Hi! I have a DGL model trained that I did with version 0.4.3. I saved the model.pt file
Now I am trying to use it for validation and checking the predictions with the current version and I get the error:
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://lg1emf2t11a-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20210825-060005-RC00_392863177#) in _call_impl(self, *input, **kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(*input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []
[/content/allmodels.py](https://lg1emf2t11a-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20210825-060005-RC00_392863177#) in forward(self, g, num_layers, pooling) 37 h = g.ndata['h_n'].float() 38 # Perform graph convolution and activation function. ---> 39 h = F.relu(self.conv1(g, h)) 40 if num_layers=='2': 41 h = F.relu(self.conv2(g, h))
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://lg1emf2t11a-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20210825-060005-RC00_392863177#) in _call_impl(self, *input, **kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(*input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []
[/usr/local/lib/python3.7/dist-packages/dgl/nn/pytorch/conv/graphconv.py](https://lg1emf2t11a-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20210825-060005-RC00_392863177#) in forward(self, graph, feat, weight, edge_weight) 377 """ 378 with graph.local_scope(): --> 379 if not self._allow_zero_in_degree: 380 if (graph.in_degrees() == 0).any(): 381 raise DGLError('There are 0-in-degree nodes in the graph, '
[/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py](https://lg1emf2t11a-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20210825-060005-RC00_392863177#) in __getattr__(self, name) 1129 return modules[name] 1130 raise AttributeError("'{}' object has no attribute '{}'".format( -> 1131 type(self).__name__, name)) 1132 1133 def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
AttributeError: 'GraphConv' object has no attribute '_allow_zero_in_degree'
Is there a way to make this work? Since it was computationally expensive to train it, I would like to avoid retraining it if it is possible 
Thanks in advance!
if it helps, this is the definition of the model used in the old version:
class Classifier_gen(nn.Module):
def __init__(self, in_dim, hidden_dim_graph,hidden_dim1,n_classes,dropout,num_layers,pooling):
super(Classifier_gen, self).__init__()
if num_layers ==1:
self.conv1 = GraphConv(in_dim, hidden_dim1)
if num_layers==2:
self.conv1 = GraphConv(in_dim, hidden_dim_graph)
self.conv2 = GraphConv(hidden_dim_graph, hidden_dim1)
if pooling == 'att':
pooling_gate_nn = nn.Linear(hidden_dim1, 1)
self.pooling = GlobalAttentionPoolingPMG(pooling_gate_nn)
self.classify = nn.Sequential(nn.Linear(hidden_dim1,hidden_dim1),nn.Dropout(dropout))
self.classify2 = nn.Sequential(nn.Linear(hidden_dim1, n_classes),nn.Dropout(dropout))
self.out_act = nn.Sigmoid()
def forward(self, g,num_layers,pooling):
# Use node degree as the initial node feature. For undirected graphs, the in-degree
# is the same as the out_degree.
# Perform graph convolution and activation function.
# Calculate graph representation by averaging all the node representations.
h = g.ndata['h_n'].float()
# Perform graph convolution and activation function.
h = F.relu(self.conv1(g, h))
if num_layers=='2':
h = F.relu(self.conv2(g, h))
g.ndata['h'] = h
# Calculate graph representation by averaging all the node representations.
#hg = dgl.mean_nodes(g, 'h')
if pooling == "max":
hg = dgl.max_nodes(g, 'h')
elif pooling=="mean":
hg = dgl.mean_nodes(g, 'h')
elif pooling == "sum":
hg = dgl.sum_nodes(g, 'h')
elif pooling =='att':
# Calculate graph representation by averaging all the node representations.
[hg,g2] = self.pooling(g,h)
g2 = hg
a2=self.classify(hg)
a3=self.classify2(a2)
return self.out_act(a3),g2,hg
