[Fix] Deprecating old type alias due to new version of numpy (#2339)

* update type alias

* fix UT

data/s3dis/indoor3d_util.py

@@ -50,5 +50,5 @@ def export(anno_path, out_filename):
     data_label[:, 0:3] -= xyz_min
 
     np.save(f'{out_filename}_point.npy', data_label[:, :6].astype(np.float32))
-    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int))
-    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int))
+    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int64))
+    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int64))

docs/en/advanced_guides/datasets/s3dis_sem_seg.md

@@ -86,8 +86,8 @@ def export(anno_path, out_filename):
     data_label[:, 0:3] -= xyz_min
 
     np.save(f'{out_filename}_point.npy', data_label[:, :6].astype(np.float32))
-    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int))
-    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int))
+    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int64))
+    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int64))
 
 ```
 

docs/zh_cn/advanced_guides/datasets/s3dis_sem_seg.md

@@ -89,8 +89,8 @@ def export(anno_path, out_filename):
     data_label[:, 0:3] -= xyz_min
 
     np.save(f'{out_filename}_point.npy', data_label[:, :6].astype(np.float32))
-    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int))
-    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int))
+    np.save(f'{out_filename}_sem_label.npy', data_label[:, 6].astype(np.int64))
+    np.save(f'{out_filename}_ins_label.npy', data_label[:, 7].astype(np.int64))
 
 ```
 

mmdet3d/datasets/s3dis_dataset.py

@@ -75,7 +75,7 @@ class S3DISDataset(Det3DDataset):
         seg_valid_cat_ids = self.METAINFO['seg_valid_class_ids']
         neg_label = len(seg_valid_cat_ids)
         seg_label_mapping = np.ones(
-            seg_max_cat_id + 1, dtype=np.int) * neg_label
+            seg_max_cat_id + 1, dtype=np.int64) * neg_label
         for cls_idx, cat_id in enumerate(seg_valid_cat_ids):
             seg_label_mapping[cat_id] = cls_idx
         self.seg_label_mapping = seg_label_mapping

mmdet3d/datasets/scannet_dataset.py

@@ -80,7 +80,7 @@ class ScanNetDataset(Det3DDataset):
         seg_valid_cat_ids = self.METAINFO['seg_valid_class_ids']
         neg_label = len(seg_valid_cat_ids)
         seg_label_mapping = np.ones(
-            seg_max_cat_id + 1, dtype=np.int) * neg_label
+            seg_max_cat_id + 1, dtype=np.int64) * neg_label
         for cls_idx, cat_id in enumerate(seg_valid_cat_ids):
             seg_label_mapping[cat_id] = cls_idx
         self.seg_label_mapping = seg_label_mapping

mmdet3d/datasets/seg3d_dataset.py

@@ -203,7 +203,7 @@ class Seg3DDataset(BaseDataset):
         seg_valid_cat_ids = self.METAINFO['seg_valid_class_ids']
         neg_label = len(seg_valid_cat_ids)
         seg_label_mapping = np.ones(
-            seg_max_cat_id + 1, dtype=np.int) * neg_label
+            seg_max_cat_id + 1, dtype=np.int64) * neg_label
         for cls_idx, cat_id in enumerate(seg_valid_cat_ids):
             seg_label_mapping[cat_id] = cls_idx
         return seg_label_mapping

mmdet3d/datasets/transforms/transforms_3d.py

@@ -878,7 +878,7 @@ class ObjectRangeFilter(BaseTransform):
         # using mask to index gt_labels_3d will cause bug when
         # len(gt_labels_3d) == 1, where mask=1 will be interpreted
         # as gt_labels_3d[1] and cause out of index error
-        gt_labels_3d = gt_labels_3d[mask.numpy().astype(np.bool)]
+        gt_labels_3d = gt_labels_3d[mask.numpy().astype(bool)]
 
         # limit rad to [-pi, pi]
         gt_bboxes_3d.limit_yaw(offset=0.5, period=2 * np.pi)
@@ -981,7 +981,7 @@ class ObjectNameFilter(BaseTransform):
         """
         gt_labels_3d = input_dict['gt_labels_3d']
         gt_bboxes_mask = np.array([n in self.labels for n in gt_labels_3d],
-                                  dtype=np.bool_)
+                                  dtype=bool)
         input_dict['gt_bboxes_3d'] = input_dict['gt_bboxes_3d'][gt_bboxes_mask]
         input_dict['gt_labels_3d'] = input_dict['gt_labels_3d'][gt_bboxes_mask]
 
@@ -1954,7 +1954,7 @@ class RandomCrop3D(RandomCrop):
     - gt_bboxes (np.float32) (optional)
     - gt_bboxes_labels (np.int64) (optional)
     - gt_masks (BitmapMasks | PolygonMasks) (optional)
-    - gt_ignore_flags (np.bool) (optional)
+    - gt_ignore_flags (bool) (optional)
     - gt_seg_map (np.uint8) (optional)
 
     Modified Keys:

mmdet3d/evaluation/functional/instance_seg_eval.py

@@ -29,7 +29,7 @@ def aggregate_predictions(masks, labels, scores, valid_class_ids):
             # match pred_instance['filename'] from assign_instances_for_scan
             file_name = f'{id}_{i}'
             info[file_name] = dict()
-            info[file_name]['mask'] = (mask == i).astype(np.int)
+            info[file_name]['mask'] = (mask == i).astype(np.int64)
             info[file_name]['label_id'] = valid_class_ids[label[i]]
             info[file_name]['conf'] = score[i]
         infos.append(info)

mmdet3d/evaluation/functional/scannet_utils/evaluate_semantic_instance.py

@@ -59,7 +59,7 @@ def evaluate_matches(matches, class_labels, options):
 
                     cur_true = np.ones(len(gt_instances))
                     cur_score = np.ones(len(gt_instances)) * (-float('inf'))
-                    cur_match = np.zeros(len(gt_instances), dtype=np.bool)
+                    cur_match = np.zeros(len(gt_instances), dtype=bool)
                     # collect matches
                     for (gti, gt) in enumerate(gt_instances):
                         found_match = False

mmdet3d/evaluation/functional/seg_eval.py

@@ -88,8 +88,8 @@ def seg_eval(gt_labels, seg_preds, label2cat, ignore_index, logger=None):
 
     hist_list = []
     for i in range(len(gt_labels)):
-        gt_seg = gt_labels[i].astype(np.int)
-        pred_seg = seg_preds[i].astype(np.int)
+        gt_seg = gt_labels[i].astype(np.int64)
+        pred_seg = seg_preds[i].astype(np.int64)
 
         # filter out ignored points
         pred_seg[gt_seg == ignore_index] = -1

mmdet3d/evaluation/metrics/seg_metric.py

@@ -87,7 +87,7 @@ class SegMetric(BaseMetric):
         ignore_index = self.dataset_meta['ignore_index']
         # need to map network output to original label idx
         cat2label = np.zeros(len(self.dataset_meta['label2cat'])).astype(
-            np.int)
+            np.int64)
         for original_label, output_idx in self.dataset_meta['label2cat'].items(
         ):
             if output_idx != ignore_index:
@@ -95,7 +95,7 @@ class SegMetric(BaseMetric):
 
         for i, (eval_ann, result) in enumerate(results):
             sample_idx = eval_ann['point_cloud']['lidar_idx']
-            pred_sem_mask = result['semantic_mask'].numpy().astype(np.int)
+            pred_sem_mask = result['semantic_mask'].numpy().astype(np.int64)
             pred_label = cat2label[pred_sem_mask]
             curr_file = f'{submission_prefix}/{sample_idx}.txt'
             np.savetxt(curr_file, pred_label, fmt='%d')

tests/test_evaluation/test_functional/test_instance_seg_eval.py

@@ -21,9 +21,9 @@ def test_instance_seg_eval():
     pred_instance_labels = []
     pred_instance_scores = []
     for n_points, gt_labels in zip(n_points_list, gt_labels_list):
-        gt_instance_mask = np.ones(n_points, dtype=np.int) * -1
-        gt_semantic_mask = np.ones(n_points, dtype=np.int) * -1
-        pred_instance_mask = np.ones(n_points, dtype=np.int) * -1
+        gt_instance_mask = np.ones(n_points, dtype=np.int64) * -1
+        gt_semantic_mask = np.ones(n_points, dtype=np.int64) * -1
+        pred_instance_mask = np.ones(n_points, dtype=np.int64) * -1
         labels = []
         scores = []
         for i, gt_label in enumerate(gt_labels):

tests/test_evaluation/test_metrics/test_instance_seg_metric.py

@@ -16,8 +16,8 @@ class TestInstanceSegMetric(unittest.TestCase):
 
         n_points = 3300
         gt_labels = [0, 0, 0, 0, 0, 0, 14, 14, 2, 1]
-        gt_instance_mask = np.ones(n_points, dtype=np.int) * -1
-        gt_semantic_mask = np.ones(n_points, dtype=np.int) * -1
+        gt_instance_mask = np.ones(n_points, dtype=np.int64) * -1
+        gt_semantic_mask = np.ones(n_points, dtype=np.int64) * -1
         for i, gt_label in enumerate(gt_labels):
             begin = i * 300
             end = begin + 300
@@ -31,7 +31,7 @@ class TestInstanceSegMetric(unittest.TestCase):
         results_dict = dict()
         n_points = 3300
         gt_labels = [0, 0, 0, 0, 0, 0, 14, 14, 2, 1]
-        pred_instance_mask = np.ones(n_points, dtype=np.int) * -1
+        pred_instance_mask = np.ones(n_points, dtype=np.int64) * -1
         labels = []
         scores = []
         for i, gt_label in enumerate(gt_labels):

tests/test_structures/test_ops/test_box_np_ops.py

@@ -72,12 +72,12 @@ def test_points_in_convex_polygon_jit():
                          [[1.0, 0.0], [1.0, 1.0], [0.5, 1.0], [0.0, 1.0]],
                          [[1.0, 0.0], [0.0, 1.0], [-1.0, 0.0], [0.0, -1.0]]])
     res = points_in_convex_polygon_jit(points, polygons)
-    expected_res = np.array([[1, 0, 1], [0, 0, 0], [0, 1, 0]]).astype(np.bool)
+    expected_res = np.array([[1, 0, 1], [0, 0, 0], [0, 1, 0]]).astype(bool)
     assert np.allclose(res, expected_res)
 
     polygons = np.array([[[0.0, 0.0], [0.0, 1.0], [0.5, 0.5], [1.0, 0.0]],
                          [[0.0, 1.0], [1.0, 1.0], [1.0, 0.5], [1.0, 0.0]],
                          [[1.0, 0.0], [0.0, -1.0], [-1.0, 0.0], [0.0, 1.1]]])
     res = points_in_convex_polygon_jit(points, polygons, clockwise=True)
-    expected_res = np.array([[1, 0, 1], [0, 0, 1], [0, 1, 0]]).astype(np.bool)
+    expected_res = np.array([[1, 0, 1], [0, 0, 1], [0, 1, 0]]).astype(bool)
     assert np.allclose(res, expected_res)

tools/dataset_converters/kitti_data_utils.py

@@ -594,9 +594,9 @@ def add_difficulty_to_annos(info):
     occlusion = annos['occluded']
     truncation = annos['truncated']
     diff = []
-    easy_mask = np.ones((len(dims), ), dtype=np.bool)
-    moderate_mask = np.ones((len(dims), ), dtype=np.bool)
-    hard_mask = np.ones((len(dims), ), dtype=np.bool)
+    easy_mask = np.ones((len(dims), ), dtype=bool)
+    moderate_mask = np.ones((len(dims), ), dtype=bool)
+    hard_mask = np.ones((len(dims), ), dtype=bool)
     i = 0
     for h, o, t in zip(height, occlusion, truncation):
         if o > max_occlusion[0] or h <= min_height[0] or t > max_trunc[0]:

tools/dataset_converters/s3dis_data_utils.py

@@ -78,8 +78,10 @@ class S3DISData(object):
                 f'{self.split}_{sample_idx}_sem_label.npy')
 
             points = np.load(pts_filename).astype(np.float32)
-            pts_instance_mask = np.load(pts_instance_mask_path).astype(np.int)
-            pts_semantic_mask = np.load(pts_semantic_mask_path).astype(np.int)
+            pts_instance_mask = np.load(pts_instance_mask_path).astype(
+                np.int64)
+            pts_semantic_mask = np.load(pts_semantic_mask_path).astype(
+                np.int64)
 
             mmengine.mkdir_or_exist(osp.join(self.root_dir, 'points'))
             mmengine.mkdir_or_exist(osp.join(self.root_dir, 'instance_mask'))
@@ -180,8 +182,8 @@ class S3DISSegData(object):
                                  12])  # used for seg task
         self.ignore_index = len(self.cat_ids)
 
-        self.cat_id2class = np.ones((self.all_ids.shape[0],), dtype=np.int) * \
-            self.ignore_index
+        self.cat_id2class = np.ones(
+            (self.all_ids.shape[0], ), dtype=np.int64) * self.ignore_index
         for i, cat_id in enumerate(self.cat_ids):
             self.cat_id2class[cat_id] = i
 

tools/dataset_converters/scannet_data_utils.py

@@ -232,8 +232,8 @@ class ScanNetSegData(object):
         ])  # used for seg task
         self.ignore_index = len(self.cat_ids)
 
-        self.cat_id2class = np.ones((self.all_ids.shape[0],), dtype=np.int) * \
-            self.ignore_index
+        self.cat_id2class = np.ones(
+            (self.all_ids.shape[0], ), dtype=np.int64) * self.ignore_index
         for i, cat_id in enumerate(self.cat_ids):
             self.cat_id2class[cat_id] = i