models/gnn.py
- 1 F841: local variable 'batch' is assigned to but never used
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3 from torch_geometric.nn import global_mean_pool, global_add_pool, GCNConv, GINConv, ChebConv -
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14 Graph Isomorphism Network augmented with virtual node for multi-task binary graph classification -
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18 - prediction (Tensor): float torch tensor of shape (num_graphs, num_tasks) -
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21 def __init__(self, gnn_type, dataset_group, num_tasks=128, num_layers=5, emb_dim=300, dropout=0.5, is_pooled=True, -
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25 - num_tasks (int): number of binary label tasks. default to 128 (number of tasks of ogbg-molpcba) -
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56 self.gnn_node = GNN_node_Virtualnode(num_layers, emb_dim, dataset_group=self.dataset_group, -
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57 gnn_type=self.gnn_type.split('_')[0], dropout=dropout, -
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60 self.gnn_node = GNN_node(num_layers, emb_dim, dataset_group=self.dataset_group, -
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61 gnn_type=self.gnn_type.split('_')[0], dropout=dropout, -
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73 self.graph_pred_linear = torch.nn.Linear(self.emb_dim, self.num_tasks) -
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84 return self.graph_pred_linear(self.pool(h_node, batched_data.batch)) -
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94 def __init__(self, num_layer, emb_dim, dataset_group='mol', gnn_type='gin', dropout=0.5, JK="last", -
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119 self.node_encoder = torch.nn.Embedding(1, emb_dim) # uniform input node embedding -
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138 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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139 mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2 * emb_dim), -
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140 torch.nn.BatchNorm1d(2 * emb_dim), torch.nn.ReLU(), -
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141 torch.nn.Linear(2 * emb_dim, emb_dim)) -
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146 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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151 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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154 self.convs.append(ChebConvNew(emb_dim, Cheb_K, self.dataset_group)) -
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156 raise ValueError('Undefined GNN type called {}'.format(gnn_type)) -
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164 x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch -
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171 # h_list = [self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze()) + perturb if perturb is not None else self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze())] -
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173 h_list = [self.node_encoder(x) + perturb if perturb is not None else self.node_encoder(x)] -
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185 h = F.dropout(F.relu(h), self.drop_ratio, training=self.training) -
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210 def __init__(self, num_layer, emb_dim, dataset_group='mol', gnn_type='gin', dropout=0.5, JK="last", -
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234 self.node_encoder = torch.nn.Embedding(1, emb_dim) # uniform input node embedding -
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261 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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262 mlp = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2 * emb_dim), -
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263 torch.nn.BatchNorm1d(2 * emb_dim), torch.nn.ReLU(), -
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264 torch.nn.Linear(2 * emb_dim, emb_dim)) -
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269 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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274 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']: -
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277 self.convs.append(ChebConvNew(emb_dim, Cheb_K, self.dataset_group)) -
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279 raise ValueError('Undefined GNN type called {}'.format(gnn_type)) -
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285 torch.nn.Sequential(torch.nn.Linear(emb_dim, 2 * emb_dim), torch.nn.BatchNorm1d(2 * emb_dim), -
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287 torch.nn.Linear(2 * emb_dim, emb_dim), torch.nn.BatchNorm1d(emb_dim), -
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295 x, edge_index, edge_attr, batch = batched_data.x, batched_data.edge_index, batched_data.edge_attr, batched_data.batch -
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299 torch.zeros(batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device)) -
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306 # h_list = [self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze()) + perturb if perturb is not None else self.node_encoder(x[:,:2]) + self.node_encoder_cate(x[:,2:].to(torch.int).squeeze())] -
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308 h_list = [self.node_encoder(x) + perturb if perturb is not None else self.node_encoder(x)] -
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322 h = F.dropout(F.relu(h), self.drop_ratio, training=self.training) -
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332 virtualnode_embedding_temp = global_add_pool(h_list[layer], batch) + virtualnode_embedding -
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337 self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), self.drop_ratio, -
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340 virtualnode_embedding = F.dropout(self.mlp_virtualnode_list[layer](virtualnode_embedding_temp), -
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341 self.drop_ratio, training=self.training)
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16
E266: too many leading '#' for block comment
- 87 ### GNN to generate node embedding
- 109 ### add residual connection or not
- 192 ### Different implementations of Jk-concat
- 203 ### Virtual GNN to generate node embedding
- 224 ### add residual connection or not
- 247 ### set the initial virtual node embedding to 0.
- 251 ### List of GNNs
- 253 ### batch norms applied to node embeddings
- 256 ### List of MLPs to transform virtual node at every layer
- 297 ### virtual node embeddings for graphs
- 311 ### add message from virtual nodes to graph nodes
- 314 ### Message passing among graph nodes
- 329 ### update the virtual nodes
- 331 ### add message from graph nodes to virtual nodes
- 333 ### transform virtual nodes using MLP
- 343 ### Different implementations of Jk-concat
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9
E203: whitespace before ':'
- 35 if self.gnn_type.endswith('layers') :
- 38 else :
- 122 elif self.dataset_group == 'ColoredMNIST' :
- 125 elif self.dataset_group == 'SBM' :
- 150 elif gnn_type == 'cheb' :
- 178 if self.batchnorm :
- 237 elif self.dataset_group == 'ColoredMNIST' :
- 240 elif self.dataset_group == 'SBM' :
- 273 elif gnn_type == 'cheb' :
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6
E271: multiple spaces after keyword
- 138 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 146 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 151 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 261 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 269 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 274 if self.dataset_group in ['RotatedMNIST', 'ColoredMNIST', 'SBM', 'UPFD']:
- 2 E261: at least two spaces before inline comment
- 1 E265: block comment should start with '# '
- 1 E502: the backslash is redundant between brackets