edge_index = knn_graph(x, self.k, batch, loop=False, flow=self.flow) return super(DynamicEdgeConv, self).forward(x, edge_index)
而这两种操作,自己去实现都会稍微有点点困难,在PyG 当中都已经有预先设定的好的函数可以进行处理: 3.4.1 add self loop 我们还是以如下这个图为例进行演示: 可以看到,add_self_loops 会在原先的edge_index 的后面添加 0->0 , 1->1 , 2->2 的连接 3.4.2 knn 在torch_geometric 的依赖库 torch_cluster ...
import torchfrom torch_geometric.nn import MessagePassingfrom torch_geometric.utils import add_self_loops, degree#实现GCNclass GCNConv(MessagePassing):def __init__(self, in_channels, out_channels):super().__init__(aggr='add')# 使用'add'聚合self.lin = torch.nn.Linear(in_channels, out_cha...
We can now optimize the model in a training loop, similar to thestandard PyTorch training procedure. importtorch.nn.functionalasF data = dataset[0] optimizer = torch.optim.Adam(model.parameters(), lr=0.01)forepochinrange(200): pred = model(data.x, data.edge_index) loss = F.cross_entropy...
We can now optimize the model in a training loop, similar to thestandard PyTorch training procedure. importtorch.nn.functionalasFdata=dataset[0]optimizer=torch.optim.Adam(model.parameters(),lr=0.01)forepochinrange(200):pred=model(data.x,data.edge_index)loss=F.cross_entropy(pred[data.train_mas...
# trainer.fit_loop.max_epochs += 100 trainer.fit(litsegger, xe_train_loader, xe_val_loader) Training without using the validation dataloader works fine but when adding the validation dataloader I get the following error. File "/home/.conda/envs/py39/lib/python3.9/site-packages/lightning/pyt...
We can now optimize the model in a training loop, similar to thestandard PyTorch training procedure. importtorch.nn.functionalasF data = dataset[0] optimizer = torch.optim.Adam(model.parameters(), lr=0.01)forepochinrange(200): pred = model(data.x, data.edge_index) loss = F.cross_entropy...
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calibration_area is not None: draw_polyline_norm( self.calibration_area, thickness=2.0, color=RGBA(0.663, 0.863, 0.463, 0.8), line_type=gl.GL_LINE_LOOP, ) 浏览完整代码 来源:accuracy_visualizer.py 项目:pupil-labs/pupil 示例9 def gl_display(self): """ use gl calls to render at least:...