[Typo] Fix typo in docs and docstring (#3254)

* fix typo in docs and docstring

* update launch utility url

.github/ISSUE_TEMPLATE/error-report.md

@@ -26,7 +26,7 @@ A placeholder for the command.
 
 **Environment**
 
-1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment infomation and paste it here.
+1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here.
 2. You may add addition that may be helpful for locating the problem, such as
     - How you installed PyTorch [e.g., pip, conda, source]
     - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)

.github/ISSUE_TEMPLATE/reimplementation_questions.md

@@ -18,7 +18,7 @@ There are several common situations in the reimplementation issues as below
 There are several things to do for different cases as below.
 - For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue.
 - For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write.
-- One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemted code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you.
+- One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you.
 
 **Checklist**
 1. I have searched related issues but cannot get the expected help.
@@ -42,7 +42,7 @@ A placeholder for the config.
 
 **Environment**
 
-1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment infomation and paste it here.
+1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here.
 2. You may add addition that may be helpful for locating the problem, such as
     - How you installed PyTorch [e.g., pip, conda, source]
     - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)

.isort.cfg

@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet
-known_third_party = PIL,asynctest,cityscapesscripts,cv2,matplotlib,numpy,onnx,pycocotools,pytest,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
+known_third_party = PIL,asynctest,cityscapesscripts,cv2,matplotlib,mmcv,numpy,onnx,pycocotools,pytest,robustness_eval,roi_align,roi_pool,seaborn,six,terminaltables,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY

docs/getting_started.md

@@ -301,7 +301,7 @@ GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco
 You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
 
 If you have just multiple machines connected with ethernet, you can refer to
-PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility).
+PyTorch [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
 Usually it is slow if you do not have high speed networking like InfiniBand.
 
 ### Launch multiple jobs on a single machine

docs/tutorials/data_pipeline.md

@@ -19,7 +19,7 @@ We present a classical pipeline in the following figure. The blue blocks are pip
 
 The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.
 
-Here is an pipeline example for Faster R-CNN.
+Here is a pipeline example for Faster R-CNN.
 ```python
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

tests/test_assigner.py

@@ -82,7 +82,7 @@ def test_max_iou_assigner_with_empty_gt():
 
 
 def test_max_iou_assigner_with_empty_boxes():
-    """Test corner case where an network might predict no boxes."""
+    """Test corner case where a network might predict no boxes."""
     self = MaxIoUAssigner(
         pos_iou_thr=0.5,
         neg_iou_thr=0.5,
@@ -106,7 +106,7 @@ def test_max_iou_assigner_with_empty_boxes():
 
 
 def test_max_iou_assigner_with_empty_boxes_and_ignore():
-    """Test corner case where an network might predict no boxes and
+    """Test corner case where a network might predict no boxes and
     ignore_iof_thr is on."""
     self = MaxIoUAssigner(
         pos_iou_thr=0.5,
@@ -140,7 +140,7 @@ def test_max_iou_assigner_with_empty_boxes_and_ignore():
 
 
 def test_max_iou_assigner_with_empty_boxes_and_gt():
-    """Test corner case where an network might predict no boxes and no gt."""
+    """Test corner case where a network might predict no boxes and no gt."""
     self = MaxIoUAssigner(
         pos_iou_thr=0.5,
         neg_iou_thr=0.5,