merge master

tests/test_indoor_sample.py

 import numpy as np
 
-from mmdet3d.datasets.pipelines.indoor_sample import PointSample
+from mmdet3d.datasets.pipelines import PointSample
 
 
 def test_indoor_sample():

tests/test_roi_extractors.py

+import pytest
+import torch
+
+from mmdet3d.models.roi_heads.roi_extractors import Single3DRoIAwareExtractor
+
+
+def test_single_roiaware_extractor():
+    if not torch.cuda.is_available():
+        pytest.skip('test requires GPU and torch+cuda')
+
+    roi_layer_cfg = dict(
+        type='RoIAwarePool3d', out_size=4, max_pts_per_voxel=128, mode='max')
+
+    self = Single3DRoIAwareExtractor(roi_layer=roi_layer_cfg)
+    feats = torch.tensor(
+        [[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
+         [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
+         [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9],
+         [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]],
+        dtype=torch.float32).cuda()
+    coordinate = feats.clone()
+    batch_inds = torch.zeros(feats.shape[0]).cuda()
+    rois = torch.tensor([[0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 0.3],
+                         [0, -10.0, 23.0, 16.0, 10, 20, 20, 0.5]],
+                        dtype=torch.float32).cuda()
+    # test forward
+    pooled_feats = self(feats, coordinate, batch_inds, rois)
+    assert pooled_feats.shape == torch.Size([2, 4, 4, 4, 3])
+    assert torch.allclose(pooled_feats.sum(),
+                          torch.tensor(51.100).cuda(), 1e-3)

tools/data_converter/sunrgbd_data_utils.py

@@ -146,9 +146,9 @@ class SUNRGBDData(object):
             pc_upright_depth = self.get_depth(sample_idx)
             pc_upright_depth_subsampled = random_sampling(
                 pc_upright_depth, SAMPLE_NUM)
-            np.savez_compressed(
-                os.path.join(self.root_dir, 'lidar', f'{sample_idx:06d}.npz'),
-                pc=pc_upright_depth_subsampled)
+            np.save(
+                os.path.join(self.root_dir, 'lidar', f'{sample_idx:06d}.npy'),
+                pc_upright_depth_subsampled)
 
             info = dict()
             pc_info = {'num_features': 6, 'lidar_idx': sample_idx}