def testSort_staticallyKnownRank_constantTransposition(self):
# The transposition array should be a constant if the rank of "values" is
# statically known.
tensor = random_ops.random_uniform(
# Rank is statically known to be 5, but the dimension lengths are not
# known.
random_ops.random_uniform(
shape=(5,), minval=0, maxval=10, dtype=dtypes.int32))
sort_ops.sort(tensor, axis=1)
transposition = (
ops.get_default_graph().get_tensor_by_name('sort/transposition:0'))
self.assertFalse(tensor_util.constant_value(transposition) is None)
self.assertAllEqual(
# Swaps "1" and "4" to put "1" at the end.
tensor_util.constant_value(transposition),
[0, 4, 2, 3, 1])
def testDescending(self):
arr = np.random.random((10, 5, 5))
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=0)[::-1],
sort_ops.sort(constant_op.constant(arr),
axis=0,
direction='DESCENDING').eval())
def testSort_staticallyKnownRank_constantTransposition(self):
# The transposition array should be a constant if the rank of "values" is
# statically known.
tensor = random_ops.random_uniform(
# Rank is statically known to be 5, but the dimension lengths are not
# known.
random_ops.random_uniform(shape=(5, ),
minval=0,
maxval=10,
dtype=dtypes.int32))
sort_ops.sort(tensor, axis=1)
transposition = (
ops.get_default_graph().get_tensor_by_name('sort/transposition:0'))
self.assertFalse(tensor_util.constant_value(transposition) is None)
self.assertAllEqual(
# Swaps "1" and "4" to put "1" at the end.
tensor_util.constant_value(transposition),
[0, 4, 2, 3, 1])
def testDescending(self):
arr = np.random.random((10, 5, 5))
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=0)[::-1],
sort_ops.sort(
constant_op.constant(arr),
axis=0,
direction='DESCENDING').eval())
def testScalar(self):
# Create an empty scalar where the static shape is unknown.
zeros_length_1 = array_ops.zeros(random_ops.random_uniform(
[1], minval=0, maxval=1, dtype=dtypes.int32),
dtype=dtypes.int32)
scalar = array_ops.zeros(zeros_length_1)
sort = sort_ops.sort(scalar)
with self.cached_session():
with self.assertRaises(errors.InvalidArgumentError):
sort.eval()
def testScalar(self):
# Create an empty scalar where the static shape is unknown.
zeros_length_1 = array_ops.zeros(
random_ops.random_uniform([1], minval=0, maxval=1, dtype=dtypes.int32),
dtype=dtypes.int32)
scalar = array_ops.zeros(zeros_length_1)
sort = sort_ops.sort(scalar)
with self.cached_session():
with self.assertRaises(errors.InvalidArgumentError):
sort.eval()
def testRandom_highDimensionality(self):
np.random.seed(100)
for _ in range(20):
rank = np.random.randint(5, 15)
shape = [np.random.randint(1, 4) for _ in range(rank)]
arr = np.random.random(shape)
sort_axis = np.random.choice(rank)
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=sort_axis),
sort_ops.sort(constant_op.constant(arr), axis=sort_axis).eval())
def testRandom_highDimensionality(self):
np.random.seed(100)
for _ in range(20):
rank = np.random.randint(5, 15)
shape = [np.random.randint(1, 4) for _ in range(rank)]
arr = np.random.random(shape)
sort_axis = np.random.choice(rank)
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=sort_axis),
sort_ops.sort(constant_op.constant(arr),
axis=sort_axis).eval())
def _testRandom_lowDimensionality(self, negative_axis):
np.random.seed(42)
for _ in range(20):
rank = np.random.randint(1, 3)
shape = [np.random.randint(0, 20) for _ in range(rank)]
arr = np.random.random(shape)
sort_axis = np.random.choice(rank)
if negative_axis:
sort_axis = -1 - sort_axis
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=sort_axis),
sort_ops.sort(constant_op.constant(arr), axis=sort_axis).eval())
def _testRandom_lowDimensionality(self, negative_axis):
np.random.seed(42)
for _ in range(20):
rank = np.random.randint(1, 3)
shape = [np.random.randint(0, 20) for _ in range(rank)]
arr = np.random.random(shape)
sort_axis = np.random.choice(rank)
if negative_axis:
sort_axis = -1 - sort_axis
with self.cached_session():
self.assertAllEqual(
np.sort(arr, axis=sort_axis),
sort_ops.sort(constant_op.constant(arr),
axis=sort_axis).eval())
def testNegativeOutOfBounds_staticShape(self):
arr = constant_op.constant([3, 4, 5])
with self.assertRaises(ValueError):
sort_ops.sort(arr, axis=-4)
def testNegativeOutOfBounds_staticShape(self):
arr = constant_op.constant([3, 4, 5])
with self.assertRaises(ValueError):
sort_ops.sort(arr, axis=-4)
