def testZeros(self):
for s in self.all_shapes:
actual = np_array_ops.zeros(s)
expected = np.zeros(s)
msg = 'shape: {}'.format(s)
self.match(actual, expected, msg)
for s, t in itertools.product(self.all_shapes, self.all_types):
actual = np_array_ops.zeros(s, t)
expected = np.zeros(s, t)
msg = 'shape: {}, dtype: {}'.format(s, t)
self.match(actual, expected, msg)
def testSize(self):
def run_test(arr, axis=None):
onp_arr = np.array(arr)
self.assertEqual(np_array_ops.size(arr, axis), np.size(onp_arr, axis))
run_test(np_array_ops.array([1]))
run_test(np_array_ops.array([1, 2, 3, 4, 5]))
run_test(np_array_ops.ones((2, 3, 2)))
run_test(np_array_ops.ones((3, 2)))
run_test(np_array_ops.zeros((5, 6, 7)))
run_test(1)
run_test(np_array_ops.ones((3, 2, 1)))
run_test(constant_op.constant(5))
run_test(constant_op.constant([1, 1, 1]))
self.assertRaises(NotImplementedError, np_array_ops.size, np.ones((2, 2)),
1)
@def_function.function(input_signature=[
tensor_spec.TensorSpec(dtype=dtypes.float64, shape=None)])
def f(arr):
arr = np_array_ops.asarray(arr)
return np_array_ops.size(arr)
self.assertEqual(f(np_array_ops.ones((3, 2))).numpy(), 6)
def testArray(self):
ndmins = [0, 1, 2, 5]
for a, dtype, ndmin, copy in itertools.product(self.all_arrays,
self.all_types, ndmins,
[True, False]):
self.match(
np_array_ops.array(a, dtype=dtype, ndmin=ndmin, copy=copy),
np.array(a, dtype=dtype, ndmin=ndmin, copy=copy))
zeros_list = np_array_ops.zeros(5)
def test_copy_equal_false():
# Backing tensor is the same if copy=False, other attributes being None.
self.assertIs(
np_array_ops.array(zeros_list, copy=False).data, zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False).data, zeros_list.data)
# Backing tensor is different if ndmin is not satisfied.
self.assertIsNot(
np_array_ops.array(zeros_list, copy=False, ndmin=2).data,
zeros_list.data)
self.assertIsNot(
np_array_ops.array(zeros_list.data, copy=False, ndmin=2).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list, copy=False, ndmin=1).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False, ndmin=1).data,
zeros_list.data)
# Backing tensor is different if dtype is not satisfied.
self.assertIsNot(
np_array_ops.array(zeros_list, copy=False, dtype=int).data,
zeros_list.data)
self.assertIsNot(
np_array_ops.array(zeros_list.data, copy=False, dtype=int).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list, copy=False, dtype=float).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False, dtype=float).data,
zeros_list.data)
test_copy_equal_false()
with ops.device('CPU:1'):
test_copy_equal_false()
self.assertNotIn('CPU:1', zeros_list.data.backing_device)
with ops.device('CPU:1'):
self.assertIn('CPU:1', np_array_ops.array(zeros_list, copy=True).data
.backing_device)
self.assertIn('CPU:1', np_array_ops.array(np.array(0), copy=True).data
.backing_device)
def testAsAnyArray(self):
for a, dtype in itertools.product(self.all_arrays, self.all_types):
self.match(np_array_ops.asanyarray(a, dtype=dtype),
np.asanyarray(a, dtype=dtype))
zeros_list = np_array_ops.zeros(5)
# Same instance is returned if no dtype is specified and input is ndarray.
self.assertIs(np_array_ops.asanyarray(zeros_list), zeros_list)
# Different instance is returned if dtype is specified and input is ndarray.
self.assertIsNot(np_array_ops.asanyarray(zeros_list, dtype=int),
zeros_list)
def testArray(self):
ndmins = [0, 1, 2, 5]
for a, dtype, ndmin, copy in itertools.product(self.all_arrays,
self.all_types, ndmins,
[True, False]):
self.match(
np_array_ops.array(a, dtype=dtype, ndmin=ndmin, copy=copy),
np.array(a, dtype=dtype, ndmin=ndmin, copy=copy))
zeros_list = np_array_ops.zeros(5)
# TODO(srbs): Test that copy=True when context.device is different from
# tensor device copies the tensor.
# Backing tensor is the same if copy=False, other attributes being None.
self.assertIs(
np_array_ops.array(zeros_list, copy=False).data, zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False).data,
zeros_list.data)
# Backing tensor is different if ndmin is not satisfied.
self.assertIsNot(
np_array_ops.array(zeros_list, copy=False, ndmin=2).data,
zeros_list.data)
self.assertIsNot(
np_array_ops.array(zeros_list.data, copy=False, ndmin=2).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list, copy=False, ndmin=1).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False, ndmin=1).data,
zeros_list.data)
# Backing tensor is different if dtype is not satisfied.
self.assertIsNot(
np_array_ops.array(zeros_list, copy=False, dtype=int).data,
zeros_list.data)
self.assertIsNot(
np_array_ops.array(zeros_list.data, copy=False, dtype=int).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list, copy=False, dtype=float).data,
zeros_list.data)
self.assertIs(
np_array_ops.array(zeros_list.data, copy=False, dtype=float).data,
zeros_list.data)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0): # pylint: disable=missing-docstring
dtype = dtypes.as_dtype(dtype) if dtype else np_utils.result_type(
start, stop, float(num), np_array_ops.zeros((), dtype))
computation_dtype = np.promote_types(dtype.as_numpy_dtype, np.float32)
start = np_array_ops.asarray(start, dtype=computation_dtype)
stop = np_array_ops.asarray(stop, dtype=computation_dtype)
# follow the numpy geomspace convention for negative and complex endpoints
start_sign = 1 - np_array_ops.sign(np_array_ops.real(start))
stop_sign = 1 - np_array_ops.sign(np_array_ops.real(stop))
signflip = 1 - start_sign * stop_sign // 2
res = signflip * logspace(log10(signflip * start),
log10(signflip * stop),
num,
endpoint=endpoint,
base=10.0,
dtype=computation_dtype,
axis=0)
if axis != 0:
res = np_array_ops.moveaxis(res, 0, axis)
return math_ops.cast(res, dtype)
