Fixed running custom onnx models.

extras/easypose/detection.py

@@ -21,18 +21,27 @@ class RTMDet(BaseModel):
 
     def preprocess(self, image: np.ndarray):
         th, tw = self.input_shape[2:]
-        image, self.dx, self.dy, self.scale = letterbox(image, (tw, th))
-        tensor = (image - np.array((103.53, 116.28, 123.675))) / np.array((57.375, 57.12, 58.395))
-        tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2)).astype(np.float32)
+        tensor, self.dx, self.dy, self.scale = letterbox(
+            image, (tw, th), fill_value=114
+        )
+        tensor -= np.array((123.675, 116.28, 103.53))
+        tensor /= np.array((58.395, 57.12, 57.375))
+        tensor = tensor[..., ::-1]
+        tensor = (
+            np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2)).astype(np.float32)
+        )
         return tensor
 
     def postprocess(self, tensor: List[np.ndarray]):
         boxes = tensor[0]
+        classes = tensor[1]
         boxes = np.squeeze(boxes, axis=0)
-        boxes[..., [4, 5]] = boxes[..., [5, 4]]
+        classes = np.squeeze(classes, axis=0)
+        classes = np.expand_dims(classes, axis=-1)
+        boxes = np.concatenate([boxes, classes], axis=-1)
 
         boxes = nms(boxes, self.iou_threshold, self.conf_threshold)
-        
+
         if boxes.shape[0] == 0:
             return boxes