def test_attr_sparse_tensor_repeated_protos(self): # type: () -> None
dense_shape = [3, 3]
sparse_values = [
1.764052391052246, 0.40015721321105957, 0.978738009929657
]
values_tensor = helper.make_tensor(name='sparse_values',
data_type=TensorProto.FLOAT,
dims=[len(sparse_values)],
vals=np.array(sparse_values).astype(
np.float32),
raw=False)
linear_indicies = [2, 3, 5]
indicies_tensor = helper.make_tensor(
name='indicies',
data_type=TensorProto.INT64,
dims=[len(linear_indicies)],
vals=np.array(linear_indicies).astype(np.int64),
raw=False)
sparse_tensor = helper.make_sparse_tensor(values_tensor,
indicies_tensor, dense_shape)
repeated_sparse = [sparse_tensor, sparse_tensor]
attr = helper.make_attribute("sparse_attrs", repeated_sparse)
self.assertEqual(attr.name, "sparse_attrs")
checker.check_attribute(attr)
for s in helper.get_attribute_value(attr):
checker.check_sparse_tensor(s)
def test_check_sparse_tensor_coo_format(self): # type: () -> None
sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 2],
[0, 9, 2, 7, 8, 1])
checker.check_sparse_tensor(sparse)
def test_check_sparse_tensor(self): # type: () -> None
sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81])
checker.check_sparse_tensor(sparse)
