/
example_parser_configuration.py
206 lines (171 loc) · 7.97 KB
/
example_parser_configuration.py
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
# 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.
# ==============================================================================
"""Extract parse_example op configuration to a proto."""
from tensorflow.core.example import example_parser_configuration_pb2
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
def extract_example_parser_configuration(parse_example_op, sess):
"""Returns an ExampleParserConfig proto.
Args:
parse_example_op: A ParseExample or ParseExampleV2 `Operation`
sess: A tf.compat.v1.Session needed to obtain some configuration values.
Returns:
A ExampleParserConfig proto.
Raises:
ValueError: If attributes are inconsistent.
"""
if parse_example_op.type == "ParseExample":
return _extract_from_parse_example(parse_example_op, sess)
elif parse_example_op.type == "ParseExampleV2":
return _extract_from_parse_example_v2(parse_example_op, sess)
else:
raise ValueError(
"Found unexpected type when parsing example. Expected `ParseExample` "
f"object. Received type: {parse_example_op.type}")
def _extract_from_parse_example(parse_example_op, sess):
"""Extract ExampleParserConfig from ParseExample op."""
config = example_parser_configuration_pb2.ExampleParserConfiguration()
num_sparse = parse_example_op.get_attr("Nsparse")
num_dense = parse_example_op.get_attr("Ndense")
total_features = num_dense + num_sparse
sparse_types = parse_example_op.get_attr("sparse_types")
dense_types = parse_example_op.get_attr("Tdense")
dense_shapes = parse_example_op.get_attr("dense_shapes")
if len(sparse_types) != num_sparse:
raise ValueError("len(sparse_types) attribute does not match "
"Nsparse attribute (%d vs %d)" %
(len(sparse_types), num_sparse))
if len(dense_types) != num_dense:
raise ValueError("len(dense_types) attribute does not match "
"Ndense attribute (%d vs %d)" %
(len(dense_types), num_dense))
if len(dense_shapes) != num_dense:
raise ValueError("len(dense_shapes) attribute does not match "
"Ndense attribute (%d vs %d)" %
(len(dense_shapes), num_dense))
# Skip over the serialized input, and the names input.
fetch_list = parse_example_op.inputs[2:]
# Fetch total_features key names and num_dense default values.
if len(fetch_list) != (total_features + num_dense):
raise ValueError("len(fetch_list) does not match total features + "
"num_dense (%d vs %d)" %
(len(fetch_list), (total_features + num_dense)))
fetched = sess.run(fetch_list)
if len(fetched) != len(fetch_list):
raise ValueError("len(fetched) does not match len(fetch_list) "
"(%d vs %d)" % (len(fetched), len(fetch_list)))
# Fetch indices.
sparse_keys_start = 0
dense_keys_start = sparse_keys_start + num_sparse
dense_def_start = dense_keys_start + num_dense
# Output tensor indices.
sparse_indices_start = 0
sparse_values_start = num_sparse
sparse_shapes_start = sparse_values_start + num_sparse
dense_values_start = sparse_shapes_start + num_sparse
# Dense features.
for i in range(num_dense):
key = fetched[dense_keys_start + i]
feature_config = config.feature_map[key]
# Convert the default value numpy array fetched from the session run
# into a TensorProto.
fixed_config = feature_config.fixed_len_feature
fixed_config.default_value.CopyFrom(
tensor_util.make_tensor_proto(fetched[dense_def_start + i]))
# Convert the shape from the attributes
# into a TensorShapeProto.
fixed_config.shape.CopyFrom(
tensor_shape.TensorShape(dense_shapes[i]).as_proto())
fixed_config.dtype = dense_types[i].as_datatype_enum
# Get the output tensor name.
fixed_config.values_output_tensor_name = parse_example_op.outputs[
dense_values_start + i].name
# Sparse features.
for i in range(num_sparse):
key = fetched[sparse_keys_start + i]
feature_config = config.feature_map[key]
var_len_feature = feature_config.var_len_feature
var_len_feature.dtype = sparse_types[i].as_datatype_enum
var_len_feature.indices_output_tensor_name = parse_example_op.outputs[
sparse_indices_start + i].name
var_len_feature.values_output_tensor_name = parse_example_op.outputs[
sparse_values_start + i].name
var_len_feature.shapes_output_tensor_name = parse_example_op.outputs[
sparse_shapes_start + i].name
return config
def _extract_from_parse_example_v2(parse_example_op, sess):
"""Extract ExampleParserConfig from ParseExampleV2 op."""
config = example_parser_configuration_pb2.ExampleParserConfiguration()
dense_types = parse_example_op.get_attr("Tdense")
num_sparse = parse_example_op.get_attr("num_sparse")
sparse_types = parse_example_op.get_attr("sparse_types")
ragged_value_types = parse_example_op.get_attr("ragged_value_types")
ragged_split_types = parse_example_op.get_attr("ragged_split_types")
dense_shapes = parse_example_op.get_attr("dense_shapes")
num_dense = len(dense_types)
num_ragged = len(ragged_value_types)
assert len(ragged_value_types) == len(ragged_split_types)
assert len(parse_example_op.inputs) == 5 + num_dense
# Skip over the serialized input, and the names input.
fetched = sess.run(parse_example_op.inputs[2:])
sparse_keys = fetched[0].tolist()
dense_keys = fetched[1].tolist()
ragged_keys = fetched[2].tolist()
dense_defaults = fetched[3:]
assert len(sparse_keys) == num_sparse
assert len(dense_keys) == num_dense
assert len(ragged_keys) == num_ragged
# Output tensor indices.
sparse_indices_start = 0
sparse_values_start = num_sparse
sparse_shapes_start = sparse_values_start + num_sparse
dense_values_start = sparse_shapes_start + num_sparse
ragged_values_start = dense_values_start + num_dense
ragged_row_splits_start = ragged_values_start + num_ragged
# Dense features.
for i in range(num_dense):
key = dense_keys[i]
feature_config = config.feature_map[key]
# Convert the default value numpy array fetched from the session run
# into a TensorProto.
fixed_config = feature_config.fixed_len_feature
fixed_config.default_value.CopyFrom(
tensor_util.make_tensor_proto(dense_defaults[i]))
# Convert the shape from the attributes
# into a TensorShapeProto.
fixed_config.shape.CopyFrom(
tensor_shape.TensorShape(dense_shapes[i]).as_proto())
fixed_config.dtype = dense_types[i].as_datatype_enum
# Get the output tensor name.
fixed_config.values_output_tensor_name = parse_example_op.outputs[
dense_values_start + i].name
# Sparse features.
for i in range(num_sparse):
key = sparse_keys[i]
feature_config = config.feature_map[key]
var_len_feature = feature_config.var_len_feature
var_len_feature.dtype = sparse_types[i].as_datatype_enum
var_len_feature.indices_output_tensor_name = parse_example_op.outputs[
sparse_indices_start + i].name
var_len_feature.values_output_tensor_name = parse_example_op.outputs[
sparse_values_start + i].name
var_len_feature.shapes_output_tensor_name = parse_example_op.outputs[
sparse_shapes_start + i].name
if num_ragged != 0:
del ragged_values_start # unused
del ragged_row_splits_start # unused
raise ValueError("Ragged features are not yet supported by "
"example_parser_configuration.proto")
return config