Path: blob/main/sagemaker/26_document_ai_donut/scripts/inference.py
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from transformers import DonutProcessor, VisionEncoderDecoderModel1import torch23device = "cuda" if torch.cuda.is_available() else "cpu"45def model_fn(model_dir):6# Load our model from Hugging Face7processor = DonutProcessor.from_pretrained(model_dir)8model = VisionEncoderDecoderModel.from_pretrained(model_dir)910# Move model to GPU11model.to(device)1213return model, processor141516def predict_fn(data, model_and_processor):17# unpack model and tokenizer18model, processor = model_and_processor1920image = data.get("inputs")21pixel_values = processor.feature_extractor(image, return_tensors="pt").pixel_values22task_prompt = "<s>" # start of sequence token for decoder since we are not having a user prompt23decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids2425# run inference26outputs = model.generate(27pixel_values.to(device),28decoder_input_ids=decoder_input_ids.to(device),29max_length=model.decoder.config.max_position_embeddings,30early_stopping=True,31pad_token_id=processor.tokenizer.pad_token_id,32eos_token_id=processor.tokenizer.eos_token_id,33use_cache=True,34num_beams=1,35bad_words_ids=[[processor.tokenizer.unk_token_id]],36return_dict_in_generate=True,37)3839# process output40prediction = processor.batch_decode(outputs.sequences)[0]41prediction = processor.token2json(prediction)4243return prediction44454647