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Update expected slices for pillow > 9 (#16117)

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* Update expected slices for pillow > 9

* Add expected slices depending on pillow version

* Add different slices depending on pillow version for other models

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
This commit is contained in:
NielsRogge
2022-03-18 09:46:45 +01:00
committed by GitHub
parent 12d1f07770
commit ec4e421b7d
3 changed files with 51 additions and 11 deletions
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29
tests/beit/test_modeling_beit.py
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@@ -19,6 +19,7 @@ import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
@@ -44,6 +45,7 @@ if is_torch_available():
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitFeatureExtractor
@@ -536,12 +538,25 @@ class BeitModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 150, 160, 160))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
]
).to(torch_device)
is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0")
if is_pillow_less_than_9:
expected_slice = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
],
device=torch_device,
)
else:
expected_slice = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))