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fix marigold depth failure in xpu and A100 (#13886)
fix marigold depth failure in xpu Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
1 parent 41875e0 commit 2a57cc4

1 file changed

Lines changed: 92 additions & 12 deletions

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‎tests/pipelines/marigold/test_marigold_depth.py‎

Lines changed: 92 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -341,15 +341,24 @@ def _test_marigold_depth(
341341
pipe_kwargs.get("processing_resolution", 768),
342342
"Unexpected output resolution",
343343
)
344-
345344
self.assertTrue(np.allclose(prediction_slice, expected_slice, atol=atol))
346345

347346
def test_marigold_depth_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):
347+
# fmt: off
348+
expected_slices = Expectations(
349+
{
350+
("cuda", 7): np.array([0.4328, 0.4328, 0.4328, 0.4328, 0.4328, 0.4328, 0.4328, 0.4328, 0.4328]),
351+
("xpu", None): np.array([0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323]),
352+
}
353+
)
354+
expected_slice = expected_slices.get_expectation()
355+
# fmt: on
356+
348357
self._test_marigold_depth(
349358
is_fp16=False,
350359
device="cpu",
351360
generator_seed=0,
352-
expected_slice=np.array([0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323, 0.4323]),
361+
expected_slice=expected_slice,
353362
num_inference_steps=1,
354363
processing_resolution=32,
355364
ensemble_size=1,
@@ -358,11 +367,20 @@ def test_marigold_depth_einstein_f32_cpu_G0_S1_P32_E1_B1_M1(self):
358367
)
359368

360369
def test_marigold_depth_einstein_f32_accelerator_G0_S1_P768_E1_B1_M1(self):
370+
# fmt: off
371+
expected_slices = Expectations(
372+
{
373+
("cuda", 7): np.array([0.1243, 0.1265, 0.1291, 0.1239, 0.1252, 0.1266, 0.1245, 0.1225, 0.1179]),
374+
("xpu", None): np.array([0.1244, 0.1265, 0.1292, 0.1240, 0.1252, 0.1266, 0.1246, 0.1226, 0.1180]),
375+
}
376+
)
377+
expected_slice = expected_slices.get_expectation()
378+
# fmt: on
361379
self._test_marigold_depth(
362380
is_fp16=False,
363381
device=torch_device,
364382
generator_seed=0,
365-
expected_slice=np.array([0.1244, 0.1265, 0.1292, 0.1240, 0.1252, 0.1266, 0.1246, 0.1226, 0.1180]),
383+
expected_slice=expected_slice,
366384
num_inference_steps=1,
367385
processing_resolution=768,
368386
ensemble_size=1,
@@ -371,11 +389,21 @@ def test_marigold_depth_einstein_f32_accelerator_G0_S1_P768_E1_B1_M1(self):
371389
)
372390

373391
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E1_B1_M1(self):
392+
# fmt: off
393+
expected_slices = Expectations(
394+
{
395+
("cuda", 7): np.array([0.1242, 0.1263, 0.1290, 0.1237, 0.1249, 0.1263, 0.1243, 0.1223, 0.1177]),
396+
("xpu", 3): np.array([0.1241, 0.1262, 0.1290, 0.1237, 0.125, 0.1265, 0.1243, 0.1223, 0.1179]),
397+
("xpu", 5): np.array([0.1244, 0.1265, 0.1292, 0.1240, 0.1252, 0.1266, 0.1245, 0.1225, 0.1179]),
398+
}
399+
)
400+
expected_slice = expected_slices.get_expectation()
401+
# fmt: on
374402
self._test_marigold_depth(
375403
is_fp16=True,
376404
device=torch_device,
377405
generator_seed=0,
378-
expected_slice=np.array([0.1241, 0.1262, 0.1290, 0.1238, 0.1250, 0.1265, 0.1244, 0.1225, 0.1179]),
406+
expected_slice=expected_slice,
379407
num_inference_steps=1,
380408
processing_resolution=768,
381409
ensemble_size=1,
@@ -384,11 +412,21 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E1_B1_M1(self):
384412
)
385413

386414
def test_marigold_depth_einstein_f16_accelerator_G2024_S1_P768_E1_B1_M1(self):
415+
# fmt: off
416+
expected_slices = Expectations(
417+
{
418+
("cuda", 7): np.array([0.1709, 0.1722, 0.1736, 0.1699, 0.1699, 0.1693, 0.1697, 0.1661, 0.1589]),
419+
("xpu", 3): np.array([0.1709, 0.1722, 0.1736, 0.1699, 0.1698, 0.1693, 0.1696, 0.1660, 0.1589]),
420+
("xpu", 5): np.array([0.1709, 0.1722, 0.1738, 0.1699, 0.1699, 0.1694, 0.1697, 0.1661, 0.1589]),
421+
}
422+
)
423+
expected_slice = expected_slices.get_expectation()
424+
# fmt: on
387425
self._test_marigold_depth(
388426
is_fp16=True,
389427
device=torch_device,
390428
generator_seed=2024,
391-
expected_slice=np.array([0.1710, 0.1725, 0.1738, 0.1700, 0.1700, 0.1696, 0.1698, 0.1663, 0.1592]),
429+
expected_slice=expected_slice,
392430
num_inference_steps=1,
393431
processing_resolution=768,
394432
ensemble_size=1,
@@ -400,9 +438,9 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S2_P768_E1_B1_M1(self):
400438
# fmt: off
401439
expected_slices = Expectations(
402440
{
403-
("cuda", 7): np.array([0.1085, 0.1098, 0.1110, 0.1081, 0.1085, 0.1082, 0.1085, 0.1057, 0.0996]),
404-
("xpu", 3): np.array([0.1084, 0.1096, 0.1108, 0.1080, 0.1083, 0.1080,
405-
0.1085, 0.1057, 0.0996]),
441+
("cuda", 7): np.array([0.1083, 0.1096, 0.1107, 0.1078, 0.1082, 0.1079, 0.1084, 0.1055, 0.0994]),
442+
("xpu", 3): np.array([0.1083, 0.1096, 0.1107, 0.1079, 0.1082, 0.1079, 0.1083, 0.1055, 0.0994]),
443+
("xpu", 5): np.array([0.1086, 0.1098, 0.1111, 0.1082, 0.1085, 0.1081, 0.1086, 0.1057, 0.0996]),
406444
}
407445
)
408446
expected_slice = expected_slices.get_expectation()
@@ -421,11 +459,21 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S2_P768_E1_B1_M1(self):
421459
)
422460

423461
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P512_E1_B1_M1(self):
462+
# fmt: off
463+
expected_slices = Expectations(
464+
{
465+
("cuda", 7): np.array([0.2683, 0.2693, 0.2698, 0.2666, 0.2632, 0.2615, 0.2656, 0.2603, 0.2573]),
466+
("xpu", 3): np.array([0.2678, 0.2688, 0.2695, 0.2661, 0.2629, 0.2612, 0.2654, 0.2600, 0.2573]),
467+
("xpu", 5): np.array([0.2683, 0.2693, 0.2698, 0.2666, 0.2634, 0.2617,0.2659, 0.2603, 0.2576]),
468+
}
469+
)
470+
expected_slice = expected_slices.get_expectation()
471+
# fmt: on
424472
self._test_marigold_depth(
425473
is_fp16=True,
426474
device=torch_device,
427475
generator_seed=0,
428-
expected_slice=np.array([0.2683, 0.2693, 0.2698, 0.2666, 0.2632, 0.2615, 0.2656, 0.2603, 0.2573]),
476+
expected_slice=expected_slice,
429477
num_inference_steps=1,
430478
processing_resolution=512,
431479
ensemble_size=1,
@@ -434,11 +482,22 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S1_P512_E1_B1_M1(self):
434482
)
435483

436484
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E3_B1_M1(self):
485+
# fmt: off
486+
expected_slices = Expectations(
487+
{
488+
("cuda", 7): np.array([0.1075, 0.1099, 0.1121, 0.1069, 0.1070, 0.1066, 0.1063, 0.1011, 0.0931]),
489+
("xpu", 3): np.array([0.1074, 0.1097, 0.1120, 0.1068, 0.1069, 0.1064, 0.1061, 0.1010, 0.0930]),
490+
("xpu", 5): np.array([0.1196, 0.1212, 0.1234, 0.1189, 0.1193, 0.1199, 0.1192, 0.1162, 0.1105]),
491+
}
492+
)
493+
expected_slice = expected_slices.get_expectation()
494+
# fmt: on
495+
437496
self._test_marigold_depth(
438497
is_fp16=True,
439498
device=torch_device,
440499
generator_seed=0,
441-
expected_slice=np.array([0.1200, 0.1215, 0.1237, 0.1193, 0.1197, 0.1202, 0.1196, 0.1166, 0.1109]),
500+
expected_slice=expected_slice,
442501
num_inference_steps=1,
443502
processing_resolution=768,
444503
ensemble_size=3,
@@ -448,11 +507,22 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E3_B1_M1(self):
448507
)
449508

450509
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E4_B2_M1(self):
510+
# fmt: off
511+
expected_slices = Expectations(
512+
{
513+
("cuda", 7): np.array([0.0933, 0.0952, 0.0969, 0.0927, 0.0927, 0.0918, 0.0922, 0.0872, 0.0792]),
514+
("xpu", 3): np.array([0.1010, 0.1031, 0.1052, 0.1006, 0.1010, 0.1007, 0.1005, 0.0961, 0.0890]),
515+
("xpu", 5): np.array([0.1118, 0.1132, 0.1153, 0.1110, 0.1112, 0.1115, 0.1108, 0.1076, 0.1016]),
516+
}
517+
)
518+
expected_slice = expected_slices.get_expectation()
519+
# fmt: on
520+
451521
self._test_marigold_depth(
452522
is_fp16=True,
453523
device=torch_device,
454524
generator_seed=0,
455-
expected_slice=np.array([0.1121, 0.1135, 0.1155, 0.1111, 0.1115, 0.1118, 0.1111, 0.1079, 0.1019]),
525+
expected_slice=expected_slice,
456526
num_inference_steps=1,
457527
processing_resolution=768,
458528
ensemble_size=4,
@@ -462,11 +532,21 @@ def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E4_B2_M1(self):
462532
)
463533

464534
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P512_E1_B1_M0(self):
535+
# fmt: off
536+
expected_slices = Expectations(
537+
{
538+
("cuda", 7): np.array([0.2671, 0.2690, 0.2720, 0.2659, 0.2676, 0.2739, 0.2664, 0.2686, 0.2573]),
539+
("xpu", 3): np.array([0.2671, 0.2686, 0.2715, 0.2656, 0.2671, 0.2734, 0.2661, 0.2681, 0.2573]),
540+
("xpu", 5): np.array([0.2671, 0.2690, 0.2720, 0.2659, 0.2676, 0.2739, 0.2664, 0.2686, 0.2576]),
541+
}
542+
)
543+
expected_slice = expected_slices.get_expectation()
544+
# fmt: on
465545
self._test_marigold_depth(
466546
is_fp16=True,
467547
device=torch_device,
468548
generator_seed=0,
469-
expected_slice=np.array([0.2671, 0.2690, 0.2720, 0.2659, 0.2676, 0.2739, 0.2664, 0.2686, 0.2573]),
549+
expected_slice=expected_slice,
470550
num_inference_steps=1,
471551
processing_resolution=512,
472552
ensemble_size=1,

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