/
utils_impl.py
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/
utils_impl.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SavedModel utility functions implementation."""
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.core.protobuf import struct_pb2
from tensorflow.python.eager import context
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.lib.io import file_io
from tensorflow.python.saved_model import constants
from tensorflow.python.saved_model import nested_structure_coder
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
# TensorInfo helpers.
@tf_export(v1=["saved_model.build_tensor_info",
"saved_model.utils.build_tensor_info"])
@deprecation.deprecated(
None,
"This function will only be available through the v1 compatibility "
"library as tf.compat.v1.saved_model.utils.build_tensor_info or "
"tf.compat.v1.saved_model.build_tensor_info.")
def build_tensor_info(tensor):
"""Utility function to build TensorInfo proto from a Tensor.
Args:
tensor: Tensor or SparseTensor whose name, dtype and shape are used to
build the TensorInfo. For SparseTensors, the names of the three
constituent Tensors are used.
Returns:
A TensorInfo protocol buffer constructed based on the supplied argument.
Raises:
RuntimeError: If eager execution is enabled.
@compatibility(TF2)
This API is not compatible with eager execution as `tensor` needs to be a
graph tensor, and there is no replacement for it in TensorFlow 2.x. To start
writing programs using TensorFlow 2.x, please refer to the [Effective
TensorFlow 2](https://www.tensorflow.org/guide/effective_tf2) guide.
@end_compatibility
"""
if context.executing_eagerly():
raise RuntimeError("`build_tensor_info` is not supported in eager "
"execution.")
return build_tensor_info_internal(tensor)
def build_tensor_info_internal(tensor):
"""Utility function to build TensorInfo proto from a Tensor."""
if (isinstance(tensor, composite_tensor.CompositeTensor) and
not isinstance(tensor, sparse_tensor.SparseTensor)):
return _build_composite_tensor_info_internal(tensor)
tensor_info = meta_graph_pb2.TensorInfo(
dtype=dtypes.as_dtype(tensor.dtype).as_datatype_enum,
tensor_shape=tensor.get_shape().as_proto())
if isinstance(tensor, sparse_tensor.SparseTensor):
tensor_info.coo_sparse.values_tensor_name = tensor.values.name
tensor_info.coo_sparse.indices_tensor_name = tensor.indices.name
tensor_info.coo_sparse.dense_shape_tensor_name = tensor.dense_shape.name
else:
tensor_info.name = tensor.name
return tensor_info
def _build_composite_tensor_info_internal(tensor):
"""Utility function to build TensorInfo proto from a CompositeTensor."""
spec = tensor._type_spec # pylint: disable=protected-access
tensor_info = meta_graph_pb2.TensorInfo()
spec_proto = nested_structure_coder.encode_structure(spec)
tensor_info.composite_tensor.type_spec.CopyFrom(spec_proto.type_spec_value)
for component in nest.flatten(tensor, expand_composites=True):
tensor_info.composite_tensor.components.add().CopyFrom(
build_tensor_info_internal(component))
return tensor_info
def build_tensor_info_from_op(op):
"""Utility function to build TensorInfo proto from an Op.
Note that this function should be used with caution. It is strictly restricted
to TensorFlow internal use-cases only. Please make sure you do need it before
using it.
This utility function overloads the TensorInfo proto by setting the name to
the Op's name, dtype to DT_INVALID and tensor_shape as None. One typical usage
is for the Op of the call site for the defunned function:
```python
@function.defun
def some_variable_initialization_fn(value_a, value_b):
a = value_a
b = value_b
value_a = constant_op.constant(1, name="a")
value_b = constant_op.constant(2, name="b")
op_info = utils.build_op_info(
some_variable_initialization_fn(value_a, value_b))
```
Args:
op: An Op whose name is used to build the TensorInfo. The name that points
to the Op could be fetched at run time in the Loader session.
Returns:
A TensorInfo protocol buffer constructed based on the supplied argument.
Raises:
RuntimeError: If eager execution is enabled.
"""
if context.executing_eagerly():
raise RuntimeError(
"`build_tensor_info_from_op` is not supported in eager execution.")
return meta_graph_pb2.TensorInfo(
dtype=types_pb2.DT_INVALID,
tensor_shape=tensor_shape.unknown_shape().as_proto(),
name=op.name)
@tf_export(v1=["saved_model.get_tensor_from_tensor_info",
"saved_model.utils.get_tensor_from_tensor_info"])
@deprecation.deprecated(
None,
"This function will only be available through the v1 compatibility "
"library as tf.compat.v1.saved_model.utils.get_tensor_from_tensor_info or "
"tf.compat.v1.saved_model.get_tensor_from_tensor_info.")
def get_tensor_from_tensor_info(tensor_info, graph=None, import_scope=None):
"""Returns the Tensor or CompositeTensor described by a TensorInfo proto.
Args:
tensor_info: A TensorInfo proto describing a Tensor or SparseTensor or
CompositeTensor.
graph: The tf.Graph in which tensors are looked up. If None, the
current default graph is used.
import_scope: If not None, names in `tensor_info` are prefixed with this
string before lookup.
Returns:
The Tensor or SparseTensor or CompositeTensor in `graph` described by
`tensor_info`.
Raises:
KeyError: If `tensor_info` does not correspond to a tensor in `graph`.
ValueError: If `tensor_info` is malformed.
"""
graph = graph or ops.get_default_graph()
def _get_tensor(name):
return graph.get_tensor_by_name(
ops.prepend_name_scope(name, import_scope=import_scope))
encoding = tensor_info.WhichOneof("encoding")
if encoding == "name":
return _get_tensor(tensor_info.name)
elif encoding == "coo_sparse":
return sparse_tensor.SparseTensor(
_get_tensor(tensor_info.coo_sparse.indices_tensor_name),
_get_tensor(tensor_info.coo_sparse.values_tensor_name),
_get_tensor(tensor_info.coo_sparse.dense_shape_tensor_name))
elif encoding == "composite_tensor":
spec_proto = struct_pb2.StructuredValue(
type_spec_value=tensor_info.composite_tensor.type_spec)
spec = nested_structure_coder.decode_proto(spec_proto)
components = [_get_tensor(component.name) for component in
tensor_info.composite_tensor.components]
return nest.pack_sequence_as(spec, components, expand_composites=True)
else:
raise ValueError(f"Invalid TensorInfo.encoding: {encoding}. Expected `"
"coo_sparse`, `composite_tensor`, or `name` for a dense "
"tensor.")
def get_element_from_tensor_info(tensor_info, graph=None, import_scope=None):
"""Returns the element in the graph described by a TensorInfo proto.
Args:
tensor_info: A TensorInfo proto describing an Op or Tensor by name.
graph: The tf.Graph in which tensors are looked up. If None, the current
default graph is used.
import_scope: If not None, names in `tensor_info` are prefixed with this
string before lookup.
Returns:
Op or tensor in `graph` described by `tensor_info`.
Raises:
KeyError: If `tensor_info` does not correspond to an op or tensor in `graph`
"""
graph = graph or ops.get_default_graph()
return graph.as_graph_element(
ops.prepend_name_scope(tensor_info.name, import_scope=import_scope))
# Path helpers.
def get_or_create_variables_dir(export_dir):
"""Return variables sub-directory, or create one if it doesn't exist."""
variables_dir = get_variables_dir(export_dir)
file_io.recursive_create_dir(variables_dir)
return variables_dir
def get_variables_dir(export_dir):
"""Return variables sub-directory in the SavedModel."""
return file_io.join(
compat.as_text(export_dir), compat.as_text(constants.VARIABLES_DIRECTORY))
def get_variables_path(export_dir):
"""Return the variables path, used as the prefix for checkpoint files."""
return file_io.join(
compat.as_text(get_variables_dir(export_dir)),
compat.as_text(constants.VARIABLES_FILENAME))
def get_or_create_assets_dir(export_dir):
"""Return assets sub-directory, or create one if it doesn't exist."""
assets_destination_dir = get_assets_dir(export_dir)
file_io.recursive_create_dir(assets_destination_dir)
return assets_destination_dir
def get_assets_dir(export_dir):
"""Return path to asset directory in the SavedModel."""
return file_io.join(
compat.as_text(export_dir), compat.as_text(constants.ASSETS_DIRECTORY))
def get_or_create_debug_dir(export_dir):
"""Returns path to the debug sub-directory, creating if it does not exist."""
debug_dir = get_debug_dir(export_dir)
file_io.recursive_create_dir(debug_dir)
return debug_dir
def get_saved_model_pbtxt_path(export_dir):
return file_io.join(
compat.as_bytes(compat.path_to_str(export_dir)),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
def get_saved_model_pb_path(export_dir):
return file_io.join(
compat.as_bytes(compat.path_to_str(export_dir)),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
def get_debug_dir(export_dir):
"""Returns path to the debug sub-directory in the SavedModel."""
return file_io.join(
compat.as_text(export_dir), compat.as_text(constants.DEBUG_DIRECTORY))
# Based on tensor_bundle/byte_swap.cc
byte_swappable = [
dtypes.float16, dtypes.float32, dtypes.float64, dtypes.bfloat16,
dtypes.complex64, dtypes.complex128, dtypes.uint16, dtypes.uint32,
dtypes.uint64, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.qint16,
dtypes.quint16, dtypes.qint32
]
def swap_function_tensor_content(meta_graph_def, from_endiness, to_endiness):
functions = meta_graph_def.graph_def.library.function
for function in functions:
node_def = function.node_def
for node in node_def:
if node.op == "Const":
tensor = node.attr["value"].tensor
byte_swap_tensor_content(tensor, from_endiness, to_endiness)
def byte_swap_tensor_content(tensor, from_endiness, to_endiness):
"""Byte swaps."""
if tensor.dtype in byte_swappable:
tshape = tensor.tensor_shape.dim
tensor_bytes = tensor.tensor_content
if tensor_bytes:
tensor_size = 1
for sz in tshape:
tensor_size = tensor_size * sz.size
chunksize = int(len(tensor_bytes) / tensor_size)
# Split tensor_data into chunks for byte swapping.
to_swap = [
tensor_bytes[i:i + chunksize]
for i in range(0, len(tensor_bytes), chunksize)
]
# Swap and replace tensor_content.
tensor.tensor_content = b"".join([
int.from_bytes(byteswap,
from_endiness).to_bytes(chunksize, to_endiness)
for byteswap in to_swap
])