Use isort to sort imports and setup travis (#1085)

* add isort config

* use isort to sort imports

* add isort to travis

.isort.cfg

+[isort]
+line_length = 79
+multi_line_output = 0
+known_first_party = mmdet
+known_third_party = mmcv,numpy,matplotlib,pycocotools,six,seaborn,terminaltables,torch,torchvision
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY
\ No newline at end of file

.travis.yml

@@ -2,7 +2,7 @@ dist: xenial
 language: python
 
 install:
-  - pip install flake8 yapf
+  - pip install isort flake8 yapf 
 
 python:
   - "3.5"
@@ -11,4 +11,5 @@ python:
 
 script:
   - flake8
+  - isort -rc --diff mmdet/ tools/
   - yapf -r -d --style .style.yapf mmdet/ tools/
\ No newline at end of file

mmdet/apis/train.py

 from __future__ import division
-
 import re
 from collections import OrderedDict
 
 import torch
-from mmcv.runner import Runner, DistSamplerSeedHook, obj_from_dict
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
 
 from mmdet import datasets
-from mmdet.core import (DistOptimizerHook, DistEvalmAPHook,
-                        CocoDistEvalRecallHook, CocoDistEvalmAPHook,
-                        Fp16OptimizerHook)
-from mmdet.datasets import build_dataloader, DATASETS
+from mmdet.core import (CocoDistEvalmAPHook, CocoDistEvalRecallHook,
+                        DistEvalmAPHook, DistOptimizerHook, Fp16OptimizerHook)
+from mmdet.datasets import DATASETS, build_dataloader
 from mmdet.models import RPN
 from .env import get_root_logger
 

mmdet/core/anchor/anchor_target.py

 import torch
 
-from ..bbox import assign_and_sample, build_assigner, PseudoSampler, bbox2delta
+from ..bbox import PseudoSampler, assign_and_sample, bbox2delta, build_assigner
 from ..utils import multi_apply
 
 

mmdet/core/anchor/guided_anchor_target.py

 import torch
 
-from ..bbox import build_assigner, build_sampler, PseudoSampler
-from ..utils import unmap, multi_apply
+from ..bbox import PseudoSampler, build_assigner, build_sampler
+from ..utils import multi_apply, unmap
 
 
 def calc_region(bbox, ratio, featmap_size=None):

mmdet/core/bbox/assigners/approx_max_iou_assigner.py

 import torch
 
-from .max_iou_assigner import MaxIoUAssigner
 from ..geometry import bbox_overlaps
+from .max_iou_assigner import MaxIoUAssigner
 
 
 class ApproxMaxIoUAssigner(MaxIoUAssigner):

mmdet/core/bbox/assigners/max_iou_assigner.py

 import torch
 
-from .base_assigner import BaseAssigner
-from .assign_result import AssignResult
 from ..geometry import bbox_overlaps
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
 
 
 class MaxIoUAssigner(BaseAssigner):

mmdet/core/bbox/bbox_target.py

 import torch
 
-from .transforms import bbox2delta
 from ..utils import multi_apply
+from .transforms import bbox2delta
 
 
 def bbox_target(pos_bboxes_list,

mmdet/core/bbox/samplers/combined_sampler.py

-from .base_sampler import BaseSampler
 from ..assign_sampling import build_sampler
+from .base_sampler import BaseSampler
 
 
 class CombinedSampler(BaseSampler):

mmdet/core/bbox/samplers/ohem_sampler.py

 import torch
 
-from .base_sampler import BaseSampler
 from ..transforms import bbox2roi
+from .base_sampler import BaseSampler
 
 
 class OHEMSampler(BaseSampler):

mmdet/core/evaluation/eval_hooks.py

@@ -5,14 +5,14 @@ import mmcv
 import numpy as np
 import torch
 import torch.distributed as dist
+from mmcv.parallel import collate, scatter
 from mmcv.runner import Hook
-from mmcv.parallel import scatter, collate
 from pycocotools.cocoeval import COCOeval
 from torch.utils.data import Dataset
 
-from .coco_utils import results2json, fast_eval_recall
-from .mean_ap import eval_map
 from mmdet import datasets
+from .coco_utils import fast_eval_recall, results2json
+from .mean_ap import eval_map
 
 
 class DistEvalHook(Hook):

mmdet/core/fp16/hooks.py

 import copy
+
 import torch
 import torch.nn as nn
 from mmcv.runner import OptimizerHook
 
-from .utils import cast_tensor_type
 from ..utils.dist_utils import allreduce_grads
+from .utils import cast_tensor_type
 
 
 class Fp16OptimizerHook(OptimizerHook):

mmdet/core/mask/mask_target.py

-import torch
-import numpy as np
 import mmcv
+import numpy as np
+import torch
 
 
 def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,

mmdet/core/post_processing/merge_augs.py

-import torch
-
 import numpy as np
+import torch
 
 from mmdet.ops import nms
 from ..bbox import bbox_mapping_back

mmdet/core/utils/dist_utils.py

 from collections import OrderedDict
 
 import torch.distributed as dist
-from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
-                          _take_tensors)
 from mmcv.runner import OptimizerHook
+from torch._utils import (_flatten_dense_tensors, _take_tensors,
+                          _unflatten_dense_tensors)
 
 
 def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):

mmdet/datasets/custom.py

@@ -6,11 +6,11 @@ import numpy as np
 from mmcv.parallel import DataContainer as DC
 from torch.utils.data import Dataset
 
-from .registry import DATASETS
-from .transforms import (ImageTransform, BboxTransform, MaskTransform,
-                         SegMapTransform, Numpy2Tensor)
-from .utils import to_tensor, random_scale
 from .extra_aug import ExtraAugmentation
+from .registry import DATASETS
+from .transforms import (BboxTransform, ImageTransform, MaskTransform,
+                         Numpy2Tensor, SegMapTransform)
+from .utils import random_scale, to_tensor
 
 
 @DATASETS.register_module

mmdet/datasets/loader/build_loader.py

 import platform
 from functools import partial
 
-from mmcv.runner import get_dist_info
 from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
 from torch.utils.data import DataLoader
 
-from .sampler import GroupSampler, DistributedGroupSampler, DistributedSampler
+from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
 
 if platform.system() != 'Windows':
     # https://github.com/pytorch/pytorch/issues/973

mmdet/datasets/loader/sampler.py

 from __future__ import division
-
 import math
-import torch
-import numpy as np
 
+import numpy as np
+import torch
 from mmcv.runner.utils import get_dist_info
-from torch.utils.data import Sampler
 from torch.utils.data import DistributedSampler as _DistributedSampler
+from torch.utils.data import Sampler
 
 
 class DistributedSampler(_DistributedSampler):

mmdet/models/anchor_heads/anchor_head.py

@@ -5,8 +5,8 @@ import torch
 import torch.nn as nn
 from mmcv.cnn import normal_init
 
-from mmdet.core import (AnchorGenerator, anchor_target, delta2bbox,
-                        multi_apply, multiclass_nms, force_fp32)
+from mmdet.core import (AnchorGenerator, anchor_target, delta2bbox, force_fp32,
+                        multi_apply, multiclass_nms)
 from ..builder import build_loss
 from ..registry import HEADS
 

mmdet/models/anchor_heads/fcos_head.py

@@ -2,10 +2,10 @@ import torch
 import torch.nn as nn
 from mmcv.cnn import normal_init
 
-from mmdet.core import multi_apply, multiclass_nms, distance2bbox, force_fp32
+from mmdet.core import distance2bbox, force_fp32, multi_apply, multiclass_nms
 from ..builder import build_loss
 from ..registry import HEADS
-from ..utils import bias_init_with_prob, Scale, ConvModule
+from ..utils import ConvModule, Scale, bias_init_with_prob
 
 INF = 1e8