def body(list_, m):
list_ = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(list_), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype),
lambda: list_)
list_ = list_ops.tensor_list_push_back(list_, m)
return list_, m
def body(i, m, t1):
t1 = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(t1), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: t1)
t1 = list_ops.tensor_list_push_back(t1, m * i)
i += 1.0
return i, m, t1
def testTensorListFromTensor(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=[]) l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 2.0) l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0)
def body(i, m, t1):
t1 = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(t1), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: t1)
t1 = list_ops.tensor_list_push_back(t1, m * i)
i += 1.0
return i, m, t1
def testTensorListFromTensor(self):
t = constant_op.constant([1.0, 2.0])
l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape())
l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 2.0)
l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(e), 1.0)
self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0)
def build_graph(parameters):
"""Build the TensorListLength op testing graph."""
data = tf.placeholder(dtype=parameters["element_dtype"],
shape=[parameters["num_elements"]] +
parameters["element_shape"])
tensor_list = list_ops.tensor_list_from_tensor(
data, parameters["element_shape"])
out = list_ops.tensor_list_length(tensor_list)
return [data], [out]
def testListFromTensor(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=[])
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e, 1.0)
l, e0 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 2.0)
l, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e1, 1.0)
self.assertAllEqual(list_ops.tensor_list_length(l), 0)
def testListFromTensor(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=[])
e = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
self.assertAllEqual(e, 1.0)
l, e0 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e0, 2.0)
l, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(e1, 1.0)
self.assertAllEqual(list_ops.tensor_list_length(l), 0)
def dynamic_len(list_or_tensor):
"""Implementation of len using dynamic dispatch."""
if _is_tensor_list(list_or_tensor):
return list_ops.tensor_list_length(list_or_tensor)
elif tensor_util.is_tensor(list_or_tensor):
shape = list_or_tensor.shape
if not shape.ndims:
raise ValueError(
'len requires non-zero rank for tensor "%s"' % list_or_tensor)
return array_ops.shape(list_or_tensor)[0]
return len(list_or_tensor)
def testUnevenSplit(self):
l = list_ops.tensor_list_split([1., 2., 3., 4., 5],
element_shape=None,
lengths=[3, 2])
self.assertAllEqual(list_ops.tensor_list_length(l), 2)
self.assertAllEqual(
list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32),
[1., 2., 3.])
self.assertAllEqual(
list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32),
[4., 5.])
def _tf_tensor_list_append(list_, x):
"""Overload of list_append that stages a Tensor list write."""
def empty_list_of_elements_like_x():
tensor_x = ops.convert_to_tensor(x)
return list_ops.empty_tensor_list(
element_shape=array_ops.shape(tensor_x),
element_dtype=tensor_x.dtype)
list_ = control_flow_ops.cond(
list_ops.tensor_list_length(list_) > 0,
lambda: list_,
empty_list_of_elements_like_x,
)
return list_ops.tensor_list_push_back(list_, x)
def body(list_, m):
list_ = control_flow_ops.cond(
math_ops.equal(list_ops.tensor_list_length(list_), 0),
lambda: list_ops.empty_tensor_list(m.shape, m.dtype), lambda: list_)
list_ = list_ops.tensor_list_push_back(list_, m)
return list_, m
def count(self): return list_ops.tensor_list_length(self.list_)
def _tf_tensor_list_len(s): return list_ops.tensor_list_length(s)
def _tf_tensor_list_len(s): return list_ops.tensor_list_length(s)
def size(self, name=None): """See TensorArray.""" return list_ops.tensor_list_length(input_handle=self._flow, name=name)
def size(self, name=None): """See TensorArray.""" return list_ops.tensor_list_length(input_handle=self._flow, name=name)
def count(self): return list_ops.tensor_list_length(self.list_)
