fixing tensor.cpu() error in boxes

nemaquant.py

@@ -160,7 +160,7 @@ def main():
         box_classes = [result.names[int(x)] for x in result.boxes.cls]
         # NOTE - filtering by class is not necessary, but would make this easier to extend to multi-class models
         # e.g. if we want to add hatched, empty eggs, etc
-        egg_xy = [x.numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
+        egg_xy = [x.cpu().numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
         print('Target image:\n%s' % str(args.imgpath))
         print('n eggs:\n%s' % len(egg_xy))
         if args.annotated:
@@ -203,7 +203,7 @@ def main():
                 results = model.predict(img, imgsz = 1440, verbose=False, conf=args.conf)
                 result = results[0]
                 box_classes = [result.names[int(x)] for x in result.boxes.cls]
-                egg_xy = [x.numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
+                egg_xy = [x.cpu().numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
                 # append relevant output to temporary lists
                 tmp_well.append(well)
                 tmp_numeggs.append(len(egg_xy))
@@ -240,7 +240,7 @@ def main():
                 results = model.predict(img, imgsz = 1440, verbose=False, conf= args.conf)
                 result = results[0]
                 box_classes = [result.names[int(x)] for x in result.boxes.cls]
-                egg_xy = [x.numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
+                egg_xy = [x.cpu().numpy().astype(np.int32) for i,x in enumerate(result.boxes.xyxy) if box_classes[i] == 'egg']
                 tmp_numeggs.append(len(egg_xy))
                 tmp_filenames.append(impath.name)
                 # annotate if needed