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

* add isort config

* use isort to sort imports

* add isort to travis

mmdet/models/detectors/mask_rcnn.py

-from .two_stage import TwoStageDetector
 from ..registry import DETECTORS
+from .two_stage import TwoStageDetector
 
 
 @DETECTORS.register_module

mmdet/models/detectors/mask_scoring_rcnn.py

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

mmdet/models/detectors/retinanet.py

-from .single_stage import SingleStageDetector
 from ..registry import DETECTORS
+from .single_stage import SingleStageDetector
 
 
 @DETECTORS.register_module

mmdet/models/detectors/rpn.py

 import mmcv
 
-from mmdet.core import tensor2imgs, bbox_mapping
-from .base import BaseDetector
-from .test_mixins import RPNTestMixin
+from mmdet.core import bbox_mapping, tensor2imgs
 from .. import builder
 from ..registry import DETECTORS
+from .base import BaseDetector
+from .test_mixins import RPNTestMixin
 
 
 @DETECTORS.register_module

mmdet/models/detectors/single_stage.py

 import torch.nn as nn
 
-from .base import BaseDetector
+from mmdet.core import bbox2result
 from .. import builder
 from ..registry import DETECTORS
-from mmdet.core import bbox2result
+from .base import BaseDetector
 
 
 @DETECTORS.register_module

mmdet/models/detectors/test_mixins.py

-from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_proposals,
-                        merge_aug_bboxes, merge_aug_masks, multiclass_nms)
+from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
+                        merge_aug_masks, merge_aug_proposals, multiclass_nms)
 
 
 class RPNTestMixin(object):

mmdet/models/detectors/two_stage.py

 import torch
 import torch.nn as nn
 
-from .base import BaseDetector
-from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin
+from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
 from .. import builder
 from ..registry import DETECTORS
-from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler
+from .base import BaseDetector
+from .test_mixins import BBoxTestMixin, MaskTestMixin, RPNTestMixin
 
 
 @DETECTORS.register_module

mmdet/models/losses/balanced_l1_loss.py

@@ -2,8 +2,8 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from .utils import weighted_loss
 from ..registry import LOSSES
+from .utils import weighted_loss
 
 
 @weighted_loss

mmdet/models/losses/cross_entropy_loss.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .utils import weight_reduce_loss
 from ..registry import LOSSES
+from .utils import weight_reduce_loss
 
 
 def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):

mmdet/models/losses/focal_loss.py

@@ -2,8 +2,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from mmdet.ops import sigmoid_focal_loss as _sigmoid_focal_loss
-from .utils import weight_reduce_loss
 from ..registry import LOSSES
+from .utils import weight_reduce_loss
 
 
 # This method is only for debugging

mmdet/models/losses/iou_loss.py

@@ -2,8 +2,8 @@ import torch
 import torch.nn as nn
 
 from mmdet.core import bbox_overlaps
-from .utils import weighted_loss
 from ..registry import LOSSES
+from .utils import weighted_loss
 
 
 @weighted_loss

mmdet/models/losses/mse_loss.py

 import torch.nn as nn
 import torch.nn.functional as F
 
-from .utils import weighted_loss
 from ..registry import LOSSES
+from .utils import weighted_loss
 
 mse_loss = weighted_loss(F.mse_loss)
 

mmdet/models/losses/smooth_l1_loss.py

 import torch
 import torch.nn as nn
 
-from .utils import weighted_loss
 from ..registry import LOSSES
+from .utils import weighted_loss
 
 
 @weighted_loss

mmdet/models/mask_heads/fcn_mask_head.py

@@ -4,10 +4,10 @@ import pycocotools.mask as mask_util
 import torch
 import torch.nn as nn
 
+from mmdet.core import auto_fp16, force_fp32, mask_target
 from ..builder import build_loss
 from ..registry import HEADS
 from ..utils import ConvModule
-from mmdet.core import mask_target, force_fp32, auto_fp16
 
 
 @HEADS.register_module

mmdet/models/mask_heads/htc_mask_head.py

-from .fcn_mask_head import FCNMaskHead
 from ..registry import HEADS
 from ..utils import ConvModule
+from .fcn_mask_head import FCNMaskHead
 
 
 @HEADS.register_module

mmdet/models/mask_heads/maskiou_head.py

@@ -2,8 +2,8 @@ import numpy as np
 import torch
 import torch.nn as nn
 from mmcv.cnn import kaiming_init, normal_init
-from mmdet.core import force_fp32
 
+from mmdet.core import force_fp32
 from ..builder import build_loss
 from ..registry import HEADS
 

mmdet/models/necks/hrfpn.py

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.checkpoint import checkpoint
 from mmcv.cnn.weight_init import caffe2_xavier_init
+from torch.utils.checkpoint import checkpoint
 
-from ..utils import ConvModule
 from ..registry import NECKS
+from ..utils import ConvModule
 
 
 @NECKS.register_module

mmdet/models/plugins/generalized_attention.py

+import math
+
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
-import math
-import numpy as np
 from mmcv.cnn import kaiming_init
 
 

mmdet/models/utils/conv_module.py

 import warnings
 
 import torch.nn as nn
-from mmcv.cnn import kaiming_init, constant_init
+from mmcv.cnn import constant_init, kaiming_init
 
 from .conv_ws import ConvWS2d
 from .norm import build_norm_layer

mmdet/ops/nms/nms_wrapper.py

 import numpy as np
 import torch
 
-from . import nms_cuda, nms_cpu
+from . import nms_cpu, nms_cuda
 from .soft_nms_cpu import soft_nms_cpu