{{ message }}
Repository navigation
Expand file tree
/
Copy pathtesting_utils.py
More file actions
1161 lines (903 loc) · 40.7 KB
/
Copy pathtesting_utils.py
File metadata and controls
1161 lines (903 loc) · 40.7 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
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
import functools
import importlib
import importlib.metadata
import inspect
import io
import logging
import multiprocessing
import os
import random
import re
import struct
import sys
import tempfile
import time
import unittest
import urllib.parse
from contextlib import contextmanager
from io import BytesIO, StringIO
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
import numpy as np
import PIL.Image
import PIL.ImageOps
import requests
from numpy.linalg import norm
from packaging import version
from .import_utils import (
BACKENDS_MAPPING,
is_accelerate_available,
is_bitsandbytes_available,
is_compel_available,
is_flax_available,
is_gguf_available,
is_note_seq_available,
is_onnx_available,
is_opencv_available,
is_peft_available,
is_timm_available,
is_torch_available,
is_torch_version,
is_torchao_available,
is_torchsde_available,
is_transformers_available,
)
from .logging import get_logger
global_rng = random.Random()
logger = get_logger(__name__)
_required_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) > version.parse("0.5")
_required_transformers_version = is_transformers_available() and version.parse(
version.parse(importlib.metadata.version("transformers")).base_version
) > version.parse("4.33")
USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version
BIG_GPU_MEMORY = int(os.getenv("BIG_GPU_MEMORY", 40))
if is_torch_available():
import torch
# Set a backend environment variable for any extra module import required for a custom accelerator
if "DIFFUSERS_TEST_BACKEND" in os.environ:
backend = os.environ["DIFFUSERS_TEST_BACKEND"]
try:
_ = importlib.import_module(backend)
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
f"Failed to import `DIFFUSERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module \
to enable a specified backend.):\n{e}"
) from e
if "DIFFUSERS_TEST_DEVICE" in os.environ:
torch_device = os.environ["DIFFUSERS_TEST_DEVICE"]
try:
# try creating device to see if provided device is valid
_ = torch.device(torch_device)
except RuntimeError as e:
raise RuntimeError(
f"Unknown testing device specified by environment variable `DIFFUSERS_TEST_DEVICE`: {torch_device}"
) from e
logger.info(f"torch_device overrode to {torch_device}")
else:
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
is_torch_higher_equal_than_1_12 = version.parse(
version.parse(torch.__version__).base_version
) >= version.parse("1.12")
if is_torch_higher_equal_than_1_12:
# Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details
mps_backend_registered = hasattr(torch.backends, "mps")
torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device
def torch_all_close(a, b, *args, **kwargs):
if not is_torch_available():
raise ValueError("PyTorch needs to be installed to use this function.")
if not torch.allclose(a, b, *args, **kwargs):
assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}."
return True
def numpy_cosine_similarity_distance(a, b):
similarity = np.dot(a, b) / (norm(a) * norm(b))
distance = 1.0 - similarity.mean()
return distance
def print_tensor_test(
tensor,
limit_to_slices=None,
max_torch_print=None,
filename="test_corrections.txt",
expected_tensor_name="expected_slice",
):
if max_torch_print:
torch.set_printoptions(threshold=10_000)
test_name = os.environ.get("PYTEST_CURRENT_TEST")
if not torch.is_tensor(tensor):
tensor = torch.from_numpy(tensor)
if limit_to_slices:
tensor = tensor[0, -3:, -3:, -1]
tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "")
# format is usually:
# expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])
output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array")
test_file, test_class, test_fn = test_name.split("::")
test_fn = test_fn.split()[0]
with open(filename, "a") as f:
print("::".join([test_file, test_class, test_fn, output_str]), file=f)
def get_tests_dir(append_path=None):
"""
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
joined after the `tests` dir the former is provided.
"""
# this function caller's __file__
caller__file__ = inspect.stack()[1][1]
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
while not tests_dir.endswith("tests"):
tests_dir = os.path.dirname(tests_dir)
if append_path:
return Path(tests_dir, append_path).as_posix()
else:
return tests_dir
# Taken from the following PR:
# https://github.com/huggingface/accelerate/pull/1964
def str_to_bool(value) -> int:
"""
Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`,
`on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1
elif value in ("n", "no", "f", "false", "off", "0"):
return 0
else:
raise ValueError(f"invalid truth value {value}")
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = str_to_bool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f"If set, {key} must be yes or no.")
return _value
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False)
_run_compile_tests = parse_flag_from_env("RUN_COMPILE", default=False)
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
def slow(test_case):
"""
Decorator marking a test as slow.
Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
def nightly(test_case):
"""
Decorator marking a test that runs nightly in the diffusers CI.
Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_nightly_tests, "test is nightly")(test_case)
def is_torch_compile(test_case):
"""
Decorator marking a test that runs compile tests in the diffusers CI.
Compile tests are skipped by default. Set the RUN_COMPILE environment variable to a truthy value to run them.
"""
return unittest.skipUnless(_run_compile_tests, "test is torch compile")(test_case)
def require_torch(test_case):
"""
Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.
"""
return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)
def require_torch_2(test_case):
"""
Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed.
"""
return unittest.skipUnless(is_torch_available() and is_torch_version(">=", "2.0.0"), "test requires PyTorch 2")(
test_case
)
def require_torch_version_greater_equal(torch_version):
"""Decorator marking a test that requires torch with a specific version or greater."""
def decorator(test_case):
correct_torch_version = is_torch_available() and is_torch_version(">=", torch_version)
return unittest.skipUnless(
correct_torch_version, f"test requires torch with the version greater than or equal to {torch_version}"
)(test_case)
return decorator
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch."""
return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")(
test_case
)
# These decorators are for accelerator-specific behaviours that are not GPU-specific
def require_torch_accelerator(test_case):
"""Decorator marking a test that requires an accelerator backend and PyTorch."""
return unittest.skipUnless(is_torch_available() and torch_device != "cpu", "test requires accelerator+PyTorch")(
test_case
)
def require_torch_multi_gpu(test_case):
"""
Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without
multiple GPUs. To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests
-k "multi_gpu"
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case)
def require_torch_accelerator_with_fp16(test_case):
"""Decorator marking a test that requires an accelerator with support for the FP16 data type."""
return unittest.skipUnless(_is_torch_fp16_available(torch_device), "test requires accelerator with fp16 support")(
test_case
)
def require_torch_accelerator_with_fp64(test_case):
"""Decorator marking a test that requires an accelerator with support for the FP64 data type."""
return unittest.skipUnless(_is_torch_fp64_available(torch_device), "test requires accelerator with fp64 support")(
test_case
)
def require_big_gpu_with_torch_cuda(test_case):
"""
Decorator marking a test that requires a bigger GPU (24GB) for execution. Some example pipelines: Flux, SD3, Cog,
etc.
"""
if not is_torch_available():
return unittest.skip("test requires PyTorch")(test_case)
import torch
if not torch.cuda.is_available():
return unittest.skip("test requires PyTorch CUDA")(test_case)
device_properties = torch.cuda.get_device_properties(0)
total_memory = device_properties.total_memory / (1024**3)
return unittest.skipUnless(
total_memory >= BIG_GPU_MEMORY, f"test requires a GPU with at least {BIG_GPU_MEMORY} GB memory"
)(test_case)
def require_torch_accelerator_with_training(test_case):
"""Decorator marking a test that requires an accelerator with support for training."""
return unittest.skipUnless(
is_torch_available() and backend_supports_training(torch_device),
"test requires accelerator with training support",
)(test_case)
def skip_mps(test_case):
"""Decorator marking a test to skip if torch_device is 'mps'"""
return unittest.skipUnless(torch_device != "mps", "test requires non 'mps' device")(test_case)
def require_flax(test_case):
"""
Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed
"""
return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
def require_compel(test_case):
"""
Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when
the library is not installed.
"""
return unittest.skipUnless(is_compel_available(), "test requires compel")(test_case)
def require_onnxruntime(test_case):
"""
Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.
"""
return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case)
def require_note_seq(test_case):
"""
Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed.
"""
return unittest.skipUnless(is_note_seq_available(), "test requires note_seq")(test_case)
def require_accelerator(test_case):
"""
Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no
hardware accelerator available.
"""
return unittest.skipUnless(torch_device != "cpu", "test requires a hardware accelerator")(test_case)
def require_torchsde(test_case):
"""
Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed.
"""
return unittest.skipUnless(is_torchsde_available(), "test requires torchsde")(test_case)
def require_peft_backend(test_case):
"""
Decorator marking a test that requires PEFT backend, this would require some specific versions of PEFT and
transformers.
"""
return unittest.skipUnless(USE_PEFT_BACKEND, "test requires PEFT backend")(test_case)
def require_timm(test_case):
"""
Decorator marking a test that requires timm. These tests are skipped when timm isn't installed.
"""
return unittest.skipUnless(is_timm_available(), "test requires timm")(test_case)
def require_bitsandbytes(test_case):
"""
Decorator marking a test that requires bitsandbytes. These tests are skipped when bitsandbytes isn't installed.
"""
return unittest.skipUnless(is_bitsandbytes_available(), "test requires bitsandbytes")(test_case)
def require_accelerate(test_case):
"""
Decorator marking a test that requires accelerate. These tests are skipped when accelerate isn't installed.
"""
return unittest.skipUnless(is_accelerate_available(), "test requires accelerate")(test_case)
def require_peft_version_greater(peft_version):
"""
Decorator marking a test that requires PEFT backend with a specific version, this would require some specific
versions of PEFT and transformers.
"""
def decorator(test_case):
correct_peft_version = is_peft_available() and version.parse(
version.parse(importlib.metadata.version("peft")).base_version
) > version.parse(peft_version)
return unittest.skipUnless(
correct_peft_version, f"test requires PEFT backend with the version greater than {peft_version}"
)(test_case)
return decorator
def require_transformers_version_greater(transformers_version):
"""
Decorator marking a test that requires transformers with a specific version, this would require some specific
versions of PEFT and transformers.
"""
def decorator(test_case):
correct_transformers_version = is_transformers_available() and version.parse(
version.parse(importlib.metadata.version("transformers")).base_version
) > version.parse(transformers_version)
return unittest.skipUnless(
correct_transformers_version,
f"test requires transformers with the version greater than {transformers_version}",
)(test_case)
return decorator
def require_accelerate_version_greater(accelerate_version):
def decorator(test_case):
correct_accelerate_version = is_accelerate_available() and version.parse(
version.parse(importlib.metadata.version("accelerate")).base_version
) > version.parse(accelerate_version)
return unittest.skipUnless(
correct_accelerate_version, f"Test requires accelerate with the version greater than {accelerate_version}."
)(test_case)
return decorator
def require_bitsandbytes_version_greater(bnb_version):
def decorator(test_case):
correct_bnb_version = is_bitsandbytes_available() and version.parse(
version.parse(importlib.metadata.version("bitsandbytes")).base_version
) > version.parse(bnb_version)
return unittest.skipUnless(
correct_bnb_version, f"Test requires bitsandbytes with the version greater than {bnb_version}."
)(test_case)
return decorator
def require_hf_hub_version_greater(hf_hub_version):
def decorator(test_case):
correct_hf_hub_version = version.parse(
version.parse(importlib.metadata.version("huggingface_hub")).base_version
) > version.parse(hf_hub_version)
return unittest.skipUnless(
correct_hf_hub_version, f"Test requires huggingface_hub with the version greater than {hf_hub_version}."
)(test_case)
return decorator
def require_gguf_version_greater_or_equal(gguf_version):
def decorator(test_case):
correct_gguf_version = is_gguf_available() and version.parse(
version.parse(importlib.metadata.version("gguf")).base_version
) >= version.parse(gguf_version)
return unittest.skipUnless(
correct_gguf_version, f"Test requires gguf with the version greater than {gguf_version}."
)(test_case)
return decorator
def require_torchao_version_greater_or_equal(torchao_version):
def decorator(test_case):
correct_torchao_version = is_torchao_available() and version.parse(
version.parse(importlib.metadata.version("torchao")).base_version
) >= version.parse(torchao_version)
return unittest.skipUnless(
correct_torchao_version, f"Test requires torchao with version greater than {torchao_version}."
)(test_case)
return decorator
def deprecate_after_peft_backend(test_case):
"""
Decorator marking a test that will be skipped after PEFT backend
"""
return unittest.skipUnless(not USE_PEFT_BACKEND, "test skipped in favor of PEFT backend")(test_case)
def get_python_version():
sys_info = sys.version_info
major, minor = sys_info.major, sys_info.minor
return major, minor
def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -> np.ndarray:
if isinstance(arry, str):
if local_path is not None:
# local_path can be passed to correct images of tests
return Path(local_path, arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]).as_posix()
elif arry.startswith("http://") or arry.startswith("https://"):
response = requests.get(arry)
response.raise_for_status()
arry = np.load(BytesIO(response.content))
elif os.path.isfile(arry):
arry = np.load(arry)
else:
raise ValueError(
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path"
)
elif isinstance(arry, np.ndarray):
pass
else:
raise ValueError(
"Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a"
" ndarray."
)
return arry
def load_pt(url: str):
response = requests.get(url)
response.raise_for_status()
arry = torch.load(BytesIO(response.content))
return arry
def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
"""
Loads `image` to a PIL Image.
Args:
image (`str` or `PIL.Image.Image`):
The image to convert to the PIL Image format.
Returns:
`PIL.Image.Image`:
A PIL Image.
"""
if isinstance(image, str):
if image.startswith("http://") or image.startswith("https://"):
image = PIL.Image.open(requests.get(image, stream=True).raw)
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
raise ValueError(
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path"
)
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise ValueError(
"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image."
)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
def preprocess_image(image: PIL.Image, batch_size: int):
w, h = image.size
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def export_to_gif(image: List[PIL.Image.Image], output_gif_path: str = None) -> str:
if output_gif_path is None:
output_gif_path = tempfile.NamedTemporaryFile(suffix=".gif").name
image[0].save(
output_gif_path,
save_all=True,
append_images=image[1:],
optimize=False,
duration=100,
loop=0,
)
return output_gif_path
@contextmanager
def buffered_writer(raw_f):
f = io.BufferedWriter(raw_f)
yield f
f.flush()
def export_to_ply(mesh, output_ply_path: str = None):
"""
Write a PLY file for a mesh.
"""
if output_ply_path is None:
output_ply_path = tempfile.NamedTemporaryFile(suffix=".ply").name
coords = mesh.verts.detach().cpu().numpy()
faces = mesh.faces.cpu().numpy()
rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)
with buffered_writer(open(output_ply_path, "wb")) as f:
f.write(b"ply\n")
f.write(b"format binary_little_endian 1.0\n")
f.write(bytes(f"element vertex {len(coords)}\n", "ascii"))
f.write(b"property float x\n")
f.write(b"property float y\n")
f.write(b"property float z\n")
if rgb is not None:
f.write(b"property uchar red\n")
f.write(b"property uchar green\n")
f.write(b"property uchar blue\n")
if faces is not None:
f.write(bytes(f"element face {len(faces)}\n", "ascii"))
f.write(b"property list uchar int vertex_index\n")
f.write(b"end_header\n")
if rgb is not None:
rgb = (rgb * 255.499).round().astype(int)
vertices = [
(*coord, *rgb)
for coord, rgb in zip(
coords.tolist(),
rgb.tolist(),
)
]
format = struct.Struct("<3f3B")
for item in vertices:
f.write(format.pack(*item))
else:
format = struct.Struct("<3f")
for vertex in coords.tolist():
f.write(format.pack(*vertex))
if faces is not None:
format = struct.Struct("<B3I")
for tri in faces.tolist():
f.write(format.pack(len(tri), *tri))
return output_ply_path
def export_to_obj(mesh, output_obj_path: str = None):
if output_obj_path is None:
output_obj_path = tempfile.NamedTemporaryFile(suffix=".obj").name
verts = mesh.verts.detach().cpu().numpy()
faces = mesh.faces.cpu().numpy()
vertex_colors = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1)
vertices = [
"{} {} {} {} {} {}".format(*coord, *color) for coord, color in zip(verts.tolist(), vertex_colors.tolist())
]
faces = ["f {} {} {}".format(str(tri[0] + 1), str(tri[1] + 1), str(tri[2] + 1)) for tri in faces.tolist()]
combined_data = ["v " + vertex for vertex in vertices] + faces
with open(output_obj_path, "w") as f:
f.writelines("\n".join(combined_data))
def export_to_video(video_frames: List[np.ndarray], output_video_path: str = None) -> str:
if is_opencv_available():
import cv2
else:
raise ImportError(BACKENDS_MAPPING["opencv"][1].format("export_to_video"))
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
h, w, c = video_frames[0].shape
video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=8, frameSize=(w, h))
for i in range(len(video_frames)):
img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
video_writer.write(img)
return output_video_path
def load_hf_numpy(path) -> np.ndarray:
base_url = "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main"
if not path.startswith("http://") and not path.startswith("https://"):
path = os.path.join(base_url, urllib.parse.quote(path))
return load_numpy(path)
# --- pytest conf functions --- #
# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once
pytest_opt_registered = {}
def pytest_addoption_shared(parser):
"""
This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.
It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`
option.
"""
option = "--make-reports"
if option not in pytest_opt_registered:
parser.addoption(
option,
action="store",
default=False,
help="generate report files. The value of this option is used as a prefix to report names",
)
pytest_opt_registered[option] = 1
def pytest_terminal_summary_main(tr, id):
"""
Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
directory. The report files are prefixed with the test suite name.
This function emulates --duration and -rA pytest arguments.
This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined
there.
Args:
- tr: `terminalreporter` passed from `conftest.py`
- id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is
needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.
NB: this functions taps into a private _pytest API and while unlikely, it could break should
pytest do internal changes - also it calls default internal methods of terminalreporter which
can be hijacked by various `pytest-` plugins and interfere.
"""
from _pytest.config import create_terminal_writer
if not len(id):
id = "tests"
config = tr.config
orig_writer = config.get_terminal_writer()
orig_tbstyle = config.option.tbstyle
orig_reportchars = tr.reportchars
dir = "reports"
Path(dir).mkdir(parents=True, exist_ok=True)
report_files = {
k: f"{dir}/{id}_{k}.txt"
for k in [
"durations",
"errors",
"failures_long",
"failures_short",
"failures_line",
"passes",
"stats",
"summary_short",
"warnings",
]
}
# custom durations report
# note: there is no need to call pytest --durations=XX to get this separate report
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66
dlist = []
for replist in tr.stats.values():
for rep in replist:
if hasattr(rep, "duration"):
dlist.append(rep)
if dlist:
dlist.sort(key=lambda x: x.duration, reverse=True)
with open(report_files["durations"], "w") as f:
durations_min = 0.05 # sec
f.write("slowest durations\n")
for i, rep in enumerate(dlist):
if rep.duration < durations_min:
f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")
break
f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")
def summary_failures_short(tr):
# expecting that the reports were --tb=long (default) so we chop them off here to the last frame
reports = tr.getreports("failed")
if not reports:
return
tr.write_sep("=", "FAILURES SHORT STACK")
for rep in reports:
msg = tr._getfailureheadline(rep)
tr.write_sep("_", msg, red=True, bold=True)
# chop off the optional leading extra frames, leaving only the last one
longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S)
tr._tw.line(longrepr)
# note: not printing out any rep.sections to keep the report short
# use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814
# note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.
# pytest-instafail does that)
# report failures with line/short/long styles
config.option.tbstyle = "auto" # full tb
with open(report_files["failures_long"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_failures()
# config.option.tbstyle = "short" # short tb
with open(report_files["failures_short"], "w") as f:
tr._tw = create_terminal_writer(config, f)
summary_failures_short(tr)
config.option.tbstyle = "line" # one line per error
with open(report_files["failures_line"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_failures()
with open(report_files["errors"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_errors()
with open(report_files["warnings"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_warnings() # normal warnings
tr.summary_warnings() # final warnings
tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary())
with open(report_files["passes"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_passes()
with open(report_files["summary_short"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.short_test_summary()
with open(report_files["stats"], "w") as f:
tr._tw = create_terminal_writer(config, f)
tr.summary_stats()
# restore:
tr._tw = orig_writer
tr.reportchars = orig_reportchars
config.option.tbstyle = orig_tbstyle
# Copied from https://github.com/huggingface/transformers/blob/000e52aec8850d3fe2f360adc6fd256e5b47fe4c/src/transformers/testing_utils.py#L1905
def is_flaky(max_attempts: int = 5, wait_before_retry: Optional[float] = None, description: Optional[str] = None):
"""
To decorate flaky tests. They will be retried on failures.
Args:
max_attempts (`int`, *optional*, defaults to 5):
The maximum number of attempts to retry the flaky test.
wait_before_retry (`float`, *optional*):
If provided, will wait that number of seconds before retrying the test.
description (`str`, *optional*):
A string to describe the situation (what / where / why is flaky, link to GH issue/PR comments, errors,
etc.)
"""
def decorator(test_func_ref):
@functools.wraps(test_func_ref)
def wrapper(*args, **kwargs):
retry_count = 1
while retry_count < max_attempts:
try:
return test_func_ref(*args, **kwargs)
except Exception as err:
print(f"Test failed with {err} at try {retry_count}/{max_attempts}.", file=sys.stderr)
if wait_before_retry is not None:
time.sleep(wait_before_retry)
retry_count += 1
return test_func_ref(*args, **kwargs)
return wrapper
return decorator
# Taken from: https://github.com/huggingface/transformers/blob/3658488ff77ff8d45101293e749263acf437f4d5/src/transformers/testing_utils.py#L1787
def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None):
"""
To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.
Args:
test_case (`unittest.TestCase`):
The test that will run `target_func`.
target_func (`Callable`):
The function implementing the actual testing logic.
inputs (`dict`, *optional*, defaults to `None`):
The inputs that will be passed to `target_func` through an (input) queue.
timeout (`int`, *optional*, defaults to `None`):
The timeout (in seconds) that will be passed to the input and output queues. If not specified, the env.
variable `PYTEST_TIMEOUT` will be checked. If still `None`, its value will be set to `600`.
"""
if timeout is None:
timeout = int(os.environ.get("PYTEST_TIMEOUT", 600))
start_methohd = "spawn"
ctx = multiprocessing.get_context(start_methohd)
input_queue = ctx.Queue(1)
output_queue = ctx.JoinableQueue(1)
# We can't send `unittest.TestCase` to the child, otherwise we get issues regarding pickle.
input_queue.put(inputs, timeout=timeout)
process = ctx.Process(target=target_func, args=(input_queue, output_queue, timeout))
process.start()
# Kill the child process if we can't get outputs from it in time: otherwise, the hanging subprocess prevents
# the test to exit properly.
try:
results = output_queue.get(timeout=timeout)
output_queue.task_done()
except Exception as e:
process.terminate()
test_case.fail(e)
process.join(timeout=timeout)
if results["error"] is not None:
test_case.fail(f'{results["error"]}')
class CaptureLogger:
"""
Args:
Context manager to capture `logging` streams
logger: 'logging` logger object
Returns:
The captured output is available via `self.out`
Example:
```python
>>> from diffusers import logging
>>> from diffusers.testing_utils import CaptureLogger
>>> msg = "Testing 1, 2, 3"
>>> logging.set_verbosity_info()
>>> logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py")
>>> with CaptureLogger(logger) as cl:
... logger.info(msg)
>>> assert cl.out, msg + "\n"
```
"""
def __init__(self, logger):
self.logger = logger
self.io = StringIO()
self.sh = logging.StreamHandler(self.io)
self.out = ""
def __enter__(self):
self.logger.addHandler(self.sh)
return self
def __exit__(self, *exc):
self.logger.removeHandler(self.sh)
self.out = self.io.getvalue()
def __repr__(self):
return f"captured: {self.out}\n"
def enable_full_determinism():
"""
Helper function for reproducible behavior during distributed training. See
You can’t perform that action at this time.