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

* add isort config

* use isort to sort imports

* add isort to travis

mmdet/ops/roi_align/gradcheck.py

+import os.path as osp
+import sys
+
 import numpy as np
 import torch
 from torch.autograd import gradcheck
 
-import os.path as osp
-import sys
 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
-from roi_align import RoIAlign  # noqa: E402
+from roi_align import RoIAlign  # noqa: E402, isort:skip
 
 feat_size = 15
 spatial_scale = 1.0 / 8

mmdet/ops/roi_pool/gradcheck.py

+import os.path as osp
+import sys
+
 import torch
 from torch.autograd import gradcheck
 
-import os.path as osp
-import sys
 sys.path.append(osp.abspath(osp.join(__file__, '../../')))
-from roi_pool import RoIPool  # noqa: E402
+from roi_pool import RoIPool  # noqa: E402, isort:skip
 
 feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda()
 rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55],

tools/publish_model.py

 import argparse
 import subprocess
+
 import torch
 
 

tools/test.py

@@ -7,11 +7,11 @@ import tempfile
 import mmcv
 import torch
 import torch.distributed as dist
-from mmcv.runner import load_checkpoint, get_dist_info
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import get_dist_info, load_checkpoint
 
 from mmdet.apis import init_dist
-from mmdet.core import results2json, coco_eval, wrap_fp16_model
+from mmdet.core import coco_eval, results2json, wrap_fp16_model
 from mmdet.datasets import build_dataloader, build_dataset
 from mmdet.models import build_detector
 

tools/train.py

 from __future__ import division
-
 import argparse
 import os
+
+import torch
 from mmcv import Config
 
 from mmdet import __version__
+from mmdet.apis import (get_root_logger, init_dist, set_random_seed,
+                        train_detector)
 from mmdet.datasets import build_dataset
-from mmdet.apis import (train_detector, init_dist, get_root_logger,
-                        set_random_seed)
 from mmdet.models import build_detector
-import torch
 
 
 def parse_args():