File size: 1,588 Bytes
2a261b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
from typing import Dict, List, Any
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch
# check for GPU
device = 0 if torch.cuda.is_available() else -1
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
|