def test_serialize_tensors():
# create a temporary file and immediately release it so DGL can open it.
f = tempfile.NamedTemporaryFile(delete=False)
path = f.name
f.close()
tensor_dict = {
"a": F.tensor([1, 3, -1, 0], dtype=F.int64),
"1@1": F.tensor([1.5, 2], dtype=F.float32)
}
save_tensors(path, tensor_dict)
load_tensor_dict = load_tensors(path)
for key in tensor_dict:
assert key in load_tensor_dict
assert np.array_equal(F.asnumpy(load_tensor_dict[key]),
F.asnumpy(tensor_dict[key]))
load_nd_dict = load_tensors(path, return_dgl_ndarray=True)
for key in tensor_dict:
assert key in load_nd_dict
assert isinstance(load_nd_dict[key], nd.NDArray)
assert np.array_equal(load_nd_dict[key].asnumpy(),
F.asnumpy(tensor_dict[key]))
os.unlink(path)
def test_serialize_empty_dict():
# create a temporary file and immediately release it so DGL can open it.
f = tempfile.NamedTemporaryFile(delete=False)
path = f.name
f.close()
tensor_dict = {}
save_tensors(path, tensor_dict)
load_tensor_dict = load_tensors(path)
assert isinstance(load_tensor_dict, dict)
assert len(load_tensor_dict) == 0
os.unlink(path)
elif args.dataset == 'pubmed':
part_config = os.path.join(part_config, "Libra_result_pubmed",
str(nc) + "Communities", "pubmed.json")
else:
print("Error: Dataset not found !!!")
sys.exit(1)
print("Dataset/partition location: ", part_config)
with open(part_config) as conf_f:
part_metadata = json.load(conf_f)
part_files = part_metadata['part-{}'.format(args.rank)]
assert 'node_feats' in part_files, "the partition does not contain node features."
assert 'edge_feats' in part_files, "the partition does not contain edge feature."
assert 'part_graph' in part_files, "the partition does not contain graph structure."
node_feats = load_tensors(part_files['node_feats'])
# edge_feats = load_tensors(part_files['edge_feats'])
graph = load_graphs(part_files['part_graph'])[0][0]
num_parts = part_metadata['num_parts']
node_map = part_metadata['node_map']
graph.ndata['feat'] = node_feats['feat']
graph.ndata['lf'] = node_feats['lf']
graph.ndata['label'] = node_feats['label']
graph.ndata['train_mask'] = node_feats['train_mask']
graph.ndata['test_mask'] = node_feats['test_mask']
graph.ndata['val_mask'] = node_feats['val_mask']
g_orig = load_data(args)[0]
