PointNet2 classfication Failure Too Many Resources

❓ Questions & Help

Hi,

I implemented the program with reference to examples/pointnet2_classification.py and used Google Colablatory’s GPU to learn the model.

I was able to save the model I had learned in Colab and call that model in Colab to make an inference.

However, when I save the model that I learned in Colab and try to call that model in Jetson Nano to make an inference, I get the following error.

What is the reason for this? Do you have any solutions?

Thank you very much.

Traceback (most recent call last):
  File "pointnet.py", line 274, in <module>
    test_acc = test(test_loader)
  File "pointnet.py", line 149, in test
    pred = model(data).max(1)[1]
  File "/home/jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "pointnet.py", line 99, in forward
    sa1_out = self.sa1_module(*sa0_out)
  File "/home/jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "pointnet.py", line 38, in forward
    row, col = radius(pos, pos[idx], self.r, batch, batch[idx], max_num_neighbors=64)
  File "/home/jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch_geometric/nn/pool/__init__.py", line 159, in radius
    return torch_cluster.radius(x, y, r, batch_x, batch_y, max_num_neighbors)
  File "/home/jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch_cluster/radius.py", line 76, in radius
    max_num_neighbors)
RuntimeError: CUDA error: too many resources requested for launch (launch_kernel at /media/nvidia/WD_BLUE_2.5_1TB/pytorch/20200116/pytorch-v1.4.0/aten/src/ATen/native/cuda/Loops.cuh:103)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x78 (0x7f80044258 in /home/jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libc10.so)


⋮


frame #33: <unknown
 function> + 0x2b06a34 (0x7f82bafa34 in /home/kohei-jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libtorch.so)
frame #34: <unknown function> + 0x48e2098 (0x7f8498b098 in /home/kohei-jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libtorch.so)
frame #35: <unknown function> + 0x68b034 (0x7faa04a034 in /home/kohei-jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #36: <unknown function> + 0x652d28 (0x7faa011d28 in /home/kohei-jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #37: <unknown function> + 0x296ce4 (0x7fa9c55ce4 in /home/kohei-jetson/.virtualenvs/py33d/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #39: python() [0x52ba70]
frame #41: python() [0x52b108]
frame #42: python() [0x52b69c]
frame #43: python() [0x52b8f4]
frame #45: python() [0x52b108]
frame #46: python() [0x52b69c]
frame #47: python() [0x52b8f4]
frame #49: python() [0x529978]
frame #51: python() [0x5f4d34]
frame #54: python() [0x52b108]
frame #57: python() [0x5f4d34]
frame #59: python() [0x597fb4]
frame #62: python() [0x529978]

[rusty1s]

Glad that it is working, although I am still unsure what may cause this behavior on GPU.

[rusty1s]

just add cpu() calls to all input arguments, and put the output back to the GPU.