| 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() |
|
|
