def prepare_sample(self, sample):
sample = u.Namespace(sample)
for i, adj in enumerate(sample.hist_adj_list):
adj = u.sparse_prepare_tensor(adj, torch_size=[self.num_nodes])
sample.hist_adj_list[i] = adj.to(self.args.device)
nodes = self.tasker.prepare_node_feats(sample.hist_ndFeats_list[i])
sample.hist_ndFeats_list[i] = nodes.to(self.args.device)
node_mask = sample.node_mask_list[i]
sample.node_mask_list[i] = node_mask.to(self.args.device).t(
) #transposed to have same dimensions as scorer
label_sp = self.ignore_batch_dim(sample.label_sp)
if self.args.task in ["link_pred", "edge_cls"]:
label_sp['idx'] = label_sp['idx'].to(self.args.device).t(
) ####### ALDO TO CHECK why there was the .t() -----> because I concatenate embeddings when there are pairs of them, the embeddings are row vectors after the transpose
else:
label_sp['idx'] = label_sp['idx'].to(self.args.device)
label_sp['vals'] = label_sp['vals'].type(torch.long).to(
self.args.device)
sample.label_sp = label_sp
return sample
def __init__(self, args, dataset):
self.data = dataset
self.args = args
self.num_classes = 3
self.adj_matrix = tu.get_static_sp_adj(edges=self.data.edges,
weighted=False)
if args.use_2_hot_node_feats:
max_deg_out, max_deg_in = tu.get_max_degs_static(
self.data.num_nodes, self.adj_matrix)
self.feats_per_node = max_deg_out + max_deg_in
#print ('feats_per_node',self.feats_per_node ,max_deg_out, max_deg_in)
self.nodes_feats = tu.get_2_hot_deg_feats(self.adj_matrix,
max_deg_out, max_deg_in,
dataset.num_nodes)
#print('XXXX self.nodes_feats',self.nodes_feats)
self.nodes_feats = u.sparse_prepare_tensor(
self.nodes_feats,
torch_size=[self.data.num_nodes, self.feats_per_node],
ignore_batch_dim=False)
else:
self.feats_per_node = dataset.feats_per_node
self.nodes_feats = self.data.node_feats
self.adj_matrix = tu.normalize_adj(adj=self.adj_matrix,
num_nodes=self.data.num_nodes)
self.is_static = True
def __init__(self, args, splitter, gcn, classifier, comp_loss, dataset,
num_classes):
self.args = args
self.splitter = splitter
self.tasker = splitter.tasker
self.gcn = gcn
self.classifier = classifier
self.comp_loss = comp_loss
self.num_nodes = dataset.num_nodes
self.data = dataset
self.num_classes = num_classes
self.logger = logger.Logger(args, self.num_classes)
self.init_optimizers(args)
if self.tasker.is_static:
adj_matrix = u.sparse_prepare_tensor(self.tasker.adj_matrix,
torch_size=[self.num_nodes],
ignore_batch_dim=False)
self.hist_adj_list = [adj_matrix]
self.hist_ndFeats_list = [self.tasker.nodes_feats.float()]
def prepare_node_feats(node_feats): return u.sparse_prepare_tensor(node_feats, torch_size= [dataset.num_nodes, self.feats_per_node])
