def test_cast_tensorflow_dtype(self):
"""If the tensor is a TensorFlow tensor, casting using a TensorFlow dtype
will also work"""
t = tf.Variable([1, 2, 3])
res = fn.cast(t, tf.complex128)
assert isinstance(res, tf.Tensor)
assert res.dtype is tf.complex128
def test_cast_torch_dtype(self):
"""If the tensor is a Torch tensor, casting using a Torch dtype
will also work"""
t = torch.tensor([1, 2, 3], dtype=torch.int64)
res = fn.cast(t, torch.float64)
assert isinstance(res, torch.Tensor)
assert res.dtype is torch.float64
def test_cast_numpy_string(self, t):
"""Test that specifying a NumPy dtype via a string results in proper casting
behaviour"""
res = fn.cast(t, "float64")
assert fn.get_interface(res) == fn.get_interface(t)
if hasattr(res, "numpy"):
# if tensorflow or pytorch, extract view of underlying data
res = res.numpy()
t = t.numpy()
assert onp.issubdtype(onp.asarray(t).dtype, onp.integer)
assert res.dtype.type is onp.float64