fpv_rcnn's People
fpv_rcnn's Issues
The cooperative vehicles in 'tfs' and 'coop_cloud' are different?
Hi, I download your COMAP dataset and extract them all, but find them confusing. The cooperative vehicles in 'tfs' and 'coop_cloud' have no correspondence
Take the first frame in train_val.txt as example, 1070_000317.
keys in tfs/1070_000317.npy include ['tf_ego', '000578',''000573'',''000561'']
files in coop_cloud/1070_000317 include ['000388.bin', '000447.bin', '000386.bin', '000414.bin', '000458.bin', '000387.bin', '000391.bin', '000399.bin', '000405.bin']. And them will turn to coop_ids.
so I encounter error in line 116 in datasets/comap/comap.py, which says a key error.
images and segmantation
Greetings,
I can not find any image in data and corresponding ground truth data for semantic segmentations.
Regards,
Melih
COMAP dataset cannot download
hello, I can't download the COMAP dataset. The link https://seafile.cloud.uni-hannover.de/d/1c52826e98d34c0399a4/ interface's shows that '/COMP/' does not exist.
Run your method on other dataset
Hi, thanks for your great work! I was wondering, have you tried your method on some other dataset, such as OPV2V? It would be good if you have tried and provide a number on it. Thanks!
Get stuck when going into batch_dict = self.matcher(batch_dict).
Hi! When I try to run tools/train_fusion_detector.py
with cfg/fusion_pvrcnn_comap.py
, I find I fall in a dead loop in MatcherV3' s clustering method.
cluster_indices.shape: torch.Size([1608])
ious.shape: torch.Size([1608, 1608])
torch.where(ious[cur_idx] > 0.1)
returns all empty tensor in the end, like (tensor([], device='cuda:0', dtype=torch.int64),)
.
Then I simply replace MatcherV3 with MatcherV2, I meet another error:
File "/GPFS/data/yifanlu/FPV_RCNN/losses/smooth_l1_loss.py", line 55, in forward
anchorwise_smooth_l1norm *= weights.unsqueeze(-1) # pos_anchors multiply the weight: 1/num_pos_anchor in each sample
...
RuntimeError: Function 'MulBackward0' returned nan values in its 0th output.
There are some other questions I look forward to your answers:
I find the pretrained model cia_ssd.pth you provide don't come in handy. Where to load it?
If I do not have the class labels of points, can I train the SemSeg network? Since I want to apply this method in other dataset.
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