Refine KITTI evaluation code.

paddle3d/thirdparty/kitti_object_eval_python/eval.py

@@ -362,22 +362,22 @@ def fused_compute_statistics(overlaps,
         dc_num += dc_nums[i]
 
 
-def calculate_iou_partly(gt_annos,
-                         dt_annos,
+def calculate_iou_partly(dt_annos,
+                         gt_annos,
                          metric,
                          num_parts=50,
                          z_axis=1,
                          z_center=1.0):
     """fast iou algorithm. this function can be used independently to
     do result analysis.
     Args:
-        gt_annos: dict, must from get_label_annos() in kitti_common.py
         dt_annos: dict, must from get_label_annos() in kitti_common.py
+        gt_annos: dict, must from get_label_annos() in kitti_common.py
         metric: eval type. 0: bbox, 1: bev, 2: 3d
         num_parts: int. a parameter for fast calculate algorithm
         z_axis: height axis. kitti camera use 1, lidar use 2.
     """
-    assert len(gt_annos) == len(dt_annos)
+    assert len(dt_annos) == len(gt_annos)
     total_dt_num = np.stack([len(a["name"]) for a in dt_annos], 0)
     total_gt_num = np.stack([len(a["name"]) for a in gt_annos], 0)
     num_examples = len(gt_annos)
@@ -392,7 +392,7 @@ def calculate_iou_partly(gt_annos,
         if metric == 0:
             gt_boxes = np.concatenate([a["bbox"] for a in gt_annos_part], 0)
             dt_boxes = np.concatenate([a["bbox"] for a in dt_annos_part], 0)
-            overlap_part = image_box_overlap(gt_boxes, dt_boxes)
+            overlap_part = image_box_overlap(dt_boxes, gt_boxes)
         elif metric == 1:
             loc = np.concatenate(
                 [a["location"][:, bev_axes] for a in gt_annos_part], 0)
@@ -408,8 +408,8 @@ def calculate_iou_partly(gt_annos,
             rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)
             dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
-            overlap_part = bev_box_overlap(gt_boxes,
-                                           dt_boxes).astype(np.float64)
+            overlap_part = bev_box_overlap(dt_boxes,
+                                           gt_boxes).astype(np.float64)
         elif metric == 2:
             loc = np.concatenate([a["location"] for a in gt_annos_part], 0)
             dims = np.concatenate([a["dimensions"] for a in gt_annos_part], 0)
@@ -422,7 +422,7 @@ def calculate_iou_partly(gt_annos,
             dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
                                       axis=1)
             overlap_part = d3_box_overlap(
-                gt_boxes, dt_boxes, z_axis=z_axis,
+                dt_boxes, gt_boxes, z_axis=z_axis,
                 z_center=z_center).astype(np.float64)
         else:
             raise ValueError("unknown metric")
@@ -431,21 +431,21 @@ def calculate_iou_partly(gt_annos,
     overlaps = []
     example_idx = 0
     for j, num_part in enumerate(split_parts):
-        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
-        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
+        # gt_annos_part = gt_annos[example_idx:example_idx + num_part]
+        # dt_annos_part = dt_annos[example_idx:example_idx + num_part]
         gt_num_idx, dt_num_idx = 0, 0
         for i in range(num_part):
             gt_box_num = total_gt_num[example_idx + i]
             dt_box_num = total_dt_num[example_idx + i]
             overlaps.append(
-                parted_overlaps[j][gt_num_idx:gt_num_idx +
-                                   gt_box_num, dt_num_idx:dt_num_idx +
-                                   dt_box_num])
+                parted_overlaps[j][dt_num_idx:dt_num_idx +
+                                   dt_box_num, gt_num_idx:gt_num_idx +
+                                   gt_box_num])
             gt_num_idx += gt_box_num
             dt_num_idx += dt_box_num
         example_idx += num_part
 
-    return overlaps, parted_overlaps, total_gt_num, total_dt_num
+    return overlaps, parted_overlaps, total_dt_num, total_gt_num
 
 
 def _prepare_data(gt_annos, dt_annos, current_class, difficulty):