| 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()
|
|
|
