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

* add isort config

* use isort to sort imports

* add isort to travis

.isort.cfg

@@ -0,0 +1,7 @@
+[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

.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/

mmdet/apis/train.py

@@ -1,17 +1,15 @@
 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

@@ -1,6 +1,6 @@
 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

@@ -1,7 +1,7 @@
 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

@@ -1,7 +1,7 @@
 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

@@ -1,8 +1,8 @@
 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

@@ -1,7 +1,7 @@
 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

@@ -1,5 +1,5 @@
-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

@@ -1,7 +1,7 @@
 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 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

@@ -1,10 +1,11 @@
 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

@@ -1,6 +1,6 @@
-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

@@ -1,6 +1,5 @@
-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

@@ -1,9 +1,9 @@
 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 @@
 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

@@ -1,11 +1,11 @@
 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

@@ -1,12 +1,11 @@
 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.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.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
 

mmdet/models/anchor_heads/ga_retina_head.py

@@ -1,10 +1,10 @@
 import torch.nn as nn
 from mmcv.cnn import normal_init
 
-from .guided_anchor_head import GuidedAnchorHead, FeatureAdaption
-from ..registry import HEADS
-from ..utils import bias_init_with_prob, ConvModule
 from mmdet.ops import MaskedConv2d
+from ..registry import HEADS
+from ..utils import ConvModule, bias_init_with_prob
+from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
 
 
 @HEADS.register_module

mmdet/models/anchor_heads/ga_rpn_head.py

@@ -5,8 +5,8 @@
 
 from mmdet.core import delta2bbox
 from mmdet.ops import nms
-from .guided_anchor_head import GuidedAnchorHead
 from ..registry import HEADS
+from .guided_anchor_head import GuidedAnchorHead
 
 
 @HEADS.register_module

mmdet/models/anchor_heads/guided_anchor_head.py

@@ -5,14 +5,14 @@
 import torch.nn as nn
 from mmcv.cnn import normal_init
 
-from mmdet.core import (AnchorGenerator, anchor_target, anchor_inside_flags,
-                        ga_loc_target, ga_shape_target, delta2bbox,
-                        multi_apply, multiclass_nms, force_fp32)
+from mmdet.core import (AnchorGenerator, anchor_inside_flags, anchor_target,
+                        delta2bbox, force_fp32, ga_loc_target, ga_shape_target,
+                        multi_apply, multiclass_nms)
 from mmdet.ops import DeformConv, MaskedConv2d
 from ..builder import build_loss
-from .anchor_head import AnchorHead
 from ..registry import HEADS
 from ..utils import bias_init_with_prob
+from .anchor_head import AnchorHead
 
 
 class FeatureAdaption(nn.Module):

mmdet/models/anchor_heads/retina_head.py

@@ -2,9 +2,9 @@
 import torch.nn as nn
 from mmcv.cnn import normal_init
 
-from .anchor_head import AnchorHead
 from ..registry import HEADS
-from ..utils import bias_init_with_prob, ConvModule
+from ..utils import ConvModule, bias_init_with_prob
+from .anchor_head import AnchorHead
 
 
 @HEADS.register_module

mmdet/models/anchor_heads/rpn_head.py

@@ -5,8 +5,8 @@
 
 from mmdet.core import delta2bbox
 from mmdet.ops import nms
-from .anchor_head import AnchorHead
 from ..registry import HEADS
+from .anchor_head import AnchorHead
 
 
 @HEADS.register_module

mmdet/models/anchor_heads/ssd_head.py

@@ -5,9 +5,9 @@
 from mmcv.cnn import xavier_init
 
 from mmdet.core import AnchorGenerator, anchor_target, multi_apply
-from .anchor_head import AnchorHead
 from ..losses import smooth_l1_loss
 from ..registry import HEADS
+from .anchor_head import AnchorHead
 
 
 # TODO: add loss evaluator for SSD

mmdet/models/backbones/hrnet.py

@@ -6,7 +6,7 @@
 from torch.nn.modules.batchnorm import _BatchNorm
 
 from ..registry import BACKBONES
-from ..utils import build_norm_layer, build_conv_layer
+from ..utils import build_conv_layer, build_norm_layer
 from .resnet import BasicBlock, Bottleneck
 
 

mmdet/models/backbones/resnet.py

@@ -2,14 +2,12 @@
 
 import torch.nn as nn
 import torch.utils.checkpoint as cp
-from torch.nn.modules.batchnorm import _BatchNorm
-
 from mmcv.cnn import constant_init, kaiming_init
 from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmdet.ops import DeformConv, ModulatedDeformConv, ContextBlock
 from mmdet.models.plugins import GeneralizedAttention
-
+from mmdet.ops import ContextBlock, DeformConv, ModulatedDeformConv
 from ..registry import BACKBONES
 from ..utils import build_conv_layer, build_norm_layer
 

mmdet/models/backbones/resnext.py

@@ -3,10 +3,10 @@
 import torch.nn as nn
 
 from mmdet.ops import DeformConv, ModulatedDeformConv
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNet
 from ..registry import BACKBONES
 from ..utils import build_conv_layer, build_norm_layer
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResNet
 
 
 class Bottleneck(_Bottleneck):

mmdet/models/backbones/ssd_vgg.py

@@ -3,8 +3,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mmcv.cnn import (VGG, xavier_init, constant_init, kaiming_init,
-                      normal_init)
+from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
 from mmcv.runner import load_checkpoint
 
 from ..registry import BACKBONES

mmdet/models/bbox_heads/bbox_head.py

@@ -2,8 +2,8 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from mmdet.core import (delta2bbox, multiclass_nms, bbox_target, force_fp32,
-                        auto_fp16)
+from mmdet.core import (auto_fp16, bbox_target, delta2bbox, force_fp32,
+                        multiclass_nms)
 from ..builder import build_loss
 from ..losses import accuracy
 from ..registry import HEADS

mmdet/models/bbox_heads/convfc_bbox_head.py

@@ -1,8 +1,8 @@
 import torch.nn as nn
 
-from .bbox_head import BBoxHead
 from ..registry import HEADS
 from ..utils import ConvModule
+from .bbox_head import BBoxHead
 
 
 @HEADS.register_module

mmdet/models/builder.py

@@ -1,8 +1,8 @@
 from torch import nn
 
 from mmdet.utils import build_from_cfg
-from .registry import (BACKBONES, NECKS, ROI_EXTRACTORS, SHARED_HEADS, HEADS,
-                       LOSSES, DETECTORS)
+from .registry import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
+                       ROI_EXTRACTORS, SHARED_HEADS)
 
 
 def build(cfg, registry, default_args=None):