fix flake8 error

mmdet/__init__.py

 from .version import __version__, short_version
+
+__all__ = ['__version__', 'short_version']

mmdet/core/__init__.py

-from .rpn_ops import *
-from .bbox_ops import *
-from .mask_ops import *
-from .losses import *
-from .eval import *
-from .parallel import *
-from .post_processing import *
-from .utils import *
+from .rpn_ops 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 .post_processing import *  # noqa: F401, F403
+from .utils import *  # noqa: F401, F403

mmdet/core/mask_ops/segms.py

+# flake8: noqa
 # This file is copied from Detectron.
 
 # Copyright (c) 2017-present, Facebook, Inc.

mmdet/core/rpn_ops/__init__.py

-from .anchor_generator import *
-from .anchor_target import *
+from .anchor_generator import AnchorGenerator
+from .anchor_target import anchor_target
+
+__all__ = ['AnchorGenerator', 'anchor_target']

mmdet/core/utils/dist_utils.py

@@ -38,7 +38,8 @@ def _init_dist_slurm(backend, **kwargs):
     raise NotImplementedError
 
 
-# modified from https://github.com/NVIDIA/apex/blob/master/apex/parallel/distributed.py#L9
+# modified from
+# https://github.com/NVIDIA/apex/blob/master/apex/parallel/distributed.py#L9
 def all_reduce_coalesced(tensors):
     buckets = OrderedDict()
     for tensor in tensors:

mmdet/datasets/__init__.py

 from .coco import CocoDataset
+from .loader import (collate, GroupSampler, DistributedGroupSampler,
+                     build_dataloader)
+from .utils import DataContainer, to_tensor, random_scale, show_ann
 
-__all__ = ['CocoDataset']
+__all__ = [
+    'CocoDataset', 'collate', 'GroupSampler', 'DistributedGroupSampler',
+    'build_dataloader', 'DataContainer', 'to_tensor', 'random_scale',
+    'show_ann'
+]

mmdet/datasets/coco.py

@@ -117,7 +117,10 @@ class CocoDataset(Dataset):
         gt_bboxes = []
         gt_labels = []
         gt_bboxes_ignore = []
-        # each mask consists of one or several polys, each poly is a list of float.
+        # Two formats are provided.
+        # 1. mask: a binary map of the same size of the image.
+        # 2. polys: each mask consists of one or several polys, each poly is a
+        # list of float.
         if with_mask:
             gt_masks = []
             gt_mask_polys = []

mmdet/datasets/utils/__init__.py

 from .data_container import DataContainer
-from .misc import *
+from .misc import to_tensor, random_scale, show_ann
+
+__all__ = ['DataContainer', 'to_tensor', 'random_scale', 'show_ann']

mmdet/models/__init__.py

-from .detectors import *
-from .builder import *
+from .detectors import BaseDetector, RPN, FasterRCNN, MaskRCNN
+from .builder import (build_neck, build_rpn_head, build_roi_extractor,
+                      build_bbox_head, build_mask_head, build_detector)
+
+__all__ = [
+    'BaseDetector', 'RPN', 'FasterRCNN', 'MaskRCNN', 'build_backbone',
+    'build_neck', 'build_rpn_head', 'build_roi_extractor', 'build_bbox_head',
+    'build_mask_head', 'build_detector'
+]

mmdet/models/backbones/__init__.py

 from .resnet import resnet
+
+__all__ = ['resnet']

mmdet/models/bbox_heads/convfc_bbox_head.py

@@ -43,17 +43,21 @@ class ConvFCRoIHead(BBoxHead):
         self.fc_out_channels = fc_out_channels
 
         # add shared convs and fcs
-        self.shared_convs, self.shared_fcs, last_layer_dim = self._add_conv_fc_branch(
-            self.num_shared_convs, self.num_shared_fcs, self.in_channels, True)
+        self.shared_convs, self.shared_fcs, last_layer_dim = \
+            self._add_conv_fc_branch(
+                self.num_shared_convs, self.num_shared_fcs, self.in_channels,
+                True)
         self.shared_out_channels = last_layer_dim
 
         # add cls specific branch
-        self.cls_convs, self.cls_fcs, self.cls_last_dim = self._add_conv_fc_branch(
-            self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
+        self.cls_convs, self.cls_fcs, self.cls_last_dim = \
+            self._add_conv_fc_branch(
+                self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
 
         # add reg specific branch
-        self.reg_convs, self.reg_fcs, self.reg_last_dim = self._add_conv_fc_branch(
-            self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
+        self.reg_convs, self.reg_fcs, self.reg_last_dim = \
+            self._add_conv_fc_branch(
+                self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
 
         if self.num_shared_fcs == 0 and not self.with_avg_pool:
             if self.num_cls_fcs == 0:

mmdet/models/necks/fpn.py

@@ -111,7 +111,8 @@ class FPN(nn.Module):
         ]
         # part 2: add extra levels
         if self.num_outs > len(outs):
-            # use max pool to get more levels on top of outputs (Faster R-CNN, Mask R-CNN)
+            # use max pool to get more levels on top of outputs
+            # (e.g., Faster R-CNN, Mask R-CNN)
             if not self.add_extra_convs:
                 for i in range(self.num_outs - used_backbone_levels):
                     outs.append(F.max_pool2d(outs[-1], 1, stride=2))

mmdet/models/utils/__init__.py

 from .conv_module import ConvModule
 from .norm import build_norm_layer
-from .weight_init import *
+from .weight_init import xavier_init, normal_init, uniform_init, kaiming_init
 
-__all__ = ['ConvModule', 'build_norm_layer']
+__all__ = [
+    'ConvModule', 'build_norm_layer', 'xavier_init', 'normal_init',
+    'uniform_init', 'kaiming_init'
+]

mmdet/ops/__init__.py

 from .nms import nms, soft_nms
 from .roi_align import RoIAlign, roi_align
 from .roi_pool import RoIPool, roi_pool
+
+__all__ = ['nms', 'soft_nms', 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool']

mmdet/ops/nms/__init__.py

 from .nms_wrapper import nms, soft_nms
+
+__all__ = ['nms', 'soft_nms']

mmdet/ops/roi_align/__init__.py

 from .functions.roi_align import roi_align
 from .modules.roi_align import RoIAlign
+
+__all__ = ['roi_align', 'RoIAlign']

mmdet/ops/roi_align/gradcheck.py

@@ -5,7 +5,7 @@ from torch.autograd import gradcheck
 import os.path as osp
 import sys
 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
-from roi_align import RoIAlign
+from roi_align import RoIAlign  # noqa: E402
 
 feat_size = 15
 spatial_scale = 1.0 / 8

mmdet/ops/roi_pool/__init__.py

 from .functions.roi_pool import roi_pool
 from .modules.roi_pool import RoIPool
+
+__all__ = ['roi_pool', 'RoIPool']

mmdet/ops/roi_pool/gradcheck.py

@@ -4,7 +4,7 @@ from torch.autograd import gradcheck
 import os.path as osp
 import sys
 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
-from roi_pool import RoIPool
+from roi_pool import RoIPool  # noqa: E402
 
 feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda()
 rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55],

setup.py

@@ -61,7 +61,7 @@ def get_hash():
 
 
 def write_version_py():
-    content = """# GENERATED VERSION FILE 
+    content = """# GENERATED VERSION FILE
 # TIME: {}
 
 __version__ = '{}'