Pytorch TypeError: scatter_add() 从 2 到 5 位置 arguments 但给出了 6

Question

有人可以解释如何解决我直接从 Pytorch 文档中举个例子的情况吗：

import torch
from torch_geometric.datasets import TUDataset
from torch_geometric.data import Data, Dataset,DataLoader
dataset = TUDataset(root='data/TUDataset', name='MUTAG')
print()
print(f'Dataset: {dataset}:')
print('====================')
print(f'Number of graphs: {len(dataset)}')
print(f'Number of features: {dataset.num_features}')
print(f'Number of classes: {dataset.num_classes}')
data = dataset[0]  # Get the first graph object.
print()
print(data)
print('=============================================================')
# Gather some statistics about the first graph.
print(f'Number of nodes: {data.num_nodes}')
print(f'Number of edges: {data.num_edges}')
print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')

#print(f'Has isolated nodes: {data.has_isolated_nodes()}')
#print(f'Has self-loops: {data.has_self_loops()}')
#print(f'Is undirected: {data.is_undirected()}')
torch.manual_seed(12345)
dataset = dataset.shuffle()
train_dataset = dataset[:150]
test_dataset = dataset[150:]
print(f'Number of training graphs: {len(train_dataset)}')
print(f'Number of test graphs: {len(test_dataset)}')
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

for step, data in enumerate(train_loader):
    print(f'Step {step + 1}:')
    print('=======')
    print(f'Number of graphs in the current batch: {data.num_graphs}')
    print(data)
    print()

from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_pool
class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        self.conv3 = GCNConv(hidden_channels, hidden_channels)
        self.lin = Linear(hidden_channels, dataset.num_classes)

    def forward(self, x, edge_index, batch):
        # 1. Obtain node embeddings 
        x = self.conv1(x, edge_index)
        x = x.relu()
        x = self.conv2(x, edge_index)
        x = x.relu()
        x = self.conv3(x, edge_index)

        # 2. Readout layer
        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]
        # 3. Apply a final classifier

        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin(x)
        
        return x

model = GCN(hidden_channels=64)
print(model)
model = GCN(hidden_channels=64)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()

def train():
    model.train()
    for data in train_loader:  # Iterate in batches over the training dataset.
         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.
         loss = criterion(out, data.y)  # Compute the loss.
         loss.backward()  # Derive gradients.
         optimizer.step()  # Update parameters based on gradients.
         optimizer.zero_grad()  # Clear gradients.

def test(loader):
     model.eval()
     correct = 0
     for data in loader:  # Iterate in batches over the training/test dataset.
         out = model(data.x, data.edge_index, data.batch)  
         pred = out.argmax(dim=1)  # Use the class with highest probability.
         correct += int((pred == data.y).sum())  # Check against ground-truth labels.
     return correct / len(loader.dataset)  # Derive ratio of correct predictions.

for epoch in range(1, 171):
    train()
    train_acc = test(train_loader)
    test_acc = test(test_loader)
    print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')

我收到错误：

    out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.
  File "/root/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "base_test.py", line 67, in forward
    x = self.conv1(x, edge_index)
  File "/root/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/miniconda3/lib/python3.7/site-packages/torch_geometric/nn/conv/gcn_conv.py", line 103, in forward
    return self.propagate(edge_index, x=x, norm=norm)
  File "/root/miniconda3/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py", line 127, in propagate
    out = scatter_(self.aggr, out, edge_index[i], dim, dim_size=size[i])
  File "/root/miniconda3/lib/python3.7/site-packages/torch_geometric/utils/scatter.py", line 34, in scatter_
    out = op(src, index, dim, None, dim_size, fill_value)
TypeError: scatter_add() takes from 2 to 5 positional arguments but 6 were given

我在用：

torch                   1.11.0
torch-cluster           1.6.0
torch-geometric         1.3.2
torch-scatter           2.0.9
torch-sparse            0.6.13
torchmetrics            0.9.1

Answer 1

顺便提一下，答案只是卸载torch-scatter ，然后重新安装完全相同的版本。