|
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer |
|
import torch |
|
from PIL import Image |
|
from typing import Dict, List, Any |
|
|
|
class EndpointHandler(): |
|
def __init__(self, path=""): |
|
model = VisionEncoderDecoderModel.from_pretrained( |
|
"nlpconnect/vit-gpt2-image-captioning") |
|
feature_extractor = ViTImageProcessor.from_pretrained( |
|
"nlpconnect/vit-gpt2-image-captioning") |
|
tokenizer = AutoTokenizer.from_pretrained( |
|
"nlpconnect/vit-gpt2-image-captioning") |
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
model.to(device) |
|
self.model = model |
|
self.feature_extractor = feature_extractor |
|
self.tokenizer = tokenizer |
|
|
|
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
|
""" |
|
data args: |
|
inputs (:obj: `str`) |
|
date (:obj: `str`) |
|
Return: |
|
A :obj:`list` | `dict`: will be serialized and returned |
|
""" |
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
max_length = 128 |
|
num_beams = 4 |
|
gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
|
image_paths = data.pop("image_paths", data) |
|
images = [] |
|
for image_path in image_paths: |
|
i_image = Image.open(image_path) |
|
if i_image.mode != "RGB": |
|
i_image = i_image.convert(mode="RGB") |
|
|
|
images.append(i_image) |
|
|
|
pixel_values = self.feature_extractor( |
|
images=images, return_tensors="pt").pixel_values |
|
pixel_values = pixel_values.to(device) |
|
|
|
output_ids = self.model.generate(pixel_values, **gen_kwargs) |
|
|
|
preds = self.tokenizer.batch_decode( |
|
output_ids, skip_special_tokens=True) |
|
preds = [pred.strip() for pred in preds] |
|
return preds |
|
|
