Create handler.py

handler.py

@@ -0,0 +1,39 @@
+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()