Reformat use yapf 0.27.0 & Add yapf style file (#672)

* Reformat use yapf 0.27.0

* Add yapf style file

.style.yapf

+[style]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true

configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py

@@ -11,9 +11,7 @@ model = dict(
         frozen_stages=1,
         style='pytorch',
         dcn=dict(
-            modulated=False,
-            deformable_groups=1,
-            fallback_on_stride=False),
+            modulated=False, deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py

@@ -11,9 +11,7 @@ model = dict(
         frozen_stages=1,
         style='pytorch',
         dcn=dict(
-            modulated=False,
-            deformable_groups=1,
-            fallback_on_stride=False),
+            modulated=False, deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py

@@ -10,9 +10,7 @@ model = dict(
         frozen_stages=1,
         style='pytorch',
         dcn=dict(
-            modulated=False,
-            deformable_groups=1,
-            fallback_on_stride=False),
+            modulated=False, deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py

@@ -10,9 +10,7 @@ model = dict(
         frozen_stages=1,
         style='pytorch',
         dcn=dict(
-            modulated=True,
-            deformable_groups=1,
-            fallback_on_stride=False),
+            modulated=True, deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py

@@ -10,9 +10,7 @@ model = dict(
         frozen_stages=1,
         style='pytorch',
         dcn=dict(
-            modulated=False,
-            deformable_groups=1,
-            fallback_on_stride=False),
+            modulated=False, deformable_groups=1, fallback_on_stride=False),
         stage_with_dcn=(False, True, True, True)),
     neck=dict(
         type='FPN',

configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py

@@ -99,9 +99,7 @@ test_cfg = dict(
         nms_thr=0.7,
         min_bbox_size=0),
     rcnn=dict(
-        score_thr=0.05,
-        nms=dict(type='nms', iou_thr=0.5),
-        max_per_img=100))
+        score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100))
 # dataset settings
 dataset_type = 'CocoDataset'
 data_root = 'data/coco/'

mmdet/apis/inference.py

@@ -124,8 +124,7 @@ def show_result(img, result, class_names, score_thr=0.3, out_file=None):
         segms = mmcv.concat_list(segm_result)
         inds = np.where(bboxes[:, -1] > score_thr)[0]
         for i in inds:
-            color_mask = np.random.randint(
-                0, 256, (1, 3), dtype=np.uint8)
+            color_mask = np.random.randint(0, 256, (1, 3), dtype=np.uint8)
             mask = maskUtils.decode(segms[i]).astype(np.bool)
             img[mask] = img[mask] * 0.5 + color_mask * 0.5
     # draw bounding boxes

mmdet/apis/train.py

@@ -91,8 +91,8 @@ def build_optimizer(model, optimizer_cfg):
     paramwise_options = optimizer_cfg.pop('paramwise_options', None)
     # if no paramwise option is specified, just use the global setting
     if paramwise_options is None:
-        return obj_from_dict(optimizer_cfg, torch.optim,
-                             dict(params=model.parameters()))
+        return obj_from_dict(
+            optimizer_cfg, torch.optim, dict(params=model.parameters()))
     else:
         assert isinstance(paramwise_options, dict)
         # get base lr and weight decay

mmdet/core/bbox/assign_sampling.py

@@ -7,8 +7,7 @@ def build_assigner(cfg, **kwargs):
     if isinstance(cfg, assigners.BaseAssigner):
         return cfg
     elif isinstance(cfg, dict):
-        return mmcv.runner.obj_from_dict(
-            cfg, assigners, default_args=kwargs)
+        return mmcv.runner.obj_from_dict(cfg, assigners, default_args=kwargs)
     else:
         raise TypeError('Invalid type {} for building a sampler'.format(
             type(cfg)))
@@ -18,8 +17,7 @@ def build_sampler(cfg, **kwargs):
     if isinstance(cfg, samplers.BaseSampler):
         return cfg
     elif isinstance(cfg, dict):
-        return mmcv.runner.obj_from_dict(
-            cfg, samplers, default_args=kwargs)
+        return mmcv.runner.obj_from_dict(cfg, samplers, default_args=kwargs)
     else:
         raise TypeError('Invalid type {} for building a sampler'.format(
             type(cfg)))

mmdet/core/evaluation/mean_ap.py

@@ -261,8 +261,8 @@ def eval_map(det_results,
         cls_dets, cls_gts, cls_gt_ignore = get_cls_results(
             det_results, gt_bboxes, gt_labels, gt_ignore, i)
         # calculate tp and fp for each image
-        tpfp_func = (tpfp_imagenet
-                     if dataset in ['det', 'vid'] else tpfp_default)
+        tpfp_func = (
+            tpfp_imagenet if dataset in ['det', 'vid'] else tpfp_default)
         tpfp = [
             tpfp_func(cls_dets[j], cls_gts[j], cls_gt_ignore[j], iou_thr,
                       area_ranges) for j in range(len(cls_dets))

mmdet/core/post_processing/bbox_nms.py

@@ -45,9 +45,8 @@ def multiclass_nms(multi_bboxes,
             _scores *= score_factors[cls_inds]
         cls_dets = torch.cat([_bboxes, _scores[:, None]], dim=1)
         cls_dets, _ = nms_op(cls_dets, **nms_cfg_)
-        cls_labels = multi_bboxes.new_full((cls_dets.shape[0], ),
-                                           i - 1,
-                                           dtype=torch.long)
+        cls_labels = multi_bboxes.new_full(
+            (cls_dets.shape[0], ), i - 1, dtype=torch.long)
         bboxes.append(cls_dets)
         labels.append(cls_labels)
     if bboxes:

mmdet/datasets/extra_aug.py

@@ -91,9 +91,7 @@ class Expand(object):
 
 class RandomCrop(object):
 
-    def __init__(self,
-                 min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
-                 min_crop_size=0.3):
+    def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3):
         # 1: return ori img
         self.sample_mode = (1, *min_ious, 0)
         self.min_crop_size = min_crop_size

mmdet/datasets/loader/__init__.py

 from .build_loader import build_dataloader
 from .sampler import GroupSampler, DistributedGroupSampler
 
-__all__ = [
-    'GroupSampler', 'DistributedGroupSampler', 'build_dataloader'
-]
+__all__ = ['GroupSampler', 'DistributedGroupSampler', 'build_dataloader']

mmdet/datasets/loader/build_loader.py

@@ -25,10 +25,8 @@ def build_dataloader(dataset,
             sampler = DistributedGroupSampler(dataset, imgs_per_gpu,
                                               world_size, rank)
         else:
-            sampler = DistributedSampler(dataset,
-                                         world_size,
-                                         rank,
-                                         shuffle=False)
+            sampler = DistributedSampler(
+                dataset, world_size, rank, shuffle=False)
         batch_size = imgs_per_gpu
         num_workers = workers_per_gpu
     else:
@@ -36,13 +34,13 @@ def build_dataloader(dataset,
         batch_size = num_gpus * imgs_per_gpu
         num_workers = num_gpus * workers_per_gpu
 
-    data_loader = DataLoader(dataset,
-                             batch_size=batch_size,
-                             sampler=sampler,
-                             num_workers=num_workers,
-                             collate_fn=partial(collate,
-                                                samples_per_gpu=imgs_per_gpu),
-                             pin_memory=False,
-                             **kwargs)
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),
+        pin_memory=False,
+        **kwargs)
 
     return data_loader

mmdet/datasets/loader/sampler.py

@@ -139,8 +139,8 @@ class DistributedGroupSampler(Sampler):
 
         indices = [
             indices[j] for i in list(
-                torch.randperm(len(indices) // self.samples_per_gpu,
-                               generator=g))
+                torch.randperm(
+                    len(indices) // self.samples_per_gpu, generator=g))
             for j in range(i * self.samples_per_gpu, (i + 1) *
                            self.samples_per_gpu)
         ]

mmdet/datasets/transforms.py

@@ -34,8 +34,8 @@ class ImageTransform(object):
         else:
             img, w_scale, h_scale = mmcv.imresize(
                 img, scale, return_scale=True)
-            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                    dtype=np.float32)
+            scale_factor = np.array(
+                [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
         img_shape = img.shape
         img = mmcv.imnormalize(img, self.mean, self.std, self.to_rgb)
         if flip:

mmdet/models/anchor_heads/anchor_head.py

@@ -196,8 +196,9 @@ class AnchorHead(nn.Module):
             return None
         (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
          num_total_pos, num_total_neg) = cls_reg_targets
-        num_total_samples = (num_total_pos if self.cls_focal_loss else
-                             num_total_pos + num_total_neg)
+        num_total_samples = (
+            num_total_pos
+            if self.cls_focal_loss else num_total_pos + num_total_neg)
         losses_cls, losses_reg = multi_apply(
             self.loss_single,
             cls_scores,

mmdet/models/backbones/resnet.py

@@ -43,12 +43,7 @@ class BasicBlock(nn.Module):
             bias=False)
         self.add_module(self.norm1_name, norm1)
         self.conv2 = build_conv_layer(
-            conv_cfg,
-            planes,
-            planes,
-            3,
-            padding=1,
-            bias=False)
+            conv_cfg, planes, planes, 3, padding=1, bias=False)
         self.add_module(self.norm2_name, norm2)
 
         self.relu = nn.ReLU(inplace=True)

mmdet/models/bbox_heads/convfc_bbox_head.py

@@ -94,8 +94,8 @@ class ConvFCBBoxHead(BBoxHead):
         branch_convs = nn.ModuleList()
         if num_branch_convs > 0:
             for i in range(num_branch_convs):
-                conv_in_channels = (last_layer_dim
-                                    if i == 0 else self.conv_out_channels)
+                conv_in_channels = (
+                    last_layer_dim if i == 0 else self.conv_out_channels)
                 branch_convs.append(
                     ConvModule(
                         conv_in_channels,
@@ -114,8 +114,8 @@ class ConvFCBBoxHead(BBoxHead):
                     or self.num_shared_fcs == 0) and not self.with_avg_pool:
                 last_layer_dim *= (self.roi_feat_size * self.roi_feat_size)
             for i in range(num_branch_fcs):
-                fc_in_channels = (last_layer_dim
-                                  if i == 0 else self.fc_out_channels)
+                fc_in_channels = (
+                    last_layer_dim if i == 0 else self.fc_out_channels)
                 branch_fcs.append(
                     nn.Linear(fc_in_channels, self.fc_out_channels))
             last_layer_dim = self.fc_out_channels