Hello (Mufei, I’m sure you’ll respond to this first ),

So,

I keep getting “Target size (torch.Size([20804])) must be the same as input size (torch.Size([15321])” in one of my batches. There is no graph with 15,321 nodes or 20804 labels. Any idea where I should be looking for: Here is my load_graphs code,

" if self.mode == ‘train’:

graph_files = ‘dglgraph/training’

self.train_list = [f for f in listdir(graph_files)]

onlyfiles = self.train_list

self.train_graphs = []

self.train_labels = []

self.graph_id = []

self.train_features = []

for i in range(len(onlyfiles)):

fn = str(onlyfiles[i])

fn = ‘dglgraph/training/’+fn

g, l = load_graphs(fn)

graph = g[0]

num_nodes = graph.number_of_nodes()

for j in range(num_nodes):

self.graph_id.append(self.counter)

label = l

self.train_graphs.append(graph)

self.train_labels.append(label)

self.train_features.append(graph.ndata[‘node_attributes’])

self.counter += 1

print('labels: ', len(self.train_labels))

"

And here is the collate function:

"def collate(sample):

graphs, labels_d =map(list, zip(*sample))

labels = []

for g in labels_d:

for k, v in g.items():

labels.append(v)

feats =[]

for g in graphs:

feat = g.ndata[‘node_attributes’]

feats.append(feat)

graph = dgl.batch(graphs)

feats = torch.from_numpy(np.concatenate(feats))

labels = torch.from_numpy(np.concatenate(labels))

return graph, feats, labels

"

Thanks so much.