fromtorch_geometric.nnimportGINConv, global_add_pool# 初始化GIN并指定参数num_layers =5hidden_dim =1024model = GIN(hidden_dim=hidden_dim, num_layers=num_layers).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-06) loss_func = nn.CrossEntropyLoss()#...
完整的架构我们直接通过print(model)就可以看到,这个模型可以实现端到端的图分类了! fromtorch.nnimportLinearimporttorch.nn.functionalasFfromtorch_geometric.nnimportGCNConvfromtorch_geometric.nnimportglobal_mean_poolclassGCN(torch.nn.Module):def__init__(self,hidden_channels):super(GCN,self).__init__()...
File "/home/qmy/cyp/models/gat_gcn.py", line 5, in from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool File "/home/qmy/anaconda3/envs/deeplearning/lib/python3.7/site-packages/torch_geometric/init.py", line 7, in import torch_geometric.data File "/home/qmy/ana...
self.lin = torch.nn.Linear(64, 10) def forward(self, x, edge_index): x = self.conv1(x, edge_index) x = F.relu(x) x = self.conv2(x, edge_index) x = F.relu(x) x = self.conv3(x, edge_index) x = F.relu(x) x = global_mean_pool(x, batch) x = self.lin(x) re...
Based on [EdgePooling](https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/pool/edge_pool.py) --- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>Loading branch...
from torch_geometric.nn import global_max_pool pooling = global_max_pool(x, batch=torch.tensor([0, 0, 0, 0, 1, 1])) ``` 在这里,我们使用了global_max_pool函数进行邻居汇聚,您可以使用global_max_pool、global_median_pool、global_add_pool、global_mean_pool等函数。该函数把节点$\{0,1,2...
normal':torch.nn.init.xavier_normal_(params)elifself.kernel_initializer=='he_normal':torch.nn.init.kaiming_normal_(params)elifself.kernel_initializer=='random_uniform':torch.nn.init.uniform_(params)elifself.kernel_initializer=='glorot_uniform':torch.nn.init.xavier_uniform_(params)elifself.kernel...
(x, edge_index)) x = global_add_pool(x, batch) x = F.relu(self.lin1(x)) x = F.dropout(x, p=0.5, training=self.training) x = self.lin2(x) return F.log_softmax(x, dim=-1) def __repr__(self): return self.__class__.__name__ class GraphSAGEWithJK(torch.nn.Module)...
nn/modules/module.py in to(self, *args, **kwargs) 897 return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) 898 --> 899 return self._apply(convert) 900 901 def register_backward_hook( /usr/local/lib/python3.8/dist-packages/torch/nn/...
randint(0, 2, (2, 2)) model(x, edge_index) return predict def inference_multithread(model): thr = Pool(10) thr.map(inference(model), range(50)) When running inference_multithread(tg.nn.GATv2Conv(1, 1)) This results in an AssertionError, in particular this line. The following ...