72 lines
No EOL
2.8 KiB
Python
72 lines
No EOL
2.8 KiB
Python
from transformers import BlipProcessor, BlipForQuestionAnswering
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from transformers import ViltProcessor, ViltForQuestionAnswering
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from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
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from transformers import GitVisionConfig, GitVisionModel, AutoProcessor, GitProcessor
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class VQA:
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name = "Not defined"
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def query(image, question: str) -> str:
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pass
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class Blip(VQA):
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name = "Blip"
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def query(self, image, question):
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processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
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model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large")
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inputs = processor(image, question, return_tensors="pt")
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out = model.generate(max_new_tokens=50000, **inputs)
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return processor.decode(out[0], skip_special_tokens=True)
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class Vilt(VQA):
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name = "Vilt"
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def query(self, image, question):
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processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
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# prepare inputs
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encoding = processor(image, question, return_tensors="pt")
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# forward pass
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outputs = model(**encoding)
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logits = outputs.logits
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idx = logits.argmax(-1).item()
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return model.config.id2label[idx]
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class Deplot(VQA):
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name = "Deplot"
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def query(self, image, question):
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processor = Pix2StructProcessor.from_pretrained('google/deplot')
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model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')
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inputs = processor(images=image, text=question, return_tensors="pt")
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predictions = model.generate(**inputs, max_new_tokens=512)
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return processor.decode(predictions[0], skip_special_tokens=True)
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class PixStructDocVA(VQA):
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name = "google/pix2struct-docvqa-large"
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def query(self, image, question):
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print(question)
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model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large")
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processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large")
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inputs = processor(images=image, text=question, return_tensors="pt")
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predictions = model.generate(**inputs, max_new_tokens=10000)
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answer = processor.decode(predictions[0], skip_special_tokens=True)
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print(answer)
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return answer
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class MicrosoftGIT(VQA):
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name = "microsoft/git-base-textvqa"
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def query(self, image, question):
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processor = GitProcessor.from_pretrained("microsoft/git-base")
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model = GitVisionModel.from_pretrained("microsoft/git-base")
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inputs = processor(images=image, text=question, return_tensors="pt")
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predictions = model.generate(**inputs, max_new_tokens=10000)
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answer = processor.decode(predictions[0], skip_special_tokens=True)
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return answer
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