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Remove functions from TFLite public Python API. #21105

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Jul 26, 2018
Merged

Remove functions from TFLite public Python API. #21105

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61 changes: 29 additions & 32 deletions tensorflow/contrib/lite/python/lite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,24 +40,23 @@
from google.protobuf.message import DecodeError
from tensorflow.contrib.lite.python import lite_constants as constants
from tensorflow.contrib.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import
from tensorflow.contrib.lite.python.convert import tensor_name
from tensorflow.contrib.lite.python.convert import tensor_name as _tensor_name
from tensorflow.contrib.lite.python.convert import toco_convert
from tensorflow.contrib.lite.python.convert import toco_convert_protos # pylint: disable=unused-import
from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model
from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names
from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes
from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model as _freeze_saved_model
from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names as _get_tensors_from_tensor_names
from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes as _set_tensor_shapes
from tensorflow.contrib.lite.python.interpreter import Interpreter # pylint: disable=unused-import
from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import
from tensorflow.contrib.lite.python.op_hint import OpHint # pylint: disable=unused-import
from tensorflow.core.framework import graph_pb2 as _graph_pb2
from tensorflow.python import keras as _keras
from tensorflow.python.client import session as _session
from tensorflow.python.framework import graph_util as tf_graph_util
from tensorflow.python.framework.importer import import_graph_def
from tensorflow.python.ops.variables import global_variables_initializer
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
# from tensorflow.python.util.all_util import remove_undocumented
from tensorflow.python.framework import graph_util as _tf_graph_util
from tensorflow.python.framework.importer import import_graph_def as _import_graph_def
from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer
from tensorflow.python.saved_model import signature_constants as _signature_constants
from tensorflow.python.saved_model import tag_constants as _tag_constants


class TocoConverter(object):
Expand Down Expand Up @@ -196,7 +195,7 @@ def from_frozen_graph(cls,
input_arrays or output_arrays contains an invalid tensor name.
"""
with _session.Session() as sess:
sess.run(global_variables_initializer())
sess.run(_global_variables_initializer())

# Read GraphDef from file.
graph_def = _graph_pb2.GraphDef()
Expand All @@ -218,12 +217,12 @@ def from_frozen_graph(cls,
raise ValueError(
"Unable to parse input file '{}'.".format(graph_def_file))
sess.graph.as_default()
import_graph_def(graph_def, name="")
_import_graph_def(graph_def, name="")

# Get input and output tensors.
input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays)
output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays)
set_tensor_shapes(input_tensors, input_shapes)
input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays)
output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays)
_set_tensor_shapes(input_tensors, input_shapes)

# Check if graph is frozen.
if not _is_frozen_graph(sess):
Expand Down Expand Up @@ -261,12 +260,12 @@ def from_saved_model(cls,
TocoConverter class.
"""
if tag_set is None:
tag_set = set([tag_constants.SERVING])
tag_set = set([_tag_constants.SERVING])
if signature_key is None:
signature_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY

result = freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key)
result = _freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key)
return cls(
graph_def=result[0], input_tensors=result[1], output_tensors=result[2])

Expand Down Expand Up @@ -299,15 +298,15 @@ def from_keras_model_file(cls,

# Get input and output tensors.
if input_arrays:
input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays)
input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays)
else:
input_tensors = keras_model.inputs

if output_arrays:
output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays)
output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays)
else:
output_tensors = keras_model.outputs
set_tensor_shapes(input_tensors, input_shapes)
_set_tensor_shapes(input_tensors, input_shapes)

graph_def = _freeze_graph(sess, output_tensors)
return cls(graph_def, input_tensors, output_tensors)
Expand All @@ -328,12 +327,12 @@ def convert(self):
for tensor in self._input_tensors:
if not tensor.get_shape():
raise ValueError("Provide an input shape for input array '{0}'.".format(
tensor_name(tensor)))
_tensor_name(tensor)))
shape = tensor.get_shape().as_list()
if None in shape[1:]:
raise ValueError(
"None is only supported in the 1st dimension. Tensor '{0}' has "
"invalid shape '{1}'.".format(tensor_name(tensor), shape))
"invalid shape '{1}'.".format(_tensor_name(tensor), shape))
elif shape[0] is None:
self._set_batch_size(batch_size=1)

Expand All @@ -343,7 +342,7 @@ def convert(self):
quantized_stats = []
invalid_stats = []
for tensor in self._input_tensors:
name = tensor_name(tensor)
name = _tensor_name(tensor)
if name in self.quantized_input_stats:
quantized_stats.append(self.quantized_input_stats[name])
else:
Expand Down Expand Up @@ -381,7 +380,7 @@ def get_input_arrays(self):
Returns:
List of strings.
"""
return [tensor_name(tensor) for tensor in self._input_tensors]
return [_tensor_name(tensor) for tensor in self._input_tensors]

def _set_batch_size(self, batch_size):
"""Sets the first dimension of the input tensor to `batch_size`.
Expand Down Expand Up @@ -428,11 +427,9 @@ def _freeze_graph(sess, output_tensors):
Frozen GraphDef.
"""
if not _is_frozen_graph(sess):
sess.run(global_variables_initializer())
output_arrays = [tensor_name(tensor) for tensor in output_tensors]
return tf_graph_util.convert_variables_to_constants(sess, sess.graph_def,
output_arrays)
sess.run(_global_variables_initializer())
output_arrays = [_tensor_name(tensor) for tensor in output_tensors]
return _tf_graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_arrays)
else:
return sess.graph_def

# remove_undocumented(__name__)