pytorch_geometric安装方法

github地址

主要不要直接pip install安装，会由于依赖无法安装而失败

点击here手动安装依赖

选择对应的pytorch版本，我的是Win10 Python3.8.3+Pytorch1.8.1+CUDA10.2

手动下载四个依赖包本地安装：

主要不要直接：pip install torch_geometric

这样会安装最新的torch_geometric，后面在使用时候会出现Pytorch AttributeError: module 'torch' has no attribute 'sparse_scs'

这里手动指定低版本安装：

pip install torch_geometric==2.0.4

接下来就可以跑一个图卷积神经网络试试：

import torch
import networkx as nx
import matplotlib.pyplot as plt
from torch_geometric.datasets import KarateClub
from torch_geometric.utils import to_networkxdataset = KarateClub()def visualize_graph(G, color):plt.figure(figsize=(7,7))plt.xticks([])plt.yticks([])nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,node_color=color, cmap="Set2")plt.show()def visualize_embedding(h, color, epoch=None, loss=None):plt.figure(figsize=(7,7))plt.xticks([])plt.yticks([])h = h.detach().cpu().numpy()plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap="Set2")if epoch is not None and loss is not None:plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)plt.show()G = to_networkx(data, to_undirected=True)
visualize_graph(G, color=data.y)    

import torch
from torch.nn import Linear
from torch_geometric.nn import GCNConv
class GCN(torch.nn.Module):def __init__(self):super().__init__()torch.manual_seed(1234)self.conv1 = GCNConv(dataset.num_features, 4) # 只需定义好输入特征和输出特征即可self.conv2 = GCNConv(4, 4)self.conv3 = GCNConv(4, 2)self.classifier = Linear(2, dataset.num_classes)def forward(self, x, edge_index):h = self.conv1(x, edge_index) # 输入特征与邻接矩阵（注意格式，上面那种）h = h.tanh()h = self.conv2(h, edge_index)h = h.tanh()h = self.conv3(h, edge_index)h = h.tanh()  # 分类层out = self.classifier(h)return out, hmodel = GCN()
_, h = model(data.x, data.edge_index)
print(f'Embedding shape: {list(h.shape)}')visualize_embedding(h, color=data.y)

Embedding shape: [34, 2]

训练模型

import timemodel = GCN()
criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.def train(data):optimizer.zero_grad()  out, h = model(data.x, data.edge_index) #h是两维向量，主要是为了咱们画个图 loss = criterion(out[data.train_mask], data.y[data.train_mask])  # semi-supervisedloss.backward()  optimizer.step()  return loss, hfor epoch in range(401):loss, h = train(data)if epoch % 10 == 0:visualize_embedding(h, color=data.y, epoch=epoch, loss=loss)time.sleep(0.3)