File size: 1,913 Bytes
865f97a |
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 48 49 50 51 52 53 |
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
"""
# get inputs
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
|