Add code spell hook to pre-commit and fix typos (#1384)

* Add code-spell hook to pre-commit

* Add code-spell hook to pre-commit and fix typos

* fix setup.cfg

.pre-commit-config.yaml

@@ -35,6 +35,10 @@ repos:
       - id: markdownlint
         args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034",
               "-t", "allow_different_nesting"]
+  - 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

@@ -43,7 +43,7 @@ There are two versions of MMCV:
 - **mmcv-full**: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
 - **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
 
-**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full verion is highly recommended if CUDA is avaliable`.
+**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full version is highly recommended if CUDA is available`.
 
 a. Install the full version.
 

docs/deployment/tensorrt_plugin.md

@@ -73,7 +73,7 @@ pip install $TENSORRT_DIR/onnx_graphsurgeon/onnx_graphsurgeon-0.2.6-py2.py3-none
 pip install $TENSORRT_DIR/graphsurgeon/graphsurgeon-0.4.5-py2.py3-none-any.whl
 ```
 
-For more detailed infomation of installing TensorRT using tar, please refer to [Nvidia' website](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-721/install-guide/index.html#installing-tar).
+For more detailed information of installing TensorRT using tar, please refer to [Nvidia' website](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-721/install-guide/index.html#installing-tar).
 
 #### Build on Linux
 

docs/get_started/build.md

@@ -189,7 +189,7 @@ pip list
    <... omit some lines ...>
    ```
 
-   This should already be done by CUDA installer. If not, or you have multiple version of CUDA tookit installed, set it with
+   This should already be done by CUDA installer. If not, or you have multiple version of CUDA toolkit installed, set it with
 
    ```shell
    $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
@@ -202,7 +202,7 @@ pip list
    ```shell
    # Suppose you are using GTX 1080, which is of capability 6.1
    $env:TORCH_CUDA_ARCH_LIST="6.1"
-   # OR build all suppoted arch, will be slow
+   # OR build all supported arch, will be slow
    $env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
    ```
 

docs/get_started/installation.md

@@ -6,7 +6,7 @@ There are two versions of MMCV:
 - **mmcv**: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
 
 ```{warning}
-Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full verion is highly recommended if CUDA is avaliable`.
+Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full version is highly recommended if CUDA is avaliable`.
 ```
 
 a. Install the full version.

docs/understand_mmcv/cnn.md

@@ -140,7 +140,7 @@ We provide the following initialization methods.
 
 - kaiming_init
 
-  Initialize module paramters with the valuse according to the method
+  Initialize module parameters with the values according to the method
   described in [Delving deep into rectifiers: Surpassing human-level
   performance on ImageNet classification - He, K. et al. (2015)](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf)
 
@@ -430,7 +430,7 @@ Let us introduce the usage of `initialize` in detail.
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
 
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     model1 = FooConv1d(init_cfg1)
     model2 = FooConv2d(init_cfg2)
     init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
@@ -473,7 +473,7 @@ Let us introduce the usage of `initialize` in detail.
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
 
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     model1 = FooConv1d(init_cfg1)
     model2 = FooConv2d(init_cfg2)
     init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)

docs/understand_mmcv/runner.md

@@ -92,7 +92,7 @@ We will walk you through the usage of runner with a classification task. The fol
 model=...
 # initialize optimizer, typically, we set: cfg.optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
 optimizer = build_optimizer(model, cfg.optimizer)
-# intialize the dataloader corresponding to the workflow(train/val)
+# initialize the dataloader corresponding to the workflow(train/val)
 data_loaders = [
         build_dataloader(
             ds,
@@ -119,7 +119,7 @@ runner = build_runner(
 **(3) Register training hooks and customized hooks.**
 
 ```python
-# register defalt hooks neccesary for traning
+# register default hooks necessary for training
 runner.register_training_hooks(
     # configs of learning rate, it is typically set as:
     # lr_config = dict(policy='step', step=[100, 150])

docs_zh_CN/understand_mmcv/cnn.md

@@ -419,7 +419,7 @@ conv = ConvModule(
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
 
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     model1 = FooConv1d(init_cfg1)
     model2 = FooConv2d(init_cfg2)
     init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
@@ -462,7 +462,7 @@ conv = ConvModule(
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
 
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     model1 = FooConv1d(init_cfg1)
     model2 = FooConv2d(init_cfg2)
     init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)

mmcv/cnn/utils/weight_init.py

@@ -383,7 +383,7 @@ class UniformInit(BaseInit):
 
 @INITIALIZERS.register_module(name='Kaiming')
 class KaimingInit(BaseInit):
-    r"""Initialize module paramters with the valuse according to the method
+    r"""Initialize module parameters with the values according to the method
     described in `Delving deep into rectifiers: Surpassing human-level
     performance on ImageNet classification - He, K. et al. (2015).
     <https://www.cv-foundation.org/openaccess/content_iccv_2015/

mmcv/fileio/parse.py

@@ -4,7 +4,7 @@ def list_from_file(filename, prefix='', offset=0, max_num=0, encoding='utf-8'):
 
     Args:
         filename (str): Filename.
-        prefix (str): The prefix to be inserted to the begining of each item.
+        prefix (str): The prefix to be inserted to the beginning of each item.
         offset (int): The offset of lines.
         max_num (int): The maximum number of lines to be read,
             zeros and negatives mean no limitation.

mmcv/image/colorspace.py

@@ -86,7 +86,7 @@ def _convert_input_type_range(img):
 
     It converts the input image to np.float32 type and range of [0, 1].
     It is mainly used for pre-processing the input image in colorspace
-    convertion functions such as rgb2ycbcr and ycbcr2rgb.
+    conversion functions such as rgb2ycbcr and ycbcr2rgb.
 
     Args:
         img (ndarray): The input image. It accepts:
@@ -116,7 +116,7 @@ def _convert_output_type_range(img, dst_type):
     images will be converted to np.uint8 type with range [0, 255]. If
     `dst_type` is np.float32, it converts the image to np.float32 type with
     range [0, 1].
-    It is mainly used for post-processing images in colorspace convertion
+    It is mainly used for post-processing images in colorspace conversion
     functions such as rgb2ycbcr and ycbcr2rgb.
 
     Args:

mmcv/onnx/symbolic.py

@@ -51,7 +51,7 @@ def _interpolate(name, dim, interpolate_mode):
                 'Resize',
                 input,
                 empty_tensor,
-                # roi only takes effect whith
+                # roi only takes effect with
                 # coordinate_transformation_mode="tf_crop_and_resize"
                 scales,  # scales is not needed since we are sending out_size
                 output_size,

mmcv/ops/ball_query.py

@@ -22,7 +22,7 @@ class BallQuery(Function):
             center_xyz (Tensor): (B, npoint, 3) centers of the ball query.
 
         Returns:
-            Tensor: (B, npoint, nsample) tensor with the indicies of
+            Tensor: (B, npoint, nsample) tensor with the indices of
                 the features that form the query balls.
         """
         assert center_xyz.is_contiguous()

mmcv/ops/csrc/onnxruntime/cpu/gridSample.cpp

@@ -249,7 +249,7 @@ void GridSampleKernel::Compute(OrtKernelContext *context) {
           int64_t ix_nearest = static_cast<int64_t>(std::nearbyint(ix));
           int64_t iy_nearest = static_cast<int64_t>(std::nearbyint(iy));
 
-          // assign nearest neighor pixel value to output pixel
+          // assign nearest neighbor pixel value to output pixel
           float *out_ptr_NCHW = out_ptr + n * out_sN + h * out_sH + w * out_sW;
           const float *inp_ptr_NC = inp_ptr_N;
           for (int64_t c = 0; c < C;
@@ -284,7 +284,7 @@ void GridSampleKernel::Compute(OrtKernelContext *context) {
                ++c, out_ptr_NCHW += out_sC, inp_ptr_NC += inp_sC) {
             float coefficients[4];
 
-            // Interpolate 4 values in the x directon
+            // Interpolate 4 values in the x direction
             for (int64_t i = 0; i < 4; ++i) {
               coefficients[i] = cubic_interp1d<float>(
                   get_value_bounded<float>(inp_ptr_NC, ix_nw - 1, iy_nw - 1 + i,

mmcv/ops/csrc/onnxruntime/cpu/reduce_ops.cpp

@@ -122,7 +122,7 @@ void CumMax_CumMin_CPU(const T1 *input, T1 *output, T2 *indices,
           reversed_dim_cumprod[dim_i + 1] * out_dimensions.data()[dim_i];
     }
 
-    // do cummax or cummin besed on `Operation` type
+    // do cummax or cummin based on `Operation` type
     tensor_dim_apply3<float, int64_t>(
         input, output, indices, dim, ndims, reversed_dim_cumprod,
         cummax_cummin_helper<float, int64_t, Operation>);
@@ -135,7 +135,7 @@ void MMCVCumMaxKernel::Compute(OrtKernelContext *context) {
   const float *input_data =
       reinterpret_cast<const float *>(ort_.GetTensorData<float>(input));
 
-  // get ouput
+  // get output
   OrtTensorDimensions out_dimensions(ort_, input);
   OrtValue *output = ort_.KernelContext_GetOutput(
       context, 0, out_dimensions.data(), out_dimensions.size());
@@ -164,7 +164,7 @@ void MMCVCumMinKernel::Compute(OrtKernelContext *context) {
   const float *input_data =
       reinterpret_cast<const float *>(ort_.GetTensorData<float>(input));
 
-  // get ouput
+  // get output
   OrtTensorDimensions out_dimensions(ort_, input);
   OrtValue *output = ort_.KernelContext_GetOutput(
       context, 0, out_dimensions.data(), out_dimensions.size());

mmcv/ops/csrc/pytorch/info.cpp

@@ -25,7 +25,7 @@ std::string get_compiling_cuda_version() {
   printCudaStyleVersion(get_cudart_version());
   return oss.str();
 #else
-  return std::string("rocm not vailable");
+  return std::string("rocm not available");
 #endif
 #else
   return std::string("not available");

mmcv/ops/csrc/pytorch/modulated_deform_conv.cpp

@@ -99,10 +99,10 @@ void modulated_deform_conv_forward(
   const int kernel_w_ = weight.size(3);
 
   if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
+    AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
              kernel_h_, kernel_w, kernel_h_, kernel_w_);
   if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
+    AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
              channels, channels_kernel * group);
 
   const int height_out =
@@ -220,10 +220,10 @@ void modulated_deform_conv_backward(
   const int kernel_h_ = weight.size(2);
   const int kernel_w_ = weight.size(3);
   if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
-    AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
+    AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).",
              kernel_h_, kernel_w, kernel_h_, kernel_w_);
   if (channels != channels_kernel * group)
-    AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
+    AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).",
              channels, channels_kernel * group);
 
   const int height_out =

mmcv/ops/csrc/tensorrt/plugins/trt_grid_sampler_kernel.cu

@@ -148,7 +148,7 @@ __global__ void grid_sampler_2d_kernel(
     const int n = index / (out_H * out_W);
     const int grid_offset = n * grid_sN + h * grid_sH + w * grid_sW;
 
-    // get the corresponding input x, y co-ordinates from grid
+    // get the corresponding input x, y coordinates from grid
     scalar_t ix = grid[grid_offset];
     scalar_t iy = grid[grid_offset + grid_sCoor];
 
@@ -197,7 +197,7 @@ __global__ void grid_sampler_2d_kernel(
       int ix_nearest = static_cast<int>(::round(ix));
       int iy_nearest = static_cast<int>(::round(iy));
 
-      // assign nearest neighor pixel value to output pixel
+      // assign nearest neighbor pixel value to output pixel
       auto inp_ptr_NC = input + n * inp_sN;
       auto out_ptr_NCHW = output + n * out_sN + h * out_sH + w * out_sW;
       for (int c = 0; c < C;
@@ -249,7 +249,7 @@ __global__ void grid_sampler_3d_kernel(
     const int grid_offset =
         n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW;
 
-    // get the corresponding input x, y, z co-ordinates from grid
+    // get the corresponding input x, y, z coordinates from grid
     scalar_t ix = grid[grid_offset];
     scalar_t iy = grid[grid_offset + grid_sCoor];
     scalar_t iz = grid[grid_offset + 2 * grid_sCoor];
@@ -367,7 +367,7 @@ __global__ void grid_sampler_3d_kernel(
       int iy_nearest = static_cast<int>(::round(iy));
       int iz_nearest = static_cast<int>(::round(iz));
 
-      // assign nearest neighor pixel value to output pixel
+      // assign nearest neighbor pixel value to output pixel
       auto inp_ptr_NC = input + n * inp_sN;
       auto out_ptr_NCDHW =
           output + n * out_sN + d * out_sD + h * out_sH + w * out_sW;

mmcv/ops/csrc/tensorrt/plugins/trt_nms_kernel.cu

@@ -144,7 +144,7 @@ size_t get_onnxnms_workspace_size(size_t num_batches, size_t spatial_dimension,
  * @param[in] score_threshold threshold of scores
  * @param[in] offset box offset, only 0 or 1 is valid
  * @param[out] output with shape [output_length, 3], each row contain index
- *     (batch_id, class_id, boxes_id), filling -1 if result is not vaild.
+ *     (batch_id, class_id, boxes_id), filling -1 if result is not valid.
  * @param[in] center_point_box 0 if boxes is [left, top, right, bottom] 1 if
  *     boxes is [center_x, center_y, width, height]
  * @param[in] num_batches batch size of boxes and scores

mmcv/parallel/data_parallel.py

@@ -30,7 +30,7 @@ class MMDataParallel(DataParallel):
     def forward(self, *inputs, **kwargs):
         """Override the original forward function.
 
-        The main difference lies in the CPU inference where the datas in
+        The main difference lies in the CPU inference where the data in
         :class:`DataContainers` will still be gathered.
         """
         if not self.device_ids:

mmcv/runner/fp16_utils.py

@@ -15,7 +15,7 @@ try:
     # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
     # and used; otherwise, auto fp16 will adopt mmcv's implementation.
     # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
-    # manually, so the behavior may not be consistant with real amp.
+    # manually, so the behavior may not be consistent with real amp.
     from torch.cuda.amp import autocast
 except ImportError:
     pass

mmcv/runner/hooks/logger/mlflow.py

@@ -28,7 +28,7 @@ class MlflowLoggerHook(LoggerHook):
             tags (dict of str: str, optional): Tags for the current run.
                 Default None.
                 If not None, set tags for the current run.
-            log_model (bool, optional): Wheter to log an MLflow artifact.
+            log_model (bool, optional): Whether to log an MLflow artifact.
                 Default True.
                 If True, log runner.model as an MLflow artifact
                 for the current run.

mmcv/runner/optimizer/default_constructor.py

@@ -53,7 +53,7 @@ class DefaultOptimizerConstructor:
             offset layer in deformable convs, set ``dcn_offset_lr_mult``
             to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``.
         2. If the option ``dcn_offset_lr_mult`` is used, the constructor will
-            apply it to all the DCN layers in the model. So be carefull when
+            apply it to all the DCN layers in the model. So be careful when
             the model contains multiple DCN layers in places other than
             backbone.
 

mmcv/utils/config.py

@@ -257,7 +257,7 @@ class Config:
                                    f'Duplicate keys: {duplicate_keys}')
                 base_cfg_dict.update(c)
 
-            # Subtitute base variables from strings to their actual values
+            # Substitute base variables from strings to their actual values
             cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict,
                                                     base_cfg_dict)
 

mmcv/utils/path.py

@@ -47,7 +47,7 @@ def scandir(dir_path, suffix=None, recursive=False):
             directory. Default: False.
 
     Returns:
-        A generator for all the interested files with relative pathes.
+        A generator for all the interested files with relative paths.
     """
     if isinstance(dir_path, (str, Path)):
         dir_path = str(dir_path)

mmcv/utils/registry.py

@@ -68,7 +68,7 @@ class Registry:
 
     Please refer to
     https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
-    advanced useage.
+    advanced usage.
 
     Args:
         name (str): Registry name.

mmcv/utils/testing.py

@@ -33,7 +33,7 @@ def _any(judge_result):
             if _any(element):
                 return True
     except TypeError:
-        # Maybe encouter the case: torch.tensor(True) | torch.tensor(False)
+        # Maybe encounter the case: torch.tensor(True) | torch.tensor(False)
         if judge_result:
             return True
     return False

setup.cfg

@@ -17,3 +17,6 @@ known_first_party = mmcv
 known_third_party = addict,cv2,m2r,numpy,onnx,onnxruntime,packaging,pytest,pytorch_sphinx_theme,recommonmark,scipy,sphinx,tensorrt,torch,torchvision,yaml,yapf
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
+
+[codespell]
+ignore-words-list = inout,hist

tests/test_image/test_geometric.py

@@ -88,7 +88,7 @@ class TestGeometric:
         assert resized_img.shape == (
             912, 810, 3) and h_scale == 912 / 300 and w_scale == 810 / 400
 
-        # one of size and scale_factor shuld be given
+        # one of size and scale_factor should be given
         with pytest.raises(ValueError):
             mmcv.imresize_to_multiple(
                 self.img, divisor=16, size=(1000, 600), scale_factor=2)

tests/test_ops/test_border_align.py

@@ -15,7 +15,7 @@ boxes_arr = [[[0, 0, 2, 1], [1, 0, 3, 1], [1, 0, 2, 1], [0, 0, 3, 1],
               [0, 1, 1, 2], [0, 0, 3, 2], [1, 0, 3, 2], [2, 0, 3, 2]]]
 output_dict = {
     # [1,c,h*w,4] for each value,
-    # the ouput is manually checked for its correctness
+    # the output is manually checked for its correctness
 
     # pool_size=1
     1: [[[[3., 6., 1., 2.], [4., 7., -1., 1.], [3., 7., 1., 2.],

tests/test_ops/test_tensorrt.py

@@ -80,7 +80,7 @@ def test_roialign():
                 opset_version=11)
         onnx_model = onnx.load(onnx_file)
 
-        # create trt engine and wraper
+        # create trt engine and wrapper
         opt_shape_dict = {
             'input': [list(input.shape),
                       list(input.shape),
@@ -141,7 +141,7 @@ def test_nms():
             opset_version=11)
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'boxes': [list(boxes.shape),
                   list(boxes.shape),
@@ -220,7 +220,7 @@ def test_batched_nms():
             output_names=output_names,
             opset_version=11)
     onnx_model = onnx.load(onnx_file)
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'boxes': [list(boxes.shape),
                   list(boxes.shape),
@@ -295,7 +295,7 @@ def test_scatternd():
 
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'input': [list(data.shape),
                   list(data.shape),
@@ -372,7 +372,7 @@ def test_deform_conv():
 
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'input': [list(x.shape), list(x.shape),
                   list(x.shape)],
@@ -443,7 +443,7 @@ def test_modulated_deform_conv(with_bias):
 
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'input': [list(x.shape), list(x.shape),
                   list(x.shape)],
@@ -515,7 +515,7 @@ def test_grid_sample(mode, padding_mode, align_corners):
 
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     opt_shape_dict = {
         'input': [list(input.shape),
                   list(input.shape),
@@ -602,7 +602,7 @@ def test_cummin_cummax(func: Callable):
 
             onnx_model = onnx.load(onnx_file)
 
-            # create trt engine and wraper
+            # create trt engine and wrapper
             opt_shape_dict = {
                 'input':
                 [list(input.shape),
@@ -688,7 +688,7 @@ def test_instance_norm(dynamic_export, fp16_mode):
 
     onnx_model = onnx.load(onnx_file)
 
-    # create trt engine and wraper
+    # create trt engine and wrapper
     if dynamic_export:
         opt_shape_dict = {
             'input':
@@ -777,7 +777,7 @@ def test_corner_pool(mode):
                 opset_version=opset)
         onnx_model = onnx.load(onnx_file)
 
-        # create trt engine and wraper
+        # create trt engine and wrapper
         opt_shape_dict = {
             'input': [list(input.shape),
                       list(input.shape),

tests/test_parallel.py

@@ -29,7 +29,7 @@ def test_is_module_wrapper():
             return self.conv(x)
 
     # _verify_model_across_ranks is added in torch1.9.0 so we should check
-    # wether _verify_model_across_ranks is the member of torch.distributed
+    # whether _verify_model_across_ranks is the member of torch.distributed
     # before mocking
     if hasattr(torch.distributed, '_verify_model_across_ranks'):
         torch.distributed._verify_model_across_ranks = mock

tests/test_runner/test_basemodule.py

@@ -503,7 +503,7 @@ def test_sequential_model_weight_init():
                        torch.full(seq_model[1].conv2d.weight.shape, 2.))
     assert torch.equal(seq_model[1].conv2d.bias,
                        torch.full(seq_model[1].conv2d.bias.shape, 3.))
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     layers = [build_from_cfg(cfg, COMPONENTS) for cfg in seq_model_cfg]
     seq_model = Sequential(
         *layers,
@@ -540,7 +540,7 @@ def test_modulelist_weight_init():
                        torch.full(modellist[1].conv2d.weight.shape, 2.))
     assert torch.equal(modellist[1].conv2d.bias,
                        torch.full(modellist[1].conv2d.bias.shape, 3.))
-    # inner init_cfg has highter priority
+    # inner init_cfg has higher priority
     layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
     modellist = ModuleList(
         layers,

tests/test_runner/test_eval_hook.py

@@ -236,7 +236,7 @@ def test_eval_hook():
         assert osp.exists(ckpt_path)
         assert runner.meta['hook_msgs']['best_score'] == -3
 
-    # Test the EvalHook when resume happend
+    # Test the EvalHook when resume happened
     data_loader = DataLoader(EvalDataset())
     eval_hook = EvalHook(data_loader, save_best='acc')
     with tempfile.TemporaryDirectory() as tmpdir: