|
from typing import Dict, List, Any |
|
from transformers import DonutProcessor, VisionEncoderDecoderModel |
|
import torch |
|
|
|
|
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
|
|
|
class EndpointHandler: |
|
def __init__(self, path=""): |
|
|
|
self.processor = DonutProcessor.from_pretrained(path) |
|
self.model = VisionEncoderDecoderModel.from_pretrained(path) |
|
|
|
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) |
|
|
|
|
|
|
|
pixel_values = self.processor(inputs, return_tensors="pt").pixel_values |
|
|
|
|
|
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, |
|
) |
|
|
|
prediction = self.processor.batch_decode(outputs.sequences)[0] |
|
prediction = self.processor.token2json(prediction) |
|
|
|
return prediction |
|
|
