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from typing import Dict, List, Any
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
# check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
# load the model
self.processor = DonutProcessor.from_pretrained(path)
self.model = VisionEncoderDecoderModel.from_pretrained(path)
# move model to device
self.model.to(device)
self.decoder_input_ids = self.processor.tokenizer(
"<s_cord-v2>", add_special_tokens=False, return_tensors="pt"
).input_ids
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
# preprocess the input
pixel_values = self.processor(inputs, return_tensors="pt").pixel_values
# forward pass
outputs = self.model.generate(
pixel_values.to(device),
decoder_input_ids=self.decoder_input_ids.to(device),
max_length=self.model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=self.processor.tokenizer.pad_token_id,
eos_token_id=self.processor.tokenizer.eos_token_id,
use_cache=True,
num_beams=1,
bad_words_ids=[[self.processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
# process output
prediction = self.processor.batch_decode(outputs.sequences)[0]
prediction = self.processor.token2json(prediction)
return prediction
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