AttributeError: 'GCNConv' object has no attribute '__check_input__'

[FabioDataGeek]

I'm trying to run the examples for explainable GNN ( xgnn), but when loading the model exported from dig.xgraph.models get the error above.

I'm currently running the experiment in conda environment with:

pytorch 2.0.0,
python 3.9,
CUDA 11.7
torch_geometric 2.3.0

Here are the traceback:

`---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[8], line 10
7 if torch.isnan(data.y[0].squeeze()):
8 continue
---> 10 logits = model(data.x, data.edge_index)
11 prediction = logits[node_idx].argmax(-1).item()
13 _, explanation_results, related_preds = explainer(data.x, data.edge_index, node_idx=node_idx, max_nodes=max_nodes)

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/dig/xgraph/models/models.py:164, in GCN_2l.forward(self, *args, **kwargs)
158 """
159 :param Required[data]: Batch - input data
160 :return:
161 """
162 x, edge_index, batch = self.arguments_read(*args, **kwargs)
--> 164 post_conv = self.relu1(self.conv1(x, edge_index))
165 for conv, relu in zip(self.convs, self.relus):
166 post_conv = relu(conv(post_conv, edge_index))

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/dig/xgraph/models/models.py:350, in GCNConv.forward(self, x, edge_index, edge_weight)
347 x = torch.matmul(x, self.weight)
349 # propagate_type: (x: Tensor, edge_weight: OptTensor)
--> 350 out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
351 size=None)
353 if self.bias is not None:
354 out += self.bias

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/dig/xgraph/models/models.py:362, in GCNConv.propagate(self, edge_index, size, **kwargs)
361 def propagate(self, edge_index: Adj, size: Size = None, **kwargs):
--> 362 size = self.check_input(edge_index, size)
364 # Run "fused" message and aggregation (if applicable).
365 if (isinstance(edge_index, SparseTensor) and self.fuse
366 and not self._explain):

File ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/torch/nn/modules/module.py:1614, in Module.getattr(self, name)
1612 if name in modules:
1613 return modules[name]
-> 1614 raise AttributeError("'{}' object has no attribute '{}'".format(
1615 type(self).name, name))

AttributeError: 'GCNConv' object has no attribute 'check_input'`

Also tried with GIN_2l but got the same result as GCN_2l

The part of the code that is failing is this:
`# --- Create data collector and explanation processor ---
from dig.xgraph.evaluation import XCollector

x_collector = XCollector()

index = -1
node_indices = torch.where(dataset[0].test_mask * dataset[0].y != 0)[0].tolist()
data = dataset[0]

from dig.xgraph.method.subgraphx import PlotUtils
from dig.xgraph.method.subgraphx import find_closest_node_result

Visualization

max_nodes = 5
node_idx = node_indices[20]
print(f'explain graph node {node_idx}')
data.to(device)
logits = model(data.x, data.edge_index)
prediction = logits[node_idx].argmax(-1).item()

_, explanation_results, related_preds = explainer(data.x, data.edge_index, node_idx=node_idx, max_nodes=max_nodes)

explanation_results = explanation_results[prediction]
explanation_results = explainer.read_from_MCTSInfo_list(explanation_results)

plotutils = PlotUtils(dataset_name='ba_shapes', is_show=True)
explainer.visualization(explanation_results,
max_nodes=max_nodes,
plot_utils=plotutils,
y=data.y)`

The code has been obtained from DIG repository: https://github.com/divelab/DIG/blob/dig-stable/examples/xgraph/subgraphx.ipynb

[LongchaoDa]

I have faced the same problem, have you solved it?

[FabioDataGeek]

No, currently I'm running explainable models in Pytorch Geometric, I would like to test this library but i cannot run the examples.

[Krith-man]

I have faced the same problem above, any updates?

[Krith-man]

Hello again, do we have any news related to this issue?

[Qianli-Wu]

I experienced the same error: AttributeError: 'GCNConv' object has no attribute '__check_input__'.

This is due to a naming convention change in torch_geometric v2.3.0, where __attribute__ changed to _attribute. This means attributes like __check_input__ and __user_args__ have been renamed to _check_input and _user_args respectively. More details can be found in this pull request: pyg-team/pytorch_geometric#6999

To fix it:

Update the attribute names in your code. For instance, in ~/anaconda3/envs/dive_into_graphs/lib/python3.9/site-packages/dig/xgraph/models/models.py:362, change __check_input__ to _check_input. Same goes to other attributes.

Alternatively, downgrade torch_geometric to v2.2.0 or earlier with pip install torch_geometric==2.2.0 to avoid the naming issue. Please note, this may impact other parts of your code.

[ZhongLIFR]

I just had the same problem here. It is solved by downgrading torch_geometric to v2.2.0
pip install torch_geometric==2.2.0