RuntimeError: There were no tensor arguments to this function about softgroup HOT 4 CLOSED

The error shows that grouping doesnot return any valid proposal. Can you set semantic_only to True and tell the sematic mIoU, Acc and offset MAE.

from softgroup.

Thanks for the quick reply!

I was trying to run as you said, however I ran into another error

2022-04-17 14:48:47,455 - INFO - Load state dict from /arc-share/pc_yejin/checkpoints/SoftGroup/softgroup_scannet_spconv2.pth
2022-04-17 14:48:48,616 - INFO - unexpected key in source state_dict: tiny_unet.blocks.block0.conv_branch.0.weight, tiny_unet.blocks.block0.conv_branch.0.bias, tiny_unet.blocks.block0.conv_branch.0.running_mean, tiny_unet.blocks.block0.conv_branch.0.running_var, tiny_unet.blocks.block0.conv_branch.0.num_batches_tracked, tiny_unet.blocks.block0.conv_branch.2.weight, tiny_unet.blocks.block0.conv_branch.3.weight, tiny_unet.blocks.block0.conv_branch.3.bias, tiny_unet.blocks.block0.conv_branch.3.running_mean, tiny_unet.blocks.block0.conv_branch.3.running_var, tiny_unet.blocks.block0.conv_branch.3.num_batches_tracked, tiny_unet.blocks.block0.conv_branch.5.weight, tiny_unet.blocks.block1.conv_branch.0.weight, tiny_unet.blocks.block1.conv_branch.0.bias, tiny_unet.blocks.block1.conv_branch.0.running_mean, tiny_unet.blocks.block1.conv_branch.0.running_var, tiny_unet.blocks.block1.conv_branch.0.num_batches_tracked, tiny_unet.blocks.block1.conv_branch.2.weight, tiny_unet.blocks.block1.conv_branch.3.weight, tiny_unet.blocks.block1.conv_branch.3.bias, tiny_unet.blocks.block1.conv_branch.3.running_mean, tiny_unet.blocks.block1.conv_branch.3.running_var, tiny_unet.blocks.block1.conv_branch.3.num_batches_tracked, tiny_unet.blocks.block1.conv_branch.5.weight, tiny_unet.conv.0.weight, tiny_unet.conv.0.bias, tiny_unet.conv.0.running_mean, tiny_unet.conv.0.running_var, tiny_unet.conv.0.num_batches_tracked, tiny_unet.conv.2.weight, tiny_unet.u.blocks.block0.conv_branch.0.weight, tiny_unet.u.blocks.block0.conv_branch.0.bias, tiny_unet.u.blocks.block0.conv_branch.0.running_mean, tiny_unet.u.blocks.block0.conv_branch.0.running_var, tiny_unet.u.blocks.block0.conv_branch.0.num_batches_tracked, tiny_unet.u.blocks.block0.conv_branch.2.weight, tiny_unet.u.blocks.block0.conv_branch.3.weight, tiny_unet.u.blocks.block0.conv_branch.3.bias, tiny_unet.u.blocks.block0.conv_branch.3.running_mean, tiny_unet.u.blocks.block0.conv_branch.3.running_var, tiny_unet.u.blocks.block0.conv_branch.3.num_batches_tracked, tiny_unet.u.blocks.block0.conv_branch.5.weight, tiny_unet.u.blocks.block1.conv_branch.0.weight, tiny_unet.u.blocks.block1.conv_branch.0.bias, tiny_unet.u.blocks.block1.conv_branch.0.running_mean, tiny_unet.u.blocks.block1.conv_branch.0.running_var, tiny_unet.u.blocks.block1.conv_branch.0.num_batches_tracked, tiny_unet.u.blocks.block1.conv_branch.2.weight, tiny_unet.u.blocks.block1.conv_branch.3.weight, tiny_unet.u.blocks.block1.conv_branch.3.bias, tiny_unet.u.blocks.block1.conv_branch.3.running_mean, tiny_unet.u.blocks.block1.conv_branch.3.running_var, tiny_unet.u.blocks.block1.conv_branch.3.num_batches_tracked, tiny_unet.u.blocks.block1.conv_branch.5.weight, tiny_unet.deconv.0.weight, tiny_unet.deconv.0.bias, tiny_unet.deconv.0.running_mean, tiny_unet.deconv.0.running_var, tiny_unet.deconv.0.num_batches_tracked, tiny_unet.deconv.2.weight, tiny_unet.blocks_tail.block0.i_branch.0.weight, tiny_unet.blocks_tail.block0.conv_branch.0.weight, tiny_unet.blocks_tail.block0.conv_branch.0.bias, tiny_unet.blocks_tail.block0.conv_branch.0.running_mean, tiny_unet.blocks_tail.block0.conv_branch.0.running_var, tiny_unet.blocks_tail.block0.conv_branch.0.num_batches_tracked, tiny_unet.blocks_tail.block0.conv_branch.2.weight, tiny_unet.blocks_tail.block0.conv_branch.3.weight, tiny_unet.blocks_tail.block0.conv_branch.3.bias, tiny_unet.blocks_tail.block0.conv_branch.3.running_mean, tiny_unet.blocks_tail.block0.conv_branch.3.running_var, tiny_unet.blocks_tail.block0.conv_branch.3.num_batches_tracked, tiny_unet.blocks_tail.block0.conv_branch.5.weight, tiny_unet.blocks_tail.block1.conv_branch.0.weight, tiny_unet.blocks_tail.block1.conv_branch.0.bias, tiny_unet.blocks_tail.block1.conv_branch.0.running_mean, tiny_unet.blocks_tail.block1.conv_branch.0.running_var, tiny_unet.blocks_tail.block1.conv_branch.0.num_batches_tracked, tiny_unet.blocks_tail.block1.conv_branch.2.weight, tiny_unet.blocks_tail.block1.conv_branch.3.weight, tiny_unet.blocks_tail.block1.conv_branch.3.bias, tiny_unet.blocks_tail.block1.conv_branch.3.running_mean, tiny_unet.blocks_tail.block1.conv_branch.3.running_var, tiny_unet.blocks_tail.block1.conv_branch.3.num_batches_tracked, tiny_unet.blocks_tail.block1.conv_branch.5.weight, tiny_unet_outputlayer.0.weight, tiny_unet_outputlayer.0.bias, tiny_unet_outputlayer.0.running_mean, tiny_unet_outputlayer.0.running_var, tiny_unet_outputlayer.0.num_batches_tracked, iou_score_linear.weight, iou_score_linear.bias, cls_linear.weight, cls_linear.bias, mask_linear.0.weight, mask_linear.0.bias, mask_linear.2.weight, mask_linear.2.bias
2022-04-17 14:48:48,620 - INFO - Load test dataset: 312 scans
0%| | 0/312 [00:00<?, ?it/s]None
Traceback (most recent call last):
File "./tools/test.py", line 146, in
main()
File "./tools/test.py", line 102, in main
result = model(batch)
TypeError: 'NoneType' object is not callable
0%|

It seems like the checkpoint does not match the model..
I found out after doing model.eval() in test.py:98, the model somehow becomes None.
Any solution for this?

from softgroup.

The checkpoint mismatch is expected because we truncate the instance segmentation components. Which pytorch version are you using? Could you try to replace model = model.eval() by model.eval()

from softgroup.

Replacing model=model.eval() to model.eval() works well! Thanks

I'm sorry, I found out the cause of the error "RuntimeError: There were no tensor arguments to this function".
It was because of spconv. I found out that spconv v2 was somehow not built properly. I built it from the source and I might have done something wrong.
I installed spconv with pip (pip install spconv-cu111) and it works perfectly!

Sorry again, nice work!

from softgroup.