forked from SciSharp/TensorFlow.NET
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathmeta_graph.cs
More file actions
349 lines (307 loc) · 14.5 KB
/
meta_graph.cs
File metadata and controls
349 lines (307 loc) · 14.5 KB
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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
/*****************************************************************************
Copyright 2018 The TensorFlow.NET 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.
******************************************************************************/
using Google.Protobuf;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Tensorflow.Operations;
using static Tensorflow.CollectionDef;
using static Tensorflow.MetaGraphDef.Types;
using static Tensorflow.Binding;
namespace Tensorflow
{
public class meta_graph
{
public static MetaGraphDef read_meta_graph_file(string filename)
{
var bytes = File.ReadAllBytes(filename);
var meta_graph_def = MetaGraphDef.Parser.ParseFrom(bytes);
return meta_graph_def;
}
public static (Dictionary<string, IVariableV1>, ITensorOrOperation[]) import_scoped_meta_graph_with_return_elements(MetaGraphDef meta_graph_or_file,
bool clear_devices = false,
string import_scope = "",
Dictionary<string, Tensor> input_map = null,
string unbound_inputs_col_name = "unbound_inputs",
string[] return_elements = null)
{
var meta_graph_def = meta_graph_or_file;
if (!string.IsNullOrEmpty(unbound_inputs_col_name))
{
foreach (var col in meta_graph_def.CollectionDef)
{
if (col.Key == unbound_inputs_col_name)
{
throw new NotImplementedException("import_scoped_meta_graph_with_return_elements");
}
}
}
// Sets graph to default graph if it's not passed in.
var graph = ops.get_default_graph();
// Gathers the list of nodes we are interested in.
OpList producer_op_list = null;
if (meta_graph_def.MetaInfoDef.StrippedOpList != null)
producer_op_list = meta_graph_def.MetaInfoDef.StrippedOpList;
var input_graph_def = meta_graph_def.GraphDef;
// Remove all the explicit device specifications for this node. This helps to
// make the graph more portable.
if (clear_devices)
foreach (var node in input_graph_def.Node)
node.Device = "";
var scope_to_prepend_to_names = graph.unique_name("", mark_as_used: false);
var imported_return_elements = importer.import_graph_def(input_graph_def,
name: scope_to_prepend_to_names,
input_map: input_map,
producer_op_list: producer_op_list,
return_elements: return_elements);
// Restores all the other collections.
var variable_objects = new Dictionary<ByteString, IVariableV1>();
foreach (var col in meta_graph_def.CollectionDef.OrderBy(x => x.Key))
{
// Don't add unbound_inputs to the new graph.
if (col.Key == unbound_inputs_col_name)
continue;
switch (col.Value.KindCase)
{
case KindOneofCase.NodeList:
foreach (var value in col.Value.NodeList.Value)
{
var col_op = graph.as_graph_element(ops.prepend_name_scope(value, scope_to_prepend_to_names));
graph.add_to_collection(col.Key, col_op);
}
break;
case KindOneofCase.BytesList:
//var proto_type = ops.get_collection_proto_type(key)
if (tf.GraphKeys._VARIABLE_COLLECTIONS.Contains(col.Key))
{
foreach (var value in col.Value.BytesList.Value)
{
IVariableV1 variable = null;
if (!variable_objects.ContainsKey(value))
{
var proto = VariableDef.Parser.ParseFrom(value);
if (proto.IsResource)
variable = new ResourceVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
else
variable = new RefVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
variable_objects[value] = variable;
}
variable = variable_objects[value];
graph.add_to_collection(col.Key, variable);
}
}
else
{
foreach (var value in col.Value.BytesList.Value)
{
switch (col.Key)
{
case "cond_context":
{
var proto = CondContextDef.Parser.ParseFrom(value);
var condContext = new CondContext().from_proto(proto, import_scope);
graph.add_to_collection(col.Key, condContext);
}
break;
case "while_context":
{
var proto = WhileContextDef.Parser.ParseFrom(value);
var whileContext = new WhileContext().from_proto(proto, import_scope);
graph.add_to_collection(col.Key, whileContext);
}
break;
default:
Console.WriteLine($"import_scoped_meta_graph_with_return_elements {col.Key}");
continue;
}
}
}
break;
default:
Console.WriteLine($"Cannot identify data type for collection {col.Key}. Skipping.");
break;
}
}
var variables = graph.get_collection<IVariableV1>(tf.GraphKeys.GLOBAL_VARIABLES,
scope: scope_to_prepend_to_names);
var var_list = new Dictionary<string, IVariableV1>();
variables.ForEach(v => var_list[ops.strip_name_scope(v.Name, scope_to_prepend_to_names)] = v);
return (var_list, imported_return_elements);
}
/// <summary>
/// Returns `MetaGraphDef` proto. Optionally writes it to filename.
/// </summary>
/// <param name="filename"></param>
/// <param name="graph_def"></param>
/// <param name="as_text"></param>
/// <param name="unbound_inputs_col_name"></param>
/// <param name="clear_devices"></param>
/// <param name="saver_def"></param>
/// <param name="clear_extraneous_savers"></param>
/// <param name="strip_default_attrs"></param>
/// <param name="meta_info_def"></param>
/// <returns></returns>
public static (MetaGraphDef, Dictionary<string, IVariableV1>) export_scoped_meta_graph(string filename = "",
GraphDef graph_def = null,
bool as_text = false,
string unbound_inputs_col_name = "unbound_inputs",
bool clear_devices = false,
SaverDef saver_def = null,
bool clear_extraneous_savers = false,
bool strip_default_attrs = false,
byte[] meta_info_def = null)
{
var graph = ops.get_default_graph();
var var_list = new Dictionary<string, IVariableV1>();
var variables = graph.get_collection<IVariableV1>(tf.GraphKeys.GLOBAL_VARIABLES);
if (variables != null)
{
foreach (var v in variables)
{
var_list[v.Name] = v;
}
}
var scoped_meta_graph_def = create_meta_graph_def(
graph_def: graph_def,
export_scope: "",
exclude_nodes: "",
clear_extraneous_savers: clear_extraneous_savers,
saver_def: saver_def,
strip_default_attrs: strip_default_attrs);
if (!string.IsNullOrEmpty(filename))
graph_io.write_graph(scoped_meta_graph_def, "", filename, as_text: as_text);
return (scoped_meta_graph_def, var_list);
}
private static bool _should_include_node()
{
return true;
}
private static MetaGraphDef create_meta_graph_def(MetaInfoDef meta_info_def = null,
GraphDef graph_def = null,
string export_scope = "",
string exclude_nodes = "",
SaverDef saver_def = null,
bool clear_extraneous_savers = false,
bool strip_default_attrs = false)
{
// Sets graph to default graph if it's not passed in.
var graph = ops.get_default_graph().as_default();
// Creates a MetaGraphDef proto.
var meta_graph_def = new MetaGraphDef();
if (meta_info_def == null)
meta_info_def = new MetaInfoDef();
// Set the tf version strings to the current tf build.
meta_info_def.TensorflowVersion = tf.VERSION;
meta_info_def.TensorflowGitVersion = "unknown";
meta_graph_def.MetaInfoDef = meta_info_def;
// Adds graph_def or the default.
if (graph_def == null)
meta_graph_def.GraphDef = graph.as_graph_def(add_shapes: true);
else
meta_graph_def.GraphDef = graph_def;
// Fills in meta_info_def.stripped_op_list using the ops from graph_def.
if (meta_graph_def.MetaInfoDef.StrippedOpList == null ||
meta_graph_def.MetaInfoDef.StrippedOpList.Op.Count == 0)
meta_graph_def.MetaInfoDef.StrippedOpList = stripped_op_list_for_graph(meta_graph_def.GraphDef);
var clist = graph.get_all_collection_keys();
foreach (var ctype in clist)
{
if (clear_extraneous_savers)
{
throw new NotImplementedException("create_meta_graph_def clear_extraneous_savers");
}
else
{
add_collection_def(meta_graph_def, ctype, graph);
}
}
return meta_graph_def;
}
private static void add_collection_def(MetaGraphDef meta_graph_def,
string key,
Graph graph = null,
string export_scope = "")
{
if (!meta_graph_def.CollectionDef.ContainsKey(key))
meta_graph_def.CollectionDef[key] = new CollectionDef();
var col_def = meta_graph_def.CollectionDef[key];
switch (graph.get_collection(key))
{
case List<IVariableV1> collection_list:
col_def.BytesList = new Types.BytesList();
foreach (var x in collection_list)
{
if(x is RefVariable x_ref_var)
{
var proto = x_ref_var.to_proto(export_scope);
col_def.BytesList.Value.Add(proto.ToByteString());
}
else if(x is ResourceVariable x_res_var)
{
var proto = x_res_var.to_proto(export_scope);
col_def.BytesList.Value.Add(proto.ToByteString());
}
}
break;
case List<RefVariable> collection_list:
col_def.BytesList = new Types.BytesList();
foreach (var x in collection_list)
{
var proto = x.to_proto(export_scope);
col_def.BytesList.Value.Add(proto.ToByteString());
}
break;
case List<object> collection_list:
col_def.NodeList = new Types.NodeList();
foreach (var x in collection_list)
if (x is ITensorOrOperation x2)
col_def.NodeList.Value.Add(ops.strip_name_scope(x2.name, export_scope));
break;
case List<Operation> collection_list:
break;
}
}
private static OpList stripped_op_list_for_graph(GraphDef graph_def)
{
var used_ops = ops_used_by_graph_def(graph_def);
// Verify that all used ops are registered.
// var registered_ops = op_def_registry.get_registered_ops();
var op_list = new OpList();
/*used_ops.OrderBy(x => x).Select(x => {
}).ToArray();*/
return op_list;
}
/// <summary>
/// Collect the list of ops used by a graph.
/// </summary>
/// <param name="graph_def"></param>
/// <returns></returns>
private static string[] ops_used_by_graph_def(GraphDef graph_def)
{
var used_ops = new List<string>();
Action<string> mark_op_as_used = (op) =>
{
if (!used_ops.Contains(op))
{
}
used_ops.Add(op);
};
foreach (var node in graph_def.Node)
{
mark_op_as_used(node.Op);
}
return used_ops.ToArray();
}
}
}