deploy: fb8481effdf5a0b23ff86fad414906046d7620bd

layout-non-alignment.py

@@ -12,7 +12,23 @@ Computes the extent of spatial non-alignment between elements.
 """
 
 _KWARGS_DESCRIPTION = """\
-FIXME
+Args:
+    predictions (`list` of `list` of `float`): A list of lists of floats representing normalized `ltrb`-format bounding boxes.
+    gold_labels (`list` of `list` of `int`): A list of lists of integers representing class labels.
+    canvas_width (`int`, *optional*): Width of the canvas in pixels. Can be provided at initialization or during computation.
+    canvas_height (`int`, *optional*): Height of the canvas in pixels. Can be provided at initialization or during computation.
+
+Returns:
+    float: The extent of spatial non-alignment between elements. Lower values indicate better alignment. Evaluates alignment across six aspects: left edge, top edge, center X, center Y, right edge, and bottom edge.
+
+Examples:
+    >>> import evaluate
+    >>> metric = evaluate.load("creative-graphic-design/layout-non-alignment")
+    >>> # Normalized bounding boxes (left, top, right, bottom)
+    >>> predictions = [[[0.1, 0.1, 0.3, 0.3], [0.1, 0.4, 0.3, 0.6]]]  # Left-aligned elements
+    >>> gold_labels = [[1, 2]]
+    >>> result = metric.compute(predictions=predictions, gold_labels=gold_labels, canvas_width=512, canvas_height=512)
+    >>> print(f"Non-alignment score: {result:.4f}")
 """
 
 _CITATION = """\
@@ -40,8 +56,8 @@ _CITATION = """\
 class LayoutNonAlignment(evaluate.Metric):
     def __init__(
         self,
-        canvas_width: int,
-        canvas_height: int,
+        canvas_width: int | None = None,
+        canvas_height: int | None = None,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
@@ -77,20 +93,24 @@ class LayoutNonAlignment(evaluate.Metric):
         return -math.log(1 - x, 10)
 
     def get_rid_of_invalid(
-        self, predictions: npt.NDArray[np.float64], gold_labels: npt.NDArray[np.int64]
+        self,
+        predictions: npt.NDArray[np.float64],
+        gold_labels: npt.NDArray[np.int64],
+        canvas_width: int,
+        canvas_height: int,
     ) -> npt.NDArray[np.int64]:
         assert len(predictions) == len(gold_labels)
 
-        w = self.canvas_width / 100
-        h = self.canvas_height / 100
+        w = canvas_width / 100
+        h = canvas_height / 100
 
         for i, prediction in enumerate(predictions):
             for j, b in enumerate(prediction):
                 xl, yl, xr, yr = b
                 xl = max(0, xl)
                 yl = max(0, yl)
-                xr = min(self.canvas_width, xr)
-                yr = min(self.canvas_height, yr)
+                xr = min(canvas_width, xr)
+                yr = min(canvas_height, yr)
                 if abs((xr - xl) * (yr - yl)) < w * h * 10:
                     if gold_labels[i, j]:
                         gold_labels[i, j] = 0
@@ -101,15 +121,32 @@ class LayoutNonAlignment(evaluate.Metric):
         *,
         predictions: Union[npt.NDArray[np.float64], List[List[float]]],
         gold_labels: Union[npt.NDArray[np.int64], List[int]],
+        canvas_width: int | None = None,
+        canvas_height: int | None = None,
     ) -> float:
+        # パラメータの優先順位処理
+        canvas_width = canvas_width if canvas_width is not None else self.canvas_width
+        canvas_height = (
+            canvas_height if canvas_height is not None else self.canvas_height
+        )
+
+        if canvas_width is None or canvas_height is None:
+            raise ValueError(
+                "canvas_width and canvas_height must be provided either "
+                "at initialization or during computation"
+            )
+
         predictions = np.array(predictions)
         gold_labels = np.array(gold_labels)
 
-        predictions[:, :, ::2] *= self.canvas_width
-        predictions[:, :, 1::2] *= self.canvas_height
+        predictions[:, :, ::2] *= canvas_width
+        predictions[:, :, 1::2] *= canvas_height
 
         gold_labels = self.get_rid_of_invalid(
-            predictions=predictions, gold_labels=gold_labels
+            predictions=predictions,
+            gold_labels=gold_labels,
+            canvas_width=canvas_width,
+            canvas_height=canvas_height,
         )
 
         metrics: float = 0.0
@@ -121,10 +158,10 @@ class LayoutNonAlignment(evaluate.Metric):
             theda = []
             for mb in mask_box:
                 pos = copy.deepcopy(mb)
-                pos[0] /= self.canvas_width
-                pos[2] /= self.canvas_width
-                pos[1] /= self.canvas_height
-                pos[3] /= self.canvas_height
+                pos[0] /= canvas_width
+                pos[2] /= canvas_width
+                pos[1] /= canvas_height
+                pos[3] /= canvas_height
                 theda.append(
                     [
                         pos[0],