fix some problems

mmdet3d/datasets/__init__.py

@@ -7,10 +7,10 @@ from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
 from .nuscenes_dataset import NuScenesDataset
 from .pipelines import (GlobalRotScale, IndoorFlipData, IndoorGlobalRotScale,
                         IndoorLoadAnnotations3D, IndoorLoadPointsFromFile,
-                        IndoorPointsColorJitter, IndoorPointsColorNormalize,
-                        ObjectNoise, ObjectRangeFilter, ObjectSample,
-                        PointSample, PointShuffle, PointsRangeFilter,
-                        RandomFlip3D)
+                        IndoorPointSample, IndoorPointsColorJitter,
+                        IndoorPointsColorNormalize, ObjectNoise,
+                        ObjectRangeFilter, ObjectSample, PointShuffle,
+                        PointsRangeFilter, RandomFlip3D)
 
 __all__ = [
     'KittiDataset', 'GroupSampler', 'DistributedGroupSampler',
@@ -18,7 +18,7 @@ __all__ = [
     'CocoDataset', 'Kitti2DDataset', 'NuScenesDataset', 'ObjectSample',
     'RandomFlip3D', 'ObjectNoise', 'GlobalRotScale', 'PointShuffle',
     'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
-    'IndoorLoadPointsFromFile', 'IndoorPointsColorNormalize', 'PointSample',
-    'IndoorLoadAnnotations3D', 'IndoorPointsColorJitter',
+    'IndoorLoadPointsFromFile', 'IndoorPointsColorNormalize',
+    'IndoorPointSample', 'IndoorLoadAnnotations3D', 'IndoorPointsColorJitter',
     'IndoorGlobalRotScale', 'IndoorFlipData'
 ]

mmdet3d/datasets/pipelines/__init__.py

@@ -5,7 +5,7 @@ from .indoor_augment import (IndoorFlipData, IndoorGlobalRotScale,
                              IndoorPointsColorJitter)
 from .indoor_loading import (IndoorLoadAnnotations3D, IndoorLoadPointsFromFile,
                              IndoorPointsColorNormalize)
-from .indoor_sample import PointSample
+from .indoor_sample import IndoorPointSample
 from .loading import LoadMultiViewImageFromFiles, LoadPointsFromFile
 from .train_aug import (GlobalRotScale, ObjectNoise, ObjectRangeFilter,
                         ObjectSample, PointShuffle, PointsRangeFilter,
@@ -18,5 +18,6 @@ __all__ = [
     'DefaultFormatBundle', 'DefaultFormatBundle3D', 'DataBaseSampler',
     'IndoorGlobalRotScale', 'IndoorPointsColorJitter', 'IndoorFlipData',
     'MMDataBaseSampler', 'IndoorLoadPointsFromFile',
-    'IndoorPointsColorNormalize', 'IndoorLoadAnnotations3D', 'PointSample'
+    'IndoorPointsColorNormalize', 'IndoorLoadAnnotations3D',
+    'IndoorPointSample'
 ]

mmdet3d/datasets/pipelines/indoor_sample.py

@@ -4,7 +4,7 @@ from mmdet.datasets.builder import PIPELINES
 
 
 @PIPELINES.register_module()
-class PointSample(object):
+class IndoorPointSample(object):
     """Point Sample.
 
     Sampling data to a certain number.
@@ -46,7 +46,7 @@ class PointSample(object):
             return points[choices]
 
     def __call__(self, results):
-        points = results.get('points', None)
+        points = results['points']
         points, choices = self.points_random_sampling(
             points, self.num_points, return_choices=True)
         pts_instance_mask = results.get('pts_instance_mask', None)

tests/test_indoor_loading.py

@@ -65,7 +65,7 @@ def test_load_annotations3D():
     scannet_results['pts_instance_mask_path'] = osp.join(
         data_path, f'{scan_name}_ins_label.npy')
     scannet_results['pts_semantic_mask_path'] = osp.join(
-        data_path, scan_name + '_sem_label.npy')
+        data_path, f'{scan_name}_sem_label.npy')
     scannet_results['info'] = scannet_info
     scannet_results['gt_bboxes_3d'] = scannet_gt_bboxes_3d
     scannet_results['gt_labels'] = scannet_gt_labels

tests/test_indoor_sample.py

 import numpy as np
 
-from mmdet3d.datasets.pipelines import PointSample
+from mmdet3d.datasets.pipelines import IndoorPointSample
 
 
 def test_indoor_sample():
     np.random.seed(0)
-    scannet_sample_points = PointSample(5)
+    scannet_sample_points = IndoorPointSample(5)
     scannet_results = dict()
     scannet_points = np.array([[1.0719866, -0.7870435, 0.8408122, 0.9196809],
                                [1.103661, 0.81065744, 2.6616862, 2.7405548],
@@ -24,11 +24,9 @@ def test_indoor_sample():
     scannet_pts_semantic_mask = np.array([38, 1, 1, 40, 0, 40, 1, 1, 1, 0])
     scannet_results['pts_semantic_mask'] = scannet_pts_semantic_mask
     scannet_results = scannet_sample_points(scannet_results)
-    scannet_points_result = scannet_results.get('points', None)
-    scannet_instance_labels_result = scannet_results.get(
-        'pts_instance_mask', None)
-    scannet_semantic_labels_result = scannet_results.get(
-        'pts_semantic_mask', None)
+    scannet_points_result = scannet_results['points']
+    scannet_instance_labels_result = scannet_results['pts_instance_mask']
+    scannet_semantic_labels_result = scannet_results['pts_semantic_mask']
     scannet_choices = np.array([2, 8, 4, 9, 1])
     assert np.allclose(scannet_points[scannet_choices], scannet_points_result)
     assert np.all(scannet_pts_instance_mask[scannet_choices] ==
@@ -37,7 +35,7 @@ def test_indoor_sample():
                   scannet_semantic_labels_result)
 
     np.random.seed(0)
-    sunrgbd_sample_points = PointSample(5)
+    sunrgbd_sample_points = IndoorPointSample(5)
     sunrgbd_results = dict()
     sunrgbd_point_cloud = np.array(
         [[-1.8135729e-01, 1.4695230e+00, -1.2780589e+00, 7.8938007e-03],
@@ -53,6 +51,6 @@ def test_indoor_sample():
     sunrgbd_results['points'] = sunrgbd_point_cloud
     sunrgbd_results = sunrgbd_sample_points(sunrgbd_results)
     sunrgbd_choices = np.array([2, 8, 4, 9, 1])
-    sunrgbd_points_result = sunrgbd_results.get('points', None)
+    sunrgbd_points_result = sunrgbd_results['points']
     assert np.allclose(sunrgbd_point_cloud[sunrgbd_choices],
                        sunrgbd_points_result)