Cuda out of memory

I’m trying to run the example.py with colab and when I load a pre-trained model as:

model = spvnas_specialized('SemanticKITTI_val_SPVNAS@20GMACs').to(device)

I get the following out of memory which is strange as I think that the network actually does not have so many parameters:

RuntimeError: CUDA out of memory. Tried to allocate 127.56 GiB (GPU 0; 14.76 GiB total capacity; 1.25 GiB already allocated; 12.37 GiB free; 1.36 GiB reserved in total by PyTorch)

Also I want to notify you that I opened an issue in the torch sparse repository as there are some problems running test on CPU with Docker environment configured as you request.

[luiscastro1995]

I am having the exact same problem when running SPVNAS/Tutorial in COLAB, with GPU backend. When I run the commands:

• model = spvnas_specialized(‘SemanticKITTI_val_SPVNAS@20GMACs’) or
• model = spvnas_specialized(‘SemanticKITTI_val_SPVNAS@65GMACs’).to(device) I get the following error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-18-f5ea08b4d2a2> in <module>()
      1 # import SPVNAS model from model zoo
      2 from model_zoo import spvnas_specialized
----> 3 model = spvnas_specialized('SemanticKITTI_val_SPVNAS@20GMACs')
      4 # model = spvnas_specialized('SemanticKITTI_val_SPVNAS@65GMACs').to(device)
      5 

18 frames
/usr/local/lib/python3.7/dist-packages/torchsparse/nn/functional/conv.py in forward(ctx, features, kernel, neighbor_map, neighbor_offset, sizes, transpose)
     38             torchsparse_backend.sparseconv_forward(features, out, kernel,
     39                                                    neighbor_map,
---> 40                                                    neighbor_offset, transpose)
     41         else:
     42             # use the native pytorch XLA APIs for the TPU.

RuntimeError: CUDA out of memory. Tried to allocate 115.36 GiB (GPU 0; 14.76 GiB total capacity; 754.88 MiB already allocated; 12.97 GiB free; 770.00 MiB reserved in total by PyTorch)

Could this error be related to the version of torchsparse library installed?

Thank you!

[luiscastro1995]

Thank you @zhijian-liu. With your changes I can now run successfully your tutorial both in Colab and in my local machine!