mit-han-lab / torchsparse

[MICRO'23, MLSys'22] TorchSparse: Efficient Training and Inference Framework for Sparse Convolution on GPUs.

Home Page:https://torchsparse.mit.edu

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[BUG] what 2.0.0b0 diffrence about 2.1.0? use 2.1.0 and SparseResUNet42, at foward encode at last layer, coord.shape is [0,4],but use 2.0.0.b0,it is ok.

yeyan00 opened this issue · comments

Is there an existing issue for this?

  • I have searched the existing issues

Current Behavior

use 2.1.0 and SparseResUNet42 as backbone, same data,after encoder last layer of at foward downsample , the coord.shape is [0,4], but switch 2.0.0.b0,nothing modifed,it work well.
for pypi server is blocked, i install 2.1.0 from source code by use

python -m pip install -e .

when use 2.1.0,i set coordinate order is [batch, x, y, z],when use 2.0.0b0,the order is[x, y, z, batch]
is there other parameters should i set?

unet.py-->_unet_forward
# len(encoders)=1
# downsample
xd = encoders[0](x)


# set coords order 
 if ts_version>='2.1.0':
          x.coords =  torch.cat((batch.reshape(-1,1),x.coords), dim=1).int()
      else:
          # version<=2.0.0
          x.coords =  torch.cat((x.coords, batch.reshape(-1,1)), dim=1).int()

Expected Behavior

No response

Environment

- GCC:9.4.0
- NVCC:11.6
- PyTorch:1.13
- PyTorch CUDA:11.6
- TorchSparse:2.1.0

Anything else?

No response

We have just fixed our PyPI server. Could you please try installing TorchSparse with PyPI? Thanks!

Hi @yeyan00 , could you please provide a runnable snippet code for us to reproduce the error if it still exists?

Hi @yeyan00 , could you please provide a runnable snippet code for us to reproduce the error if it still exists?

ok,i will install use pypi server first, if it still has this error, i will provide runnable code

We have just fixed our PyPI server. Could you please try installing TorchSparse with PyPI? Thanks!

i install it from download whl file,train 3 epoch, it seems ok.
is there any difference between compiled version and source code?

i install from latest source code like this:

export CUDA_HOME=/home/szlt/anaconda3/envs/torch_113     
python -m pip install -e .

I think there is no major difference between the pre-compiled version and the source code. Please feel free to use the pre-compiled version if you cannot build it from source successfully.

Close this issue as completed. Feel free to reopen it if you have any further questions.