enhance unittest for yolo_box (#33070)

python/paddle/fluid/tests/unittests/test_yolo_box_op.py

@@ -36,7 +36,8 @@ def YoloBox(x, img_size, attrs):
     clip_bbox = attrs['clip_bbox']
     scale_x_y = attrs['scale_x_y']
     bias_x_y = -0.5 * (scale_x_y - 1.)
-    input_size = downsample * h
+    input_h = downsample * h
+    input_w = downsample * w
 
     x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
 
@@ -50,7 +51,7 @@ def YoloBox(x, img_size, attrs):
 
     anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
     anchors_s = np.array(
-        [(an_w / input_size, an_h / input_size) for an_w, an_h in anchors])
+        [(an_w / input_w, an_h / input_h) for an_w, an_h in anchors])
     anchor_w = anchors_s[:, 0:1].reshape((1, an_num, 1, 1))
     anchor_h = anchors_s[:, 1:2].reshape((1, an_num, 1, 1))
     pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
@@ -191,5 +192,19 @@ class TestYoloBoxStatic(unittest.TestCase):
         assert boxes is not None and scores is not None
 
 
+class TestYoloBoxOpHW(TestYoloBoxOp):
+    def initTestCase(self):
+        self.anchors = [10, 13, 16, 30, 33, 23]
+        an_num = int(len(self.anchors) // 2)
+        self.batch_size = 32
+        self.class_num = 2
+        self.conf_thresh = 0.5
+        self.downsample = 32
+        self.clip_bbox = False
+        self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 9)
+        self.imgsize_shape = (self.batch_size, 2)
+        self.scale_x_y = 1.
+
+
 if __name__ == "__main__":
     unittest.main()