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Remove script datasets in tests (#38940)

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* remove trust_remote_code

* again

* Revert "Skip some tests for now (#38931)"

This reverts commit 31d30b7224.

* again

* style

* again

* again

* style

* fix integration test

* fix tests

* style

* fix

* fix

* fix the last ones

* style

* last one

* fix last

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Quentin Lhoest
2025-06-25 16:31:20 +02:00
committed by GitHub
parent 3c322c9cdf
commit 858f9b71a8
51 changed files with 154 additions and 293 deletions
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24
tests/models/beit/test_image_processing_beit.py
View File

@@ -27,8 +27,6 @@ if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
if is_torchvision_available():
@@ -98,23 +96,14 @@ class BeitImageProcessingTester:
def prepare_semantic_single_inputs():
dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
image = Image.open(dataset[0]["file"])
map = Image.open(dataset[1]["file"])
return image, map
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
example = ds[0]
return example["image"], example["map"]
def prepare_semantic_batch_inputs():
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
image1 = Image.open(ds[0]["file"])
map1 = Image.open(ds[1]["file"])
image2 = Image.open(ds[2]["file"])
map2 = Image.open(ds[3]["file"])
return [image1, image2], [map1, map2]
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
return list(ds["image"][:2]), list(ds["map"][:2])
@require_torch
@@ -157,7 +146,6 @@ class BeitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
self.assertEqual(image_processor.do_reduce_labels, True)
@unittest.skip("temporary to avoid failing on circleci")
def test_call_segmentation_maps(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
@@ -265,7 +253,6 @@ class BeitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
@unittest.skip("temporary to avoid failing on circleci")
def test_reduce_labels(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
@@ -282,7 +269,6 @@ class BeitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(encoding["labels"].min().item() >= 0)
self.assertTrue(encoding["labels"].max().item() <= 255)
@unittest.skip("temporary to avoid failing on circleci")
def test_slow_fast_equivalence(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")

39
tests/models/beit/test_modeling_beit.py
View File

@@ -16,7 +16,6 @@
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.testing_utils import (
@@ -53,7 +52,6 @@ if is_torch_available():
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
@@ -504,8 +502,8 @@ class BeitModelIntegrationTest(unittest.TestCase):
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
image = Image.open(ds[0]["file"])
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image = ds[0]["image"].convert("RGB")
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
@@ -517,27 +515,14 @@ class BeitModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 150, 160, 160))
self.assertEqual(logits.shape, expected_shape)
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,
)
expected_slice = torch.tensor(
[
[[-4.8963, -2.3696, -3.0359], [-2.8485, -0.9842, -1.7426], [-2.9453, -1.3338, -2.1463]],
[[-5.8099, -3.4140, -4.1025], [-3.8578, -2.2100, -3.0337], [-3.8383, -2.4615, -3.3681]],
[[-0.0314, 3.9864, 4.0536], [2.9637, 4.6879, 4.9976], [3.2074, 4.7690, 4.9946]],
],
device=torch_device,
)
torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
@@ -547,8 +532,8 @@ class BeitModelIntegrationTest(unittest.TestCase):
image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False)
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
image = Image.open(ds[0]["file"])
ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
image = ds[0]["image"].convert("RGB")
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass