rename bbox_ops/mask_ops to bbox/mask

mmdet/core/__init__.py

 from .anchor import *  # noqa: F401, F403
-from .bbox_ops import *  # noqa: F401, F403
-from .mask_ops import *  # noqa: F401, F403
+from .bbox import *  # noqa: F401, F403
+from .mask import *  # noqa: F401, F403
 from .losses import *  # noqa: F401, F403
 from .eval import *  # noqa: F401, F403
 from .parallel import *  # noqa: F401, F403

mmdet/core/anchor/anchor_target.py

 import torch
 
-from ..bbox_ops import bbox_assign, bbox2delta, bbox_sampling
+from ..bbox import bbox_assign, bbox2delta, bbox_sampling
 from ..utils import multi_apply
 
 

mmdet/core/bbox_ops/sampling.py → mmdet/core/bbox/sampling.py

@@ -5,6 +5,11 @@ from .geometry import bbox_overlaps
 
 
 def random_choice(gallery, num):
+    """Random select some elements from the gallery.
+
+    It seems that Pytorch's implementation is slower than numpy so we use numpy
+    to randperm the indices.
+    """
     assert len(gallery) >= num
     if isinstance(gallery, list):
         gallery = np.array(gallery)
@@ -12,9 +17,7 @@ def random_choice(gallery, num):
     np.random.shuffle(cands)
     rand_inds = cands[:num]
     if not isinstance(gallery, np.ndarray):
-        rand_inds = torch.from_numpy(rand_inds).long()
-        if gallery.is_cuda:
-            rand_inds = rand_inds.cuda(gallery.get_device())
+        rand_inds = torch.from_numpy(rand_inds).long().to(gallery.device)
     return gallery[rand_inds]
 
 

mmdet/core/post_processing/merge_augs.py

@@ -3,7 +3,7 @@ import torch
 import numpy as np
 
 from mmdet.ops import nms
-from ..bbox_ops import bbox_mapping_back
+from ..bbox import bbox_mapping_back
 
 
 def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):

mmdet/datasets/transforms.py

@@ -2,7 +2,7 @@ import mmcv
 import numpy as np
 import torch
 
-from mmdet.core.mask_ops import segms
+from mmdet.core.mask import segms
 
 __all__ = [
     'ImageTransform', 'BboxTransform', 'PolyMaskTransform', 'Numpy2Tensor'