1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
|
# Copyright 2017 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.
# ==============================================================================
"""TensorFlow Lite tooling helper functionality.
EXPERIMENTAL: APIs here are unstable and likely to change without notice.
@@toco_convert
@@toco_convert_protos
@@OpHint
@@convert_op_hints_to_stubs
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os as _os
import subprocess as _subprocess
import tempfile as _tempfile
# pylint: disable=unused-import
from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs
from tensorflow.contrib.lite.python.op_hint import OpHint
# pylint: enable=unused-import
from tensorflow.contrib.lite.toco import model_flags_pb2 as _model_flags_pb2
from tensorflow.contrib.lite.toco import toco_flags_pb2 as _toco_flags_pb2
from tensorflow.contrib.lite.toco import types_pb2 as _types_pb2
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.platform import resource_loader as _resource_loader
from tensorflow.python.util.all_util import remove_undocumented
from tensorflow.python.util.lazy_loader import LazyLoader
# Lazy load since some of the performance benchmark skylark rules
# break dependencies.
_toco_python = LazyLoader(
"tensorflow_wrap_toco", globals(),
"tensorflow.contrib.lite.toco.python."
"tensorflow_wrap_toco")
del LazyLoader
# Enum types from the protobuf promoted to the API
FLOAT = _types_pb2.FLOAT
INT32 = _types_pb2.INT32
INT64 = _types_pb2.INT64
STRING = _types_pb2.STRING
QUANTIZED_UINT8 = _types_pb2.QUANTIZED_UINT8
TENSORFLOW_GRAPHDEF = _toco_flags_pb2.TENSORFLOW_GRAPHDEF
TFLITE = _toco_flags_pb2.TFLITE
GRAPHVIZ_DOT = _toco_flags_pb2.GRAPHVIZ_DOT
# Currently the default mode of operation is to shell to another python process
# to protect against crashes. However, it breaks some dependent targets because
# it forces us to depend on an external py_binary. The experimental API doesn't
# have that drawback.
EXPERIMENTAL_USE_TOCO_API_DIRECTLY = False
# Find the toco_from_protos binary using the resource loader if using from
# bazel, otherwise we are in a pip where console_scripts already has
# the toco_from_protos tool.
if EXPERIMENTAL_USE_TOCO_API_DIRECTLY:
_toco_from_proto_bin = ""
else:
_toco_from_proto_bin = _resource_loader.get_path_to_datafile(
"../toco/python/toco_from_protos")
if _toco_from_proto_bin and not _os.path.exists(_toco_from_proto_bin):
_toco_from_proto_bin = "toco_from_protos"
def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str):
"""Convert `input_data_str` according to model and toco parameters.
Unless you know what you are doing consider using
the more friendly @{tf.contrib.lite.toco_convert}}.
Args:
model_flags_str: Serialized proto describing model properties, see
`toco/model_flags.proto`.
toco_flags_str: Serialized proto describing conversion properties, see
`toco/toco_flags.proto`.
input_data_str: Input data in serialized form (e.g. a graphdef is common)
Returns:
Converted model in serialized form (e.g. a TFLITE model is common).
Raises:
RuntimeError: When conversion fails, an exception is raised with the error
message embedded.
"""
# TODO(aselle): When toco does not use fatal errors for failure, we can
# switch this on.
if not _toco_from_proto_bin:
return _toco_python.TocoConvert(
model_flags_str, toco_flags_str, input_data_str)
with _tempfile.NamedTemporaryFile() as fp_toco, \
_tempfile.NamedTemporaryFile() as fp_model, \
_tempfile.NamedTemporaryFile() as fp_input, \
_tempfile.NamedTemporaryFile() as fp_output:
fp_model.write(model_flags_str)
fp_toco.write(toco_flags_str)
fp_input.write(input_data_str)
fp_model.flush()
fp_toco.flush()
fp_input.flush()
cmd = [
_toco_from_proto_bin, fp_model.name, fp_toco.name, fp_input.name,
fp_output.name
]
cmdline = " ".join(cmd)
proc = _subprocess.Popen(
cmdline,
shell=True,
stdout=_subprocess.PIPE,
stderr=_subprocess.STDOUT,
close_fds=True)
stdout, stderr = proc.communicate()
exitcode = proc.returncode
if exitcode == 0:
stuff = fp_output.read()
return stuff
else:
raise RuntimeError("TOCO failed see console for info.\n%s\n%s\n" %
(stdout, stderr))
def _tensor_name(x):
return x.name.split(":")[0]
def toco_convert(input_data,
input_tensors,
output_tensors,
inference_type=FLOAT,
input_format=TENSORFLOW_GRAPHDEF,
output_format=TFLITE,
quantized_input_stats=None,
drop_control_dependency=True):
"""Convert a model using TOCO from `input_format` to `output_format`.
Typically this is to convert from TensorFlow GraphDef to TFLite, in which
case the default `input_format` and `output_format` are sufficient.
Args:
input_data: Input data (i.e. often `sess.graph_def`).
input_tensors: List of input tensors. Type and shape are computed using
`foo.get_shape()` and `foo.dtype`.
output_tensors: List of output tensors (only .name is used from this).
inference_type: Currently must be `{FLOAT, QUANTIZED_UINT8}`.
input_format: Type of data to read (currently must be TENSORFLOW_GRAPHDEF).
output_format: Type of data to write (currently must be TFLITE or
GRAPHVIZ_DOT)
quantized_input_stats: For each member of input_tensors the mean and
std deviation of training data. Only needed if `inference_type` is
`QUANTIZED_UINT8`.
drop_control_dependency: Drops control dependencies silently. This is due
to tf lite not supporting control dependencies.
Returns:
The converted data. For example if tflite was the destination, then
this will be a tflite flatbuffer in a bytes array.
Raises:
ValueError: If the input tensor type is unknown
RuntimeError: If TOCO fails to convert (in which case the runtime error's
error text will contain the TOCO error log)
"""
toco = _toco_flags_pb2.TocoFlags()
toco.input_format = input_format
toco.output_format = output_format
toco.drop_control_dependency = drop_control_dependency
model = _model_flags_pb2.ModelFlags()
toco.inference_type = inference_type
for idx, input_tensor in enumerate(input_tensors):
if input_tensor.dtype == _dtypes.float32:
tflite_input_type = FLOAT
elif input_tensor.dtype == _dtypes.int32:
tflite_input_type = INT32
elif input_tensor.dtype == _dtypes.int64:
tflite_input_type = INT64
# TODO(aselle): Insert strings when they are available
else:
raise ValueError("Tensors %s not known type %r" % (input_tensor.name,
input_tensor.dtype))
input_array = model.input_arrays.add()
if inference_type == QUANTIZED_UINT8:
if tflite_input_type == FLOAT:
tflite_input_type = QUANTIZED_UINT8
input_array.mean_value, input_array.std_value = quantized_input_stats[idx]
input_array.name = _tensor_name(input_tensor)
input_array.shape.dims.extend(map(int, input_tensor.get_shape()))
for output_tensor in output_tensors:
model.output_arrays.append(_tensor_name(output_tensor))
# TODO(aselle): Consider handling the case of allowing quantized
# inputs to be converted to float (via the toco.inference_input_type field).
data = toco_convert_protos(model.SerializeToString(),
toco.SerializeToString(),
input_data.SerializeToString())
return data
_allowed_symbols = [
"FLOAT",
"INT32",
"INT64",
"STRING",
"QUANTIZED_UINT8",
"TENSORFLOW_GRAPHDEF",
"TFLITE",
"GRAPHVIZ_DOT",
"EXPERIMENTAL_USE_TOCO_API_DIRECTLY",
]
remove_undocumented(__name__, _allowed_symbols)
|
