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# +
from typing import Dict, List, Any
from PIL import Image
import torch
import requests
from io import BytesIO
from transformers import BlipForConditionalGeneration, BlipProcessor
from functools import reduce
# -
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = "Salesforce/blip-image-captioning-large"
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
self.processor = BlipProcessor.from_pretrained(model)
self.model = BlipForConditionalGeneration.from_pretrained(model).to(device)
self.model.eval()
self.model = self.model.to(device)
def __call__(self, data: Any) -> Dict[str, Any]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. The object returned should be a dict of one list like {"captions": ["A hugging face at the office"]} containing :
- "caption": A string corresponding to the generated caption.
"""
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", {})
raw_images = [Image.open(BytesIO(requests.get(_img).content)) for _img in inputs]
processed_image = self.processor(images=raw_images, return_tensors="pt")
processed_image["pixel_values"] = processed_image["pixel_values"].to(device)
processed_image = {**processed_image, **parameters}
with torch.no_grad():
out = self.model.generate(
**processed_image
)
captions = self.processor.batch_decode(out, skip_special_tokens=True)
replace_sets = ("arafed", ""), ("araffe", ""), ("araff", "")
filtered_captions = [reduce(lambda a, kv: a.replace(*kv).strip(), replace_sets, caption) for caption in captions]
# postprocess the prediction
return {"captions": filtered_captions}
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