dgl._ffi.base.DGLError: Cannot assign node feature "n_feat" on device cuda:0 to a graph on device cpu. 很显然,我们没有把向量同步到我们模型的所用的计算设备上。 一般,我们用<你的dgl对象名称>.to(<你的设备>)来解决。如下。 代码语言:javascript 代码运行次数:0 g.to(torch.device('cuda:0'...
A new API DGLGraph.to(device) that can move all node/edge data to the given device. A new API dgl.to_simple that can convert a graph to a simple graph with no multi-edges. A new API dgl.to_bidirected that can convert a graph to a bidirectional graph. A new API dgl.contrib.sampl...
graph = graph.to(device) x = graph.ndata['feat'].to(device) e = graph.edata['feat'].to(device).float() h = model(graph, x, e) # Positive samples edge = train_edges[perm].t() pos_out = model.edge_predictor( h[edge[0]], h[edge[1]] ) # Just do some trivial random ...
我们可以看到 RelGraphConvLayer 就是我们的 异构图卷积层layer , 其主要是调用了DGL实现的 HeteroGraphConv算子,从上面第一小节我们也详细阐述了异构图卷积算子其实就是: 对各种关系分别进行卷积然后进行同类型的节点的融合。 这里我们需要重点关注的是:RelGraphConvLayer层的返回,从代码中,我们可以看到,对于每种节点...
dgl._ffi.base.DGLError: Cannot assign node feature “n_feat” on device cuda:0 to a graph on device cpu. 很显然,我们没有把向量同步到我们模型的所用的计算设备上。 一般,我们用<你的dgl对象名称>.to(<你的设备>)来解决。如下。 g.to(torch.device('cuda:0')) ...
VoVAllen wjw1309397040 commentedon Sep 14, 2020 wjw1309397040 I use pytorch. The same problem exists. Note that moving aDGLGraphto a specified device is not an in-place operation, you need to useg = g.to(device)rather thang.to(device). ...
self.model = DDP(model, device_ids=[gpu_id]) # 使用DDP包装模型 def _run_batch(self, batch): """ 运行单个批次。 参数: batch: 单个批次的数据。 """ self.optimizer.zero_grad() # 梯度清零 graph, features, labels = batch graph = graph.to(self.gpu_id) # 将图移动到GPU ...
ex:graph.update_all(fn.u_mul_e('ft', 'a', 'm')将源节点特征tf和边特征a相乘生成消息m,fn.sum('m', 'ft')再对所有消息求和来更新节点特征ft,再乘2后得到最终结果final_ft。调用后,中间消息m会被清除。 1.2 编写高效的消息传递代码 关于dgl内置函数是如何优化消息传递的内存消耗和计算速度的, 详见...
torch.int64>>>g32_2 = g64.int()# convert to int32>>>g32_2.idtype torch.int32 1.3 DGL中节点特征和边的特征 DGL中可以给节点和边添加特征,节点特征可以访问ndata,边特征可以访问edata。 01.>>>importdgl02.>>>importtorchasth03.>>> g = dgl.graph(([0,0,1,5], [1,2,2,0]))# 6...
Deep Graph Library (DGL) is a Python package built for the implementation and training of graph neural networks on top of existing DL frameworks. The DGL NGC Container is built with the latest versions of DGL, PyTorch, and their dependencies.