[Enhance] Add spell hook (#116)

* add spell hook

* fix code spell

.pre-commit-config.yaml

@@ -1,4 +1,3 @@
-exclude: ^tests/data/
 repos:
   - repo: https://gitlab.com/pycqa/flake8.git
     rev: 3.8.3
@@ -40,6 +39,10 @@ repos:
             "-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:

backend_ops/onnxruntime/grid_sample/grid_sample.cpp

@@ -250,7 +250,7 @@ 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;
@@ -285,7 +285,7 @@ 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,

backend_ops/tensorrt/batched_nms/allClassNMS.cu

@@ -69,7 +69,7 @@ template <typename T_BBOX>
 __device__ void emptyBboxInfo(BboxInfo<T_BBOX> *bbox_info) {
   bbox_info->conf_score = T_BBOX(0);
   bbox_info->label =
-      -2;  // -1 is used for all labels when shared_location is ture
+      -2;  // -1 is used for all labels when shared_location is true
   bbox_info->bbox_idx = -1;
   bbox_info->kept = false;
 }

backend_ops/tensorrt/batched_nms/trt_batched_nms.cpp

@@ -124,14 +124,14 @@ void TRTBatchedNMS::configurePlugin(
 }
 
 bool TRTBatchedNMS::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc* ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
   if (pos == 3) {
-    return inOut[pos].type == nvinfer1::DataType::kINT32 &&
-           inOut[pos].format == nvinfer1::TensorFormat::kLINEAR;
+    return ioDesc[pos].type == nvinfer1::DataType::kINT32 &&
+           ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR;
   }
-  return inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-         inOut[pos].format == nvinfer1::TensorFormat::kLINEAR;
+  return ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+         ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR;
 }
 
 const char* TRTBatchedNMS::getPluginType() const TRT_NOEXCEPT {

backend_ops/tensorrt/batched_nms/trt_batched_nms.hpp

@@ -42,7 +42,7 @@ class TRTBatchedNMS : public TRTPluginBase {
                        int nbOutputs) TRT_NOEXCEPT override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
+                                 const nvinfer1::PluginTensorDesc* ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
 

backend_ops/tensorrt/grid_sampler/trt_grid_sampler.cpp

@@ -51,14 +51,14 @@ nvinfer1::DimsExprs TRTGridSampler::getOutputDimensions(
 }
 
 bool TRTGridSampler::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc *inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc *ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
   if (pos == 0) {
-    return (inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-            inOut[pos].format == nvinfer1::TensorFormat::kLINEAR);
+    return (ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+            ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR);
   } else {
-    return inOut[pos].type == inOut[0].type &&
-           inOut[pos].format == inOut[0].format;
+    return ioDesc[pos].type == ioDesc[0].type &&
+           ioDesc[pos].format == ioDesc[0].format;
   }
 }
 

backend_ops/tensorrt/grid_sampler/trt_grid_sampler.hpp

@@ -29,7 +29,7 @@ class TRTGridSampler : public TRTPluginBase {
       nvinfer1::IExprBuilder &exprBuilder) TRT_NOEXCEPT override;
 
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc *inOut,
+                                 const nvinfer1::PluginTensorDesc *ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
 

backend_ops/tensorrt/grid_sampler/trt_grid_sampler_kernel.cu

@@ -144,7 +144,7 @@ __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];
 
@@ -193,7 +193,7 @@ __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;
@@ -245,7 +245,7 @@ __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];
@@ -363,7 +363,7 @@ __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;

backend_ops/tensorrt/instance_norm/trt_instance_norm.cpp

@@ -107,12 +107,12 @@ void TRTInstanceNormalization::serialize(void* buffer) const TRT_NOEXCEPT {
 }
 
 bool TRTInstanceNormalization::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc* ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
-  return ((inOut[pos].type == nvinfer1::DataType::kFLOAT ||
-           inOut[pos].type == nvinfer1::DataType::kHALF) &&
-          inOut[pos].format == nvinfer1::PluginFormat::kLINEAR &&
-          inOut[pos].type == inOut[0].type);
+  return ((ioDesc[pos].type == nvinfer1::DataType::kFLOAT ||
+           ioDesc[pos].type == nvinfer1::DataType::kHALF) &&
+          ioDesc[pos].format == nvinfer1::PluginFormat::kLINEAR &&
+          ioDesc[pos].type == ioDesc[0].type);
 }
 
 const char* TRTInstanceNormalization::getPluginType() const TRT_NOEXCEPT {

backend_ops/tensorrt/instance_norm/trt_instance_norm.hpp

@@ -48,7 +48,7 @@ class TRTInstanceNormalization final : public TRTPluginBase {
 
   // DynamicExt plugin supportsFormat update.
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc* inOut,
+                                 const nvinfer1::PluginTensorDesc* ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
 

backend_ops/tensorrt/modulated_deform_conv/trt_modulated_deform_conv.cpp

@@ -73,15 +73,15 @@ nvinfer1::DimsExprs ModulatedDeformableConvPluginDynamic::getOutputDimensions(
 }
 
 bool ModulatedDeformableConvPluginDynamic::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc *inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc *ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
   if (pos == 0) {
-    return (inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-            inOut[pos].format == nvinfer1::TensorFormat::kLINEAR);
+    return (ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+            ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR);
 
   } else {
-    return inOut[pos].type == inOut[0].type &&
-           inOut[pos].format == inOut[0].format;
+    return ioDesc[pos].type == ioDesc[0].type &&
+           ioDesc[pos].format == ioDesc[0].format;
   }
 }
 

backend_ops/tensorrt/modulated_deform_conv/trt_modulated_deform_conv.hpp

@@ -31,7 +31,7 @@ class ModulatedDeformableConvPluginDynamic : public TRTPluginBase {
       int outputIndex, const nvinfer1::DimsExprs *inputs, int nbInputs,
       nvinfer1::IExprBuilder &exprBuilder) TRT_NOEXCEPT override;
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc *inOut,
+                                 const nvinfer1::PluginTensorDesc *ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc *in,

backend_ops/tensorrt/multi_level_roi_align/trt_multi_level_roi_align.cpp

@@ -65,10 +65,10 @@ nvinfer1::DimsExprs TRTMultiLevelRoiAlign::getOutputDimensions(
 }
 
 bool TRTMultiLevelRoiAlign::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc *inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc *ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
-  return inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-         inOut[pos].format == nvinfer1::TensorFormat::kLINEAR;
+  return ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+         ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR;
 }
 
 void TRTMultiLevelRoiAlign::configurePlugin(

backend_ops/tensorrt/multi_level_roi_align/trt_multi_level_roi_align.hpp

@@ -29,7 +29,7 @@ class TRTMultiLevelRoiAlign : public TRTPluginBase {
       int outputIndex, const nvinfer1::DimsExprs *inputs, int nbInputs,
       nvinfer1::IExprBuilder &exprBuilder) TRT_NOEXCEPT override;
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc *inOut,
+                                 const nvinfer1::PluginTensorDesc *ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc *in,

backend_ops/tensorrt/roi_align/trt_roi_align.cpp

@@ -59,10 +59,10 @@ nvinfer1::DimsExprs TRTRoIAlign::getOutputDimensions(
 }
 
 bool TRTRoIAlign::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc *inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc *ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
-  return inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-         inOut[pos].format == nvinfer1::TensorFormat::kLINEAR;
+  return ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+         ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR;
 }
 
 void TRTRoIAlign::configurePlugin(

backend_ops/tensorrt/roi_align/trt_roi_align.hpp

@@ -23,7 +23,7 @@ class TRTRoIAlign : public TRTPluginBase {
       int outputIndex, const nvinfer1::DimsExprs *inputs, int nbInputs,
       nvinfer1::IExprBuilder &exprBuilder) TRT_NOEXCEPT override;
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc *inOut,
+                                 const nvinfer1::PluginTensorDesc *ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc *in,

backend_ops/tensorrt/scatternd/trt_scatternd.cpp

@@ -34,24 +34,24 @@ nvinfer1::DimsExprs TRTScatterND::getOutputDimensions(
 }
 
 bool TRTScatterND::supportsFormatCombination(
-    int pos, const nvinfer1::PluginTensorDesc *inOut, int nbInputs,
+    int pos, const nvinfer1::PluginTensorDesc *ioDesc, int nbInputs,
     int nbOutputs) TRT_NOEXCEPT {
   if (pos < nbInputs) {
     switch (pos) {
       case 0:
         // data
-        return (inOut[pos].type == nvinfer1::DataType::kFLOAT &&
-                inOut[pos].format == nvinfer1::TensorFormat::kLINEAR) ||
-               (inOut[pos].type == nvinfer1::DataType::kINT32 &&
-                inOut[pos].format == nvinfer1::TensorFormat::kLINEAR);
+        return (ioDesc[pos].type == nvinfer1::DataType::kFLOAT &&
+                ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR) ||
+               (ioDesc[pos].type == nvinfer1::DataType::kINT32 &&
+                ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR);
       case 1:
         // indices
-        return inOut[pos].type == nvinfer1::DataType::kINT32 &&
-               inOut[pos].format == nvinfer1::TensorFormat::kLINEAR;
+        return ioDesc[pos].type == nvinfer1::DataType::kINT32 &&
+               ioDesc[pos].format == nvinfer1::TensorFormat::kLINEAR;
       case 2:
         // updates
-        return inOut[pos].type == inOut[0].type &&
-               inOut[pos].format == inOut[0].format;
+        return ioDesc[pos].type == ioDesc[0].type &&
+               ioDesc[pos].format == ioDesc[0].format;
       default:
         return true;
     }
@@ -59,8 +59,8 @@ bool TRTScatterND::supportsFormatCombination(
     switch (pos - nbInputs) {
       case 0:
         // output
-        return inOut[pos].type == inOut[0].type &&
-               inOut[pos].format == inOut[0].format;
+        return ioDesc[pos].type == ioDesc[0].type &&
+               ioDesc[pos].format == ioDesc[0].format;
       default:
         return true;
     }

backend_ops/tensorrt/scatternd/trt_scatternd.hpp

@@ -23,7 +23,7 @@ class TRTScatterND : public TRTPluginBase {
       int outputIndex, const nvinfer1::DimsExprs *inputs, int nbInputs,
       nvinfer1::IExprBuilder &exprBuilder) TRT_NOEXCEPT override;
   bool supportsFormatCombination(int pos,
-                                 const nvinfer1::PluginTensorDesc *inOut,
+                                 const nvinfer1::PluginTensorDesc *ioDesc,
                                  int nbInputs,
                                  int nbOutputs) TRT_NOEXCEPT override;
   void configurePlugin(const nvinfer1::DynamicPluginTensorDesc *in,

mmdeploy/apis/pytorch2onnx.py

@@ -29,7 +29,7 @@ def torch2onnx_impl(model: torch.nn.Module, input: torch.Tensor,
     backend = get_backend(deploy_cfg).value
     opset_version = pytorch2onnx_cfg.get('opset_version', 11)
 
-    # load registed symbolic
+    # load registered symbolic
     register_extra_symbolics(deploy_cfg, backend=backend, opset=opset_version)
 
     # patch model

mmdeploy/core/rewriters/function_rewriter.py

@@ -163,7 +163,7 @@ class RewriterContext(object):
     Examples:
         >>> from mmdeploy.core import RewriterContext
         >>> with RewriterContext(cfg, backend='onnxruntime'):
-        >>>     # the rewrite has been actived inside the context
+        >>>     # the rewrite has been activated inside the context
         >>>     torch.onnx.export(model, inputs, onnx_file)
     """
 

mmdeploy/core/rewriters/module_rewriter.py

@@ -97,7 +97,7 @@ def patch_model(model: nn.Module,
                 backend: str = 'default',
                 recursive: bool = True,
                 **kwargs) -> nn.Module:
-    """Patch the model, replace the modules that can be rewrited.
+    """Patch the model, replace the modules that can be rewritten.
 
     Args:
         model (torch.nn.Module): The model to patch.

mmdeploy/core/rewriters/symbolic_register.py

@@ -60,7 +60,7 @@ SYMBOLIC_REGISTER = Registry('symbolics', build_func=set_symbolic, scope=None)
 class SymbolicWrapper:
     """The wrapper of the symbolic function.
 
-    The wrapper is used to pass context and enviroment to the symbolic.
+    The wrapper is used to pass context and environment to the symbolic.
 
     Args:
         cfg (Dict): Config dictionary of deployment.
@@ -86,7 +86,7 @@ def register_symbolic(func_name: str,
     """The decorator of the custom symbolic.
 
     Args:
-        func_name (str): The function name/path to overide the symbolic.
+        func_name (str): The function name/path to override the symbolic.
         backend (str): The inference engine name.
         is_pytorch (bool): Enable this flag if func_name is the name of \
             a pytorch builtin function.

mmdeploy/mmcv/ops/roi_align.py

@@ -3,7 +3,7 @@ from mmdeploy.core import SYMBOLIC_REGISTER
 
 # Here using mmcv.ops.roi_align.__self__ to find
 # mmcv.ops.roi_align.RoIAlignFunction, because RoIAlignFunction is not
-# visiable in mmcv.
+# visible in mmcv.
 @SYMBOLIC_REGISTER.register_symbolic(
     'mmcv.ops.roi_align.__self__', backend='default')
 def roi_align_default(ctx, g, input, rois, output_size, spatial_scale,

mmdeploy/mmdet/export/prepare_input.py

@@ -54,7 +54,7 @@ def create_input(task: Task,
 
     cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
     test_pipeline = Compose(cfg.data.test.pipeline)
-    datas = []
+    data_list = []
     for img in imgs:
         # prepare data
         if isinstance(img, np.ndarray):
@@ -65,9 +65,9 @@ def create_input(task: Task,
             data = dict(img_info=dict(filename=img), img_prefix=None)
         # build the data pipeline
         data = test_pipeline(data)
-        datas.append(data)
+        data_list.append(data)
 
-    data = collate(datas, samples_per_gpu=len(imgs))
+    data = collate(data_list, samples_per_gpu=len(imgs))
 
     data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
     data['img'] = [img.data[0] for img in data['img']]

mmdeploy/mmedit/apis/inference.py

@@ -35,8 +35,8 @@ class DeployBaseRestorer(BaseModel):
     def forward(self, lq: torch.Tensor, test_mode: bool = False, **kwargs):
         """Run test inference for restorer.
 
-        We want forward() to output an image or a evalution result.
-        When test_mode is set, the output is evalution result. Otherwise
+        We want forward() to output an image or a evaluation result.
+        When test_mode is set, the output is evaluation result. Otherwise
         it is an image.
 
         Args:
@@ -104,7 +104,7 @@ class DeployBaseRestorer(BaseModel):
                            outputs: torch.Tensor,
                            lq: torch.Tensor,
                            gt: Optional[torch.Tensor] = None):
-        """Get evalution results by post-processing model outputs.
+        """Get evaluation results by post-processing model outputs.
 
         Args:
             output (torch.Tensor) : The output high resolution image.

mmdeploy/mmedit/export/prepare_input.py

@@ -18,7 +18,7 @@ def _preprocess_cfg(config: Union[str, mmcv.Config]):
         model_cfg (str | mmcv.Config): The input model config.
     """
 
-    # TODO: Differentiate the editting tasks (e.g. restorers and mattors
+    # TODO: Differentiate the editing tasks (e.g. restorers and mattors
     # preprocess the data in differenet ways)
 
     keys_to_remove = ['gt', 'gt_path']

mmdeploy/mmocr/export/prepare_input.py

@@ -71,7 +71,7 @@ def create_input(task: Task,
     from mmocr.datasets import build_dataset  # noqa: F401
     test_pipeline = Compose(model_cfg.data.test.pipeline)
 
-    datas = []
+    data_list = []
     for img in imgs:
         # prepare data
         if is_ndarray:
@@ -84,14 +84,14 @@ def create_input(task: Task,
         # build the data pipeline
         data = test_pipeline(data)
         # get tensor from list to stack for batch mode (text detection)
-        datas.append(data)
+        data_list.append(data)
 
-    if isinstance(datas[0]['img'], list) and len(datas) > 1:
+    if isinstance(data_list[0]['img'], list) and len(data_list) > 1:
         raise Exception('aug test does not support '
                         f'inference with batch size '
-                        f'{len(datas)}')
+                        f'{len(data_list)}')
 
-    data = collate(datas, samples_per_gpu=len(imgs))
+    data = collate(data_list, samples_per_gpu=len(imgs))
 
     # process img_metas
     if isinstance(data['img_metas'], list):

mmdeploy/mmseg/export/prepare_input.py

@@ -47,15 +47,15 @@ def create_input(task: Task,
     cfg.data.test.pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
 
     test_pipeline = Compose(cfg.data.test.pipeline)
-    datas = []
+    data_list = []
     for img in imgs:
         # prepare data
         data = dict(img=img)
         # build the data pipeline
         data = test_pipeline(data)
-        datas.append(data)
+        data_list.append(data)
 
-    data = collate(datas, samples_per_gpu=len(imgs))
+    data = collate(data_list, samples_per_gpu=len(imgs))
 
     data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
     data['img'] = [img.data[0][None, :] for img in data['img']]

mmdeploy/utils/config_utils.py

@@ -34,7 +34,7 @@ def get_task_type(deploy_cfg: Union[str, mmcv.Config], default=None) -> Task:
 
     Args:
         deploy_cfg (str | mmcv.Config): The path or content of config.
-        default (str): If the "task" field of config is emtpy, then return
+        default (str): If the "task" field of config is empty, then return
         default task type.
 
     Returns:
@@ -56,7 +56,7 @@ def get_codebase(deploy_cfg: Union[str, mmcv.Config],
 
     Args:
         deploy_cfg (str | mmcv.Config): The path or content of config.
-        default (str): If the "codebase" field of config is emtpy, then return
+        default (str): If the "codebase" field of config is empty, then return
         default codebase type.
 
     Returns:
@@ -77,7 +77,7 @@ def get_backend(deploy_cfg: Union[str, mmcv.Config], default=None) -> Backend:
 
     Args:
         deploy_cfg (str | mmcv.Config): The path or content of config.
-        default (str): If the "backend" field of config is emtpy, then return
+        default (str): If the "backend" field of config is empty, then return
         default backend type.
 
     Returns:

mmdeploy/utils/timer.py

@@ -88,7 +88,7 @@ class TimeCounter:
                 registried function will be activated.
             warmup (int): the warm up steps, default 1.
             log_interval (int): interval between each log, default 1.
-            with_sync (bool): wheather use cuda synchronize for time counting,
+            with_sync (bool): whether use cuda synchronize for time counting,
                 default False.
         """
         assert warmup >= 1

tests/test_core/test_register.py

@@ -97,7 +97,7 @@ def test_module_rewriter():
     rewrited_result = rewrited_bottle_nect(x)
     torch.testing.assert_allclose(rewrited_result, result * 2)
 
-    # wrong backend should not be rewrited
+    # wrong backend should not be rewritten
 
     rewrited_model = patch_model(model, cfg=cfg)
     rewrited_bottle_nect = rewrited_model.layer1[0]