-
Notifications
You must be signed in to change notification settings - Fork 74k
/
c_api_test.cc
2676 lines (2289 loc) · 96 KB
/
c_api_test.cc
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
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/* Copyright 2015 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.
==============================================================================*/
#include "tensorflow/c/c_api.h"
#include <algorithm>
#include <cstddef>
#include <iterator>
#include <memory>
#include <vector>
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/c_test_util.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/cc/saved_model/signature_constants.h"
#include "tensorflow/cc/saved_model/tag_constants.h"
#include "tensorflow/core/example/example.pb.h"
#include "tensorflow/core/example/feature.pb.h"
#include "tensorflow/core/framework/api_def.pb.h"
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/str_util.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/error_codes.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/util/equal_graph_def.h"
namespace tensorflow {
TF_Tensor* TF_TensorFromTensor(const Tensor& src, Status* status);
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);
namespace {
static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
EXPECT_TRUE(absl::StrContains(s, expected))
<< "'" << s << "' does not contain '" << expected << "'";
}
// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
TF_NewStatus(), TF_DeleteStatus);
TF_Status* s = status.get();
std::unique_ptr<TF_DeviceList, decltype(&TF_DeleteDeviceList)> list(
TF_SessionListDevices(session, s), TF_DeleteDeviceList);
TF_DeviceList* device_list = list.get();
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
const int num_devices = TF_DeviceListCount(device_list);
LOG(INFO) << "There are " << num_devices << " devices.";
for (int i = 0; i < num_devices; ++i) {
const char* device_name = TF_DeviceListName(device_list, i, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
const char* device_type = TF_DeviceListType(device_list, i, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
LOG(INFO) << "Device " << i << " has name " << device_name << ", type "
<< device_type;
if (string(device_type) == DEVICE_GPU) {
return device_name;
}
}
// No GPU device found.
return "";
}
string GPUDeviceName() {
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
TF_NewStatus(), TF_DeleteStatus);
TF_Status* s = status.get();
std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)> graph(TF_NewGraph(),
TF_DeleteGraph);
TF_SessionOptions* opts = TF_NewSessionOptions();
TF_Session* sess = TF_NewSession(graph.get(), opts, s);
TF_DeleteSessionOptions(opts);
const string gpu_device_name = GPUDeviceName(sess);
TF_DeleteSession(sess, s);
CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
return gpu_device_name;
}
TEST(CAPI, Version) { EXPECT_STRNE("", TF_Version()); }
TEST(CAPI, Status) {
TF_Status* s = TF_NewStatus();
EXPECT_EQ(TF_OK, TF_GetCode(s));
EXPECT_EQ(string(), TF_Message(s));
TF_SetStatus(s, TF_CANCELLED, "cancel");
EXPECT_EQ(TF_CANCELLED, TF_GetCode(s));
EXPECT_EQ(string("cancel"), TF_Message(s));
TF_DeleteStatus(s);
}
void Deallocator(void* data, size_t, void* arg) {
tensorflow::cpu_allocator()->DeallocateRaw(data);
*reinterpret_cast<bool*>(arg) = true;
}
TEST(CAPI, Tensor) {
const int num_bytes = 6 * sizeof(float);
float* values =
reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
EIGEN_MAX_ALIGN_BYTES, num_bytes));
int64_t dims[] = {2, 3};
bool deallocator_called = false;
TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
&Deallocator, &deallocator_called);
EXPECT_FALSE(deallocator_called);
EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
EXPECT_EQ(2, TF_NumDims(t));
EXPECT_EQ(dims[0], TF_Dim(t, 0));
EXPECT_EQ(dims[1], TF_Dim(t, 1));
EXPECT_EQ(num_bytes, TF_TensorByteSize(t));
EXPECT_EQ(static_cast<void*>(values), TF_TensorData(t));
TF_DeleteTensor(t);
EXPECT_TRUE(deallocator_called);
}
void NoOpDeallocator(void* data, size_t, void*) {}
TEST(CAPI, MalformedTensor) {
// See https://github.com/tensorflow/tensorflow/issues/7394
// num_dims = 0 implies a scalar, so should be backed by at least 4 bytes of
// data.
TF_Tensor* t =
TF_NewTensor(TF_FLOAT, nullptr, 0, nullptr, 0, &NoOpDeallocator, nullptr);
ASSERT_TRUE(t == nullptr);
}
TEST(CAPI, AllocateTensor) {
const int num_bytes = 6 * sizeof(float);
int64_t dims[] = {2, 3};
TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, num_bytes);
EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
EXPECT_EQ(2, TF_NumDims(t));
EXPECT_EQ(dims[0], TF_Dim(t, 0));
EXPECT_EQ(dims[1], TF_Dim(t, 1));
EXPECT_EQ(num_bytes, TF_TensorByteSize(t));
EXPECT_EQ(6, TF_TensorElementCount(t));
TF_DeleteTensor(t);
}
TEST(CAPI, MaybeMove) {
const int num_bytes = 6 * sizeof(float);
float* values =
reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
EIGEN_MAX_ALIGN_BYTES, num_bytes));
int64_t dims[] = {2, 3};
bool deallocator_called = false;
TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
&Deallocator, &deallocator_called);
TF_Tensor* o = TF_TensorMaybeMove(t);
ASSERT_TRUE(o == nullptr); // It is unsafe to move memory TF might not own.
TF_DeleteTensor(t);
EXPECT_TRUE(deallocator_called);
}
TEST(CAPI, LibraryLoadFunctions) {
// TODO(b/73318067): Fix linking for the GPU test generated by the
// tf_cuda_cc_test() bazel rule and remove the next line.
if (!GPUDeviceName().empty()) return;
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
{
// Load the library.
TF_Status* status = TF_NewStatus();
string lib_path = tensorflow::GetDataDependencyFilepath(
tensorflow::io::JoinPath("tensorflow", "c", "test_op1.so"));
TF_Library* lib = TF_LoadLibrary(lib_path.c_str(), status);
TF_Code code = TF_GetCode(status);
string status_msg(TF_Message(status));
TF_DeleteStatus(status);
ASSERT_EQ(TF_OK, code) << status_msg;
// Test op list.
TF_Buffer op_list_buf = TF_GetOpList(lib);
tensorflow::OpList op_list;
EXPECT_TRUE(op_list.ParseFromArray(op_list_buf.data, op_list_buf.length));
ASSERT_EQ(op_list.op_size(), 1);
EXPECT_EQ("TestCApi1", op_list.op(0).name());
TF_DeleteLibraryHandle(lib);
}
#endif // !defined(TENSORFLOW_NO_SHARED_OBJECTS)
{
TF_Buffer* op_list_buffer = TF_GetAllOpList();
tensorflow::OpList op_list;
op_list.ParseFromArray(op_list_buffer->data, op_list_buffer->length);
ASSERT_GE(op_list.op_size(), 1);
typedef tensorflow::protobuf::RepeatedPtrField<tensorflow::OpDef> OpDefs;
const OpDefs& ops = op_list.op();
bool found = std::find_if(ops.begin(), ops.end(),
[](const tensorflow::OpDef& op_def) {
return op_def.name() == "TestCApi";
}) != ops.end();
EXPECT_TRUE(found);
TF_DeleteBuffer(op_list_buffer);
}
}
void TestEncodeDecode(int line, const std::vector<string>& data) {
const int64_t n = data.size();
Status status;
for (const std::vector<int64_t>& dims :
std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
// Create C++ Tensor
Tensor src(tensorflow::DT_STRING, TensorShape(dims));
for (int64_t i = 0; i < src.NumElements(); ++i) {
src.flat<tstring>()(i) = data[i];
}
TF_Tensor* dst = TF_TensorFromTensor(src, &status);
ASSERT_TRUE(status.ok()) << status.message();
// Convert back to a C++ Tensor and ensure we get expected output.
Tensor output;
ASSERT_EQ(OkStatus(), TF_TensorToTensor(dst, &output)) << line;
ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
for (int64_t i = 0; i < src.NumElements(); ++i) {
ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
}
TF_DeleteTensor(dst);
}
}
TEST(CAPI, TensorEncodeDecodeStrings) {
TestEncodeDecode(__LINE__, {});
TestEncodeDecode(__LINE__, {"hello"});
TestEncodeDecode(__LINE__,
{"the", "quick", "brown", "fox", "jumped", "over"});
string big(1000, 'a');
TestEncodeDecode(__LINE__, {"small", big, "small2"});
}
TEST(CAPI, SessionOptions) {
TF_SessionOptions* opt = TF_NewSessionOptions();
TF_DeleteSessionOptions(opt);
}
TEST(CAPI, DeprecatedSession) {
TF_Status* s = TF_NewStatus();
TF_SessionOptions* opt = TF_NewSessionOptions();
TF_DeprecatedSession* session = TF_NewDeprecatedSession(opt, s);
TF_DeleteSessionOptions(opt);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Buffer* run_options = TF_NewBufferFromString("", 0);
TF_Buffer* run_metadata = TF_NewBuffer();
TF_Run(session, run_options, nullptr, nullptr, 0, nullptr, nullptr, 0,
nullptr, 0, run_metadata, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ("Session was not created with a graph before Run()!",
string(TF_Message(s)));
TF_DeleteBuffer(run_metadata);
TF_DeleteBuffer(run_options);
TF_DeleteDeprecatedSession(session, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_DeleteStatus(s);
}
TEST(CAPI, DataTypeEnum) {
EXPECT_EQ(TF_FLOAT, static_cast<TF_DataType>(tensorflow::DT_FLOAT));
EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));
EXPECT_EQ(TF_STRING, static_cast<TF_DataType>(tensorflow::DT_STRING));
EXPECT_EQ(TF_COMPLEX64, static_cast<TF_DataType>(tensorflow::DT_COMPLEX64));
EXPECT_EQ(TF_COMPLEX, TF_COMPLEX64);
EXPECT_EQ(TF_INT64, static_cast<TF_DataType>(tensorflow::DT_INT64));
EXPECT_EQ(TF_BOOL, static_cast<TF_DataType>(tensorflow::DT_BOOL));
EXPECT_EQ(TF_QINT8, static_cast<TF_DataType>(tensorflow::DT_QINT8));
EXPECT_EQ(TF_QUINT8, static_cast<TF_DataType>(tensorflow::DT_QUINT8));
EXPECT_EQ(TF_QINT32, static_cast<TF_DataType>(tensorflow::DT_QINT32));
EXPECT_EQ(TF_BFLOAT16, static_cast<TF_DataType>(tensorflow::DT_BFLOAT16));
EXPECT_EQ(TF_QINT16, static_cast<TF_DataType>(tensorflow::DT_QINT16));
EXPECT_EQ(TF_QUINT16, static_cast<TF_DataType>(tensorflow::DT_QUINT16));
EXPECT_EQ(TF_UINT16, static_cast<TF_DataType>(tensorflow::DT_UINT16));
EXPECT_EQ(TF_COMPLEX128, static_cast<TF_DataType>(tensorflow::DT_COMPLEX128));
EXPECT_EQ(TF_HALF, static_cast<TF_DataType>(tensorflow::DT_HALF));
EXPECT_EQ(TF_DataTypeSize(TF_DOUBLE),
tensorflow::DataTypeSize(tensorflow::DT_DOUBLE));
EXPECT_EQ(TF_DataTypeSize(TF_STRING),
tensorflow::DataTypeSize(tensorflow::DT_STRING));
// Test with invalid type; should always return 0 as documented
EXPECT_EQ(TF_DataTypeSize(static_cast<TF_DataType>(0)), 0);
}
TEST(CAPI, StatusEnum) {
EXPECT_EQ(TF_OK, static_cast<TF_Code>(tensorflow::error::OK));
EXPECT_EQ(TF_CANCELLED, static_cast<TF_Code>(tensorflow::error::CANCELLED));
EXPECT_EQ(TF_UNKNOWN, static_cast<TF_Code>(tensorflow::error::UNKNOWN));
EXPECT_EQ(TF_INVALID_ARGUMENT,
static_cast<TF_Code>(absl::StatusCode::kInvalidArgument));
EXPECT_EQ(TF_DEADLINE_EXCEEDED,
static_cast<TF_Code>(tensorflow::error::DEADLINE_EXCEEDED));
EXPECT_EQ(TF_NOT_FOUND, static_cast<TF_Code>(tensorflow::error::NOT_FOUND));
EXPECT_EQ(TF_ALREADY_EXISTS,
static_cast<TF_Code>(tensorflow::error::ALREADY_EXISTS));
EXPECT_EQ(TF_PERMISSION_DENIED,
static_cast<TF_Code>(tensorflow::error::PERMISSION_DENIED));
EXPECT_EQ(TF_UNAUTHENTICATED,
static_cast<TF_Code>(tensorflow::error::UNAUTHENTICATED));
EXPECT_EQ(TF_RESOURCE_EXHAUSTED,
static_cast<TF_Code>(tensorflow::error::RESOURCE_EXHAUSTED));
EXPECT_EQ(TF_FAILED_PRECONDITION,
static_cast<TF_Code>(tensorflow::error::FAILED_PRECONDITION));
EXPECT_EQ(TF_ABORTED, static_cast<TF_Code>(tensorflow::error::ABORTED));
EXPECT_EQ(TF_OUT_OF_RANGE,
static_cast<TF_Code>(tensorflow::error::OUT_OF_RANGE));
EXPECT_EQ(TF_UNIMPLEMENTED,
static_cast<TF_Code>(tensorflow::error::UNIMPLEMENTED));
EXPECT_EQ(TF_INTERNAL, static_cast<TF_Code>(tensorflow::error::INTERNAL));
EXPECT_EQ(TF_UNAVAILABLE,
static_cast<TF_Code>(tensorflow::error::UNAVAILABLE));
EXPECT_EQ(TF_DATA_LOSS, static_cast<TF_Code>(tensorflow::error::DATA_LOSS));
}
TEST(CAPI, GetAllOpList) {
TF_Buffer* buf = TF_GetAllOpList();
tensorflow::OpList op_list;
EXPECT_TRUE(op_list.ParseFromArray(buf->data, buf->length));
EXPECT_GT(op_list.op_size(), 0);
TF_DeleteBuffer(buf);
}
TEST(CAPI, SetShape) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
TF_Operation* feed = Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Output feed_out_0 = TF_Output{feed, 0};
int num_dims;
// Fetch the shape, it should be completely unknown.
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(-1, num_dims);
// Set the shape to be unknown, expect no change.
TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(-1, num_dims);
// Set the shape to be 2 x Unknown
int64_t dims[] = {2, -1};
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch the shape and validate it is 2 by -1.
num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
// Resize the dimension vector appropriately.
int64_t returned_dims[2];
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(dims[0], returned_dims[0]);
EXPECT_EQ(dims[1], returned_dims[1]);
// Set to a new valid shape: [2, 3]
dims[1] = 3;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch and see that the new value is returned.
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(dims[0], returned_dims[0]);
EXPECT_EQ(dims[1], returned_dims[1]);
// Try to set 'unknown' with unknown rank on the shape and see that
// it doesn't change.
TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, returned_dims[0]);
EXPECT_EQ(3, returned_dims[1]);
// Try to set 'unknown' with same rank on the shape and see that
// it doesn't change.
dims[0] = -1;
dims[1] = -1;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Fetch and see that the new value is returned.
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, returned_dims[0]);
EXPECT_EQ(3, returned_dims[1]);
// Try to fetch a shape with the wrong num_dims
TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
// Try to set an invalid shape (cannot change 2x3 to a 2x5).
dims[1] = 5;
TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
// Test for a scalar.
TF_Operation* three = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Output three_out_0 = TF_Output{three, 0};
num_dims = TF_GraphGetTensorNumDims(graph, three_out_0, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(0, num_dims);
TF_GraphGetTensorShape(graph, three_out_0, returned_dims, num_dims, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, Graph) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make a placeholder operation.
TF_Operation* feed = Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Test TF_Operation*() query functions.
EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
EXPECT_EQ(string("Placeholder"), string(TF_OperationOpType(feed)));
EXPECT_EQ(string(""), string(TF_OperationDevice(feed)));
EXPECT_EQ(1, TF_OperationNumOutputs(feed));
EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{feed, 0}));
EXPECT_EQ(1, TF_OperationOutputListLength(feed, "output", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(0, TF_OperationNumInputs(feed));
EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));
tensorflow::AttrValue attr_value;
ASSERT_TRUE(GetAttrValue(feed, "dtype", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
// Test not found errors in TF_Operation*() query functions.
EXPECT_EQ(-1, TF_OperationOutputListLength(feed, "bogus", s));
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
ASSERT_FALSE(GetAttrValue(feed, "missing", &attr_value, s));
EXPECT_EQ(string("Operation 'feed' has no attr named 'missing'."),
string(TF_Message(s)));
// Make a constant oper with the scalar "3".
TF_Operation* three = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add oper.
TF_Operation* add = Add(feed, three, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Test TF_Operation*() query functions.
EXPECT_EQ(string("add"), string(TF_OperationName(add)));
EXPECT_EQ(string("AddN"), string(TF_OperationOpType(add)));
EXPECT_EQ(string(""), string(TF_OperationDevice(add)));
EXPECT_EQ(1, TF_OperationNumOutputs(add));
EXPECT_EQ(TF_INT32, TF_OperationOutputType(TF_Output{add, 0}));
EXPECT_EQ(1, TF_OperationOutputListLength(add, "sum", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(2, TF_OperationNumInputs(add));
EXPECT_EQ(2, TF_OperationInputListLength(add, "inputs", s));
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
EXPECT_EQ(TF_INT32, TF_OperationInputType(TF_Input{add, 0}));
EXPECT_EQ(TF_INT32, TF_OperationInputType(TF_Input{add, 1}));
TF_Output add_in_0 = TF_OperationInput(TF_Input{add, 0});
EXPECT_EQ(feed, add_in_0.oper);
EXPECT_EQ(0, add_in_0.index);
TF_Output add_in_1 = TF_OperationInput(TF_Input{add, 1});
EXPECT_EQ(three, add_in_1.oper);
EXPECT_EQ(0, add_in_1.index);
EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{add, 0}));
EXPECT_EQ(0, TF_OperationNumControlInputs(add));
EXPECT_EQ(0, TF_OperationNumControlOutputs(add));
ASSERT_TRUE(GetAttrValue(add, "T", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
ASSERT_TRUE(GetAttrValue(add, "N", &attr_value, s)) << TF_Message(s);
EXPECT_EQ(attr_value.i(), 2);
// Placeholder oper now has a consumer.
ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
TF_Input feed_port;
EXPECT_EQ(1, TF_OperationOutputConsumers(TF_Output{feed, 0}, &feed_port, 1));
EXPECT_EQ(add, feed_port.oper);
EXPECT_EQ(0, feed_port.index);
// The scalar const oper also has a consumer.
ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{three, 0}));
TF_Input three_port;
EXPECT_EQ(1,
TF_OperationOutputConsumers(TF_Output{three, 0}, &three_port, 1));
EXPECT_EQ(add, three_port.oper);
EXPECT_EQ(1, three_port.index);
// Serialize to GraphDef.
GraphDef graph_def;
ASSERT_TRUE(GetGraphDef(graph, &graph_def));
// Validate GraphDef is what we expect.
bool found_placeholder = false;
bool found_scalar_const = false;
bool found_add = false;
for (const auto& n : graph_def.node()) {
if (IsPlaceholder(n)) {
EXPECT_FALSE(found_placeholder);
found_placeholder = true;
} else if (IsScalarConst(n, 3)) {
EXPECT_FALSE(found_scalar_const);
found_scalar_const = true;
} else if (IsAddN(n, 2)) {
EXPECT_FALSE(found_add);
found_add = true;
} else {
ADD_FAILURE() << "Unexpected NodeDef: " << n.DebugString();
}
}
EXPECT_TRUE(found_placeholder);
EXPECT_TRUE(found_scalar_const);
EXPECT_TRUE(found_add);
// Add another oper to the graph.
TF_Operation* neg = Neg(add, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Serialize to NodeDef.
NodeDef node_def;
ASSERT_TRUE(GetNodeDef(neg, &node_def));
// Validate NodeDef is what we expect.
EXPECT_TRUE(IsNeg(node_def, "add"));
// Serialize to GraphDef.
GraphDef graph_def2;
ASSERT_TRUE(GetGraphDef(graph, &graph_def2));
// Compare with first GraphDef + added NodeDef.
NodeDef* added_node = graph_def.add_node();
*added_node = node_def;
EXPECT_EQ(graph_def.DebugString(), graph_def2.DebugString());
// Look up some nodes by name.
TF_Operation* neg2 = TF_GraphOperationByName(graph, "neg");
EXPECT_TRUE(neg == neg2);
NodeDef node_def2;
ASSERT_TRUE(GetNodeDef(neg2, &node_def2));
EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());
TF_Operation* feed2 = TF_GraphOperationByName(graph, "feed");
EXPECT_TRUE(feed == feed2);
ASSERT_TRUE(GetNodeDef(feed, &node_def));
ASSERT_TRUE(GetNodeDef(feed2, &node_def2));
EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());
// Test iterating through the nodes of a graph.
found_placeholder = false;
found_scalar_const = false;
found_add = false;
bool found_neg = false;
size_t pos = 0;
TF_Operation* oper;
while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
if (oper == feed) {
EXPECT_FALSE(found_placeholder);
found_placeholder = true;
} else if (oper == three) {
EXPECT_FALSE(found_scalar_const);
found_scalar_const = true;
} else if (oper == add) {
EXPECT_FALSE(found_add);
found_add = true;
} else if (oper == neg) {
EXPECT_FALSE(found_neg);
found_neg = true;
} else {
ASSERT_TRUE(GetNodeDef(oper, &node_def));
ADD_FAILURE() << "Unexpected Node: " << node_def.DebugString();
}
}
EXPECT_TRUE(found_placeholder);
EXPECT_TRUE(found_scalar_const);
EXPECT_TRUE(found_add);
EXPECT_TRUE(found_neg);
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, UpdateEdge) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make two scalar constants.
TF_Operation* one = ScalarConst(1, graph, s, "one");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* two = ScalarConst(2, graph, s, "two");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add oper.
TF_Operation* add = Add(one, two, graph, s, "add");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add another oper to the graph.
TF_Operation* neg = Neg(add, graph, s, "neg");
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
NodeDef node_def_neg;
ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));
EXPECT_EQ(string("add"), node_def_neg.input(0));
// update edge of neg
TF_UpdateEdge(graph, TF_Output{one, 0}, TF_Input{neg, 0}, s);
ASSERT_TRUE(GetNodeDef(neg, &node_def_neg));
EXPECT_EQ(string("one:0"), node_def_neg.input(0));
// Clean up
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
/*
TODO(skyewm): this test currently DCHECKs, change to bad status
TEST(CAPI, InputFromDifferentGraphError) {
TF_Status* s = TF_NewStatus();
TF_Graph* g1 = TF_NewGraph();
TF_Graph* g2 = TF_NewGraph();
TF_Operation* feed = Placeholder(g1, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Attempt to create node in g2 with input from g1
Neg(feed, g2, s);
EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s));
EXPECT_STREQ("foo", TF_Message(s));
TF_DeleteGraph(g1);
TF_DeleteGraph(g2);
TF_DeleteStatus(s);
}
*/
TEST(CAPI, ImportGraphDef) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a simple graph.
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it, with a prefix, in a fresh graph.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported");
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "imported/scalar");
TF_Operation* feed = TF_GraphOperationByName(graph, "imported/feed");
TF_Operation* neg = TF_GraphOperationByName(graph, "imported/neg");
ASSERT_TRUE(scalar != nullptr);
ASSERT_TRUE(feed != nullptr);
ASSERT_TRUE(neg != nullptr);
// Test basic structure of the imported graph.
EXPECT_EQ(0, TF_OperationNumInputs(scalar));
EXPECT_EQ(0, TF_OperationNumInputs(feed));
ASSERT_EQ(1, TF_OperationNumInputs(neg));
TF_Output neg_input = TF_OperationInput({neg, 0});
EXPECT_EQ(scalar, neg_input.oper);
EXPECT_EQ(0, neg_input.index);
// Test that we can't see control edges involving the source and sink nodes.
TF_Operation* control_ops[100];
EXPECT_EQ(0, TF_OperationNumControlInputs(scalar));
EXPECT_EQ(0, TF_OperationGetControlInputs(scalar, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(scalar));
EXPECT_EQ(0, TF_OperationGetControlOutputs(scalar, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
EXPECT_EQ(0, TF_OperationGetControlInputs(feed, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));
EXPECT_EQ(0, TF_OperationGetControlOutputs(feed, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlInputs(neg));
EXPECT_EQ(0, TF_OperationGetControlInputs(neg, control_ops, 100));
EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));
// Import it again, with an input mapping, return outputs, and a return
// operation, into the same graph.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported2");
TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar");
EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));
TF_ImportGraphDefResults* results =
TF_GraphImportGraphDefWithResults(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar2 = TF_GraphOperationByName(graph, "imported2/scalar");
TF_Operation* feed2 = TF_GraphOperationByName(graph, "imported2/feed");
TF_Operation* neg2 = TF_GraphOperationByName(graph, "imported2/neg");
ASSERT_TRUE(scalar2 != nullptr);
ASSERT_TRUE(feed2 != nullptr);
ASSERT_TRUE(neg2 != nullptr);
// Check input mapping
neg_input = TF_OperationInput({neg, 0});
EXPECT_EQ(scalar, neg_input.oper);
EXPECT_EQ(0, neg_input.index);
// Check return outputs
TF_Output* return_outputs;
int num_return_outputs;
TF_ImportGraphDefResultsReturnOutputs(results, &num_return_outputs,
&return_outputs);
ASSERT_EQ(2, num_return_outputs);
EXPECT_EQ(feed2, return_outputs[0].oper);
EXPECT_EQ(0, return_outputs[0].index);
EXPECT_EQ(scalar, return_outputs[1].oper); // remapped
EXPECT_EQ(0, return_outputs[1].index);
// Check return operation
TF_Operation** return_opers;
int num_return_opers;
TF_ImportGraphDefResultsReturnOperations(results, &num_return_opers,
&return_opers);
ASSERT_EQ(1, num_return_opers);
EXPECT_EQ(scalar2, return_opers[0]); // not remapped
TF_DeleteImportGraphDefResults(results);
// Import again, with control dependencies, into the same graph.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported3");
TF_ImportGraphDefOptionsAddControlDependency(opts, feed);
TF_ImportGraphDefOptionsAddControlDependency(opts, feed2);
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar3 = TF_GraphOperationByName(graph, "imported3/scalar");
TF_Operation* feed3 = TF_GraphOperationByName(graph, "imported3/feed");
TF_Operation* neg3 = TF_GraphOperationByName(graph, "imported3/neg");
ASSERT_TRUE(scalar3 != nullptr);
ASSERT_TRUE(feed3 != nullptr);
ASSERT_TRUE(neg3 != nullptr);
// Check that newly-imported scalar and feed have control deps (neg3 will
// inherit them from input)
TF_Operation* control_inputs[100];
int num_control_inputs = TF_OperationGetControlInputs(
scalar3, control_inputs, TF_OperationNumControlInputs(scalar3));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed2, control_inputs[1]);
num_control_inputs = TF_OperationGetControlInputs(
feed3, control_inputs, TF_OperationNumControlInputs(feed3));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed2, control_inputs[1]);
// Export to a graph def so we can import a graph with control dependencies
TF_DeleteBuffer(graph_def);
graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import again, with remapped control dependency, into the same graph
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported4");
TF_ImportGraphDefOptionsRemapControlDependency(opts, "imported/feed", feed);
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar4 =
TF_GraphOperationByName(graph, "imported4/imported3/scalar");
TF_Operation* feed4 =
TF_GraphOperationByName(graph, "imported4/imported2/feed");
// Check that imported `imported3/scalar` has remapped control dep from
// original graph and imported control dep
num_control_inputs = TF_OperationGetControlInputs(
scalar4, control_inputs, TF_OperationNumControlInputs(scalar4));
ASSERT_EQ(2, num_control_inputs);
EXPECT_EQ(feed, control_inputs[0]);
EXPECT_EQ(feed4, control_inputs[1]);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
// Can add nodes to the imported graph without trouble.
Add(feed, scalar, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, ImportGraphDef_WithReturnOutputs) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a graph with two nodes: x and 3
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it in a fresh graph with return outputs.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
TF_Output return_outputs[2];
TF_GraphImportGraphDefWithReturnOutputs(graph, graph_def, opts,
return_outputs, 2, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar");
TF_Operation* feed = TF_GraphOperationByName(graph, "feed");
TF_Operation* neg = TF_GraphOperationByName(graph, "neg");
ASSERT_TRUE(scalar != nullptr);
ASSERT_TRUE(feed != nullptr);
ASSERT_TRUE(neg != nullptr);
// Check return outputs
EXPECT_EQ(feed, return_outputs[0].oper);
EXPECT_EQ(0, return_outputs[0].index);
EXPECT_EQ(scalar, return_outputs[1].oper);
EXPECT_EQ(0, return_outputs[1].index);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, ImportGraphDef_MissingUnusedInputMappings) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Create a graph with two nodes: x and 3
Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
TF_Operation* oper = ScalarConst(3, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
Neg(oper, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
ASSERT_TRUE(TF_GraphOperationByName(graph, "neg") != nullptr);
// Export to a GraphDef.
TF_Buffer* graph_def = TF_NewBuffer();
TF_GraphToGraphDef(graph, graph_def, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Import it in a fresh graph.
TF_DeleteGraph(graph);
graph = TF_NewGraph();
TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions();
TF_GraphImportGraphDef(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar");
// Import it in a fresh graph with an unused input mapping.
TF_DeleteImportGraphDefOptions(opts);
opts = TF_NewImportGraphDefOptions();
TF_ImportGraphDefOptionsSetPrefix(opts, "imported");
TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
TF_ImportGraphDefOptionsAddInputMapping(opts, "fake", 0, {scalar, 0});
TF_ImportGraphDefResults* results =
TF_GraphImportGraphDefWithResults(graph, graph_def, opts, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Check unused input mappings
int num_unused_input_mappings;
const char** src_names;
int* src_indexes;
TF_ImportGraphDefResultsMissingUnusedInputMappings(
results, &num_unused_input_mappings, &src_names, &src_indexes);
ASSERT_EQ(1, num_unused_input_mappings);
EXPECT_EQ(string("fake"), string(src_names[0]));
EXPECT_EQ(0, src_indexes[0]);
TF_DeleteImportGraphDefResults(results);
TF_DeleteImportGraphDefOptions(opts);
TF_DeleteBuffer(graph_def);
TF_DeleteGraph(graph);
TF_DeleteStatus(s);
}
TEST(CAPI, Session) {
TF_Status* s = TF_NewStatus();
TF_Graph* graph = TF_NewGraph();
// Make a placeholder operation.
TF_Operation* feed = Placeholder(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Make a constant operation with the scalar "2".
TF_Operation* two = ScalarConst(2, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Add operation.
TF_Operation* add = Add(feed, two, graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Create a session for this graph.
CSession csession(graph, s);
ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
// Run the graph.