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