Fix kitti evaluation bug

mmdet3d/core/bbox/box_torch_ops.py

@@ -21,9 +21,9 @@ def corners_nd(dims, origin=0.5):
             where x0 < x1, y0 < y1, z0 < z1
     """
     ndim = int(dims.shape[1])
-    corners_norm = np.stack(
-        np.unravel_index(np.arange(2**ndim), [2] * ndim),
-        axis=1).astype(dims.dtype)
+    corners_norm = torch.from_numpy(
+        np.stack(np.unravel_index(np.arange(2**ndim), [2] * ndim), axis=1)).to(
+            device=dims.device, dtype=dims.dtype)
     # now corners_norm has format: (2d) x0y0, x0y1, x1y0, x1y1
     # (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1
     # so need to convert to a format which is convenient to do other computing.
@@ -34,7 +34,7 @@ def corners_nd(dims, origin=0.5):
         corners_norm = corners_norm[[0, 1, 3, 2]]
     elif ndim == 3:
         corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]]
-    corners_norm = corners_norm - np.array(origin, dtype=dims.dtype)
+    corners_norm = corners_norm - dims.new_tensor(origin)
     corners = dims.reshape([-1, 1, ndim]) * corners_norm.reshape(
         [1, 2**ndim, ndim])
     return corners

mmdet3d/datasets/kitti_dataset.py

@@ -44,8 +44,7 @@ class KittiDataset(torch_data.Dataset):
         self.pcd_limit_range = [0, -40, -3, 70.4, 40, 0.0]
 
         self.ann_file = ann_file
-        with open(ann_file, 'rb') as f:
-            self.kitti_infos = mmcv.load(f)
+        self.kitti_infos = mmcv.load(ann_file)
 
         # set group flag for the sampler
         if not self.test_mode:
@@ -284,7 +283,7 @@ class KittiDataset(torch_data.Dataset):
             result_files = self.bbox2result_kitti(outputs, self.class_names,
                                                   pklfile_prefix,
                                                   submission_prefix)
-        return result_files
+        return result_files, tmp_dir
 
     def evaluate(self,
                  results,
@@ -321,7 +320,7 @@ class KittiDataset(torch_data.Dataset):
 
         if tmp_dir is not None:
             tmp_dir.cleanup()
-        return ap_dict
+        return ap_dict, tmp_dir
 
     def bbox2result_kitti(self,
                           net_outputs,
@@ -332,7 +331,7 @@ class KittiDataset(torch_data.Dataset):
             mmcv.mkdir_or_exist(submission_prefix)
 
         det_annos = []
-        print('Converting prediction to KITTI format')
+        print('\nConverting prediction to KITTI format')
         for idx, pred_dicts in enumerate(
                 mmcv.track_iter_progress(net_outputs)):
             annos = []