def testGatherEmptyList(self, max_num_elements):
# Should be able to gather from empty lists with fully defined
# element_shape.
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=[1, 2],
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [], element_dtype=dtypes.float32)
self.assertAllEqual((0, 1, 2), self.evaluate(t).shape)
# Should not be able to gather from empty lists with partially defined
# element_shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"non-fully-defined"):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=[None, 2],
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [], element_dtype=dtypes.float32)
self.evaluate(t)
# Should not be able to gather from empty lists with undefined
# element_shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"non-fully-defined"):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=None,
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [], element_dtype=dtypes.float32)
self.evaluate(t)
def testGatherEmptyList(self, max_num_elements):
# Should be able to gather from empty lists with fully defined
# element_shape.
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=[1, 2],
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [], element_dtype=dtypes.float32)
self.assertAllEqual((0, 1, 2), self.evaluate(t).shape)
# Should not be able to gather from empty lists with partially defined
# element_shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"non-fully-defined"):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=[-1, 2],
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [],
element_dtype=dtypes.float32)
self.evaluate(t)
# Should not be able to gather from empty lists with undefined
# element_shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"non-fully-defined"):
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=-1,
max_num_elements=max_num_elements)
t = list_ops.tensor_list_gather(l, [],
element_dtype=dtypes.float32)
self.evaluate(t)
def testZerosLikeUninitialized(self):
l0 = list_ops.tensor_list_reserve([], 3, element_dtype=dtypes.float32)
l1 = list_ops.tensor_list_set_item(l0, 0, 1.) # [1., _, _]
zeros_1 = array_ops.zeros_like(l1) # [0., _, _]
l2 = list_ops.tensor_list_set_item(l1, 2, 2.) # [1., _, 2.]
zeros_2 = array_ops.zeros_like(l2) # [0., _, 0.]
# Gather indices with zeros in `zeros_1`.
res_1 = list_ops.tensor_list_gather(
zeros_1, [0], element_dtype=dtypes.float32)
# Gather indices with zeros in `zeros_2`.
res_2 = list_ops.tensor_list_gather(
zeros_2, [0, 2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(res_1), [0.])
self.assertAllEqual(self.evaluate(res_2), [0., 0.])
def testGather(self, input_list, element_shape, indices, output):
with self.session(), self.test_scope():
tensor_list = list_ops.tensor_list_from_tensor(
input_list, element_shape=element_shape)
gather_t = list_ops.tensor_list_gather(
tensor_list, indices, element_dtype=dtypes.float32)
self.assertAllEqual(gather_t, output)
def testZerosLikeUninitialized(self):
l0 = list_ops.tensor_list_reserve([], 3, element_dtype=dtypes.float32)
l1 = list_ops.tensor_list_set_item(l0, 0, 1.) # [1., _, _]
zeros_1 = array_ops.zeros_like(l1) # [0., _, _]
l2 = list_ops.tensor_list_set_item(l1, 2, 2.) # [1., _, 2.]
zeros_2 = array_ops.zeros_like(l2) # [0., _, 0.]
# Gather indices with zeros in `zeros_1`.
res_1 = list_ops.tensor_list_gather(
zeros_1, [0], element_dtype=dtypes.float32)
# Gather indices with zeros in `zeros_2`.
res_2 = list_ops.tensor_list_gather(
zeros_2, [0, 2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(res_1), [0.])
self.assertAllEqual(self.evaluate(res_2), [0., 0.])
def testGatherWithPartiallyDefinedElementShape(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[-1])
l = list_ops.tensor_list_push_back(l, constant_op.constant([1.0]))
l = list_ops.tensor_list_push_back(l, constant_op.constant([2.0, 3.0]))
l = list_ops.tensor_list_push_back(l, constant_op.constant([4.0, 5.0]))
t = list_ops.tensor_list_gather(l, [0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[1.0]])
t = list_ops.tensor_list_gather(l, [1, 2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[2.0, 3.0], [4.0, 5.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2], element_dtype=dtypes.float32)
self.evaluate(t)
def gather(self, indices, name=None):
"""See TensorArray."""
value = list_ops.tensor_list_gather(input_handle=self._flow,
indices=indices,
element_dtype=self._dtype,
name=name)
if self._element_shape and self._element_shape[0].dims is not None:
value.set_shape([None] + self._element_shape[0].dims)
return value
def gather(self, indices, name=None):
"""See TensorArray."""
value = list_ops.tensor_list_gather(
input_handle=self._flow,
indices=indices,
element_dtype=self._dtype,
name=name)
if self._element_shape and self._element_shape[0].dims is not None:
value.set_shape([None] + self._element_shape[0].dims)
return value
def testGatherWithUnknownElementShape(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=None,
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant([3.0, 4.0]))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
t = list_ops.tensor_list_gather(l, [2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[3.0, 4.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2], element_dtype=dtypes.float32)
self.evaluate(t)
def testGatherWithUnknownElementShape(self, max_num_elements):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32,
element_shape=None,
max_num_elements=max_num_elements)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l = list_ops.tensor_list_push_back(l, constant_op.constant([3.0, 4.0]))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
t = list_ops.tensor_list_gather(l, [2], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [[3.0, 4.0]])
# Should raise an error when the requested tensors do not all have the same
# shape.
with self.assertRaisesRegexp(errors.InvalidArgumentError, "unequal shapes"):
t = list_ops.tensor_list_gather(l, [0, 2], element_dtype=dtypes.float32)
self.evaluate(t)
def gather(self, indices, name=None):
"""See TensorArray."""
if self._element_shape:
element_shape = self._element_shape[0]
else:
element_shape = tensor_shape.unknown_shape(None)
value = list_ops.tensor_list_gather(
input_handle=self._flow,
indices=indices,
element_dtype=self._dtype,
element_shape=element_shape,
name=name)
return value
def testGatherGrad(self):
with backprop.GradientTape() as tape:
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=scalar_shape())
c0 = constant_op.constant(1.0)
tape.watch(c0)
l = list_ops.tensor_list_push_back(l, c0)
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
s = (t[0] + t[1]) * (t[0] + t[1])
dt = tape.gradient(s, c0)
self.assertAllEqual(self.evaluate(dt), 6.0)
def testGatherGrad(self):
with backprop.GradientTape() as tape:
l = list_ops.empty_tensor_list(element_dtype=dtypes.float32,
element_shape=scalar_shape())
c0 = constant_op.constant(1.0)
tape.watch(c0)
l = list_ops.tensor_list_push_back(l, c0)
l = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
t = list_ops.tensor_list_gather(l, [1, 0], element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [2.0, 1.0])
s = (t[0] + t[1]) * (t[0] + t[1])
dt = tape.gradient(s, c0)
self.assertAllEqual(self.evaluate(dt), 6.0)
