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

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: