Fix the order of import statements in __init__.py (#1094)

* fix the imports in __init__.py

* update contribution guide

.travis.yml

@@ -11,5 +11,5 @@ python:
 
 script:
   - flake8
-  - isort -rc --diff mmdet/ tools/
+  - isort -rc --check-only --diff mmdet/ tools/
   - yapf -r -d --style .style.yapf mmdet/ tools/
\ No newline at end of file

CONTRIBUTING.md

@@ -21,8 +21,13 @@ please contact Kai Chen (chenkaidev[at]gmail[dot]com). We will much appreciate y
 
 ### Python
 We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
-We use [flake8](http://flake8.pycqa.org/en/latest/) as the linter and [yapf](https://github.com/google/yapf) as the formatter.
-Please upgrade to the latest yapf (>=0.27.0) and refer to the [configuration](.style.yapf).
+
+We use the following tools for linting and formatting:
+- [flake8](http://flake8.pycqa.org/en/latest/): linter
+- [yapf](https://github.com/google/yapf): formatter
+- [isort](https://github.com/timothycrosley/isort): sort imports
+
+Style configurations of yapf and isort can be found in [.style.yapf](.style.yapf) and [.isort.cfg](.isort.cfg).
 
 >Before you create a PR, make sure that your code lints and is formatted by yapf.
 

mmdet/apis/__init__.py

-from .env import init_dist, get_root_logger, set_random_seed
+from .env import get_root_logger, init_dist, set_random_seed
+from .inference import inference_detector, init_detector, show_result
 from .train import train_detector
-from .inference import init_detector, inference_detector, show_result
 
 __all__ = [
     'init_dist', 'get_root_logger', 'set_random_seed', 'train_detector',

mmdet/core/anchor/__init__.py

 from .anchor_generator import AnchorGenerator
-from .anchor_target import anchor_target, anchor_inside_flags
+from .anchor_target import anchor_inside_flags, anchor_target
 from .guided_anchor_target import ga_loc_target, ga_shape_target
 
 __all__ = [

mmdet/core/bbox/__init__.py

+from .assign_sampling import assign_and_sample, build_assigner, build_sampler
+from .assigners import AssignResult, BaseAssigner, MaxIoUAssigner
+from .bbox_target import bbox_target
 from .geometry import bbox_overlaps
-from .assigners import BaseAssigner, MaxIoUAssigner, AssignResult
-from .samplers import (BaseSampler, PseudoSampler, RandomSampler,
+from .samplers import (BaseSampler, CombinedSampler,
                        InstanceBalancedPosSampler, IoUBalancedNegSampler,
-                       CombinedSampler, SamplingResult)
-from .assign_sampling import build_assigner, build_sampler, assign_and_sample
-from .transforms import (bbox2delta, delta2bbox, bbox_flip, bbox_mapping,
-                         bbox_mapping_back, bbox2roi, roi2bbox, bbox2result,
-                         distance2bbox)
-from .bbox_target import bbox_target
+                       PseudoSampler, RandomSampler, SamplingResult)
+from .transforms import (bbox2delta, bbox2result, bbox2roi, bbox_flip,
+                         bbox_mapping, bbox_mapping_back, delta2bbox,
+                         distance2bbox, roi2bbox)
 
 __all__ = [
     'bbox_overlaps', 'BaseAssigner', 'MaxIoUAssigner', 'AssignResult',

mmdet/core/bbox/assigners/__init__.py

-from .base_assigner import BaseAssigner
-from .max_iou_assigner import MaxIoUAssigner
 from .approx_max_iou_assigner import ApproxMaxIoUAssigner
 from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+from .max_iou_assigner import MaxIoUAssigner
 
 __all__ = [
     'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult'

mmdet/core/bbox/samplers/__init__.py

 from .base_sampler import BaseSampler
-from .pseudo_sampler import PseudoSampler
-from .random_sampler import RandomSampler
+from .combined_sampler import CombinedSampler
 from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
 from .iou_balanced_neg_sampler import IoUBalancedNegSampler
-from .combined_sampler import CombinedSampler
 from .ohem_sampler import OHEMSampler
+from .pseudo_sampler import PseudoSampler
+from .random_sampler import RandomSampler
 from .sampling_result import SamplingResult
 
 __all__ = [

mmdet/core/evaluation/__init__.py

-from .class_names import (voc_classes, imagenet_det_classes,
-                          imagenet_vid_classes, coco_classes, dataset_aliases,
-                          get_classes)
+from .class_names import (coco_classes, dataset_aliases, get_classes,
+                          imagenet_det_classes, imagenet_vid_classes,
+                          voc_classes)
 from .coco_utils import coco_eval, fast_eval_recall, results2json
-from .eval_hooks import (DistEvalHook, DistEvalmAPHook, CocoDistEvalRecallHook,
-                         CocoDistEvalmAPHook)
+from .eval_hooks import (CocoDistEvalmAPHook, CocoDistEvalRecallHook,
+                         DistEvalHook, DistEvalmAPHook)
 from .mean_ap import average_precision, eval_map, print_map_summary
-from .recall import (eval_recalls, print_recall_summary, plot_num_recall,
-                     plot_iou_recall)
+from .recall import (eval_recalls, plot_iou_recall, plot_num_recall,
+                     print_recall_summary)
 
 __all__ = [
     'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',

mmdet/core/mask/__init__.py

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

mmdet/core/post_processing/__init__.py

 from .bbox_nms import multiclass_nms
-from .merge_augs import (merge_aug_proposals, merge_aug_bboxes,
-                         merge_aug_scores, merge_aug_masks)
+from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
+                         merge_aug_proposals, merge_aug_scores)
 
 __all__ = [
     'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',

mmdet/core/utils/__init__.py

-from .dist_utils import allreduce_grads, DistOptimizerHook
-from .misc import tensor2imgs, unmap, multi_apply
+from .dist_utils import DistOptimizerHook, allreduce_grads
+from .misc import multi_apply, tensor2imgs, unmap
 
 __all__ = [
     'allreduce_grads', 'DistOptimizerHook', 'tensor2imgs', 'unmap',

mmdet/datasets/__init__.py

-from .custom import CustomDataset
-from .xml_style import XMLDataset
-from .coco import CocoDataset
+from .builder import build_dataset
 from .cityscapes import CityscapesDataset
-from .voc import VOCDataset
-from .wider_face import WIDERFaceDataset
-from .loader import GroupSampler, DistributedGroupSampler, build_dataloader
-from .utils import to_tensor, random_scale, show_ann
+from .coco import CocoDataset
+from .custom import CustomDataset
 from .dataset_wrappers import ConcatDataset, RepeatDataset
 from .extra_aug import ExtraAugmentation
+from .loader import DistributedGroupSampler, GroupSampler, build_dataloader
 from .registry import DATASETS
-from .builder import build_dataset
+from .utils import random_scale, show_ann, to_tensor
+from .voc import VOCDataset
+from .wider_face import WIDERFaceDataset
+from .xml_style import XMLDataset
 
 __all__ = [
     'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',

mmdet/datasets/loader/__init__.py

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

mmdet/models/__init__.py

+from .anchor_heads import *  # noqa: F401,F403
 from .backbones import *  # noqa: F401,F403
+from .bbox_heads import *  # noqa: F401,F403
+from .builder import (build_backbone, build_detector, build_head, build_loss,
+                      build_neck, build_roi_extractor, build_shared_head)
+from .detectors import *  # noqa: F401,F403
+from .losses import *  # noqa: F401,F403
+from .mask_heads import *  # noqa: F401,F403
 from .necks import *  # noqa: F401,F403
+from .registry import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
+                       ROI_EXTRACTORS, SHARED_HEADS)
 from .roi_extractors import *  # noqa: F401,F403
-from .anchor_heads import *  # noqa: F401,F403
 from .shared_heads import *  # noqa: F401,F403
-from .bbox_heads import *  # noqa: F401,F403
-from .mask_heads import *  # noqa: F401,F403
-from .losses import *  # noqa: F401,F403
-from .detectors import *  # noqa: F401,F403
-from .registry import (BACKBONES, NECKS, ROI_EXTRACTORS, SHARED_HEADS, HEADS,
-                       LOSSES, DETECTORS)
-from .builder import (build_backbone, build_neck, build_roi_extractor,
-                      build_shared_head, build_head, build_loss,
-                      build_detector)
 
 __all__ = [
     'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES',

mmdet/models/anchor_heads/__init__.py

 from .anchor_head import AnchorHead
-from .guided_anchor_head import GuidedAnchorHead, FeatureAdaption
 from .fcos_head import FCOSHead
-from .rpn_head import RPNHead
+from .ga_retina_head import GARetinaHead
 from .ga_rpn_head import GARPNHead
+from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
 from .retina_head import RetinaHead
-from .ga_retina_head import GARetinaHead
+from .rpn_head import RPNHead
 from .ssd_head import SSDHead
 
 __all__ = [

mmdet/models/backbones/__init__.py

+from .hrnet import HRNet
 from .resnet import ResNet, make_res_layer
 from .resnext import ResNeXt
 from .ssd_vgg import SSDVGG
-from .hrnet import HRNet
 
 __all__ = ['ResNet', 'make_res_layer', 'ResNeXt', 'SSDVGG', 'HRNet']

mmdet/models/bbox_heads/double_bbox_head.py

 import torch.nn as nn
 from mmcv.cnn.weight_init import normal_init, xavier_init
 
-from .bbox_head import BBoxHead
 from ..backbones.resnet import Bottleneck
 from ..registry import HEADS
 from ..utils import ConvModule
+from .bbox_head import BBoxHead
 
 
 class BasicResBlock(nn.Module):

mmdet/models/detectors/__init__.py

 from .base import BaseDetector
-from .single_stage import SingleStageDetector
-from .two_stage import TwoStageDetector
-from .rpn import RPN
-from .fast_rcnn import FastRCNN
-from .faster_rcnn import FasterRCNN
-from .mask_rcnn import MaskRCNN
 from .cascade_rcnn import CascadeRCNN
 from .double_head_rcnn import DoubleHeadRCNN
-from .htc import HybridTaskCascade
-from .retinanet import RetinaNet
+from .fast_rcnn import FastRCNN
+from .faster_rcnn import FasterRCNN
 from .fcos import FCOS
 from .grid_rcnn import GridRCNN
+from .htc import HybridTaskCascade
+from .mask_rcnn import MaskRCNN
 from .mask_scoring_rcnn import MaskScoringRCNN
+from .retinanet import RetinaNet
+from .rpn import RPN
+from .single_stage import SingleStageDetector
+from .two_stage import TwoStageDetector
 
 __all__ = [
     'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN',

mmdet/models/detectors/double_head_rcnn.py

 import torch
 
-from .two_stage import TwoStageDetector
-from ..registry import DETECTORS
 from mmdet.core import bbox2roi, build_assigner, build_sampler
+from ..registry import DETECTORS
+from .two_stage import TwoStageDetector
 
 
 @DETECTORS.register_module

mmdet/models/losses/__init__.py

-from .accuracy import accuracy, Accuracy
-from .cross_entropy_loss import (cross_entropy, binary_cross_entropy,
-                                 mask_cross_entropy, CrossEntropyLoss)
-from .focal_loss import sigmoid_focal_loss, FocalLoss
-from .smooth_l1_loss import smooth_l1_loss, SmoothL1Loss
+from .accuracy import Accuracy, accuracy
+from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
+from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
+                                 cross_entropy, mask_cross_entropy)
+from .focal_loss import FocalLoss, sigmoid_focal_loss
 from .ghm_loss import GHMC, GHMR
-from .balanced_l1_loss import balanced_l1_loss, BalancedL1Loss
-from .mse_loss import mse_loss, MSELoss
-from .iou_loss import iou_loss, bounded_iou_loss, IoULoss, BoundedIoULoss
+from .iou_loss import BoundedIoULoss, IoULoss, bounded_iou_loss, iou_loss
+from .mse_loss import MSELoss, mse_loss
+from .smooth_l1_loss import SmoothL1Loss, smooth_l1_loss
 from .utils import reduce_loss, weight_reduce_loss, weighted_loss
 
 __all__ = [