以便同学们基于模块化的 torch-geometric 轻松建立模型。笔者基于 GAT 提供了一个简单的案例,在开展研究的初期,同学们不妨尝试使用 torch-geometric 中其他的卷积层进行实验,并分析不同卷积在 EEG 分析上的优劣以得到 insight 开展深入的自主设计。 from torch_geometric.nn import GATConv, global_mean_pool class ...
开发者ID:rusty1s,项目名称:pytorch_geometric,代码行数:23,代码来源:glob.py 示例2: global_max_pool ▲点赞 6▼ # 需要导入模块: import torch_scatter [as 别名]# 或者: from torch_scatter importscatter[as 别名]defglobal_max_pool(x, batch, size: Optional[int] = None):r"""Returns batch-wise...
torch.nn.functional as F import torch_geometric.transforms as T from torch_geometric.datasets import MNISTSuperpixels from torch_geometric.loader import DataLoader from torch_geometric.nn import ChebConv, global_mean_pool, graclus, max_pool, max_pool_x from torch_geometric.utils import normalized_...
conv2(x, edge_index, edge_weight) x = global_mean_pool(x, batch) x= `self.linear1(x) x = torch.sigmoid(x) return x 这是GNNexplainer 代码: for batch in test_loader: graphs_batch, labels_batch = batch # Choose the first graph from the batch for explanation graph_to_explain = ...
开发者ID:rusty1s,项目名称:pytorch_geometric,代码行数:24,代码来源:test_polar.py 示例6: test_permuted_global_pool ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importallclose[as 别名]deftest_permuted_global_pool():N_1, N_2 =4,6x = torch.randn(N_1 + N_2,...
Source File: test_glob.py From pytorch_geometric with MIT License 6 votes def test_permuted_global_pool(): N_1, N_2 = 4, 6 x = torch.randn(N_1 + N_2, 4) batch = torch.cat([torch.zeros(N_1), torch.ones(N_2)]).to(torch.long) perm = torch.randperm(N_1 + N_2) px...
Source File: test_glob.py From pytorch_geometric with MIT License 6 votes def test_permuted_global_pool(): N_1, N_2 = 4, 6 x = torch.randn(N_1 + N_2, 4) batch = torch.cat([torch.zeros(N_1), torch.ones(N_2)]).to(torch.long) perm = torch.randperm(N_1 + N_2) px...
Hello, I am trying to install the pytorch_geometric on my computer which doesn't have GPU. When I try to run: !pip3 install torch-scatter !pip3 install torch-sparse !pip3 install torch-cluster !pip3 install torch-spine-conv !pip3 install...
Source File: triangles_sag_pool.py From pytorch_geometric with MIT License 6 votes def forward(self, data): x, edge_index, batch = data.x, data.edge_index, data.batch x = F.relu(self.conv1(x, edge_index)) x, edge_index, _, batch, perm, score = self.pool1( x, edge_index...
bool) for i, rel in enumerate(cross_rels): cpu_bitmap[rel] = 1 self.cpu_bitmap = cpu_bitmap self.has_cross_rel = True self.global_emb = global_emb Example #22Source File: test_diff_pool.py From pytorch_geometric with MIT License 5 votes def test_dense_diff_pool(): batch_...