Upgrade pre commit hooks master (#2155)

* Upgrade pre commit hooks

* Upgrade pre commit hooks

* mim install mmcv-full

* install mim

* install mmcv-full

* test mmcv-full 1.6.0

* fix timm

* fix timm

* fix timm

.github/workflows/build.yml

@@ -70,13 +70,14 @@ jobs:
           coverage run --branch --source mmseg -m pytest tests/
           coverage xml
           coverage report -m
-        if: ${{matrix.torch >= '1.5.0'}}
+        # timm from v0.6.11 requires torch>=1.7
+        if: ${{matrix.torch >= '1.7.0'}}
       - name: Skip timm unittests and generate coverage report
         run: |
           coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
           coverage xml
           coverage report -m
-        if: ${{matrix.torch < '1.5.0'}}
+        if: ${{matrix.torch < '1.7.0'}}
 
   build_cuda101:
     runs-on: ubuntu-18.04
@@ -144,13 +145,14 @@ jobs:
           coverage run --branch --source mmseg -m pytest tests/
           coverage xml
           coverage report -m
-        if: ${{matrix.torch >= '1.5.0'}}
+        # timm from v0.6.11 requires torch>=1.7
+        if: ${{matrix.torch >= '1.7.0'}}
       - name: Skip timm unittests and generate coverage report
         run: |
           coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
           coverage xml
           coverage report -m
-        if: ${{matrix.torch < '1.5.0'}}
+        if: ${{matrix.torch < '1.7.0'}}
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1.0.10
         with:
@@ -249,7 +251,7 @@ jobs:
         run: pip install -e .
       - name: Run unittests
         run: |
-          python -m pip install timm
+          python -m pip install 'timm<0.6.11'
           coverage run --branch --source mmseg -m pytest tests/
       - name: Generate coverage report
         run: |

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://gitlab.com/pycqa/flake8.git
-    rev: 3.8.3
+    rev: 5.0.4
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
@@ -8,11 +8,11 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
-    rev: v0.30.0
+    rev: v0.32.0
     hooks:
       - id: yapf
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.1.0
+    rev: v4.3.0
     hooks:
       - id: trailing-whitespace
       - id: check-yaml
@@ -34,7 +34,7 @@ repos:
           - mdformat_frontmatter
           - linkify-it-py
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.1.0
+    rev: v2.2.1
     hooks:
       - id: codespell
   - repo: https://github.com/myint/docformatter

docs/en/faq.md

@@ -53,7 +53,7 @@ Briefly, it is a deep supervision trick to improve the accuracy. In the training
 
 ## Why is the log file not created
 
-In the train script, we call `get_root_logger`at Line 167, and `get_root_logger` in mmseg calls `get_logger` in mmcv, mmcv will return the same logger which has beed initialized in 'mmsegmentation/tools/train.py' with the parameter `log_file`. There is only one logger (initialized with `log_file`) during training.
+In the train script, we call `get_root_logger`at Line 167, and `get_root_logger` in mmseg calls `get_logger` in mmcv, mmcv will return the same logger which has been initialized in 'mmsegmentation/tools/train.py' with the parameter `log_file`. There is only one logger (initialized with `log_file`) during training.
 Ref: [https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16](https://github.com/open-mmlab/mmcv/blob/21bada32560c7ed7b15b017dc763d862789e29a8/mmcv/utils/logging.py#L9-L16)
 
 If you find the log file not been created, you might check if `mmcv.utils.get_logger` is called elsewhere.

docs/en/tutorials/customize_datasets.md

@@ -33,7 +33,7 @@ data = dict(
 - `train`, `val` and `test`: The [`config`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/config.md)s to build dataset instances for model training, validation and testing by
   using [`build and registry`](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/registry.md) mechanism.
 
-- `samples_per_gpu`: How many samples per batch and per gpu to load during model training, and the `batch_size` of training is equal to `samples_per_gpu` times gpu number, e.g. when using 8 gpus for distributed data parallel trainig and `samples_per_gpu=4`, the `batch_size` is `8*4=32`.
+- `samples_per_gpu`: How many samples per batch and per gpu to load during model training, and the `batch_size` of training is equal to `samples_per_gpu` times gpu number, e.g. when using 8 gpus for distributed data parallel training and `samples_per_gpu=4`, the `batch_size` is `8*4=32`.
   If you would like to define `batch_size` for testing and validation, please use `test_dataloaser` and
   `val_dataloader` with mmseg >=0.24.1.
 

mmseg/models/backbones/vit.py

@@ -337,7 +337,7 @@ class VisionTransformer(BaseModule):
                     constant_init(m, val=1.0, bias=0.)
 
     def _pos_embeding(self, patched_img, hw_shape, pos_embed):
-        """Positiong embeding method.
+        """Positioning embeding method.
 
         Resize the pos_embed, if the input image size doesn't match
             the training size.

mmseg/models/losses/focal_loss.py

@@ -78,7 +78,7 @@ def sigmoid_focal_loss(pred,
                        valid_mask=None,
                        reduction='mean',
                        avg_factor=None):
-    r"""A warpper of cuda version `Focal Loss
+    r"""A wrapper of cuda version `Focal Loss
     <https://arxiv.org/abs/1708.02002>`_.
     Args:
         pred (torch.Tensor): The prediction with shape (N, C), C is the number

setup.cfg

@@ -19,4 +19,4 @@ default_section = THIRDPARTY
 skip = *.po,*.ts,*.ipynb
 count =
 quiet-level = 3
-ignore-words-list = formating,sur,hist,dota,ba
+ignore-words-list = formating,sur,hist,dota,ba,warmup