def capture_value(tensor_map, value, dtype, name):
"""Capture a value from outside the function, to pass in as an extra arg."""
captured_value = tensor_map.get(ops.tensor_id(value), None)
if captured_value is None:
captured_value = graph_placeholder(dtype=dtype or value.dtype,
shape=value.shape,
name=name)
if captured_value.dtype == dtypes_module.resource:
handle_data = value._handle_data # pylint: disable=protected-access
captured_value._handle_data = handle_data # pylint: disable=protected-access
if handle_data is not None and handle_data.is_set:
# Ensure that shapes and dtypes are propagated.
shapes, types = zip(*[(pair.shape, pair.dtype)
for pair in handle_data.shape_and_type])
ranks = [
len(s.dim) if not s.unknown_rank else -1 for s in shapes
]
shapes = [[d.size
for d in s.dim] if not s.unknown_rank else None
for s in shapes]
with errors.raise_exception_on_not_ok_status() as status:
pywrap_tensorflow.TF_GraphSetOutputHandleShapesAndTypes_wrapper(
captured_value._op._graph._c_graph, # pylint: disable=protected-access
captured_value._as_tf_output(), # pylint: disable=protected-access
shapes,
ranks,
types,
status)
tensor_map[ops.tensor_id(value)] = (value, captured_value)
else:
captured_value = captured_value[1]
tape.record_operation("captured_value", [captured_value], [value],
lambda x: [x])
return captured_value
def capture_value(tensor_map, value, dtype, name):
"""Capture a value from outside the function, to pass in as an extra arg."""
captured_value = tensor_map.get(ops.tensor_id(value), None)
if captured_value is None:
captured_value = graph_placeholder(dtype=dtype or value.dtype,
shape=value.shape,
name=name)
if captured_value.dtype == dtypes_module.resource:
if ops._USE_C_SHAPES: # pylint: disable=protected-access
if isinstance(value, ops.EagerTensor):
handle_data = value._handle_data # pylint: disable=protected-access
else:
handle_data = resource_variable_ops.get_resource_handle_data(
value)
else:
handle_data = value._handle_data # pylint: disable=protected-access
if handle_data is not None and handle_data.is_set:
# pylint: disable=protected-access
if ops._USE_C_SHAPES:
pywrap_tensorflow.TFE_SetResourceHandleShapeAndType(
captured_value.graph._c_graph,
captured_value._as_tf_output(),
handle_data.SerializeToString())
else:
captured_value._handle_data = handle_data
# pylint: enable=protected-access
# Ensure that shapes and dtypes are propagated.
shapes, types = zip(*[(pair.shape, pair.dtype)
for pair in handle_data.shape_and_type])
ranks = [
len(s.dim) if not s.unknown_rank else -1 for s in shapes
]
shapes = [[d.size
for d in s.dim] if not s.unknown_rank else None
for s in shapes]
pywrap_tensorflow.TF_GraphSetOutputHandleShapesAndTypes_wrapper(
captured_value._op._graph._c_graph, # pylint: disable=protected-access
captured_value._as_tf_output(), # pylint: disable=protected-access
shapes,
ranks,
types)
tensor_map[ops.tensor_id(value)] = (value, captured_value)
else:
captured_value = captured_value[1]
tape.record_operation("captured_value", [captured_value], [value],
lambda x: [x])
return captured_value
def copy_handle_data(source_t, target_t):
"""Copies HandleData for variant and resource type tensors if available.
The CppShapeInferenceResult::HandleData proto contains information about the
shapes and types of the element tensors of resource/variant type tensors.
We need to copy this across function boundaries, i.e., when capturing a
placeholder or when returning a function tensor as output. If we don't do this
the element tensors will have unknown shapes, e.g., if a TensorList variant
tensor is captured as a placeholder, elements popped from that list would have
unknown shape.
Args:
source_t: The tensor to copy HandleData from.
target_t: The tensor to copy HandleData to.
"""
if (target_t.dtype == dtypes.resource or target_t.dtype == dtypes.variant):
if isinstance(source_t, ops.EagerTensor):
handle_data = source_t._handle_data # pylint: disable=protected-access
else:
handle_data = resource_variable_ops.get_resource_handle_data(
source_t)
if (handle_data is not None and handle_data.is_set
and handle_data.shape_and_type):
# pylint: disable=protected-access
pywrap_tensorflow.SetHandleShapeAndType(
target_t.graph._c_graph, target_t._as_tf_output(),
handle_data.SerializeToString())
# pylint: enable=protected-access
# Ensure that shapes and dtypes are propagated.
shapes, types = zip(*[(pair.shape, pair.dtype)
for pair in handle_data.shape_and_type])
ranks = [len(s.dim) if not s.unknown_rank else -1 for s in shapes]
shapes = [
[d.size for d in s.dim] # pylint: disable=g-complex-comprehension
if not s.unknown_rank else None for s in shapes
]
pywrap_tensorflow.TF_GraphSetOutputHandleShapesAndTypes_wrapper(
target_t._op._graph._c_graph, # pylint: disable=protected-access
target_t._as_tf_output(), # pylint: disable=protected-access
shapes,
ranks,
types)
