,
120 calibrated_sensor,
31206 ego_pose,
8 log,
10 scene,
404 sample,
31206 sample_data,
18538 sample_annotation,
4 map,
Done loading in 0.476 seconds.

Reverse indexing ...
Done reverse indexing in 0.1 seconds.

=== Beginning epoch 1 of 100 ===
0%| | 0/125 [00:33<?, ?it/s]
Traceback (most recent call last):
File "\BEV\mono-semantic-maps-master\train.py", line 344, in
main()
File "\BEV\mono-semantic-maps-master\train.py", line 319, in main
train(train_loader, model, criterion, optimiser, summary, config, epoch)
File "\BEV\mono-semantic-maps-master\train.py", line 37, in train
for i, batch in enumerate(tqdm(dataloader)):
File "\Users\S\anaconda3\envs\torch\lib\site-packages\tqdm\std.py", line 1182, in iter
for obj in iterable:
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data\dataloader.py", line 631, in next
data = self._next_data()
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data\dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data\dataloader.py", line 1372, in _process_data
data.reraise()
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch_utils.py", line 722, in reraise
raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "\Users\S\anaconda3\envs\torch\lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "\BEV\mono-semantic-maps-master\src\data\augmentation.py", line 14, in getitem
image, calib, labels, mask = self.dataset[index]
File "\BEV\mono-semantic-maps-master\src\data\nuscenes\dataset.py", line 56, in getitem
image = self.load_image(token)
File "\BEV\mono-semantic-maps-master\src\data\nuscenes\dataset.py", line 66, in load_image
image = Image.open(self.nuscenes.get_sample_data_path(token))
File "\Users\S\anaconda3\envs\torch\lib\site-packages\PIL\Image.py", line 3247, in open
fp = builtins.open(filename, "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'C:\BEV\mono-semantic-maps-master\nuscenes\samples/CAM_BACK_LEFT/n015-2018-07-24-11-22-45+0800__CAM_BACK_LEFT__1532402938647423.jpg'

Please help me to resolve this issue, NOTE: I tried all the possible way to mention correct paths in config file , also when i try big dataset i am facing the memory error and when using the mini dataset i face this above mentioned error. kindly help me with this

I used your script and generated the ground truth labels and then used your code to train the model. However, the model is only overfitting. Train IoUs increase consistently but validation IoUs do not increase.

Hi!

Great work! Do you plan on releasing the code for the temporal fusion (described in Section 3.2) as well?

Cheers

Hi, thanks for the great work! I want to train this model on argoverse dataset, got following error:

=== Beginning epoch 1 of 200 ===
  0%|                                                                                                                                                | 0/50000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/media/new_data3/mono-semantic-maps-master/train.py", line 340, in <module>
    main()
  File "/media/new_data3/mono-semantic-maps-master/train.py", line 316, in main
    train(train_loader, model, criterion, optimiser, summary, config, epoch)
  File "/media/new_data3/mono-semantic-maps-master/train.py", line 34, in train
    for i, batch in enumerate(tqdm(dataloader)):
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/tqdm/std.py", line 1180, in __iter__
    for obj in iterable:
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
    data.reraise()
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/media/new_data3/anaconda3/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 49, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/media/new_data3/mono-semantic-maps-master/src/data/augmentation.py", line 14, in __getitem__
    image, calib, labels, mask = self.dataset[index]
  File "/media/new_data3/mono-semantic-maps-master/src/data/argoverse/dataset.py", line 59, in __getitem__
    split, log, camera = self.examples[timestamp]
KeyError: 30314

It seems like examples are indexed by timestamp inside argoverse, which can not index by normal numbers. How can i fix this issue? Thanks.

My env is:

python 3.9.7
1.10.2+cu113

Thanks very much for releasing the code!
It seems that you have used horizontal flip as an augmentation during training. But looking at this it seems that the images are not flipped at random but instead they are flipped all the time. Shouldn't it be flipped randomly?

Hello！Thanks for your good work ! could you please explain what GPU you used to train models, and how long does it take?

Earnestly waiting!

Exception: Error: You are using an outdated map version (%s)! Please go to https://www.nuscenes.org/download to download the latest map!

I would like to ask if it is not possible to use the v1.0 map expansion?

Thx

When I am training, I have the above error, how can I solve it?
Many thanks!

Could you please provide any trained models to evaluate directly?

Hello, the function 'render_bbox' in the utils of nuscenes seems to be undefined. Could you please support it?

Hi guys,

would it be possible to get the code to remove the overlapping segments?

Best.

i trained model in nuScenes datasets following the instruction.
how can i visualize or make a demo for generated model?

Hi, @anthonyhu I got some trouble in generating BEV semantic labels of nuScenes.
The label I generated is like:

Is it right? And how to visualize it in color? Waiting for your answer and thanks a lot!

map_extents: [-25., 1., 25., 50.]

Hello. I'm confuse about that why map _extents is not [-25., 0., 25., 50.]?

Thanks for your wonderful work and codes!

I used your scripts to generate the nuscens BEV labels, but it has some black regions in the drivable(green circle above), which have much bad effect in our performance evalutaion. I also have tried map-v1.0,-v1.2,-v1.3, and they all have the same problem.
Could you please give me any advice? Many thanks again!

how can i visualize or make a demo for generated model? thanks.