Upload handler.py

handler.py

@@ -0,0 +1,43 @@
+from typing import  Dict, List, Any
+import torch
+import numpy as np
+import torch.nn.functional as F
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.model = torch.load(path)
+
+
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
+        """
+        inputs = data.pop("inputs", data)
+        img = inputs["image"]
+
+        # Load the image
+        img = np.float32(img) / 255.  # Load and normalize the image
+
+        # Convert to torch tensor and add batch dimension
+        img_tensor = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0)
+
+        # Padding if necessary (to make image dimensions multiples of 4)
+        b, c, h, w = img_tensor.shape
+        factor = 4  # Assuming factor is 4, based on the code context
+        H, W = ((h + factor) // factor) * factor, ((w + factor) // factor) * factor
+        padh = H - h if h % factor != 0 else 0
+        padw = W - w if w % factor != 0 else 0
+        img_tensor = F.pad(img_tensor, (0, padw, 0, padh), 'reflect')
+
+        restored = self.model(img_tensor)
+        # postprocess the prediction
+        return "OKAY"