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
def test_T(t):
"""Test the simple transpose (T) function"""
res = fn.T(t)
if isinstance(t, (list, tuple)):
t = onp.asarray(t)
assert fn.get_interface(res) == fn.get_interface(t)
# if tensorflow or pytorch, extract view of underlying data
if hasattr(res, "numpy"):
res = res.numpy()
t = t.numpy()
assert np.all(res.T == t.T)
def test_sequence(self, a, interface):
"""Test that a sequence is automatically converted into
a diagonal tensor"""
t = [0.1, 0.2, a]
res = fn.diag(t)
assert fn.get_interface(res) == interface
assert fn.allclose(res, onp.diag([0.1, 0.2, 0.5]))
def test_ones_like_explicit_dtype(self, t):
"""Test that the ones like function creates the correct
shape and type tensor."""
res = fn.ones_like(t, dtype=np.float16)
if isinstance(t, (list, tuple)):
t = onp.asarray(t)
assert res.shape == t.shape
assert fn.get_interface(res) == fn.get_interface(t)
assert fn.allclose(res, np.ones(t.shape))
# if tensorflow or pytorch, extract view of underlying data
if hasattr(res, "numpy"):
res = res.numpy()
t = t.numpy()
assert onp.asarray(res).dtype.type is np.float16
def test_get_interface(t, interface):
"""Test that the interface of a tensor-like object
is correctly returned."""
res = fn.get_interface(t)
assert res == interface