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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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25
tests/models/dpt/test_image_processing_dpt.py
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@@ -29,8 +29,6 @@ if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
if is_torchvision_available():
@@ -94,24 +92,15 @@ class DPTImageProcessingTester:
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
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"]
# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
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
@@ -187,7 +176,6 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
@unittest.skip("temporary to avoid failing on circleci")
# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_call_segmentation_maps
def test_call_segmentation_maps(self):
for image_processing_class in self.image_processor_list:
@@ -296,7 +284,6 @@ class DPTImageProcessingTest(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:
image_processor = image_processing_class(**self.image_processor_dict)
@@ -319,7 +306,6 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
# Compare with non-reduced label to see if it's reduced by 1
self.assertEqual(encoding["labels"][first_non_zero_coords].item(), first_non_zero_value - 1)
@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")
@@ -341,7 +327,6 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
)
self.assertTrue(torch.allclose(image_encoding_slow.labels, image_encoding_fast.labels, atol=1e-1))
@unittest.skip("temporary to avoid failing on circleci")
def test_slow_fast_equivalence_batched(self):
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest(reason="Skipping slow/fast equivalence test")