TypeError: scatter_mean() takes from 2 to 5 positional arguments but 6 were given about pathomicfusion HOT 12 CLOSED

Hi @shubhaminnani - At the time of this publication, one of the earliest versions of torch-geometric was used. Your torch-scatter, torch-sparse, and other packages are of the latest update and may not have good backwards compatibility with previous versions of PyG.

For this study: the dependencies for PyG==1.3.0 may have been torch-scatter==1.3.1, torch-sparse==0.4.0, torch-cluster==1.4.4. Though this codebase depends on legacy code, I would recommend updating everything to the most recent versions of PyG, which may require some minor modifications to the forward pass of some of the model architectures. In addition, note that there may be some minor instabilities with previous versions of PyG, e.g. - pytorch/pytorch#50469.

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Thanks for update. Will check and revert with you!

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Hi @Richarizardd ,
I am pretty much new to Pathology, if you can help to update the code, it will be really helpful.
Thanks!

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Hi @Richarizardd

I was not able to understand whats needs to be changed here.

class GraphNet(torch.nn.Module):
    def __init__(self, features=1036, nhid=128, grph_dim=32, nonlinearity=torch.tanh, 
        dropout_rate=0.25, GNN='GCN', use_edges=0, pooling_ratio=0.20, act=None, label_dim=1, init_max=True):
        super(GraphNet, self).__init__()

        self.dropout_rate = dropout_rate
        self.use_edges = use_edges
        self.act = act

        self.conv1 = SAGEConv(features, nhid)
        self.pool1 = SAGPooling(nhid, ratio=pooling_ratio, gnn=GNN)#, nonlinearity=nonlinearity)
        self.conv2 = SAGEConv(nhid, nhid)
        self.pool2 = SAGPooling(nhid, ratio=pooling_ratio, gnn=GNN)#, nonlinearity=nonlinearity)
        self.conv3 = SAGEConv(nhid, nhid)
        self.pool3 = SAGPooling(nhid, ratio=pooling_ratio, gnn=GNN)#, nonlinearity=nonlinearity)

        self.lin1 = torch.nn.Linear(nhid*2, nhid)
        self.lin2 = torch.nn.Linear(nhid, grph_dim)
        self.lin3 = torch.nn.Linear(grph_dim, label_dim)

        self.output_range = Parameter(torch.FloatTensor([6]), requires_grad=False)
        self.output_shift = Parameter(torch.FloatTensor([-3]), requires_grad=False)

        if init_max: 
            init_max_weights(self)
            print("Initialzing with Max")

    def forward(self, **kwargs):
        data = kwargs['x_grph']
        data = NormalizeFeaturesV2()(data)
        data = NormalizeEdgesV2()(data)
        x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch
        
        #x, edge_index, edge_attr, batch = data.x.type(torch.cuda.FloatTensor), data.edge_index.type(torch.cuda.LongTensor), data.edge_attr.type(torch.cuda.FloatTensor), data.batch
        x = F.relu(self.conv1(x, edge_index)) ##need to check this

From above code, it seem we are passing 5 values only, but torch_scatter throws the Error.

Can you please suggest?

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I also encountered this problem. Have you solved it？

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@shubhaminnani

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This is version dependencies of PyG. I was not able to look into this further.
Thanks!

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i have solved. you should change the ''scatter.py'' file.

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Hi @zqy396

Can you please post the changes from the scatter.py? It will be really helpful.

Thanks!

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Is this the correct thing to be updated?

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@zqy396
May I ask how you made modifications in the scatter.py file?

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https://blog.csdn.net/m0_37052320/article/details/118368656

@zqy396 May I ask how you made modifications in the scatter.py file?

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