|
import os |
|
from typing import Dict, List, Any |
|
from PIL import Image |
|
import jax |
|
from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel |
|
|
|
|
|
class PreTrainedPipeline(): |
|
|
|
def __init__(self, path=""): |
|
|
|
model_dir = os.path.join(path, "ckpt_epoch_3_step_6900") |
|
|
|
self.model = FlaxVisionEncoderDecoderModel.from_pretrained(model_dir) |
|
self.feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir) |
|
self.tokenizer = AutoTokenizer.from_pretrained(model_dir) |
|
|
|
max_length = 16 |
|
num_beams = 4 |
|
self.gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
|
|
|
@jax.jit |
|
def _generate(pixel_values): |
|
|
|
output_ids = self.model.generate(pixel_values, **self.gen_kwargs).sequences |
|
return output_ids |
|
|
|
|
|
image_path = os.path.join(path, 'val_000000039769.jpg') |
|
image = Image.open(image_path) |
|
self(image) |
|
image.close() |
|
|
|
self.generate = _generate |
|
|
|
def __call__(self, inputs: "Image.Image") -> List[str]: |
|
""" |
|
Args: |
|
Return: |
|
""" |
|
|
|
pixel_values = self.feature_extractor(images=inputs, return_tensors="np").pixel_values |
|
|
|
output_ids = self.generate(pixel_values) |
|
preds = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
|
preds = [pred.strip() for pred in preds] |
|
|
|
return preds |
