importtorchfromtorch_geometric.dataimportData# 由于是无向图,因此有 4 条边:(0 -> 1), (1 -> 0), (1 -> 2), (2 -> 1)edge_index=torch.tensor([[0,1,1,2],[1,0,2,1]],dtype=torch.long)# 节点的特征x=torch.tensor([[-1],[0],[1]],dtype=torch.float)data=Data(x=x,edge_...
optimizer.zero_grad() # Clear gradients. out = model(data.x, data.edge_index) # Perform a single forward pass. loss = criterion(out[data.train_mask], data.y[data.train_mask]) # Compute the loss solely based on the training nodes. loss.backward() # Derive gradients. optimizer.step()...
1.n_id:由于batch_size=2,所以从n_id可知,本次采样的source节点为[1, 0](即为[2, 3, 1, 0]的后两位),根据edge_index=torch.tensor([[0,0,1,2,2,3,3,3,4,4,5,6],[2,3,2,4,5,2,4,5,5,6,1,2]]) 可知1, 0的邻居节点为2, 3 2.adjs.EdgeIndex.edge_index:是根据n_id的节点...
data.edge_index:COO 格式的图的边关系,形状为 [2, num_edges],类型为 torch.long data.edge_attr:边特征矩阵,形状为[num_edges, num_edge_features] data.y:针对训练的目标可能具有不同的形状 data.pos:节点的位置矩阵,形状为[num_nodes, num_dimensions] Data 对象不是必须有上面所有的这些属性,也不是...
data = Data(x=x, edge_index=edge_index) print(data) 1. 2. 3. 4. 5. 6. 7. 8. 9. 但发现出现如下问题: 表明torch_genmetric 的torch_sparse依赖库没有安装,下节中介绍torch_sparse安装方法。 2、torch_sparse 安装 torch_sparse的安装在此依然通过本地文件安装方法,在官网下载文件,进入官网后首先...
x, edge_index = data.x, data.edge_index x = self.conv1(x, edge_index) x = F.elu(x) x = F.dropout(x, p=0.6, training=self.training) x = self.conv2(x, edge_index) x = F.log_softmax(x, dim=1) return x #构建示例图数据 x = torch.from_numpy(np.random.randn(5, 16)...
print(f"Node feature dimension: {data.num_node_features}") #将邻接矩阵转换为PyTorch的Tensor对象 adj = torch.tensor(data.edge_index, dtype=torch.long) #将节点特征矩阵转换为PyTorch的Tensor对象 features = torch.tensor(data.x, dtype=torch.float) #将目标标签矩阵转换为PyTorch的Tensor对象 labels =...
x, edge_index = data.x, data.edge_index x = self.conv1(x, edge_index) x = torch.relu(x) x = self.conv2(x, edge_index) return torch.log_softmax(x, dim=1) 在该自定义模型类中,我们首先在构造函数中定义了两个GATConv层,分别用于输入特征到隐藏特征的转换和隐藏特征到类别特征的转换。
edge_index = torch.tensor([[0, 1, 1], [1, 0, 2], [1, 2, 0]], dtype=torch.long) #创建一个图数据对象 graph_data = Data(edge_index=edge_index, num_nodes=3) ``` 3.定义一个节点嵌入层,使用node2vec方法: ```python class NodeEmbedding(nn.Module): def __init__(self, num_...
(pass) pow data-type promotion fixed hf_GPT2 hf_GPT2_large (pass) Loosen Embedding index type requirement llama (fail) Unknown reason: doctr_reco_predictor [torchbench] doctr_reco_predictor fails to run inference on dynamo. #6832 speech_transformer [torchbench] speech_transformer fails...