models/gsn/gnn.py
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4
F401: 'copy' imported but unused
(and 3 similar)
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'copy' imported but unused:
1 import copy -
'.models_misc.mlp' imported but unused:
12 from .models_misc import mlp, choose_activation -
'ogb.graphproppred.mol_encoder.AtomEncoder' imported but unused:
15 from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder -
'ogb.graphproppred.mol_encoder.BondEncoder' imported but unused:
15 from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
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-
3
F841: local variable 'jk_mlp' is assigned to but never used
(and 2 similar)
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Local variable 'jk_mlp' is assigned to but never used:
62 jk_mlp = False -
Local variable 'degree_embedding' is assigned to but never used:
63 degree_embedding = None -
Local variable 'd_degree' is assigned to but never used:
135 d_degree = self.degree_encoder.d_out
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16
E501: line too long (100 > 79 characters)
(and 14 similar)
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Line too long (100 > 79 characters):
13 from .utils_graph_learning import global_add_pool_sparse, global_mean_pool_sparse, DiscreteEmbedding -
Line too long (111 > 79 characters):
47 self.final_projection.append(True) # self.final_projection = [False, False, False, False, False, True] -
Line too long (87 > 79 characters):
66 retain_features[0] = False # retain_features = [False, True, True, True, True] -
Line too long (107 > 79 characters):
144 # mlp_vn_temp = mlp(d_in_vn, kwargs['d_out_vn'][i-1], d_h[i], seed, activation_mlp, bn_mlp) -
Line too long (110 > 79 characters):
170 use_ids = ((i > 0 and self.inject_ids) or (i == 0)) and (self.model_name == 'GSN_edge_sparse_ogb') -
Line too long (82 > 79 characters):
173 # if self.dataset_group != 'mol' and self.dataset_group != 'ppa' : -
Line too long (119 > 79 characters):
225 # -------------- Code adopted from https://github.com/snap-stanford/ogb/tree/master/examples/graphproppred/mol. -
Line too long (98 > 79 characters):
226 # -------------- Modified accordingly to allow for the existence of structural identifiers -
Line too long (91 > 79 characters):
231 # -------------- edge index, initial node features enmbedding, initial vn embedding -
Line too long (127 > 79 characters):
234 # vn_embedding = self.vn_encoder(torch.zeros(data.batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device)) -
Line too long (116 > 79 characters):
241 kwargs['identifiers'] = self.id_encoder[i](data.identifiers) if self.inject_ids else self.id_encoder[0]( -
Line too long (82 > 79 characters):
246 kwargs['edge_features'] = self.edge_encoder[i](data.edge_features) -
Line too long (96 > 79 characters):
260 x = F.dropout(self.activation(x), self.dropout_features, training=self.training) -
Line too long (97 > 79 characters):
268 # vn_embedding_temp = self.global_vn_pool(x_interm[i], data.batch) + vn_embedding -
Line too long (144 > 79 characters):
272 # vn_embedding = vn_embedding + F.dropout(self.activation(vn_embedding), self.dropout_features[i], training = self.training) -
Line too long (129 > 79 characters):
274 # vn_embedding = F.dropout(self.activation(vn_embedding), self.dropout_features[i], training = self.training)
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- 8 E203: whitespace before ':'
- 2 W291: trailing whitespace
- 1 E711: comparison to None should be 'if cond is None:'