[Enhancement] Add codespell pre-commit hook and fix typos

.github/ISSUE_TEMPLATE/config.yml

@@ -3,4 +3,4 @@ blank_issues_enabled: false
 contact_links:
   - name: MMSegmentation Documentation
     url: https://mmsegmentation.readthedocs.io
-    about: Check the docs and FAQ to see if you question is already anwsered.
+    about: Check the docs and FAQ to see if you question is already answered.

.github/ISSUE_TEMPLATE/error-report.md

@@ -30,7 +30,7 @@ A clear and concise description of what the bug is.
 
 **Environment**
 
-1. Please run `python mmseg/utils/collect_env.py` to collect necessary environment infomation and paste it here.
+1. Please run `python mmseg/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.)

.pre-commit-config.yaml

@@ -34,6 +34,10 @@ repos:
     hooks:
       - id: markdownlint
         args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034,~MD036"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.1.0
+    hooks:
+      - id: codespell
   - repo: https://github.com/myint/docformatter
     rev: v1.3.1
     hooks:

README.md

@@ -124,7 +124,7 @@ Please refer to [get_started.md](docs/get_started.md#installation) for installat
 
 Please see [train.md](docs/train.md) and [inference.md](docs/inference.md) for the basic usage of MMSegmentation.
 There are also tutorials for [customizing dataset](docs/tutorials/customize_datasets.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing modules](docs/tutorials/customize_models.md), and [customizing runtime](docs/tutorials/customize_runtime.md).
-We also provide many [training tricks](docs/tutorials/training_tricks.md) for better training and [usefule tools](docs/useful_tools.md) for deployment.
+We also provide many [training tricks](docs/tutorials/training_tricks.md) for better training and [useful tools](docs/useful_tools.md) for deployment.
 
 A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
 

docs/tutorials/config.md

@@ -67,7 +67,7 @@ model = dict(
         channels=512,  # The intermediate channels of decode head.
         pool_scales=(1, 2, 3, 6),  # The avg pooling scales of PSPHead. Please refer to paper for details.
         dropout_ratio=0.1,  # The dropout ratio before final classification layer.
-        num_classes=19,  # Number of segmentation classs. Usually 19 for cityscapes, 21 for VOC, 150 for ADE20k.
+        num_classes=19,  # Number of segmentation class. Usually 19 for cityscapes, 21 for VOC, 150 for ADE20k.
         norm_cfg=dict(type='SyncBN', requires_grad=True),  # The configuration of norm layer.
         align_corners=False,  # The align_corners argument for resize in decoding.
         loss_decode=dict(  # Config of loss function for the decode_head.
@@ -82,7 +82,7 @@ model = dict(
         num_convs=1,  # Number of convs in FCNHead. It is usually 1 in auxiliary head.
         concat_input=False,  # Whether concat output of convs with input before classification layer.
         dropout_ratio=0.1,  # The dropout ratio before final classification layer.
-        num_classes=19,  # Number of segmentation classs. Usually 19 for cityscapes, 21 for VOC, 150 for ADE20k.
+        num_classes=19,  # Number of segmentation class. Usually 19 for cityscapes, 21 for VOC, 150 for ADE20k.
         norm_cfg=dict(type='SyncBN', requires_grad=True),  # The configuration of norm layer.
         align_corners=False,  # The align_corners argument for resize in decoding.
         loss_decode=dict(  # Config of loss function for the decode_head.
@@ -132,7 +132,7 @@ test_pipeline = [
         flip=False,  # Whether to flip images during testing
         transforms=[
             dict(type='Resize',  # Use resize augmentation
-                 keep_ratio=True),  # Whether to keep the ratio between height and width, the img_scale set here will be supressed by the img_scale set above.
+                 keep_ratio=True),  # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above.
             dict(type='RandomFlip'),  # Thought RandomFlip is added in pipeline, it is not used when flip=False
             dict(
                 type='Normalize',  # Normalization config, the values are from img_norm_cfg
@@ -245,7 +245,7 @@ runner = dict(
 checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
     by_epoch=False,  # Whether count by epoch or not.
     interval=4000)  # The save interval.
-evaluation = dict(  # The config to build the evaluation hook. Please refer to mmseg/core/evaulation/eval_hook.py for details.
+evaluation = dict(  # The config to build the evaluation hook. Please refer to mmseg/core/evaluation/eval_hook.py for details.
     interval=4000,  # The interval of evaluation.
     metric='mIoU')  # The evaluation metric.
 

docs/tutorials/customize_runtime.md

@@ -113,7 +113,7 @@ Tricks not implemented by the optimizer should be implemented through optimizer
         _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
     ```
 
-    If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to overide the unnecessary settings. See the [config documenetation](https://mmsegmentation.readthedocs.io/en/latest/config.html) for more details.
+    If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to override the unnecessary settings. See the [config documentation](https://mmsegmentation.readthedocs.io/en/latest/config.html) for more details.
 
 - __Use momentum schedule to accelerate model convergence__:
     We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
@@ -198,7 +198,7 @@ custom_hooks = [
 
 ### Modify default runtime hooks
 
-There are some common hooks that are not registerd through `custom_hooks`, they are
+There are some common hooks that are not registered through `custom_hooks`, they are
 
 - log_config
 - checkpoint_config

docs_zh-CN/tutorials/config.md

@@ -241,7 +241,7 @@ runner = dict(
 checkpoint_config = dict(  # 设置检查点钩子 (checkpoint hook) 的配置文件。执行时请参考 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py。
     by_epoch=False,  # 是否按照每个 epoch 去算 runner。
     interval=4000)  # 保存的间隔
-evaluation = dict(  # 构建评估钩 (evaluation hook) 的配置文件。细节请参考 mmseg/core/evaulation/eval_hook.py。
+evaluation = dict(  # 构建评估钩 (evaluation hook) 的配置文件。细节请参考 mmseg/core/evaluation/eval_hook.py。
     interval=4000,  # 评估的间歇点
     metric='mIoU')  # 评估的指标
 

mmseg/core/evaluation/metrics.py

@@ -7,7 +7,7 @@
 
 
 def f_score(precision, recall, beta=1):
-    """calcuate the f-score value.
+    """calculate the f-score value.
 
     Args:
         precision (float | torch.Tensor): The precision value.
@@ -40,7 +40,7 @@ def intersect_and_union(pred_label,
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. The parameter will
             work only when label is str. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. The parameter will
+        reduce_zero_label (bool): Whether ignore zero label. The parameter will
             work only when label is str. Default: False.
 
      Returns:
@@ -102,7 +102,7 @@ def total_intersect_and_union(results,
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
          ndarray: The intersection of prediction and ground truth histogram
@@ -148,7 +148,7 @@ def mean_iou(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
         dict[str, float | ndarray]:
@@ -187,7 +187,7 @@ def mean_dice(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
         dict[str, float | ndarray]: Default metrics.
@@ -228,7 +228,7 @@ def mean_fscore(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
         beta (int): Determines the weight of recall in the combined score.
             Default: False.
 
@@ -274,7 +274,7 @@ def eval_metrics(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
      Returns:
         float: Overall accuracy on all images.
         ndarray: Per category accuracy, shape (num_classes, ).

mmseg/datasets/pipelines/__init__.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .compose import Compose
-from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor,
-                        Transpose, to_tensor)
+from .formatting import (Collect, ImageToTensor, ToDataContainer, ToTensor,
+                         Transpose, to_tensor)
 from .loading import LoadAnnotations, LoadImageFromFile
 from .test_time_aug import MultiScaleFlipAug
 from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,