Hi! All,
I recently tried to train a SplineCNN using Pytorch-Geometric as in example called mnist_nn_conv.py. But, 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,
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <— HERE
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.
The error said it’s a bug and suggested to report to Pytorch. Could you please tell, if my implementation is wrong or it is a bug?
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