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import torch, re |
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from PIL import Image |
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from transformers import DonutProcessor, VisionEncoderDecoderModel |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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processor = DonutProcessor.from_pretrained("Model") |
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model = VisionEncoderDecoderModel.from_pretrained("Model") |
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model.to(device) |
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def inference(image): |
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pixel_values = processor(image, return_tensors="pt").pixel_values |
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task_prompt = "<s>" |
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decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt")["input_ids"] |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model.to(device) |
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outputs = model.generate(pixel_values.to(device), |
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decoder_input_ids=decoder_input_ids.to(device), |
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max_length=model.decoder.config.max_position_embeddings, |
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early_stopping=True, |
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pad_token_id=processor.tokenizer.pad_token_id, |
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eos_token_id=processor.tokenizer.eos_token_id, |
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use_cache=True, |
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num_beams=1, |
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bad_words_ids=[[processor.tokenizer.unk_token_id]], |
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return_dict_in_generate=True, |
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output_scores=True,) |
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sequence = processor.batch_decode(outputs.sequences)[0] |
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sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "") |
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sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() |
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return processor.token2json(sequence) |
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