Fix np.long for windows (#1270)

* change np.long to np.int64

* change all log to int64

docs/en/tutorials/customize_dataset.md

@@ -134,10 +134,10 @@ class MyDataset(Custom3DDataset):
         if info['annos']['gt_num'] != 0:
             gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
                 np.float32)  # k, 6
-            gt_labels_3d = info['annos']['class'].astype(np.long)
+            gt_labels_3d = info['annos']['class'].astype(np.int64)
         else:
             gt_bboxes_3d = np.zeros((0, 6), dtype=np.float32)
-            gt_labels_3d = np.zeros((0, ), dtype=np.long)
+            gt_labels_3d = np.zeros((0, ), dtype=np.int64)
 
         # to target box structure
         gt_bboxes_3d = DepthInstance3DBoxes(

docs/zh_cn/tutorials/customize_dataset.md

@@ -130,10 +130,10 @@ class MyDataset(Custom3DDataset):
         if info['annos']['gt_num'] != 0:
             gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
                 np.float32)  # k, 6
-            gt_labels_3d = info['annos']['class'].astype(np.long)
+            gt_labels_3d = info['annos']['class'].astype(np.int64)
         else:
             gt_bboxes_3d = np.zeros((0, 6), dtype=np.float32)
-            gt_labels_3d = np.zeros((0, ), dtype=np.long)
+            gt_labels_3d = np.zeros((0, ), dtype=np.int64)
 
         # 转换为目标标注框的结构
         gt_bboxes_3d = DepthInstance3DBoxes(

mmdet3d/datasets/pipelines/loading.py

@@ -604,7 +604,7 @@ class LoadAnnotations3D(LoadAnnotations):
         except ConnectionError:
             mmcv.check_file_exist(pts_instance_mask_path)
             pts_instance_mask = np.fromfile(
-                pts_instance_mask_path, dtype=np.long)
+                pts_instance_mask_path, dtype=np.int64)
 
         results['pts_instance_mask'] = pts_instance_mask
         results['pts_mask_fields'].append('pts_instance_mask')
@@ -631,7 +631,7 @@ class LoadAnnotations3D(LoadAnnotations):
         except ConnectionError:
             mmcv.check_file_exist(pts_semantic_mask_path)
             pts_semantic_mask = np.fromfile(
-                pts_semantic_mask_path, dtype=np.long)
+                pts_semantic_mask_path, dtype=np.int64)
 
         results['pts_semantic_mask'] = pts_semantic_mask
         results['pts_seg_fields'].append('pts_semantic_mask')

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -356,7 +356,7 @@ class ObjectSample(object):
                 input_dict['img'] = sampled_dict['img']
 
         input_dict['gt_bboxes_3d'] = gt_bboxes_3d
-        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.long)
+        input_dict['gt_labels_3d'] = gt_labels_3d.astype(np.int64)
         input_dict['points'] = points
 
         return input_dict

mmdet3d/datasets/s3dis_dataset.py

@@ -85,10 +85,10 @@ class S3DISDataset(Custom3DDataset):
         if info['annos']['gt_num'] != 0:
             gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
                 np.float32)  # k, 6
-            gt_labels_3d = info['annos']['class'].astype(np.long)
+            gt_labels_3d = info['annos']['class'].astype(np.int64)
         else:
             gt_bboxes_3d = np.zeros((0, 6), dtype=np.float32)
-            gt_labels_3d = np.zeros((0, ), dtype=np.long)
+            gt_labels_3d = np.zeros((0, ), dtype=np.int64)
 
         # to target box structure
         gt_bboxes_3d = DepthInstance3DBoxes(

mmdet3d/datasets/scannet_dataset.py

@@ -143,10 +143,10 @@ class ScanNetDataset(Custom3DDataset):
         if info['annos']['gt_num'] != 0:
             gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
                 np.float32)  # k, 6
-            gt_labels_3d = info['annos']['class'].astype(np.long)
+            gt_labels_3d = info['annos']['class'].astype(np.int64)
         else:
             gt_bboxes_3d = np.zeros((0, 6), dtype=np.float32)
-            gt_labels_3d = np.zeros((0, ), dtype=np.long)
+            gt_labels_3d = np.zeros((0, ), dtype=np.int64)
 
         # to target box structure
         gt_bboxes_3d = DepthInstance3DBoxes(

mmdet3d/datasets/sunrgbd_dataset.py

@@ -137,10 +137,10 @@ class SUNRGBDDataset(Custom3DDataset):
         if info['annos']['gt_num'] != 0:
             gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
                 np.float32)  # k, 6
-            gt_labels_3d = info['annos']['class'].astype(np.long)
+            gt_labels_3d = info['annos']['class'].astype(np.int64)
         else:
             gt_bboxes_3d = np.zeros((0, 7), dtype=np.float32)
-            gt_labels_3d = np.zeros((0, ), dtype=np.long)
+            gt_labels_3d = np.zeros((0, ), dtype=np.int64)
 
         # to target box structure
         gt_bboxes_3d = DepthInstance3DBoxes(

tests/test_data/test_pipelines/test_augmentations/test_transforms_3d.py

@@ -88,7 +88,7 @@ def test_object_sample():
             gt_labels.append(CLASSES.index(cat))
         else:
             gt_labels.append(-1)
-    gt_labels = np.array(gt_labels, dtype=np.long)
+    gt_labels = np.array(gt_labels, dtype=np.int64)
     points = LiDARPoints(points, points_dim=4)
     input_dict = dict(
         points=points, gt_bboxes_3d=gt_bboxes_3d, gt_labels_3d=gt_labels)
@@ -176,7 +176,7 @@ def test_object_name_filter():
             gt_labels.append(CLASSES.index(cat))
         else:
             gt_labels.append(-1)
-    gt_labels = np.array(gt_labels, dtype=np.long)
+    gt_labels = np.array(gt_labels, dtype=np.int64)
     input_dict = dict(
         gt_bboxes_3d=gt_bboxes_3d.clone(), gt_labels_3d=gt_labels.copy())
 
@@ -200,9 +200,9 @@ def test_point_shuffle():
     points = np.fromfile('tests/data/scannet/points/scene0000_00.bin',
                          np.float32).reshape(-1, 6)
     ins_mask = np.fromfile('tests/data/scannet/instance_mask/scene0000_00.bin',
-                           np.long)
+                           np.int64)
     sem_mask = np.fromfile('tests/data/scannet/semantic_mask/scene0000_00.bin',
-                           np.long)
+                           np.int64)
 
     points = DepthPoints(
         points.copy(), points_dim=6, attribute_dims=dict(color=[3, 4, 5]))
@@ -244,9 +244,9 @@ def test_points_range_filter():
     points = np.fromfile('tests/data/scannet/points/scene0000_00.bin',
                          np.float32).reshape(-1, 6)
     ins_mask = np.fromfile('tests/data/scannet/instance_mask/scene0000_00.bin',
-                           np.long)
+                           np.int64)
     sem_mask = np.fromfile('tests/data/scannet/semantic_mask/scene0000_00.bin',
-                           np.long)
+                           np.int64)
 
     points = DepthPoints(
         points.copy(), points_dim=6, attribute_dims=dict(color=[3, 4, 5]))
@@ -286,7 +286,7 @@ def test_object_range_filter():
          [18.7314, -18.559, 20.6547, 6.4800, 8.6000, 3.9200, -1.0100],
          [3.7314, 42.559, -0.6547, 6.4800, 8.6000, 2.9200, 3.0100]])
     gt_bboxes_3d = LiDARInstance3DBoxes(bbox, origin=(0.5, 0.5, 0.5))
-    gt_labels_3d = np.array([0, 2, 1, 1, 2, 0], dtype=np.long)
+    gt_labels_3d = np.array([0, 2, 1, 1, 2, 0], dtype=np.int64)
 
     input_dict = dict(
         gt_bboxes_3d=gt_bboxes_3d.clone(), gt_labels_3d=gt_labels_3d.copy())

tests/test_data/test_pipelines/test_indoor_pipeline.py

@@ -61,10 +61,10 @@ def test_scannet_pipeline():
     if info['annos']['gt_num'] != 0:
         scannet_gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
             np.float32)
-        scannet_gt_labels_3d = info['annos']['class'].astype(np.long)
+        scannet_gt_labels_3d = info['annos']['class'].astype(np.int64)
     else:
         scannet_gt_bboxes_3d = np.zeros((1, 6), dtype=np.float32)
-        scannet_gt_labels_3d = np.zeros((1, ), dtype=np.long)
+        scannet_gt_labels_3d = np.zeros((1, ), dtype=np.int64)
     results['ann_info'] = dict()
     results['ann_info']['pts_instance_mask_path'] = osp.join(
         data_path, info['pts_instance_mask_path'])
@@ -294,10 +294,10 @@ def test_sunrgbd_pipeline():
     if info['annos']['gt_num'] != 0:
         gt_bboxes_3d = info['annos']['gt_boxes_upright_depth'].astype(
             np.float32)
-        gt_labels_3d = info['annos']['class'].astype(np.long)
+        gt_labels_3d = info['annos']['class'].astype(np.int64)
     else:
         gt_bboxes_3d = np.zeros((1, 7), dtype=np.float32)
-        gt_labels_3d = np.zeros((1, ), dtype=np.long)
+        gt_labels_3d = np.zeros((1, ), dtype=np.int64)
 
     # prepare input of pipeline
     results['ann_info'] = dict()

tests/test_data/test_pipelines/test_indoor_sample.py

@@ -82,7 +82,7 @@ def test_indoor_seg_sample():
         scannet_points, points_dim=6, attribute_dims=dict(color=[3, 4, 5]))
 
     scannet_pts_semantic_mask = np.fromfile(
-        './tests/data/scannet/semantic_mask/scene0000_00.bin', dtype=np.long)
+        './tests/data/scannet/semantic_mask/scene0000_00.bin', dtype=np.int64)
     scannet_results['pts_semantic_mask'] = scannet_pts_semantic_mask
 
     scannet_results = scannet_seg_class_mapping(scannet_results)
@@ -174,7 +174,7 @@ def test_indoor_seg_sample():
         s3dis_points, points_dim=6, attribute_dims=dict(color=[3, 4, 5]))
 
     s3dis_pts_semantic_mask = np.fromfile(
-        './tests/data/s3dis/semantic_mask/Area_1_office_2.bin', dtype=np.long)
+        './tests/data/s3dis/semantic_mask/Area_1_office_2.bin', dtype=np.int64)
     s3dis_results['pts_semantic_mask'] = s3dis_pts_semantic_mask
 
     s3dis_results = s3dis_patch_sample_points(s3dis_results)

tools/data_converter/s3dis_data_utils.py

@@ -208,7 +208,7 @@ class S3DISSegData(object):
             if mask.endswith('npy'):
                 mask = np.load(mask)
             else:
-                mask = np.fromfile(mask, dtype=np.long)
+                mask = np.fromfile(mask, dtype=np.int64)
         label = self.cat_id2class[mask]
         return label
 

tools/data_converter/scannet_data_utils.py

@@ -138,9 +138,9 @@ class ScanNetData(object):
                     f'{sample_idx}_sem_label.npy')
 
                 pts_instance_mask = np.load(pts_instance_mask_path).astype(
-                    np.long)
+                    np.int64)
                 pts_semantic_mask = np.load(pts_semantic_mask_path).astype(
-                    np.long)
+                    np.int64)
 
                 mmcv.mkdir_or_exist(osp.join(self.root_dir, 'instance_mask'))
                 mmcv.mkdir_or_exist(osp.join(self.root_dir, 'semantic_mask'))
@@ -260,7 +260,7 @@ class ScanNetSegData(object):
             if mask.endswith('npy'):
                 mask = np.load(mask)
             else:
-                mask = np.fromfile(mask, dtype=np.long)
+                mask = np.fromfile(mask, dtype=np.int64)
         label = self.cat_id2class[mask]
         return label