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[smolvlm] skip the test (#38099)

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skip the test
This commit is contained in:
Raushan Turganbay
2025-05-13 14:50:43 +02:00
committed by GitHub
parent e27d230ddd
commit e40f301f1f
2 changed files with 35 additions and 10 deletions
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14
src/transformers/models/smolvlm/processing_smolvlm.py
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@@ -180,13 +180,15 @@ class SmolVLMProcessor(ProcessorMixin):
super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs) super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)
def process_vision(self, text, images, output_kwargs, do_image_splitting=False, image_processor_size=None): def process_vision(
self, text, images, output_kwargs, do_image_splitting=False, image_processor_size=None, processor=None
):
if text is not None: if text is not None:
n_images_in_text = [sample.count(self.image_token) for sample in text] n_images_in_text = [sample.count(self.image_token) for sample in text]
n_images_in_images = [len(sublist) for sublist in images] n_images_in_images = [len(sublist) for sublist in images]
image_inputs = self.image_processor( image_inputs = processor(
images, do_image_splitting=do_image_splitting, size=image_processor_size, **output_kwargs["images_kwargs"] images, do_image_splitting=do_image_splitting, size=image_processor_size, **output_kwargs
) )
if text is None: if text is None:
@@ -309,9 +311,10 @@ class SmolVLMProcessor(ProcessorMixin):
text, vision_inputs = self.process_vision( text, vision_inputs = self.process_vision(
text, text,
images, images,
output_kwargs, output_kwargs["images_kwargs"],
do_image_splitting=self.do_image_splitting, do_image_splitting=self.do_image_splitting,
image_processor_size=self.image_size, image_processor_size=self.image_size,
processor=self.image_processor,
) )
inputs.update(vision_inputs) inputs.update(vision_inputs)
elif videos is not None: elif videos is not None:
@@ -319,9 +322,10 @@ class SmolVLMProcessor(ProcessorMixin):
text, vision_inputs = self.process_vision( text, vision_inputs = self.process_vision(
text, text,
videos, videos,
output_kwargs, output_kwargs["videos_kwargs"],
do_image_splitting=self.do_image_splitting, do_image_splitting=self.do_image_splitting,
image_processor_size=self.video_size, image_processor_size=self.video_size,
processor=self.video_processor,
) )
inputs.update(vision_inputs) inputs.update(vision_inputs)

31
tests/models/smolvlm/test_processor_smolvlm.py
View File

@@ -22,7 +22,7 @@ import requests
from transformers import SmolVLMProcessor from transformers import SmolVLMProcessor
from transformers.models.auto.processing_auto import AutoProcessor from transformers.models.auto.processing_auto import AutoProcessor
from transformers.testing_utils import is_flaky, require_av, require_torch, require_vision from transformers.testing_utils import require_av, require_torch, require_vision
from transformers.utils import is_vision_available from transformers.utils import is_vision_available
from ...test_processing_common import ProcessorTesterMixin from ...test_processing_common import ProcessorTesterMixin
@@ -118,10 +118,6 @@ class SmolVLMProcessorTest(ProcessorTesterMixin, unittest.TestCase):
def tearDownClass(cls): def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True) shutil.rmtree(cls.tmpdirname, ignore_errors=True)
@is_flaky # fails 15 out of 100, FIXME @raushan
def test_structured_kwargs_nested_from_dict_video(self):
super().test_structured_kwargs_nested_from_dict_video()
def test_process_interleaved_images_prompts_no_image_splitting(self): def test_process_interleaved_images_prompts_no_image_splitting(self):
processor_components = self.prepare_components() processor_components = self.prepare_components()
processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left") processor_components["tokenizer"] = self.get_component("tokenizer", padding_side="left")
@@ -467,6 +463,31 @@ class SmolVLMProcessorTest(ProcessorTesterMixin, unittest.TestCase):
self.assertEqual(inputs["pixel_values"].shape[3], 300) self.assertEqual(inputs["pixel_values"].shape[3], 300)
self.assertEqual(len(inputs["input_ids"][0]), 76) self.assertEqual(len(inputs["input_ids"][0]), 76)
@require_torch
@require_vision
def test_unstructured_kwargs_batched_video(self):
if "video_processor" not in self.processor_class.attributes:
self.skipTest(f"video_processor attribute not present in {self.processor_class}")
processor_components = self.prepare_components()
processor_kwargs = self.prepare_processor_dict()
processor = self.processor_class(**processor_components, **processor_kwargs)
self.skip_processor_without_typed_kwargs(processor)
input_str = self.prepare_text_inputs(batch_size=2, modality="video")
video_input = self.prepare_video_inputs(batch_size=2)
inputs = processor(
text=input_str,
videos=video_input,
return_tensors="pt",
do_rescale=True,
rescale_factor=-1,
padding="max_length",
max_length=76,
)
self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
self.assertEqual(len(inputs["input_ids"][0]), 76)
@require_torch @require_torch
@require_vision @require_vision
def test_text_only_inference(self): def test_text_only_inference(self):