[fix] from img_size (w, h) to image_shape (h, w)

src/netspresso_trainer/postprocessors/detection.py

@@ -33,7 +33,7 @@ def rtdetr_decode(pred, original_shape, num_top_queries=300, score_thresh=0.0):
 
     num_classes = logits.shape[-1]
     h, w = original_shape[1], original_shape[2]
-    boxes = transform_bbox(boxes, "cxcywhn -> xyxy", img_size=(w, h))
+    boxes = transform_bbox(boxes, "cxcywhn -> xyxy", image_shape=(h, w))
 
     scores = torch.sigmoid(logits)
     scores, index = torch.topk(scores.flatten(1), num_top_queries, axis=-1)

src/netspresso_trainer/utils/bbox_utils.py

@@ -10,7 +10,7 @@
 
 def transform_bbox(bboxes: Union[Tensor, Proxy],
                    indicator="xywh -> xyxy",
-                   img_size: Optional[Union[int, Tuple[int, int]]]=None):
+                   image_shape: Optional[Union[int, Tuple[int, int]]]=None):
     def is_normalized(fmt: str) -> bool:
         return fmt.endswith('n')
 
@@ -21,16 +21,16 @@ def is_normalized(fmt: str) -> bool:
     assert out_type in VALID_OUT_TYPE, f"Invalid out_type: '{out_type}'. Must be one of {VALID_OUT_TYPE}."
 
     if is_normalized(in_type):
-        assert img_size is not None, f"img_size is required for normalized conversion: {indicator}"
-        if isinstance(img_size, int):
-            img_width = img_height = img_size
+        assert image_shape is not None, f"image_shape is required for normalized conversion: {indicator}"
+        if isinstance(image_shape, int):
+            img_height = img_width = image_shape
         else:
-            img_width, img_height = img_size
-            assert isinstance(img_width, int) and isinstance(img_height, int), \
-                f"Invalid type: (width: {type(img_width)}, height: {type(img_height)}. Must be (int, int))"
+            img_height, img_width = image_shape
+            assert isinstance(img_height, int) and isinstance(img_width, int), \
+                f"Invalid type: (height: {type(img_height)}, width: {type(img_width)}. Must be (int, int))"
         in_type = in_type[:-1]
     else:
-        img_width = img_height = 1.0
+        img_height = img_width = 1.0
 
     if in_type == "xyxy":
         x_min, y_min, x_max, y_max = bboxes.unbind(-1)
@@ -53,16 +53,16 @@ def is_normalized(fmt: str) -> bool:
     assert (y_max >= y_min).all(), "Invalid box: y_max < y_min"
 
     if is_normalized(out_type):
-        assert img_size is not None, f"img_size is required for normalized conversion: {indicator}"
-        if isinstance(img_size, int):
-            img_width = img_height = img_size
+        assert image_shape is not None, f"img_size is required for normalized conversion: {indicator}"
+        if isinstance(image_shape, int):
+            img_height = img_width = image_shape
         else:
-            img_width, img_height = img_size
-            assert isinstance(img_width, int) and isinstance(img_height, int), \
-                f"Invalid type: (width: {type(img_width)}, height: {type(img_height)}. Must be (int, int))"
+            img_height, img_width = image_shape
+            assert isinstance(img_height, int) and isinstance(img_width, int), \
+                f"Invalid type: (height: {type(img_height)}, width: {type(img_width)}. Must be (int, int))"
         out_type = out_type[:-1]
     else:
-        img_width = img_height = 1.0
+        img_height = img_width = 1.0
 
     x_min /= img_width
     y_min /= img_height