| |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torch.nn import init |
|
|
|
|
| class MeanAggregator(nn.Module): |
|
|
| def forward(self, features, A): |
| x = torch.bmm(A, features) |
| return x |
|
|
|
|
| class GraphConv(nn.Module): |
|
|
| def __init__(self, in_dim, out_dim): |
| super().__init__() |
| self.in_dim = in_dim |
| self.out_dim = out_dim |
| self.weight = nn.Parameter(torch.FloatTensor(in_dim * 2, out_dim)) |
| self.bias = nn.Parameter(torch.FloatTensor(out_dim)) |
| init.xavier_uniform_(self.weight) |
| init.constant_(self.bias, 0) |
| self.aggregator = MeanAggregator() |
|
|
| def forward(self, features, A): |
| b, n, d = features.shape |
| assert d == self.in_dim |
| agg_feats = self.aggregator(features, A) |
| cat_feats = torch.cat([features, agg_feats], dim=2) |
| out = torch.einsum('bnd,df->bnf', cat_feats, self.weight) |
| out = F.relu(out + self.bias) |
| return out |
|
|
|
|
| class GCN(nn.Module): |
| """Graph convolutional network for clustering. This was from repo |
| https://github.com/Zhongdao/gcn_clustering licensed under the MIT license. |
| |
| Args: |
| feat_len(int): The input node feature length. |
| """ |
|
|
| def __init__(self, feat_len): |
| super(GCN, self).__init__() |
| self.bn0 = nn.BatchNorm1d(feat_len, affine=False).float() |
| self.conv1 = GraphConv(feat_len, 512) |
| self.conv2 = GraphConv(512, 256) |
| self.conv3 = GraphConv(256, 128) |
| self.conv4 = GraphConv(128, 64) |
| self.classifier = nn.Sequential( |
| nn.Linear(64, 32), nn.PReLU(32), nn.Linear(32, 2)) |
|
|
| def forward(self, x, A, knn_inds): |
|
|
| num_local_graphs, num_max_nodes, feat_len = x.shape |
|
|
| x = x.view(-1, feat_len) |
| x = self.bn0(x) |
| x = x.view(num_local_graphs, num_max_nodes, feat_len) |
|
|
| x = self.conv1(x, A) |
| x = self.conv2(x, A) |
| x = self.conv3(x, A) |
| x = self.conv4(x, A) |
| k = knn_inds.size(-1) |
| mid_feat_len = x.size(-1) |
| edge_feat = torch.zeros((num_local_graphs, k, mid_feat_len), |
| device=x.device) |
| for graph_ind in range(num_local_graphs): |
| edge_feat[graph_ind, :, :] = x[graph_ind, knn_inds[graph_ind]] |
| edge_feat = edge_feat.view(-1, mid_feat_len) |
| pred = self.classifier(edge_feat) |
|
|
| return pred |
|
|
