Merge branch 'master' into single-stage

mmdet/datasets/coco.py

@@ -16,14 +16,14 @@ class CocoDataset(CustomDataset):
         self.img_ids = self.coco.getImgIds()
         img_infos = []
         for i in self.img_ids:
-            info = self.coco.loadImgs(i)[0]
+            info = self.coco.loadImgs([i])[0]
             info['filename'] = info['file_name']
             img_infos.append(info)
         return img_infos
 
     def get_ann_info(self, idx):
         img_id = self.img_infos[idx]['id']
-        ann_ids = self.coco.getAnnIds(imgIds=img_id)
+        ann_ids = self.coco.getAnnIds(imgIds=[img_id])
         ann_info = self.coco.loadAnns(ann_ids)
         return self._parse_ann_info(ann_info)
 

mmdet/datasets/loader/build_loader.py

@@ -25,13 +25,13 @@ def build_dataloader(dataset,
         batch_size = imgs_per_gpu
         num_workers = workers_per_gpu
     else:
-        sampler = GroupSampler(dataset, imgs_per_gpu)
+        if not kwargs.get('shuffle', True):
+            sampler = None
+        else:
+            sampler = GroupSampler(dataset, imgs_per_gpu)
         batch_size = num_gpus * imgs_per_gpu
         num_workers = num_gpus * workers_per_gpu
 
-    if not kwargs.get('shuffle', True):
-        sampler = None
-
     data_loader = DataLoader(
         dataset,
         batch_size=batch_size,

mmdet/models/bbox_heads/convfc_bbox_head.py

@@ -11,7 +11,7 @@ class ConvFCBBoxHead(BBoxHead):
                                 /-> cls convs -> cls fcs -> cls
     shared convs -> shared fcs
                                 \-> reg convs -> reg fcs -> reg
-    """
+    """  # noqa: W605
 
     def __init__(self,
                  num_shared_convs=0,

mmdet/models/detectors/two_stage.py

@@ -65,6 +65,9 @@ class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
         if self.with_bbox:
             self.bbox_roi_extractor.init_weights()
             self.bbox_head.init_weights()
+        if self.with_mask:
+            self.mask_roi_extractor.init_weights()
+            self.mask_head.init_weights()
 
     def extract_feat(self, img):
         x = self.backbone(img)

mmdet/models/rpn_heads/rpn_head.py

@@ -30,7 +30,7 @@ class RPNHead(nn.Module):
         target_stds (Iterable): Std values of regression targets.
         use_sigmoid_cls (bool): Whether to use sigmoid loss for classification.
             (softmax by default)
-    """
+    """  # noqa: W605
 
     def __init__(self,
                  in_channels,