RuntimeError: pseudo.size(1) == kernel_size.numel() INTERNAL ASSERT FAILED. Input mismatch

[Amirtmgr]

🐛 Describe the bug

I tried to train a SplineCNN as provided in the example named mnist_nn_conv.py. I got the following error:

RuntimeError: The following operation failed in the TorchScript interpreter. Traceback of TorchScript (most recent call last): File "...conda\envs\dl23\lib\site-packages\torch_spline_conv\basis.py", line 10, in spline_basis is_open_spline: torch.Tensor, degree: int) -> Tuple[torch.Tensor, torch.Tensor]: return torch.ops.torch_spline_conv.spline_basis(pseudo, kernel_size,

is_open_spline, degree)
RuntimeError: pseudo.size(1) == kernel_size.numel() INTERNAL ASSERT FAILED at "D:\\a\\pytorch_spline_conv\\pytorch_spline_conv\\csrc\\cuda\\basis_cuda.cu":104, please report a bug to PyTorch. Input mismatch

My Code

`
import os.path as osp

import torch
import torch.nn as nn
import torch.nn.functional as F

import torch_geometric.transforms as T
from torch_geometric.datasets import MNISTSuperpixels
from torch_geometric.loader import DataLoader
from torch_geometric.nn import (
    SplineConv,
    global_mean_pool,
    graclus,
    max_pool,
    max_pool_x,
)
from torch_geometric.utils import normalized_cut

//Datasets
path = osp.join(osp.dirname(osp.realpath("/")), '..', 'data', 'MNIST')
transform = T.Cartesian(cat=False)
train_dataset = MNISTSuperpixels(path, True, transform=transform)
test_dataset = MNISTSuperpixels(path, False, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
d = train_dataset`

//Normalized Cut
def normalized_cut_2d(edge_index, pos):
    row, col = edge_index
    edge_attr = torch.norm(pos[row] - pos[col], p=2, dim=1)
    return normalized_cut(edge_index, edge_attr, num_nodes=pos.size(0))

//SplineCNN
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = SplineConv(in_channels = d.num_features, out_channels= 32,dim=1, kernel_size = 3)
        self.conv2 = SplineConv(in_channels = 32, out_channels= 64, dim=1, kernel_size = 3)
        self.fc1 = torch.nn.Linear(64, 128)
        self.fc2 = torch.nn.Linear(128, d.num_classes)

    def forward(self, data):
        data.x = F.elu(self.conv1(data.x, data.edge_index, data.edge_attr))
        weight = normalized_cut_2d(data.edge_index, data.pos)
        cluster = graclus(data.edge_index, weight, data.x.size(0))
        data.edge_attr = None
        data = max_pool(cluster, data, transform=transform)

        data.x = F.elu(self.conv2(data.x, data.edge_index, data.edge_attr))
        weight = normalized_cut_2d(data.edge_index, data.facepos)
        cluster = graclus(data.edge_index, weight, data.x.size(0))
        x, batch = max_pool_x(cluster, data.x, data.batch)

        x = global_mean_pool(x, batch)
        x = F.elu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        return F.log_softmax(self.fc2(x), dim=1)

//Create Model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

//Train Function
def train(epoch):
    model.train()

    if epoch == 16:
        for param_group in optimizer.param_groups:
            param_group['lr'] = 0.001

    if epoch == 26:
        for param_group in optimizer.param_groups:
            param_group['lr'] = 0.0001

    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        F.nll_loss(model(data), data.y).backward()
        optimizer.step()

//Test Function
def test():
    model.eval()
    correct = 0

    for data in test_loader:
        data = data.to(device)
        pred = model(data).max(1)[1]
        correct += pred.eq(data.y).sum().item()
    return correct / len(test_dataset)

//Run epoch
for epoch in range(1, 31):
    train(epoch)
    test_acc = test()
    print(f'Epoch: {epoch:02d}, Test: {test_acc:.4f}')

Environment

• PyG version: 2.1.0
• PyTorch version: 1.13.0
• OS: Windows
• Python version:3.10.8
• CUDA/cuDNN version: 11.7
• How you installed PyTorch and PyG (conda, pip, source):pip
• Any other relevant information (e.g., version of torch-scatter):pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cu117.html

[rusty1s]

Do other examples of SplineConv work for you, e.g., cora.py? One thing to look out for is that edge_attr.min() >= 0 and edge_attr.max() <= 1. Can you see if this is indeed the case for you?