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from typing import Dict, List, Any
from PIL import Image
from io import BytesIO
from transformers import AutoModelForSemanticSegmentation, AutoFeatureExtractor
import base64
import torch
from torch import nn
class EndpointHandler():
def __init__(self, path="."):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = AutoModelForSemanticSegmentation.from_pretrained(path).to(self.device).eval()
self.feature_extractor = AutoFeatureExtractor.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
images (:obj:`PIL.Image`)
candiates (:obj:`list`)
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
"""
inputs = data.pop("inputs", data)
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
# preprocess image
encoding = self.feature_extractor(images=image, return_tensors="pt")
pixel_values = encoding["pixel_values"].to(self.device)
with torch.no_grad():
outputs = self.model(pixel_values=pixel_values)
logits = outputs.logits
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1],
mode="bilinear",
align_corners=False,)
pred_seg = upsampled_logits.argmax(dim=1)[0]
return pred_seg.tolist() |