Merge branch 'dev' into single-stage

tools/configs/r50_fpn_frcnn_1x.py → configs/r50_fpn_frcnn_1x.py

@@ -79,7 +79,7 @@ test_cfg = dict(
     rcnn=dict(score_thr=0.05, max_per_img=100, nms_thr=0.5))
 # dataset settings
 dataset_type = 'CocoDataset'
-data_root = '../data/coco/'
+data_root = 'data/coco/'
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 data = dict(
@@ -140,7 +140,7 @@ log_config = dict(
 # runtime settings
 total_epochs = 12
 device_ids = range(8)
-dist_params = dict(backend='gloo')
+dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/fpn_faster_rcnn_r50_1x'
 load_from = None

tools/configs/r50_fpn_maskrcnn_1x.py → configs/r50_fpn_maskrcnn_1x.py

@@ -92,7 +92,7 @@ test_cfg = dict(
         score_thr=0.05, max_per_img=100, nms_thr=0.5, mask_thr_binary=0.5))
 # dataset settings
 dataset_type = 'CocoDataset'
-data_root = '../data/coco/'
+data_root = 'data/coco/'
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 data = dict(
@@ -153,7 +153,7 @@ log_config = dict(
 # runtime settings
 total_epochs = 12
 device_ids = range(8)
-dist_params = dict(backend='gloo')
+dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/fpn_mask_rcnn_r50_1x'
 load_from = None

tools/configs/r50_fpn_rpn_1x.py → configs/r50_fpn_rpn_1x.py

@@ -50,7 +50,7 @@ test_cfg = dict(
         min_bbox_size=0))
 # dataset settings
 dataset_type = 'CocoDataset'
-data_root = '../data/coco/'
+data_root = 'data/coco/'
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 data = dict(
@@ -110,7 +110,7 @@ log_config = dict(
 # yapf:enable
 # runtime settings
 total_epochs = 12
-dist_params = dict(backend='gloo')
+dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/fpn_rpn_r50_1x'
 load_from = None

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 .losses import *  # noqa: F401, F403
-from .eval import *  # noqa: F401, F403
-from .parallel import *  # noqa: F401, F403
+from .bbox import *  # noqa: F401, F403
+from .mask import *  # noqa: F401, F403
+from .loss import *  # noqa: F401, F403
+from .evaluation import *  # noqa: F401, F403
 from .post_processing import *  # noqa: F401, F403
 from .utils 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/eval/eval_hooks.py → mmdet/core/evaluation/eval_hooks.py

@@ -7,13 +7,12 @@ import mmcv
 import numpy as np
 import torch
 from mmcv.runner import Hook, obj_from_dict
+from mmcv.parallel import scatter, collate
 from pycocotools.cocoeval import COCOeval
 from torch.utils.data import Dataset
 
 from .coco_utils import results2json, fast_eval_recall
-from ..parallel import scatter
 from mmdet import datasets
-from mmdet.datasets.loader import collate
 
 
 class DistEvalHook(Hook):

mmdet/core/mask/__init__.py

+from .utils import split_combined_polys
+from .mask_target import mask_target
+
+__all__ = ['split_combined_polys', 'mask_target']