| | 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)
|
| |
|
| |
|
| | image = Image.open(BytesIO(base64.b64decode(inputs['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()
|
| |
|
