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Uniformize kwargs for image-text-to-text processors (#32544)

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* uniformize FUYU processor kwargs

* Uniformize instructblip processor kwargs

* Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2

* Uniformize llava_next processor

* Fix save_load test for processor with chat_template only as extra init args

* Fix import Unpack

* Fix Fuyu Processor import

* Fix FuyuProcessor import

* Fix FuyuProcessor

* Add defaults for specific kwargs kosmos2

* Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs

* Add tests processor Udop

* remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature

* Fix overwrite tests kwargs processors

* Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop

* Fix processing test fuyu

* remove unnecessary pad_token check in instructblip ProcessorTest

* Fix BC tests and cleanup

* FIx imports fuyu

* Uniformize Pix2Struct

* Fix wrong name for FuyuProcessorKwargs

* Fix slow tests reversed inputs align fuyu llava-next, change udop warning

* Fix wrong logging import udop

* Add check images text input order

* Fix copies

* change text pair handling when positional arg

* rebase on main, fix imports in test_processing_common

* remove optional args and udop uniformization from this PR

* fix failing tests

* remove unnecessary test, fix processing utils and test processing common

* cleanup Unpack

* cleanup

* fix conflict grounding dino
This commit is contained in:
Yoni Gozlan
2024-09-24 21:28:19 -04:00
committed by GitHub
parent fa0bb0fe76
commit 5f0c181f4e
24 changed files with 763 additions and 852 deletions
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docs/source/en/model_doc/align.md
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@@ -46,7 +46,7 @@ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = ["an image of a cat", "an image of a dog"]
inputs = processor(text=candidate_labels, images=image, return_tensors="pt")
inputs = processor(images=image ,text=candidate_labels, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)