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LLaVa-Next: Update docs with batched inference (#30857)

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* update docs with batch ex

* Update docs/source/en/model_doc/llava_next.md

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* accept nested list of img

---------

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
This commit is contained in:
Raushan Turganbay
2024-05-20 13:45:56 +05:00
committed by GitHub
parent cd6bd0af34
commit 5d0bf59b4d
3 changed files with 84 additions and 1 deletions
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18
tests/models/llava_next/test_image_processor_llava_next.py
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@@ -199,3 +199,21 @@ class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
@unittest.skip("LlavaNextImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
def test_call_numpy_4_channels(self):
pass
def test_nested_input(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
# Test batched as a list of images
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_output_image_shape = (7, 1445, 3, 18, 18)
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
# Test batched as a nested list of images, where each sublist is one batch
image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
expected_output_image_shape = (7, 1445, 3, 18, 18)
self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
# Image processor should return same pixel values, independently of ipnut format
self.assertTrue((encoded_images_nested == encoded_images).all())