Update pre-commit hook config and fix styles (#2182)

* update pre-commit hook config

* fix styles conflicting with pre-commit hooks

.github/ISSUE_TEMPLATE/general_questions.md

@@ -6,5 +6,3 @@ labels: ''
 assignees: ''
 
 ---
-
-

.pre-commit-config.yaml

 repos:
-- repo: https://github.com/asottile/seed-isort-config
-  rev: v1.9.3
-  hooks:
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 3.7.9
+    hooks:
+      - id: flake8
+  - repo: https://github.com/asottile/seed-isort-config
+    rev: v2.1.0
+    hooks:
       - id: seed-isort-config
-- repo: https://github.com/pre-commit/mirrors-isort
-  rev: v4.3.21
-  hooks:
-      - id: isort
-- repo: https://github.com/pre-commit/mirrors-yapf
-  rev: v0.29.0
-  hooks:
-    - id: yapf
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v2.4.0
-  hooks:
-    - id: flake8
-    - id: trailing-whitespace
-    - id: check-yaml
-    - id: end-of-file-fixer
-    - id: requirements-txt-fixer
+  - repo: https://github.com/timothycrosley/isort
+    rev: 4.3.21
+    hooks:
+        - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.29.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.5.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: fix-encoding-pragma
+        args: ["--remove"]

configs/cityscapes/README.md

 ## Common settings
 
-- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate. 
+- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate.
 - All models were trained on `cityscapes_train`, and tested on `cityscapes_val`.
 - 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870)
 - All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo.
@@ -25,4 +25,3 @@ Download links and more models with different backbones and training schemes wil
 
 **Notes:**
 - In the original paper, the mask AP of Mask R-CNN R-50-FPN is 31.5.
-

configs/dcn/README.md

@@ -42,4 +42,4 @@
 - `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling.
 - The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
 - (*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance.
-- **Memory, Train/Inf time is outdated.**
\ No newline at end of file
+- **Memory, Train/Inf time is outdated.**

configs/empirical_attention/README.md

@@ -20,4 +20,3 @@
 | R-50      | 0010                | N    | 1x      | 38.2   |     -    |
 | R-50      | 1111                | Y    | 1x      | 41.0   |     -    |
 | R-50      | 0010                | Y    | 1x      | 40.8   |     -    |
-

configs/foveabox/README.md

@@ -4,7 +4,7 @@ FoveaBox is an accurate, flexible and completely anchor-free object detection sy
 Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
 
 ## Main Results
-### Results on R50/101-FPN 
+### Results on R50/101-FPN
 
 | Backbone  | Style   |  align  | ms-train| Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
 |:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
@@ -33,4 +33,4 @@ Please consider citing our paper in your publications if the project helps your
   journal={arXiv preprint arXiv:1904.03797},
   year={2019}
 }
-```
\ No newline at end of file
+```

configs/gcnet/README.md

@@ -55,4 +55,3 @@ The results on COCO 2017val are shown in the below table.
 - `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone.
 - `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone.
 - `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively.
-

configs/ghm/README.md

@@ -18,4 +18,4 @@
 |    R-50-FPN     | pytorch |   1x    |   3.9    | 0.500               | 9.4            |  36.9  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_r50_fpn_1x_20190608-b9aa5862.pth) |
 |    R-101-FPN    | pytorch |   1x    |   5.8    | 0.625               | 8.5            |  39.0  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_r101_fpn_1x_20190608-b885b74a.pth) |
 | X-101-32x4d-FPN | pytorch |   1x    |   7.0    | 0.818               | 7.6            |  40.5  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_x101_32x4d_fpn_1x_20190608-ed295d22.pth) |
-| X-101-64x4d-FPN | pytorch |   1x    |   9.9    | 1.191               | 6.1            |  41.6  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_x101_64x4d_fpn_1x_20190608-7f2037ce.pth) |
\ No newline at end of file
+| X-101-64x4d-FPN | pytorch |   1x    |   9.9    | 1.191               | 6.1            |  41.6  | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/ghm/retinanet_ghm_x101_64x4d_fpn_1x_20190608-7f2037ce.pth) |

configs/gn+ws/README.md

@@ -51,4 +51,4 @@ Note:
 
 - GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN.
 - In the paper, a 20-23-24e lr schedule is used instead of 2x.
-- The X-50-GN and X-101-GN pretrained models are also shared by the authors.
\ No newline at end of file
+- The X-50-GN and X-101-GN pretrained models are also shared by the authors.

configs/gn/README.md

@@ -25,4 +25,4 @@
 **Notes:**
 - (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk).
 - The `3x` schedule is epoch [28, 34, 36].
-- **Memory, Train/Inf time is outdated.**
\ No newline at end of file
+- **Memory, Train/Inf time is outdated.**

configs/guided_anchoring/README.md

@@ -39,4 +39,4 @@ The results on COCO 2017 val is shown in the below table. (results on test-dev a
 
 
 
-- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.
\ No newline at end of file
+- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.

configs/hrnet/README.md

@@ -12,7 +12,7 @@
 
 @article{SunZJCXLMWLW19,
   title={High-Resolution Representations for Labeling Pixels and Regions},
-  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
+  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao
   and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
   journal   = {CoRR},
   volume    = {abs/1904.04514},

configs/pascal_voc/README.md

@@ -3,4 +3,4 @@
 | Backbone | Size  | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                             Download                                                             |
 | :------: | :---: | :---: | :-----: | :------: | :-----------------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------: |
 |  VGG16   |  300  | caffe |  240e   |   2.5    |        0.159        |  35.7 / 53.6   |  77.5  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_voc_vgg16_caffe_240e_20190501-7160d09a.pth) |
-|  VGG16   |  512  | caffe |  240e   |   4.3    |        0.214        |  27.5 / 35.9   |  80.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_voc_vgg16_caffe_240e_20190501-ff194be1.pth) |
\ No newline at end of file
+|  VGG16   |  512  | caffe |  240e   |   4.3    |        0.214        |  27.5 / 35.9   |  80.0  | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_voc_vgg16_caffe_240e_20190501-ff194be1.pth) |

configs/reppoints/README.md

@@ -3,7 +3,7 @@
 By [Ze Yang](https://yangze.tech/), [Shaohui Liu](http://b1ueber2y.me/), and [Han Hu](https://ancientmooner.github.io/).
 
 We provide code support and configuration files to reproduce the results in the paper for
-["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection. 
+["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection.
 
 ## Introduction
 
@@ -55,9 +55,9 @@ The results on COCO 2017val are shown in the table below.
 
 **Notes:**
 
-- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively. 
+- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively.
 - `DCN` denotes replacing 3x3 conv with the 3x3 deformable convolution in `c3-c5` stages of backbone.
-- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted. 
+- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted.
 - `moment`, `partial MinMax`, `MinMax` in the `convert func` column are three functions to convert a point set to a pseudo box.
 - `ms` denotes multi-scale training or multi-scale test.
 - Note the results here are slightly different from those reported in the paper, due to framework change. While the original paper uses an [MXNet](https://mxnet.apache.org/) implementation, we re-implement the method in [PyTorch](https://pytorch.org/) based on mmdetection.

configs/scratch/README.md

@@ -19,4 +19,4 @@
 | Mask R-CNN   | R-50-FPN  | pytorch | 6x      | 41.0   | 37.4    | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/scratch/scratch_mask_rcnn_r50_fpn_gn_6x_20190515-96743f5e.pth) |
 
 Note:
-- The above models are trained with 16 GPUs.
\ No newline at end of file
+- The above models are trained with 16 GPUs.

docs/ROBUSTNESS_BENCHMARKING.md

@@ -7,11 +7,11 @@ This page provides basic tutorials how to use the benchmark.
 
 ```
 @article{michaelis2019winter,
-  title={Benchmarking Robustness in Object Detection: 
+  title={Benchmarking Robustness in Object Detection:
     Autonomous Driving when Winter is Coming},
-  author={Michaelis, Claudio and Mitzkus, Benjamin and 
-    Geirhos, Robert and Rusak, Evgenia and 
-    Bringmann, Oliver and Ecker, Alexander S. and 
+  author={Michaelis, Claudio and Mitzkus, Benjamin and
+    Geirhos, Robert and Rusak, Evgenia and
+    Bringmann, Oliver and Ecker, Alexander S. and
     Bethge, Matthias and Brendel, Wieland},
   journal={arXiv:1907.07484},
   year={2019}
@@ -25,7 +25,7 @@ This page provides basic tutorials how to use the benchmark.
 To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark)
 
 The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally
-published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich. 
+published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich.
 
 The image corruption functions are included in this library but can be installed separately using:
 
@@ -34,7 +34,7 @@ pip install imagecorruptions
 ```
 
 Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images.
-We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library, 
+We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library,
 which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions).
 
 ## Inference with pretrained models
@@ -106,5 +106,4 @@ Cascade Mask R-CNN | R-50-FPN  | pytorch | 1x| 41.2   | 20.7         | 50.2  | 3
 RetinaNet    | R-50-FPN  | pytorch | 1x      | 35.6   | 17.8         | 50.1  | -             | -             | -      |
 Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8         | 28.1          | 64.0   |
 
-Results may vary slightly due to the stochastic application of the corruptions. 
-
+Results may vary slightly due to the stochastic application of the corruptions.

mmdet/datasets/pipelines/transforms.py

@@ -779,10 +779,10 @@ class Albu(object):
         Returns:
             obj: The constructed object.
         """
-        assert isinstance(cfg, dict) and "type" in cfg
+        assert isinstance(cfg, dict) and 'type' in cfg
         args = cfg.copy()
 
-        obj_type = args.pop("type")
+        obj_type = args.pop('type')
         if mmcv.is_str(obj_type):
             if albumentations is None:
                 raise RuntimeError('albumentations is not installed')

mmdet/models/anchor_heads/reppoints_head.py

@@ -94,9 +94,9 @@ class RepPointsHead(nn.Module):
         self.dcn_kernel = int(np.sqrt(num_points))
         self.dcn_pad = int((self.dcn_kernel - 1) / 2)
         assert self.dcn_kernel * self.dcn_kernel == num_points, \
-            "The points number should be a square number."
+            'The points number should be a square number.'
         assert self.dcn_kernel % 2 == 1, \
-            "The points number should be an odd square number."
+            'The points number should be an odd square number.'
         dcn_base = np.arange(-self.dcn_pad,
                              self.dcn_pad + 1).astype(np.float64)
         dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)

mmdet/models/backbones/resnet.py

@@ -28,9 +28,9 @@ class BasicBlock(nn.Module):
                  gcb=None,
                  gen_attention=None):
         super(BasicBlock, self).__init__()
-        assert dcn is None, "Not implemented yet."
-        assert gen_attention is None, "Not implemented yet."
-        assert gcb is None, "Not implemented yet."
+        assert dcn is None, 'Not implemented yet.'
+        assert gen_attention is None, 'Not implemented yet.'
+        assert gcb is None, 'Not implemented yet.'
 
         self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
         self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)

mmdet/models/detectors/grid_rcnn.py

@@ -198,7 +198,7 @@ class GridRCNN(TwoStageDetector):
 
     def simple_test(self, img, img_meta, proposals=None, rescale=False):
         """Test without augmentation."""
-        assert self.with_bbox, "Bbox head must be implemented."
+        assert self.with_bbox, 'Bbox head must be implemented.'
 
         x = self.extract_feat(img)