def test_raises_if_rank_is_not_scalar_static(self):
with self.test_session():
tensor = constant_op.constant((42, 43), name="my_tensor")
desired_ranks = (np.array(1, dtype=np.int32),
np.array((2, 1), dtype=np.int32))
with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"):
check_ops.assert_rank_in(tensor, desired_ranks)
def test_raises_if_rank_is_not_scalar_static(self):
tensor = constant_op.constant((42, 43), name="my_tensor")
desired_ranks = (
np.array(1, dtype=np.int32),
np.array((2, 1), dtype=np.int32))
with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"):
check_ops.assert_rank_in(tensor, desired_ranks)
def test_raises_if_rank_is_not_integer_static(self):
with self.test_session():
tensor = constant_op.constant((42, 43), name="my_tensor")
with self.assertRaisesRegexp(TypeError,
"must be of type <dtype: 'int32'>"):
check_ops.assert_rank_in(tensor, (
1,
.5,
))
def test_rank_one_tensor_raises_if_rank_mismatches_static_rank(self):
with self.test_session():
tensor_rank1 = constant_op.constant((42, 43), name="my_tensor")
with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank1, (0, 2))]):
array_ops.identity(tensor_rank1).eval()
def test_rank_one_tensor_doesnt_raise_if_rank_matches_static_rank(self):
with self.test_session():
tensor_rank1 = constant_op.constant([42, 43], name="my_tensor")
for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank1, desired_ranks)]):
array_ops.identity(tensor_rank1).eval()
def test_rank_zero_tensor_doesnt_raise_if_rank_matches_dynamic_rank(self):
with self.test_session():
tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor")
for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, desired_ranks)]):
array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
def test_rank_zero_tensor_raises_if_rank_mismatch_dynamic_rank(self):
with self.test_session():
tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor")
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
with self.assertRaisesOpError("fail.*my_tensor.*rank"):
array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self):
tensor_rank0 = constant_op.constant(42, name="my_tensor")
with self.assertRaisesRegexp(
ValueError, "fail.*must have rank.*in.*1.*2"):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
self.evaluate(array_ops.identity(tensor_rank0))
def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self):
with self.test_session():
tensor_rank0 = constant_op.constant(42, name="my_tensor")
with self.assertRaisesRegexp(
ValueError, "fail.*my_tensor.*must have rank.*in.*1.*2"):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
array_ops.identity(tensor_rank0).eval()
def test_rank_one_tensor_raises_if_rank_mismatches_dynamic_rank(self):
with self.test_session():
tensor_rank1 = array_ops.placeholder(dtypes.float32, name="my_tensor")
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank1, (0, 2))]):
with self.assertRaisesOpError("my_tensor.*rank"):
array_ops.identity(tensor_rank1).eval(feed_dict={
tensor_rank1: (42.0, 43.0)
})
def test_raises_if_rank_is_not_integer_dynamic(self):
with self.test_session():
tensor = constant_op.constant(
(42, 43), dtype=dtypes.float32, name="my_tensor")
rank_tensor = array_ops.placeholder(dtypes.float32, name="rank_tensor")
with self.assertRaisesRegexp(TypeError,
"must be of type <dtype: 'int32'>"):
with ops.control_dependencies(
[check_ops.assert_rank_in(tensor, (1, rank_tensor))]):
array_ops.identity(tensor).eval(feed_dict={rank_tensor: .5})
def test_raises_if_rank_is_not_scalar_dynamic(self):
with self.test_session():
tensor = constant_op.constant(
(42, 43), dtype=dtypes.float32, name="my_tensor")
desired_ranks = (
array_ops.placeholder(dtypes.int32, name="rank0_tensor"),
array_ops.placeholder(dtypes.int32, name="rank1_tensor"))
with self.assertRaisesOpError("Rank must be a scalar"):
with ops.control_dependencies(
(check_ops.assert_rank_in(tensor, desired_ranks),)):
array_ops.identity(tensor).eval(feed_dict={
desired_ranks[0]: 1,
desired_ranks[1]: [2, 1],
})
def test_raises_if_rank_is_not_integer_static(self):
with self.test_session():
tensor = constant_op.constant((42, 43), name="my_tensor")
with self.assertRaisesRegexp(TypeError,
"must be of type <dtype: 'int32'>"):
check_ops.assert_rank_in(tensor, (1, .5,))
def test_rank_zero_tensor_doesnt_raise_if_rank_matches_static_rank(self):
tensor_rank0 = constant_op.constant(42, name="my_tensor")
for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, desired_ranks)]):
self.evaluate(array_ops.identity(tensor_rank0))
