Update handler.py

handler.py

@@ -1,6 +1,7 @@
 from ultralyticsplus import YOLO
 from typing import List, Dict, Any
 from sahi import ObjectPrediction
+import torch, torchvision
 
 
 DEFAULT_CONFIG = {'conf': 0.25, 'iou': 0.45, 'agnostic_nms': False, 'max_det': 1000}
@@ -21,31 +22,32 @@ class EndpointHandler():
       Return:
             object_predictions
         """
-        config = DEFAULT_CONFIG
-        # Set model parameters
-        self.model.overrides['conf'] = config.get('conf')
-        self.model.overrides['iou'] = config.get('iou')
-        self.model.overrides['agnostic_nms'] = config.get('agnostic_nms')
-        self.model.overrides['max_det'] = config.get('max_det')   
+        # config = DEFAULT_CONFIG
+        # # Set model parameters
+        # self.model.overrides['conf'] = config.get('conf')
+        # self.model.overrides['iou'] = config.get('iou')
+        # self.model.overrides['agnostic_nms'] = config.get('agnostic_nms')
+        # self.model.overrides['max_det'] = config.get('max_det')   
         
-        # perform inference
-        inputs = data.pop("inputs", data)
-        result = self.model.predict(inputs['image'])[0]
+        # # perform inference
+        # inputs = data.pop("inputs", data)
+        # result = self.model.predict(inputs['image'])[0]
 
-        names = self.model.model.names
-        boxes = result.boxes
+        # names = self.model.model.names
+        # boxes = result.boxes
 
-        object_predictions = []
-        if boxes is not None:
-            det_ind = 0
-            for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
-                object_prediction = ObjectPrediction(
-                    bbox=xyxy.tolist(),
-                    category_name=names[int(cls)],
-                    category_id=int(cls),
-                    score=conf,
-                )
-                object_predictions.append(object_prediction)
-                det_ind += 1
-        return object_predictions
+        # object_predictions = []
+        # if boxes is not None:
+        #     det_ind = 0
+        #     for xyxy, conf, cls in zip(boxes.xyxy, boxes.conf, boxes.cls):
+        #         object_prediction = ObjectPrediction(
+        #             bbox=xyxy.tolist(),
+        #             category_name=names[int(cls)],
+        #             category_id=int(cls),
+        #             score=conf,
+        #         )
+        #         object_predictions.append(object_prediction)
+        #         det_ind += 1
+        # return object_predictions
+        return torch.__version__, torchvision.__version__