Warning when skip image (#1010)

* Require an operation(Save or Show the results) when testing

* using parentheses

* Warning when skip image

* added parameter skip_img_without_anno for dataset

* print filename when warning

mmdet/datasets/custom.py

 import os.path as osp
+import warnings
 
 import mmcv
 import numpy as np
@@ -55,6 +56,7 @@ class CustomDataset(Dataset):
                  seg_scale_factor=1,
                  extra_aug=None,
                  resize_keep_ratio=True,
+                 skip_img_without_anno=True,
                  test_mode=False):
         # prefix of images path
         self.img_prefix = img_prefix
@@ -127,6 +129,7 @@ class CustomDataset(Dataset):
 
         # image rescale if keep ratio
         self.resize_keep_ratio = resize_keep_ratio
+        self.skip_img_without_anno = skip_img_without_anno
 
     def __len__(self):
         return len(self.img_infos)
@@ -203,7 +206,9 @@ class CustomDataset(Dataset):
             gt_bboxes_ignore = ann['bboxes_ignore']
 
         # skip the image if there is no valid gt bbox
-        if len(gt_bboxes) == 0:
+        if len(gt_bboxes) == 0 and self.skip_img_without_anno:
+            warnings.warn('Skip the image "%s" that has no valid gt bbox' %
+                          osp.join(self.img_prefix, img_info['filename']))
             return None
 
         # extra augmentation
@@ -220,8 +225,8 @@ class CustomDataset(Dataset):
         img = img.copy()
         if self.with_seg:
             gt_seg = mmcv.imread(
-                osp.join(self.seg_prefix, img_info['file_name'].replace(
-                    'jpg', 'png')),
+                osp.join(self.seg_prefix,
+                         img_info['file_name'].replace('jpg', 'png')),
                 flag='unchanged')
             gt_seg = self.seg_transform(gt_seg.squeeze(), img_scale, flip)
             gt_seg = mmcv.imrescale(
@@ -230,8 +235,8 @@ class CustomDataset(Dataset):
         if self.proposals is not None:
             proposals = self.bbox_transform(proposals, img_shape, scale_factor,
                                             flip)
-            proposals = np.hstack(
-                [proposals, scores]) if scores is not None else proposals
+            proposals = np.hstack([proposals, scores
+                                   ]) if scores is not None else proposals
         gt_bboxes = self.bbox_transform(gt_bboxes, img_shape, scale_factor,
                                         flip)
         if self.with_crowd:
@@ -296,8 +301,8 @@ class CustomDataset(Dataset):
                     score = None
                 _proposal = self.bbox_transform(proposal, img_shape,
                                                 scale_factor, flip)
-                _proposal = np.hstack(
-                    [_proposal, score]) if score is not None else _proposal
+                _proposal = np.hstack([_proposal, score
+                                       ]) if score is not None else _proposal
                 _proposal = to_tensor(_proposal)
             else:
                 _proposal = None