def testAddTensorLists(self):
with self.cached_session(), self.test_scope():
l1 = list_ops.tensor_list_reserve(
element_shape=[], element_dtype=dtypes.float32, num_elements=3)
l2 = list_ops.tensor_list_reserve(
element_shape=[], element_dtype=dtypes.float32, num_elements=3)
l1 = list_ops.tensor_list_set_item(l1, 0, 5.)
l2 = list_ops.tensor_list_set_item(l2, 2, 10.)
l = math_ops.add_n([l1, l2])
self.assertAllEqual(
list_ops.tensor_list_stack(l, element_dtype=dtypes.float32),
[5.0, 0.0, 10.0])
def testAddTensorLists(self):
with self.session(), self.test_scope():
l1 = list_ops.tensor_list_reserve(
element_shape=[], element_dtype=dtypes.float32, num_elements=3)
l2 = list_ops.tensor_list_reserve(
element_shape=[], element_dtype=dtypes.float32, num_elements=3)
l1 = list_ops.tensor_list_set_item(l1, 0, 5.)
l2 = list_ops.tensor_list_set_item(l2, 2, 10.)
l = math_ops.add_n([l1, l2])
self.assertAllEqual(
list_ops.tensor_list_stack(l, element_dtype=dtypes.float32),
[5.0, 0.0, 10.0])
def testSerializeListWithInvalidTensors(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=2)
l = list_ops.tensor_list_set_item(l, 0, 1.)
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps = list_ops.tensor_list_set_item(l_ps, 1, 2.)
t = list_ops.tensor_list_stack(l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_t = array_ops.identity(t)
self.assertAllEqual(self.evaluate(worker_t), [1.0, 2.0])
def testSerializeListWithInvalidTensors(self):
worker = test_util.create_local_cluster(num_workers=1, num_ps=1)[0][0]
with ops.Graph().as_default(), session.Session(target=worker.target):
with ops.device("/job:worker"):
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=2)
l = list_ops.tensor_list_set_item(l, 0, 1.)
with ops.device("/job:ps"):
l_ps = array_ops.identity(l)
l_ps = list_ops.tensor_list_set_item(l_ps, 1, 2.)
t = list_ops.tensor_list_stack(l_ps, element_dtype=dtypes.float32)
with ops.device("/job:worker"):
worker_t = array_ops.identity(t)
self.assertAllEqual(self.evaluate(worker_t), [1.0, 2.0])
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 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 testSetStackReservedUnknownElementShape(self):
with self.cached_session(), self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=None, num_elements=2)
l = list_ops.tensor_list_set_item(l, 0, [3.0, 4.0])
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [[3.0, 4.0], [0., 0.]])
def testSetStackReservedUnknownElementShape(self):
with self.cached_session(), self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=None, num_elements=2)
l = list_ops.tensor_list_set_item(l, 0, [3.0, 4.0])
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [[3.0, 4.0], [0., 0.]])
def testGetSetItem(self):
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape())
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e0), 1.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t), [3.0, 2.0])
def testSetOnEmptyListWithMaxNumElementsFails(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[], max_num_elements=3)
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Trying to modify element 0 in a list with 0 elements."):
l = list_ops.tensor_list_set_item(l, 0, 1.)
self.evaluate(l)
def testGetSetItem(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=[]) e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e0), 1.0) l = list_ops.tensor_list_set_item(l, 0, 3.0) t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [3.0, 2.0])
def testSetOnEmptyListWithMaxNumElementsFails(self):
l = list_ops.empty_tensor_list(
element_dtype=dtypes.float32, element_shape=[], max_num_elements=3)
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Trying to modify element 0 in a list with 0 elements."):
l = list_ops.tensor_list_set_item(l, 0, 1.)
self.evaluate(l)
def testGetSet(self):
with self.cached_session(), self.test_scope():
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 1.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [3.0, 2.0])
def testGetSetReserved(self):
with self.cached_session(), self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=2)
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 0.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [3.0, 0.0])
def testGetSetReserved(self):
with self.cached_session(), self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=2)
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 0.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [3.0, 0.0])
def testGetSet(self):
with self.cached_session(), self.test_scope():
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=[])
e0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 1.0)
l = list_ops.tensor_list_set_item(l, 0, 3.0)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [3.0, 2.0])
def testSetGetGrad(self):
with backprop.GradientTape() as tape:
t = constant_op.constant(5.)
tape.watch(t)
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=3)
l = list_ops.tensor_list_set_item(l, 1, 2. * t)
e = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 10.0)
self.assertAllEqual(self.evaluate(tape.gradient(e, t)), 2.0)
def testSetDoesNotUpdatePushIndex(self):
with self.cached_session(), self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=[], element_dtype=dtypes.float32, max_num_elements=2)
# SetItem should not change the push index.
l = list_ops.tensor_list_set_item(l, 1, 3.)
l = list_ops.tensor_list_push_back(l, 5.)
l = list_ops.tensor_list_push_back(l, 7.)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [5., 7.])
def testSkipEagerTensorListGetItemGradAggregation(self):
l = list_ops.tensor_list_reserve(
element_shape=[], num_elements=1, element_dtype=dtypes.float32)
x = constant_op.constant(1.0)
l = list_ops.tensor_list_set_item(l, 0, x)
l_read1 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
l_read2 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
grad = gradients_impl.gradients([l_read1, l_read2], [x])
with self.cached_session() as sess:
self.assertSequenceEqual(self.evaluate(grad), [2.])
def testSkipEagerTensorListGetItemGradAggregation(self):
l = list_ops.tensor_list_reserve(
element_shape=[], num_elements=1, element_dtype=dtypes.float32)
x = constant_op.constant(1.0)
l = list_ops.tensor_list_set_item(l, 0, x)
l_read1 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
l_read2 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
grad = gradients_impl.gradients([l_read1, l_read2], [x])
with self.cached_session() as sess:
self.assertSequenceEqual(self.evaluate(grad), [2.])
def testSetDoesNotUpdatePushIndex(self):
with self.cached_session(), self.test_scope():
l = list_ops.empty_tensor_list(
element_shape=[], element_dtype=dtypes.float32, max_num_elements=2)
# SetItem should not change the push index.
l = list_ops.tensor_list_set_item(l, 1, 3.)
l = list_ops.tensor_list_push_back(l, 5.)
l = list_ops.tensor_list_push_back(l, 7.)
t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
self.assertAllEqual(t, [5., 7.])
def testSetGetGrad(self):
with backprop.GradientTape() as tape:
t = constant_op.constant(5.)
tape.watch(t)
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32, element_shape=[], num_elements=3)
l = list_ops.tensor_list_set_item(l, 1, 2. * t)
e = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 10.0)
self.assertAllEqual(self.evaluate(tape.gradient(e, t)), 2.0)
def testSkipEagerSetItemWithMismatchedShapeFails(self):
with self.cached_session() as sess:
ph = array_ops.placeholder(dtypes.float32)
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
# Set a placeholder with unknown shape to satisfy the shape inference
# at graph building time.
l = list_ops.tensor_list_set_item(l, 0, ph)
l_0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape"):
sess.run(l_0, {ph: [3.0]})
def testGetSetReservedNonScalar(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32,
element_shape=(7, 15),
num_elements=2)
l = list_ops.tensor_list_set_item(
l, 0, constant_op.constant(1.0, shape=(7, 15)))
e1 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
e2 = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e1), np.ones((7, 15)))
self.assertAllEqual(sess.run(e2), np.zeros((7, 15)))
def testSkipEagerSetItemWithMismatchedShapeFails(self):
with self.cached_session() as sess:
ph = array_ops.placeholder(dtypes.float32)
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
# Set a placeholder with unknown shape to satisfy the shape inference
# at graph building time.
l = list_ops.tensor_list_set_item(l, 0, ph)
l_0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"incompatible shape"):
sess.run(l_0, {ph: [3.0]})
def write(self, index, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayV2Write", [self._flow, index, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape:
self._merge_element_shape(value.shape)
flow_out = list_ops.tensor_list_set_item(
input_handle=self._flow, index=index, item=value, name=name)
ta = TensorArray(dtype=self._dtype, handle=None, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
return ta
def testGetSetReservedNonScalar(self):
with self.cached_session() as sess, self.test_scope():
l = list_ops.tensor_list_reserve(
element_dtype=dtypes.float32,
element_shape=(7, 15),
num_elements=2)
l = list_ops.tensor_list_set_item(
l, 0, constant_op.constant(1.0, shape=(7, 15)))
e1 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
e2 = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e1), np.ones((7, 15)))
self.assertAllEqual(sess.run(e2), np.zeros((7, 15)))
def write(self, index, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayV2Write", [self._flow, index, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape:
self._merge_element_shape(value.shape)
flow_out = list_ops.tensor_list_set_item(
input_handle=self._flow, index=index, item=value, name=name)
ta = TensorArray(dtype=self._dtype, handle=None, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
return ta
def write(self, index, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayV2Write", [self._flow, index, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape:
self._merge_element_shape(value.shape)
flow_out = list_ops.tensor_list_set_item(
input_handle=self._flow,
index=index,
item=value,
resize_if_index_out_of_bounds=self._dynamic_size,
name=name)
return build_ta_with_new_flow(self, flow_out)
def write(self, index, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayV2Write", [self._flow, index, value]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape:
self._merge_element_shape(value.shape)
flow_out = list_ops.tensor_list_set_item(
input_handle=self._flow,
index=index,
item=value,
resize_if_index_out_of_bounds=self._dynamic_size,
name=name)
return build_ta_with_new_flow(self, flow_out)
def testGetSetGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
c2 = constant_op.constant(3.0)
tape.watch(c2)
l = list_ops.tensor_list_set_item(l, 0, c2)
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
ee = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
y = e * e + ee * ee
grad_c, grad_c2 = tape.gradient(y, [c, c2])
self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0])
self.assertAllEqual(self.evaluate(grad_c2), 6.0)
def testGetSetGradients(self):
with backprop.GradientTape() as tape:
c = constant_op.constant([1.0, 2.0])
tape.watch(c)
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
c2 = constant_op.constant(3.0)
tape.watch(c2)
l = list_ops.tensor_list_set_item(l, 0, c2)
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
ee = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
y = e * e + ee * ee
grad_c, grad_c2 = tape.gradient(y, [c, c2])
self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0])
self.assertAllEqual(self.evaluate(grad_c2), 6.0)
def build_graph(parameters):
"""Build the TensorListSetItem op testing graph."""
data = tf.placeholder(dtype=parameters["element_dtype"],
shape=[parameters["num_elements"]] +
parameters["element_shape"])
item = tf.placeholder(dtype=parameters["element_dtype"],
shape=parameters["element_shape"])
tensor_list = list_ops.tensor_list_from_tensor(
data, parameters["element_shape"])
tensor_list = list_ops.tensor_list_set_item(tensor_list,
parameters["index"], item)
out = list_ops.tensor_list_stack(
tensor_list,
num_elements=parameters["num_elements"],
element_dtype=parameters["element_dtype"])
return [data, item], [out]
def write(self, index, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayV2Write", [self._flow, index, value]):
# TODO(b/129870929): Fix after all callers provide proper init dtype.
value = ops.convert_to_tensor(
value, preferred_dtype=self._dtype, name="value")
_check_dtypes(value, self._dtype)
if self._infer_shape:
self._merge_element_shape(value.shape)
flow_out = list_ops.tensor_list_set_item(
input_handle=self._flow,
index=index,
item=value,
resize_if_index_out_of_bounds=self._dynamic_size,
name=name)
return build_ta_with_new_flow(self, flow_out)
def _tf_tensor_list_set_item(target, i, x): """Overload of set_item that stages a Tensor list update.""" return list_ops.tensor_list_set_item(target, i, x)
def testSetOutOfBounds(self):
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=[])
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(list_ops.tensor_list_set_item(l, 20, 3.0))
def __setitem__(self, key, value): self.list_ = list_ops.tensor_list_set_item(self.list_, key, value)
def _tf_tensor_list_set_item(target, i, x): """Overload of set_item that stages a Tensor list update.""" return list_ops.tensor_list_set_item(target, i, x)
def __setitem__(self, key, value): self.list_ = list_ops.tensor_list_set_item(self.list_, key, value)
def loop_body(i, tensor_list):
new_item = tf.add(
tf.add(item, item),
tf.constant(value=1, dtype=parameters["element_dtype"]))
new_list = list_ops.tensor_list_set_item(tensor_list, i, new_item)
return i + 1, new_list
def testSetOutOfBounds(self):
c = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape())
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(list_ops.tensor_list_set_item(l, 20, 3.0))
def body(i, x, l):
element = tf.where(x[i])
l = list_ops.tensor_list_set_item(l, i, element)
return i + 1, x, l
