Error on training Toronto3d On RandlaNet about open3d-ml

[msanov]

Hi, you are encountering this problem because there is a bug in the original code located in ./ml3d/torch/modules/losses/semseg_loss.py. The issue arises because the code expects a tensor with 8 values (num of valid classes), but you are passing a list of tensors instead. You should use .squeeze() to fix this. Here is my corrected code (line 40):"
`

def __init__(self, pipeline, model, dataset, device):
    super(SemSegLoss, self).__init__()
    # weighted_CrossEntropyLoss
    if 'class_weights' in dataset.cfg.keys() and len(
            dataset.cfg.class_weights) != 0:
        class_wt = DataProcessing.get_class_weights(
            dataset.cfg.class_weights)
        weights = torch.tensor(class_wt, dtype=torch.float, device=device)
        weights = weights.squeeze()
        self.weighted_CrossEntropyLoss = nn.CrossEntropyLoss(weight=weights)
    else:
        self.weighted_CrossEntropyLoss = nn.CrossEntropyLoss()

`