Hi,
I am trying to train an existing neural network from a published paper, using custom dataset.
However, why trainng this I am getting NAN as my predictions even before completeing the first batch of training (batch size = 32).
I tried to google out the error and came across multiple post from this forum and tried few things -

Reducing the learning rate (default was 0.001, reduced it to 0.0001)
Reducing batch size from 32 to 10
Using torch.autograd.detect_anomaly()


by using torch.autograd.detect_anomaly() I got the following error
Warning: Traceback of forward call that caused the error:
  File "train_classification.py", line 126, in <module>
    pred, trans, trans_feat = classifier(points)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/mydrive/My Drive/Projects/pointnet/pointnet.pytorch/pointnet/model.py", line 147, in forward
    return F.log_softmax(x, dim=1), trans, trans_feat
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1317, in log_softmax
    ret = input.log_softmax(dim)
 (print_stack at /pytorch/torch/csrc/autograd/python_anomaly_mode.cpp:57)
Traceback (most recent call last):
  File "train_classification.py", line 133, in <module>
    loss.backward()
  File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 195, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py", line 99, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output.

If I am interpreting this correctly, the error seems to be from the softmax layer -  F.log_softmax()
Now I am stuck here, do not know how to proceed, can someone help me in this regards?