File size: 2,384 Bytes
5815286 d149168 5815286 67b0b4a d149168 5815286 9e4dfd9 5815286 543ffeb 5815286 9e4dfd9 5815286 9e4dfd9 5815286 d149168 5815286 d149168 9e4dfd9 d149168 8c6d69e d149168 5815286 d149168 5815286 d149168 5815286 d149168 5815286 8615120 |
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 54 55 56 57 58 59 60 61 62 63 64 |
# +
from typing import Dict, List, Any
from PIL import Image
import torch
import os
import io
import base64
from io import BytesIO
# from transformers import BlipForConditionalGeneration, BlipProcessor
from transformers import Blip2Processor, Blip2ForConditionalGeneration
# -
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
print("####### Start Deploying #####")
self.processor = Blip2Processor.from_pretrained("ChirathD/Blip-2-test-1")
self.model = Blip2ForConditionalGeneration.from_pretrained("ChirathD/Blip-2-test-1")
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.
"""
print(data)
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", {})
print(input)
image_bytes = base64.b64decode(inputs)
image_io = io.BytesIO(image_bytes)
image = Image.open(image_io)
inputs = self.processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values
generated_ids = self.model.generate(pixel_values=pixel_values, max_length=25)
generated_caption = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)
# raw_images = [Image.open(BytesIO(_img)) 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)
return {"captions": generated_caption} |