Fix typos (#3955)

libs/ultra-infer/cmake/check.cmake

@@ -26,20 +26,20 @@ if(IOS)
     message(FATAL_ERROR "Not support OpenVINO backend for IOS now. Please set ENABLE_OPENVINO_BACKEND=OFF.")
   endif()
   if(ENABLE_TRT_BACKEND)
-    message(FATAL_ERROR "Not support TensorRT backend for Andorid/IOS now. Please set ENABLE_TRT_BACKEND=OFF.")
+    message(FATAL_ERROR "Not support TensorRT backend for Android/IOS now. Please set ENABLE_TRT_BACKEND=OFF.")
   endif()
 endif()
 
 if(WITH_GPU)
   if(APPLE)
-    message(FATAL_ERROR "Cannot enable GPU while compling in Mac OSX.")
+    message(FATAL_ERROR "Cannot enable GPU while compiling in Mac OSX.")
   elseif(IOS)
-    message(FATAL_ERROR "Cannot enable GPU while compling in IOS.")
+    message(FATAL_ERROR "Cannot enable GPU while compiling in IOS.")
   endif()
 endif()
 
 if(WITH_OPENCL)
   if(NOT ENABLE_LITE_BACKEND)
-    message(FATAL_ERROR "Cannot enable OpenCL while compling unless in Paddle Lite backend is enbaled.")
+    message(FATAL_ERROR "Cannot enable OpenCL while compiling unless in Paddle Lite backend is enabled.")
   endif()
 endif()

libs/ultra-infer/cmake/cuda.cmake

@@ -10,7 +10,7 @@ if(BUILD_ON_JETSON)
   set(fd_known_gpu_archs "53 62 72")
   set(fd_known_gpu_archs10 "53 62 72")
 else()
-  message("Using New Release Strategy - All Arches Packge")
+  message("Using New Release Strategy - All Arches Package")
   set(fd_known_gpu_archs "35 50 52 60 61 70 75 80 86")
   set(fd_known_gpu_archs10 "35 50 52 60 61 70 75")
   set(fd_known_gpu_archs11 "50 60 61 70 75 80")
@@ -58,7 +58,7 @@ function(detect_installed_gpus out_variable)
       set(CUDA_gpu_detect_output
           ${nvcc_out}
           CACHE INTERNAL
-                "Returned GPU architetures from detect_installed_gpus tool"
+                "Returned GPU architectures from detect_installed_gpus tool"
                 FORCE)
     endif()
   endif()
@@ -98,7 +98,7 @@ function(select_nvcc_arch_flags out_variable)
   # set CUDA_ARCH_NAME strings (so it will be seen as dropbox in CMake-Gui)
   set(CUDA_ARCH_NAME
       ${archs_name_default}
-      CACHE STRING "Select target NVIDIA GPU achitecture.")
+      CACHE STRING "Select target NVIDIA GPU architecture.")
   set_property(CACHE CUDA_ARCH_NAME PROPERTY STRINGS "" ${archs_names})
   mark_as_advanced(CUDA_ARCH_NAME)
 
@@ -252,7 +252,7 @@ else()
   message(WARNING "Detected custom CMAKE_CUDA_STANDARD is using: ${CMAKE_CUDA_STANDARD}")  
 endif()
 
-# (Note) For windows, if delete /W[1-4], /W1 will be added defaultly and conflic with -w
+# (Note) For windows, if delete /W[1-4], /W1 will be added defaultly and conflict with -w
 # So replace /W[1-4] with /W0
 if(WIN32)
   string(REGEX REPLACE "/W[1-4]" " /W0 " CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS}")

libs/ultra-infer/cmake/faiss.cmake

@@ -113,7 +113,7 @@ else() # Linux
   list(APPEND FAISS_LIBRARIES ${LAPACK_LIBRARIES})
 endif()
 
-# Add OpenMP (REQUIRED), OpenMP must be avaliable.
+# Add OpenMP (REQUIRED), OpenMP must be available.
 find_package(OpenMP REQUIRED)
 list(APPEND FAISS_LIBRARIES OpenMP::OpenMP_CXX)
 

libs/ultra-infer/cmake/utils.cmake

@@ -152,7 +152,7 @@ function(bundle_static_library tgt_name bundled_tgt_name fake_target)
 
   list(REMOVE_DUPLICATES static_libs)
   list(REMOVE_ITEM static_libs ${REDUNDANT_STATIC_LIBS})
-  message(STATUS "WITH_STATIC_LIB=${WITH_STATIC_LIB}, Found all needed static libs from dependecy tree: ${static_libs}")
+  message(STATUS "WITH_STATIC_LIB=${WITH_STATIC_LIB}, Found all needed static libs from dependency tree: ${static_libs}")
   message(STATUS "Exclude some redundant static libs: ${REDUNDANT_STATIC_LIBS}")
 
   set(bundled_tgt_full_name

libs/ultra-infer/cpack/debian_postinst.in

@@ -16,7 +16,7 @@ case "$1" in
             LIBS_DIRECOTRIES[${#LIBS_DIRECOTRIES[@]}]=${SO_FILE%/*}
         done
 
-        # Remove the dumplicate directories
+        # Remove the duplicate directories
         LIBS_DIRECOTRIES=($(awk -v RS=' ' '!a[$1]++' <<< ${LIBS_DIRECOTRIES[@]}))
 
         IMPORT_PATH=""

libs/ultra-infer/cpack/rpm_postinst.in

@@ -14,7 +14,7 @@ for SO_FILE in $ALL_SO_FILES;do
     LIBS_DIRECOTRIES[${#LIBS_DIRECOTRIES[@]}]=${SO_FILE%/*}
 done
 
-# Remove the dumplicate directories
+# Remove the duplicate directories
 LIBS_DIRECOTRIES=($(awk -v RS=' ' '!a[$1]++' <<< ${LIBS_DIRECOTRIES[@]}))
 
 IMPORT_PATH=""

libs/ultra-infer/python/ultra_infer/pipeline/pptinypose/__init__.py

@@ -40,7 +40,7 @@ class PPTinyPose(object):
 
     @property
     def detection_model_score_threshold(self):
-        """Atrribute of PPTinyPose pipeline model. Stating the score threshold for detectin model to filter bbox before inputting pptinypose model
+        """Attribute of PPTinyPose pipeline model. Stating the score threshold for detectin model to filter bbox before inputting pptinypose model
 
         :return: value of detection_model_score_threshold(float)
         """

libs/ultra-infer/python/ultra_infer/py_only/ts/processors.py

@@ -231,7 +231,7 @@ def _load_from_dataframe(
     fillna_window_size: int = 10,
     **kwargs,
 ):
-    dfs = []  # seperate multiple group
+    dfs = []  # separate multiple group
     if group_id is not None:
         group_unique = df[group_id].unique()
         for column in group_unique:

libs/ultra-infer/python/ultra_infer/vision/classification/contrib/resnet.py

@@ -66,14 +66,14 @@ class ResNet(UltraInferModel):
     @property
     def mean_vals(self):
         """
-        Returns the mean value of normlization, default mean_vals = [0.485f, 0.456f, 0.406f];
+        Returns the mean value of normalization, default mean_vals = [0.485f, 0.456f, 0.406f];
         """
         return self._model.mean_vals
 
     @property
     def std_vals(self):
         """
-        Returns the std value of normlization, default std_vals = [0.229f, 0.224f, 0.225f];
+        Returns the std value of normalization, default std_vals = [0.229f, 0.224f, 0.225f];
         """
         return self._model.std_vals
 

libs/ultra-infer/python/ultra_infer/vision/common/manager.py

@@ -32,7 +32,7 @@ class ProcessorManager:
     def use_cuda(self, enable_cv_cuda=False, gpu_id=-1):
         """Use CUDA processors
 
-        :param: enable_cv_cuda: Ture: use CV-CUDA, False: use CUDA only
+        :param: enable_cv_cuda: True: use CV-CUDA, False: use CUDA only
         :param: gpu_id: GPU device id
         """
         return self._manager.use_cuda(enable_cv_cuda, gpu_id)
@@ -49,7 +49,7 @@ class PyProcessorManager(ABC):
     def use_cuda(self, enable_cv_cuda=False, gpu_id=-1):
         """Use CUDA processors
 
-        :param: enable_cv_cuda: Ture: use CV-CUDA, False: use CUDA only
+        :param: enable_cv_cuda: True: use CV-CUDA, False: use CUDA only
         :param: gpu_id: GPU device id
         """
         return self._manager.use_cuda(enable_cv_cuda, gpu_id)

libs/ultra-infer/python/ultra_infer/vision/common/processors.py

@@ -67,7 +67,7 @@ class Pad(Processor):
 
 class NormalizeAndPermute(Processor):
     def __init__(self, mean=[], std=[], is_scale=True, min=[], max=[], swap_rb=False):
-        """Creae a Normalize and a Permute operation with the given parameters.
+        """Create a Normalize and a Permute operation with the given parameters.
 
         :param mean: A list containing the mean of each channel
         :param std: A list containing the standard deviation of each channel
@@ -100,7 +100,7 @@ class HWC2CHW(Processor):
 
 class Normalize(Processor):
     def __init__(self, mean, std, is_scale=True, min=[], max=[], swap_rb=False):
-        """Creat a new normalize opereator with given paremeters.
+        """Creat a new normalize opereator with given parameters.
 
         :param mean: A list containing the mean of each channel
         :param std: A list containing the standard deviation of each channel

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/nanodet_plus.py

@@ -79,7 +79,7 @@ class NanoDetPlus(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS, default 4096
+        # for offsetting the boxes by classes when using NMS, default 4096
         return self._model.max_wh
 
     @property

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/scaled_yolov4.py

@@ -75,7 +75,7 @@ class ScaledYOLOv4(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property
@@ -90,7 +90,7 @@ class ScaledYOLOv4(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS
+        # for offsetting the boxes by classes when using NMS
         return self._model.max_wh
 
     @size.setter

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolor.py

@@ -74,7 +74,7 @@ class YOLOR(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property
@@ -89,7 +89,7 @@ class YOLOR(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS
+        # for offsetting the boxes by classes when using NMS
         return self._model.max_wh
 
     @size.setter

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov5.py

@@ -56,7 +56,7 @@ class YOLOv5Preprocessor:
     @property
     def is_mini_pad(self):
         """
-        is_mini_pad for preprocessing, pad to the minimum rectange which height and width is times of stride, default false
+        is_mini_pad for preprocessing, pad to the minimum rectangle which height and width is times of stride, default false
         """
         return self._preprocessor.is_mini_pad
 

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov5lite.py

@@ -74,7 +74,7 @@ class YOLOv5Lite(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property
@@ -89,7 +89,7 @@ class YOLOv5Lite(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS
+        # for offsetting the boxes by classes when using NMS
         return self._model.max_wh
 
     @property

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov5seg.py

@@ -56,7 +56,7 @@ class YOLOv5SegPreprocessor:
     @property
     def is_mini_pad(self):
         """
-        is_mini_pad for preprocessing, pad to the minimum rectange which height and width is times of stride, default false
+        is_mini_pad for preprocessing, pad to the minimum rectangle which height and width is times of stride, default false
         """
         return self._preprocessor.is_mini_pad
 

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov6.py

@@ -74,7 +74,7 @@ class YOLOv6(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property
@@ -89,7 +89,7 @@ class YOLOv6(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS
+        # for offsetting the boxes by classes when using NMS
         return self._model.max_wh
 
     @size.setter

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov7end2end_ort.py

@@ -73,7 +73,7 @@ class YOLOv7End2EndORT(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov7end2end_trt.py

@@ -73,7 +73,7 @@ class YOLOv7End2EndTRT(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolov8.py

@@ -56,7 +56,7 @@ class YOLOv8Preprocessor:
     @property
     def is_mini_pad(self):
         """
-        is_mini_pad for preprocessing, pad to the minimum rectange which height and width is times of stride, default false
+        is_mini_pad for preprocessing, pad to the minimum rectangle which height and width is times of stride, default false
         """
         return self._preprocessor.is_mini_pad
 

libs/ultra-infer/python/ultra_infer/vision/detection/contrib/yolox.py

@@ -73,7 +73,7 @@ class YOLOX(UltraInferModel):
         whether the model_file was exported with decode module.
         The official YOLOX/tools/export_onnx.py script will export ONNX file without decode module.
         Please set it 'true' manually if the model file was exported with decode module.
-        Defalut False.
+        Default False.
         """
         return self._model.is_decode_exported
 
@@ -86,7 +86,7 @@ class YOLOX(UltraInferModel):
 
     @property
     def max_wh(self):
-        # for offseting the boxes by classes when using NMS
+        # for offsetting the boxes by classes when using NMS
         return self._model.max_wh
 
     @size.setter

libs/ultra-infer/python/ultra_infer/vision/facealign/contrib/pipnet.py

@@ -63,14 +63,14 @@ class PIPNet(UltraInferModel):
     @property
     def mean_vals(self):
         """
-        Returns the mean value of normlization, default mean_vals = [0.485f, 0.456f, 0.406f];
+        Returns the mean value of normalization, default mean_vals = [0.485f, 0.456f, 0.406f];
         """
         return self._model.mean_vals
 
     @property
     def std_vals(self):
         """
-        Returns the std value of normlization, default std_vals = [0.229f, 0.224f, 0.225f];
+        Returns the std value of normalization, default std_vals = [0.229f, 0.224f, 0.225f];
         """
         return self._model.std_vals
 

libs/ultra-infer/python/ultra_infer/vision/facedet/contrib/retinaface.py

@@ -109,7 +109,7 @@ class RetinaFace(UltraInferModel):
         ), "The value to set `variance` must be type of tuple or list."
         assert (
             len(value) == 2
-        ), "The value to set `variance` must contatins 2 elements".format(len(value))
+        ), "The value to set `variance` must contains 2 elements".format(len(value))
         self._model.variance = value
 
     @downsample_strides.setter

libs/ultra-infer/python/ultra_infer/vision/facedet/contrib/scrfd.py

@@ -86,7 +86,7 @@ class SCRFD(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property

libs/ultra-infer/python/ultra_infer/vision/facedet/contrib/yolov5face.py

@@ -74,7 +74,7 @@ class YOLOv5Face(UltraInferModel):
 
     @property
     def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
+        # only pad to the minimum rectangle which height and width is times of stride
         return self._model.is_mini_pad
 
     @property

libs/ultra-infer/python/ultra_infer/vision/keypointdetection/pptinypose/__init__.py

@@ -60,7 +60,7 @@ class PPTinyPose(UltraInferModel):
 
     @property
     def use_dark(self):
-        """Atrribute of PPTinyPose model. Stating whether using Distribution-Aware Coordinate Representation for Human Pose Estimation(DARK for short) in postprocess, default is True
+        """Attribute of PPTinyPose model. Stating whether using Distribution-Aware Coordinate Representation for Human Pose Estimation(DARK for short) in postprocess, default is True
 
         :return: value of use_dark(bool)
         """

libs/ultra-infer/python/ultra_infer/vision/matting/contrib/rvm.py

@@ -58,14 +58,14 @@ class RobustVideoMatting(UltraInferModel):
     @property
     def video_mode(self):
         """
-        Whether to open the video mode, if there are some irrelevant pictures, set it to fasle, the default is true
+        Whether to open the video mode, if there are some irrelevant pictures, set it to false, the default is true
         """
         return self._model.video_mode
 
     @property
     def swap_rb(self):
         """
-        Whether convert to RGB, Set to false if you have converted YUV format images to RGB outside the model, dafault true
+        Whether convert to RGB, Set to false if you have converted YUV format images to RGB outside the model, default true
         """
         return self._model.swap_rb
 

libs/ultra-infer/python/ultra_infer/vision/ocr/ppocr/__init__.py

@@ -1295,7 +1295,7 @@ class StructureV2Layout(UltraInferModel):
 
 class PPOCRv4(UltraInferModel):
     def __init__(self, det_model=None, cls_model=None, rec_model=None):
-        """Consruct a pipeline with text detector, direction classifier and text recognizer models
+        """Construct a pipeline with text detector, direction classifier and text recognizer models
 
         :param det_model: (UltraInferModel) The detection model object created by ultra_infer.vision.ocr.DBDetector.
         :param cls_model: (UltraInferModel) The classification model object created by ultra_infer.vision.ocr.Classifier.
@@ -1379,7 +1379,7 @@ class PPOCRSystemv4(PPOCRv4):
 
 class PPOCRv3(UltraInferModel):
     def __init__(self, det_model=None, cls_model=None, rec_model=None):
-        """Consruct a pipeline with text detector, direction classifier and text recognizer models
+        """Construct a pipeline with text detector, direction classifier and text recognizer models
 
         :param det_model: (UltraInferModel) The detection model object created by ultra_infer.vision.ocr.DBDetector.
         :param cls_model: (UltraInferModel) The classification model object created by ultra_infer.vision.ocr.Classifier.
@@ -1458,7 +1458,7 @@ class PPOCRSystemv3(PPOCRv3):
 
 class PPOCRv2(UltraInferModel):
     def __init__(self, det_model=None, cls_model=None, rec_model=None):
-        """Consruct a pipeline with text detector, direction classifier and text recognizer models
+        """Construct a pipeline with text detector, direction classifier and text recognizer models
 
         :param det_model: (UltraInferModel) The detection model object created by ultra_infer.vision.ocr.DBDetector.
         :param cls_model: (UltraInferModel) The classification model object created by ultra_infer.vision.ocr.Classifier.
@@ -1539,7 +1539,7 @@ class PPOCRSystemv2(PPOCRv2):
 
 class PPStructureV2Table(UltraInferModel):
     def __init__(self, det_model=None, rec_model=None, table_model=None):
-        """Consruct a pipeline with text detector, text recognizer and table recognizer models
+        """Construct a pipeline with text detector, text recognizer and table recognizer models
 
         :param det_model: (UltraInferModel) The detection model object created by ultra_infer.vision.ocr.DBDetector.
         :param rec_model: (UltraInferModel) The recognition model object created by ultra_infer.vision.ocr.Recognizer.
@@ -1690,7 +1690,7 @@ class StructureV2SERViLayoutXLMModelPostprocessor:
 
     def run(self, preds, batch=None, *args, **kwargs):
         """Run postprocess of  Ser-Vi-LayoutXLM model.
-        :param: preds: (list) results of infering
+        :param: preds: (list) results of inferring
         """
         return self.postprocessor_op(preds, batch, *args, **kwargs)
 
@@ -1735,7 +1735,7 @@ class StructureV2SERViLayoutXLMModel(UltraInferModel):
         self.input_name_3 = self._model.get_input_info(3).name
 
     def predict(self, image):
-        assert isinstance(image, np.ndarray), "predict recives numpy.ndarray(BGR)"
+        assert isinstance(image, np.ndarray), "predict receives numpy.ndarray(BGR)"
 
         data = self.preprocessor.run(image)
         infer_input = {
@@ -1757,7 +1757,7 @@ class StructureV2SERViLayoutXLMModel(UltraInferModel):
     def batch_predict(self, image_list):
         assert isinstance(image_list, list) and isinstance(
             image_list[0], np.ndarray
-        ), "batch_predict recives list of numpy.ndarray(BGR)"
+        ), "batch_predict receives list of numpy.ndarray(BGR)"
 
         # reading and preprocessing images
         datas = None

libs/ultra-infer/python/ultra_infer/vision/ocr/ppocr/utils/ser_vi_layoutxlm/operators.py

@@ -53,7 +53,7 @@ class Resize(object):
 
 
 class NormalizeImage(object):
-    """normalize image such as substract mean, divide std"""
+    """normalize image such as subtract mean, divide std"""
 
     def __init__(self, scale=None, mean=None, std=None, order="chw", **kwargs):
         if isinstance(scale, str):

libs/ultra-infer/python/ultra_infer/vision/segmentation/ppseg/__init__.py

@@ -121,7 +121,7 @@ class PaddleSegPreprocessor(ProcessorManager):
 
     @property
     def is_vertical_screen(self):
-        """Atrribute of PP-HumanSeg model. Stating Whether the input image is vertical image(height > width), default value is False
+        """Attribute of PP-HumanSeg model. Stating Whether the input image is vertical image(height > width), default value is False
 
         :return: value of is_vertical_screen(bool)
         """
@@ -158,7 +158,7 @@ class PaddleSegPostprocessor:
 
     @property
     def apply_softmax(self):
-        """Atrribute of PaddleSeg model. Stating Whether applying softmax operator in the postprocess, default value is False
+        """Attribute of PaddleSeg model. Stating Whether applying softmax operator in the postprocess, default value is False
 
         :return: value of apply_softmax(bool)
         """
@@ -177,7 +177,7 @@ class PaddleSegPostprocessor:
 
     @property
     def store_score_map(self):
-        """Atrribute of PaddleSeg model. Stating Whether storing score map in the SegmentationResult, default value is False
+        """Attribute of PaddleSeg model. Stating Whether storing score map in the SegmentationResult, default value is False
 
         :return: value of store_score_map(bool)
         """

libs/ultra-infer/python/ultra_infer/vision/visualize/__init__.py

@@ -135,7 +135,7 @@ def vis_matting(
 
     :param im_data: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
     :param matting_result: the result produced by model
-    :param transparent_background: whether visulizing matting result with transparent background
+    :param transparent_background: whether visualizing matting result with transparent background
     :param transparent_threshold: since the alpha value in MattringResult is a float between [0, 1], transparent_threshold is used to filter background pixel
     :param remove_small_connected_area: (bool) if remove_small_connected_area==True, the visualized result will not include the small connected areas
     :return: (numpy.ndarray) image with visualized results

libs/ultra-infer/ultra_infer/core/fd_tensor.cc

@@ -73,7 +73,7 @@ const void *FDTensor::CpuData() const {
 #else
     FDASSERT(false,
              "The UltraInfer didn't compile under -DWITH_GPU=ON, so this is "
-             "an unexpected problem happend.");
+             "an unexpected problem happened.");
 #endif
   }
   return Data();
@@ -259,7 +259,7 @@ bool FDTensor::ReallocFn(size_t nbytes) {
 #else
     FDASSERT(false, "The UltraInfer FDTensor allocator didn't compile under "
                     "-DWITH_GPU=ON,"
-                    "so this is an unexpected problem happend.");
+                    "so this is an unexpected problem happened.");
 #endif
   } else {
     if (is_pinned_memory) {
@@ -276,7 +276,7 @@ bool FDTensor::ReallocFn(size_t nbytes) {
 #else
       FDASSERT(false, "The UltraInfer FDTensor allocator didn't compile under "
                       "-DWITH_GPU=ON,"
-                      "so this is an unexpected problem happend.");
+                      "so this is an unexpected problem happened.");
 #endif
     }
     buffer_ = realloc(buffer_, nbytes);
@@ -319,7 +319,7 @@ void FDTensor::CopyBuffer(void *dst, const void *src, size_t nbytes,
     FDASSERT(false,
              "The UltraInfer didn't compile under -DWITH_GPU=ON, so copying "
              "gpu buffer is "
-             "an unexpected problem happend.");
+             "an unexpected problem happened.");
 #endif
   } else {
     if (is_pinned_memory) {
@@ -330,7 +330,7 @@ void FDTensor::CopyBuffer(void *dst, const void *src, size_t nbytes,
       FDASSERT(false,
                "The UltraInfer didn't compile under -DWITH_GPU=ON, so copying "
                "gpu buffer is "
-               "an unexpected problem happend.");
+               "an unexpected problem happened.");
 #endif
     } else {
       std::memcpy(dst, src, nbytes);

libs/ultra-infer/ultra_infer/core/fd_tensor.h

@@ -25,7 +25,7 @@
 
 namespace ultra_infer {
 
-/*! @brief FDTensor object used to represend data matrix
+/*! @brief FDTensor object used to represent data matrix
  *
  */
 struct ULTRAINFER_DECL FDTensor {
@@ -125,7 +125,7 @@ struct ULTRAINFER_DECL FDTensor {
   // The internal data will be on CPU
   // Some times, the external data is on the GPU, and we are going to use
   // GPU to inference the model
-  // so we can skip data transfer, which may improve the efficience
+  // so we can skip data transfer, which may improve the efficiency
   Device device = Device::CPU;
   // By default the device id of FDTensor is -1, which means this value is
   // invalid, and FDTensor is using the same device id as Runtime.
@@ -153,7 +153,7 @@ struct ULTRAINFER_DECL FDTensor {
   const void *Data() const;
 
   // Use this data to get the tensor data to process
-  // Since the most senario is process data in CPU
+  // Since the most scenario is process data in CPU
   // this function will return a pointer to cpu memory
   // buffer.
   // If the original data is on other device, the data

libs/ultra-infer/ultra_infer/core/float16.h

@@ -151,7 +151,7 @@ public:
     return *this;
   }
 
-// Conversion opertors
+// Conversion operators
 #ifdef FD_WITH_NATIVE_FP16
   HOSTDEVICE inline explicit operator float16_t() const {
     return *reinterpret_cast<const float16_t *>(this);

libs/ultra-infer/ultra_infer/function/clip.h

@@ -23,7 +23,7 @@ namespace function {
    Support float32, float64, int32, int64
     @param x The input tensor.
     @param min The lower bound
-    @param max The uppper bound
+    @param max The upper bound
     @param out The output tensor which stores the result.
 */
 ULTRAINFER_DECL void Clip(const FDTensor &x, double min, double max,

libs/ultra-infer/ultra_infer/function/concat.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace function {
 
-/** Excute the concatenate operation for input FDTensor along given axis.
+/** Execute the concatenate operation for input FDTensor along given axis.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param axis Axis which will be concatenated.

libs/ultra-infer/ultra_infer/function/cumprod.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace function {
 
-/** Excute the concatenate operation for input FDTensor along given axis.
+/** Execute the concatenate operation for input FDTensor along given axis.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param axisi Axis which will be concatenated.

libs/ultra-infer/ultra_infer/function/elementwise.h

@@ -21,14 +21,14 @@ namespace ultra_infer {
 
 namespace function {
 
-/** Excute the add operation for input FDTensors. *out = x + y.
+/** Execute the add operation for input FDTensors. *out = x + y.
     @param x The input tensor.
     @param y The input tensor.
     @param out The output tensor which stores the result.
 */
 ULTRAINFER_DECL void Add(const FDTensor &x, const FDTensor &y, FDTensor *out);
 
-/** Excute the subtract operation for input FDTensors.  *out = x - y.
+/** Execute the subtract operation for input FDTensors.  *out = x - y.
     @param x The input tensor.
     @param y The input tensor.
     @param out The output tensor which stores the result.
@@ -36,7 +36,7 @@ ULTRAINFER_DECL void Add(const FDTensor &x, const FDTensor &y, FDTensor *out);
 ULTRAINFER_DECL void Subtract(const FDTensor &x, const FDTensor &y,
                               FDTensor *out);
 
-/** Excute the multiply operation for input FDTensors.  *out = x * y.
+/** Execute the multiply operation for input FDTensors.  *out = x * y.
     @param x The input tensor.
     @param y The input tensor.
     @param out The output tensor which stores the result.
@@ -44,7 +44,7 @@ ULTRAINFER_DECL void Subtract(const FDTensor &x, const FDTensor &y,
 ULTRAINFER_DECL void Multiply(const FDTensor &x, const FDTensor &y,
                               FDTensor *out);
 
-/** Excute the divide operation for input FDTensors.  *out = x / y.
+/** Execute the divide operation for input FDTensors.  *out = x / y.
     @param x The input tensor.
     @param y The input tensor.
     @param out The output tensor which stores the result.
@@ -52,7 +52,7 @@ ULTRAINFER_DECL void Multiply(const FDTensor &x, const FDTensor &y,
 ULTRAINFER_DECL void Divide(const FDTensor &x, const FDTensor &y,
                             FDTensor *out);
 
-/** Excute the maximum operation for input FDTensors.  *out = max(x, y).
+/** Execute the maximum operation for input FDTensors.  *out = max(x, y).
     @param x The input tensor.
     @param y The input tensor.
     @param out The output tensor which stores the result.

libs/ultra-infer/ultra_infer/function/elementwise_functor.h

@@ -112,7 +112,7 @@ template <typename T>
 struct DivideFunctor<
     T, typename std::enable_if<std::is_integral<T>::value>::type> {
   inline T operator()(const T a, const T b) const {
-    // For int32/int64, need to check whether the divison is zero.
+    // For int32/int64, need to check whether the division is zero.
     FDASSERT(b != 0, DIV_ERROR_INFO);
     return a / b;
   }

libs/ultra-infer/ultra_infer/function/pad.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace function {
-/** Excute the pad operation for input FDTensor along given dims.
+/** Execute the pad operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param pads The size of padding for each dimension, for 3-D tensor, the pads

libs/ultra-infer/ultra_infer/function/reduce.cc

@@ -260,7 +260,7 @@ template <typename T, typename Tout, ArgMinMaxType EnumArgMinMaxValue>
 void ArgMinMaxKernel(const FDTensor &x, FDTensor *out, int64_t axis,
                      bool keepdims, bool flatten) {
   bool new_keepdims = keepdims | flatten;
-  // if flatten, will construct the new dims for the cacluate
+  // if flatten, will construct the new dims for the calculate
   std::vector<int64_t> x_dims;
   int new_axis = axis;
   if (flatten) {

libs/ultra-infer/ultra_infer/function/reduce.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace function {
-/** Excute the maximum operation for input FDTensor along given dims.
+/** Execute the maximum operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -29,7 +29,7 @@ ULTRAINFER_DECL void Max(const FDTensor &x, FDTensor *out,
                          const std::vector<int64_t> &dims,
                          bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the minimum operation for input FDTensor along given dims.
+/** Execute the minimum operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -40,7 +40,7 @@ ULTRAINFER_DECL void Min(const FDTensor &x, FDTensor *out,
                          const std::vector<int64_t> &dims,
                          bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the sum operation for input FDTensor along given dims.
+/** Execute the sum operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -51,7 +51,7 @@ ULTRAINFER_DECL void Sum(const FDTensor &x, FDTensor *out,
                          const std::vector<int64_t> &dims,
                          bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the all operation for input FDTensor along given dims.
+/** Execute the all operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -62,7 +62,7 @@ ULTRAINFER_DECL void All(const FDTensor &x, FDTensor *out,
                          const std::vector<int64_t> &dims,
                          bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the any operation for input FDTensor along given dims.
+/** Execute the any operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -73,7 +73,7 @@ ULTRAINFER_DECL void Any(const FDTensor &x, FDTensor *out,
                          const std::vector<int64_t> &dims,
                          bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the mean operation for input FDTensor along given dims.
+/** Execute the mean operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -84,7 +84,7 @@ ULTRAINFER_DECL void Mean(const FDTensor &x, FDTensor *out,
                           const std::vector<int64_t> &dims,
                           bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the product operation for input FDTensor along given dims.
+/** Execute the product operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which will be reduced.
@@ -95,7 +95,7 @@ ULTRAINFER_DECL void Prod(const FDTensor &x, FDTensor *out,
                           const std::vector<int64_t> &dims,
                           bool keep_dim = false, bool reduce_all = false);
 
-/** Excute the argmax operation for input FDTensor along given dims.
+/** Execute the argmax operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param axis The axis which will be reduced.
@@ -109,7 +109,7 @@ ULTRAINFER_DECL void ArgMax(const FDTensor &x, FDTensor *out, int64_t axis,
                             FDDataType output_dtype = FDDataType::INT64,
                             bool keep_dim = false, bool flatten = false);
 
-/** Excute the argmin operation for input FDTensor along given dims.
+/** Execute the argmin operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param axis The axis which will be reduced.

libs/ultra-infer/ultra_infer/function/softmax.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace function {
-/** Excute the softmax operation for input FDTensor along given dims.
+/** Execute the softmax operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param axis The axis to be computed softmax value.

libs/ultra-infer/ultra_infer/function/transpose.h

@@ -22,7 +22,7 @@ namespace ultra_infer {
  *
  */
 namespace function {
-/** Excute the transpose operation for input FDTensor along given dims.
+/** Execute the transpose operation for input FDTensor along given dims.
     @param x The input tensor.
     @param out The output tensor which stores the result.
     @param dims The vector of axis which the input tensor will transpose.

libs/ultra-infer/ultra_infer/pipeline/pptinypose/pipeline.h

@@ -42,7 +42,7 @@ public:
   /** \brief Predict the keypoint detection result for an input image
    *
    * \param[in] img The input image data, comes from cv::imread()
-   * \param[in] result The output keypoint detection result will be writen to
+   * \param[in] result The output keypoint detection result will be written to
    * this structure \return true if the prediction successed, otherwise false
    */
   virtual bool Predict(cv::Mat *img,

libs/ultra-infer/ultra_infer/pybind/fd_tensor.cc

@@ -143,7 +143,7 @@ pybind11::capsule FDTensorToDLPack(FDTensor &fd_tensor) {
   pybind11::handle tensor_handle = pybind11::cast(&fd_tensor);
 
   // Increase the reference count by one to make sure that the DLPack
-  // represenation doesn't become invalid when the tensor object goes out of
+  // representation doesn't become invalid when the tensor object goes out of
   // scope.
   tensor_handle.inc_ref();
 

libs/ultra-infer/ultra_infer/pybind/main.cc.in

@@ -156,7 +156,7 @@ cv::Mat PyArrayToCvMat(pybind11::array& pyarray) {
 
 PYBIND11_MODULE(@PY_LIBRARY_NAME@, m) {
   m.doc() =
-      "Make programer easier to deploy deeplearning model, save time to save "
+      "Make programmer easier to deploy deeplearning model, save time to save "
       "the world!";
 
   m.def("set_logger", &SetLogger);

libs/ultra-infer/ultra_infer/runtime/backends/backend.h

@@ -142,7 +142,7 @@ public:
  *
  * @endcode In this case, 'poros_outputs' inside a function
  * are wrapped by BEGIN and END, which may be required for
- * subsequent tasks. So, we set 'base_loop' as 0 and lanuch
+ * subsequent tasks. So, we set 'base_loop' as 0 and launch
  * another infer to get the valid outputs beyond the scope
  * of 'BEGIN ~ END' for subsequent tasks.
  */

libs/ultra-infer/ultra_infer/runtime/backends/lite/lite_backend.cc

@@ -123,7 +123,7 @@ bool LiteBackend::Init(const RuntimeOption &runtime_option) {
             config_);
     if (option_.optimized_model_dir != "") {
       FDINFO
-          << "Optimzed model dir is not empty, will save optimized model to: "
+          << "Optimized model dir is not empty, will save optimized model to: "
           << option_.optimized_model_dir << std::endl;
       predictor_->SaveOptimizedModel(
           option_.optimized_model_dir,

libs/ultra-infer/ultra_infer/runtime/backends/om/om_backend.cc

@@ -392,7 +392,7 @@ bool OmBackend::CreateInput() {
 
 bool OmBackend::CreateOutput() {
   if (modelDesc_ == nullptr) {
-    FDERROR << "no model description, create ouput failed";
+    FDERROR << "no model description, create output failed";
     return false;
   }
 

libs/ultra-infer/ultra_infer/runtime/backends/ort/ort_backend.cc

@@ -195,7 +195,7 @@ bool OrtBackend::InitFromPaddle(const std::string &model_buffer,
           true, verbose, true, true, true, ops.data(), 2, "onnxruntime",
           nullptr, 0, "", &save_external, option.enable_fp16,
           disable_fp16_ops.data(), option.ort_disabled_ops_.size())) {
-    FDERROR << "Error occured while export PaddlePaddle to ONNX format."
+    FDERROR << "Error occurred while export PaddlePaddle to ONNX format."
             << std::endl;
     return false;
   }

libs/ultra-infer/ultra_infer/runtime/backends/ort/ort_backend.h

@@ -78,7 +78,7 @@ private:
   // the ONNX model file name,
   // when ONNX is bigger than 2G, we will set this name
   std::string model_file_name;
-  // recored if the model has been converted to fp16
+  // recorded if the model has been converted to fp16
   bool converted_to_fp16 = false;
 
 #ifndef NON_64_PLATFORM

libs/ultra-infer/ultra_infer/runtime/backends/paddle/ops/grid_sample_3d.cu

@@ -232,7 +232,7 @@ GridSample3DCudaKernel(const index_t nthreads, index_t out_c, index_t out_d,
       index_t iy_nearest = static_cast<index_t>(std::round(iy));
       index_t iz_nearest = static_cast<index_t>(std::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;
@@ -583,7 +583,7 @@ __global__ void GridSample3DCudaBackwardKernel(
       auto iy_nearest = static_cast<index_t>(std::round(iy));
       auto iz_nearest = static_cast<index_t>(std::round(iz));
 
-      // assign nearest neighor pixel value to output pixel
+      // assign nearest neighbor pixel value to output pixel
       index_t gOut_offset =
           n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW;
       T *gInp_ptr_NC = grad_input + n * inp_sN;

libs/ultra-infer/ultra_infer/runtime/backends/paddle/ops/iou3d_nms.cc

@@ -143,7 +143,7 @@ std::vector<paddle::Tensor> nms_gpu(const paddle::Tensor &boxes,
   cudaFree(mask_data);
 
   // WARN(qiuyanjun): codes below will throw a compile error on windows with
-  // msvc. Thus, we choosed to use std::vectored to store the result instead.
+  // msvc. Thus, we chose to use std::vectored to store the result instead.
   // unsigned long long remv_cpu[col_blocks];
   // memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
   std::vector<unsigned long long> remv_cpu(col_blocks, 0);
@@ -210,7 +210,7 @@ int nms_normal_gpu(paddle::Tensor boxes, paddle::Tensor keep,
   cudaFree(mask_data);
 
   // WARN(qiuyanjun): codes below will throw a compile error on windows with
-  // msvc. Thus, we choosed to use std::vectored to store the result instead.
+  // msvc. Thus, we chose to use std::vectored to store the result instead.
   // unsigned long long remv_cpu[col_blocks];
   // memset(remv_cpu, 0, col_blocks * sizeof(unsigned long long));
   std::vector<unsigned long long> remv_cpu(col_blocks, 0);

libs/ultra-infer/ultra_infer/runtime/backends/paddle/ops/iou3d_nms_kernel.cu

@@ -133,7 +133,7 @@ __device__ inline void rotate_around_center(const Point &center,
   // float new_y = (p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos +
   // center.y;
   // p.set(new_x, new_y);
-  // Aligh with the implement of mmdet3d
+  // Align with the implement of mmdet3d
   float new_x =
       (p.x - center.x) * angle_cos + (p.y - center.y) * angle_sin + center.x;
   float new_y =

libs/ultra-infer/ultra_infer/runtime/backends/paddle/option.h

@@ -108,7 +108,7 @@ struct PaddleBackendOption {
   bool collect_trt_shape = false;
   /// Collect shape for model by device (for some custom ops)
   bool collect_trt_shape_by_device = false;
-  /// Cache input shape for mkldnn while the input data will change dynamiclly
+  /// Cache input shape for mkldnn while the input data will change dynamically
   int mkldnn_cache_size = -1;
   /// initialize memory size(MB) for GPU
   int gpu_mem_init_size = 100;
@@ -165,7 +165,7 @@ struct PaddleBackendOption {
   std::string model_file = "";  // Path of model file
   std::string params_file = ""; // Path of parameters file, can be empty
 
-  // load model and paramters from memory
+  // load model and parameters from memory
   bool model_from_memory_ = false;
 };
 } // namespace ultra_infer

libs/ultra-infer/ultra_infer/runtime/backends/paddle/paddle_backend.cc

@@ -205,7 +205,7 @@ bool PaddleBackend::Init(const RuntimeOption &runtime_option) {
   option.paddle_infer_option.trt_option.gpu_id = runtime_option.device_id;
   // Note(qiuyanjun): For Ipu option and XPU option, please check the
   // details of RuntimeOption::UseIpu() and RuntimeOption::UseKunlunXin().
-  // Futhermore, please check paddle_infer_option.SetIpuConfig() and
+  // Furthermore, please check paddle_infer_option.SetIpuConfig() and
   // paddle_infer_option.SetXpuConfig() for more details of extra configs.
   return InitFromPaddle(option.model_file, option.params_file,
                         option.model_from_memory_, option.paddle_infer_option);

libs/ultra-infer/ultra_infer/runtime/backends/poros/common/compile.h

@@ -84,7 +84,7 @@ private:
                        std::shared_ptr<torch::jit::Graph> &graph);
 
   /**
-   * @brief segement this calculation graph
+   * @brief segment this calculation graph
    *
    * @param [in/out] graph
    * @return  int
@@ -136,7 +136,7 @@ private:
   IEngine *select_engine(const torch::jit::Node *n);
 
   /**
-   * @brief destory
+   * @brief destroy
    *
    * @return  void
    **/

libs/ultra-infer/ultra_infer/runtime/backends/rknpu2/option.h

@@ -25,8 +25,8 @@ typedef enum _rknpu2_cpu_name {
  * RKNN_NPU_CORE_AUTO : Referring to automatic mode, meaning that it will
  * select the idle core inside the NPU.
  * RKNN_NPU_CORE_0 : Running on the NPU0 core.
- * RKNN_NPU_CORE_1: Runing on the NPU1 core.
- * RKNN_NPU_CORE_2: Runing on the NPU2 core.
+ * RKNN_NPU_CORE_1: Running on the NPU1 core.
+ * RKNN_NPU_CORE_2: Running on the NPU2 core.
  * RKNN_NPU_CORE_0_1: Running on both NPU0 and NPU1 core simultaneously.
  * RKNN_NPU_CORE_0_1_2: Running on both NPU0, NPU1 and NPU2 simultaneously.
  */

libs/ultra-infer/ultra_infer/runtime/backends/tensorrt/trt_backend.cc

@@ -187,7 +187,7 @@ bool TrtBackend::InitFromPaddle(const std::string &model_buffer,
                            verbose, true, true, true, ops.data(), 1, "tensorrt",
                            &calibration_cache_ptr, &calibration_cache_size, "",
                            &save_external_)) {
-    FDERROR << "Error occured while export PaddlePaddle to ONNX format."
+    FDERROR << "Error occurred while export PaddlePaddle to ONNX format."
             << std::endl;
     return false;
   }
@@ -671,7 +671,7 @@ bool TrtBackend::BuildTrtEngine() {
     engine_file.close();
     FDINFO << "TensorRTEngine is serialized to local file "
            << option_.serialize_file
-           << ", we can load this model from the seralized engine "
+           << ", we can load this model from the serialized engine "
               "directly next time."
            << std::endl;
   }

libs/ultra-infer/ultra_infer/runtime/backends/tensorrt/trt_backend.h

@@ -123,7 +123,7 @@ private:
   // the output order of tensorrt may not be same
   // with the original onnx model
   // So this parameter will record to origin outputs
-  // order, to help recover the rigt order
+  // order, to help recover the right order
   std::map<std::string, int> outputs_order_;
 
   // temporary store onnx model content
@@ -131,7 +131,7 @@ private:
   // it will be released
   std::string onnx_model_buffer_;
   // Stores shape information of the loaded model
-  // For dynmaic shape will record its range information
+  // For dynamic shape will record its range information
   // Also will update the range information while inferencing
   std::map<std::string, ShapeRangeInfo> shape_range_info_;
 

libs/ultra-infer/ultra_infer/runtime/runtime.cc

@@ -97,7 +97,7 @@ bool AutoSelectBackend(RuntimeOption &option) {
   }
 
   if (candidates.size() == 0) {
-    FDERROR << "Cannot found availabel inference backends by model format: "
+    FDERROR << "Cannot found available inference backends by model format: "
             << option.model_format << " with device: " << option.device
             << std::endl;
     return false;
@@ -112,7 +112,7 @@ bool AutoSelectBackend(RuntimeOption &option) {
     }
   }
   std::string debug_message = Str(candidates);
-  FDERROR << "The candiate backends for " << option.model_format << " & "
+  FDERROR << "The candidate backends for " << option.model_format << " & "
           << option.device << " are " << debug_message
           << ", but both of them have not been compiled with current "
              "UltraInfer yet."
@@ -428,7 +428,7 @@ bool Runtime::Compile(std::vector<std::vector<FDTensor>> &prewarm_tensors) {
   FDASSERT(
       casted_backend->Compile(option.model_file, prewarm_tensors,
                               option.poros_option),
-      "Load model from Torchscript failed while initliazing PorosBackend.");
+      "Load model from Torchscript failed while initializing PorosBackend.");
 #else
   FDASSERT(false, "PorosBackend is not available, please compiled with "
                   "ENABLE_POROS_BACKEND=ON.");

libs/ultra-infer/ultra_infer/runtime/runtime.h

@@ -33,7 +33,7 @@ namespace ultra_infer {
  */
 struct ULTRAINFER_DECL Runtime {
 public:
-  /// Intialize a Runtime object with RuntimeOption
+  /// Initialize a Runtime object with RuntimeOption
   bool Init(const RuntimeOption &_option);
 
   /** \brief Inference the model by the input data, and write to the output
@@ -87,7 +87,7 @@ public:
   /** \brief Clone new Runtime when multiple instances of the same model are
    * created
    *
-   * \param[in] stream CUDA Stream, defualt param is nullptr
+   * \param[in] stream CUDA Stream, default param is nullptr
    * \return new Runtime* by this clone
    */
   Runtime *Clone(void *stream = nullptr, int device_id = -1);

libs/ultra-infer/ultra_infer/vision/classification/contrib/resnet.cc

@@ -82,7 +82,7 @@ bool ResNet::Postprocess(FDTensor &infer_result, ClassifyResult *result,
                          int topk) {
   // In this function, the postprocess need be implemented according to the
   // original Repos,
-  // Finally the reslut of postprocess should be saved in ClassifyResult
+  // Finally the result of postprocess should be saved in ClassifyResult
   // variable.
   // 1. Softmax 2. Choose topk labels 3. Put the result into ClassifyResult
   // variable.

libs/ultra-infer/ultra_infer/vision/classification/contrib/resnet_pybind.cc

@@ -17,7 +17,7 @@
 namespace ultra_infer {
 // the name of Pybind function should be Bind${model_name}
 void BindResNet(pybind11::module &m) {
-  // the constructor and the predict funtion are necessary
+  // the constructor and the predict function are necessary
   // the constructor is used to initialize the python model class.
   // the necessary public functions and variables like `size`, `mean_vals`
   // should also be binded.

libs/ultra-infer/ultra_infer/vision/classification/contrib/yolov5cls/yolov5cls.h

@@ -45,7 +45,7 @@ public:
    *
    * \param[in] img The input image data, comes from cv::imread(), is a 3-D
    * array with layout HWC, BGR format \param[in] result The output
-   * classification result will be writen to this structure \return true if the
+   * classification result will be written to this structure \return true if the
    * prediction successed, otherwise false
    */
   virtual bool Predict(const cv::Mat &img, ClassifyResult *result);

libs/ultra-infer/ultra_infer/vision/classification/ppcls/model.h

@@ -59,7 +59,7 @@ public:
    * remove at 1.0 version
    *
    * \param[in] im The input image data, comes from cv::imread()
-   * \param[in] result The output classification result will be writen to this
+   * \param[in] result The output classification result will be written to this
    * structure \return true if the prediction successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, ClassifyResult *result, int topk = 1);

libs/ultra-infer/ultra_infer/vision/classification/ppcls/preprocessor.h

@@ -51,7 +51,7 @@ public:
    *     maybe it's better to run resize on CPU, because the HostToDevice memcpy
    *     is time consuming. Set this true to run the initial resize on CPU.
    *
-   * \param[in] v ture or false
+   * \param[in] v true or false
    */
   void InitialResizeOnCpu(bool v) { initial_resize_on_cpu_ = v; }
 

libs/ultra-infer/ultra_infer/vision/classification/ppshitu/ppshituv2_rec.h

@@ -57,7 +57,7 @@ public:
    * remove at 1.0 version
    *
    * \param[in] im The input image data, comes from cv::imread()
-   * \param[in] result The output feature vector result will be writen to this
+   * \param[in] result The output feature vector result will be written to this
    * structure \return true if the prediction successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, ClassifyResult *result);

libs/ultra-infer/ultra_infer/vision/classification/ppshitu/ppshituv2_rec_preprocessor.h

@@ -51,7 +51,7 @@ public:
    *     maybe it's better to run resize on CPU, because the HostToDevice memcpy
    *     is time consuming. Set this true to run the initial resize on CPU.
    *
-   * \param[in] v ture or false
+   * \param[in] v true or false
    */
   void InitialResizeOnCpu(bool v) { initial_resize_on_cpu_ = v; }
 

libs/ultra-infer/ultra_infer/vision/common/processors/cast.h

@@ -24,7 +24,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for cast images with given type deafault is float.
+/*! @brief Processor for cast images with given type default is float.
  */
 class ULTRAINFER_DECL Cast : public Processor {
 public:

libs/ultra-infer/ultra_infer/vision/common/processors/center_crop.h

@@ -24,7 +24,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for crop images in center with given type deafault is
+/*! @brief Processor for crop images in center with given type default is
  * float.
  */
 class ULTRAINFER_DECL CenterCrop : public Processor {

libs/ultra-infer/ultra_infer/vision/common/processors/color_space_convert.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for tansform images from BGR to RGB.
+/*! @brief Processor for transform images from BGR to RGB.
  */
 class ULTRAINFER_DECL BGR2RGB : public Processor {
 public:
@@ -38,7 +38,7 @@ public:
   static bool Run(FDMat *mat, ProcLib lib = ProcLib::DEFAULT);
 };
 
-/*! @brief Processor for tansform images from RGB to BGR.
+/*! @brief Processor for transform images from RGB to BGR.
  */
 class ULTRAINFER_DECL RGB2BGR : public Processor {
 public:
@@ -57,7 +57,7 @@ public:
   static bool Run(FDMat *mat, ProcLib lib = ProcLib::DEFAULT);
 };
 
-/*! @brief Processor for tansform images from BGR to GRAY.
+/*! @brief Processor for transform images from BGR to GRAY.
  */
 class ULTRAINFER_DECL BGR2GRAY : public Processor {
 public:
@@ -76,7 +76,7 @@ public:
   static bool Run(FDMat *mat, ProcLib lib = ProcLib::DEFAULT);
 };
 
-/*! @brief Processor for tansform images from RGB to GRAY.
+/*! @brief Processor for transform images from RGB to GRAY.
  */
 class ULTRAINFER_DECL RGB2GRAY : public Processor {
 public:

libs/ultra-infer/ultra_infer/vision/common/processors/convert.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace vision {
-/*! @brief Processor for convert images with given paramters.
+/*! @brief Processor for convert images with given parameters.
  */
 class ULTRAINFER_DECL Convert : public Processor {
 public:

libs/ultra-infer/ultra_infer/vision/common/processors/convert_and_permute.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace vision {
-/*! @brief Processor for convert images with given paramters and permute images
+/*! @brief Processor for convert images with given parameters and permute images
  * from HWC to CHW.
  */
 class ULTRAINFER_DECL ConvertAndPermute : public Processor {

libs/ultra-infer/ultra_infer/vision/common/processors/crop.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for crop images with given paramters.
+/*! @brief Processor for crop images with given parameters.
  */
 class ULTRAINFER_DECL Crop : public Processor {
 public:

libs/ultra-infer/ultra_infer/vision/common/processors/limit_by_stride.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for LimitByStride images with given paramters.
+/*! @brief Processor for LimitByStride images with given parameters.
  */
 class ULTRAINFER_DECL LimitByStride : public Processor {
 public:
@@ -38,8 +38,8 @@ public:
   /** \brief Process the input images
    *
    * \param[in] mat The input image data
-   * \param[in] stride limit image stride, deafult is 32
-   * \param[in] interp interpolation method, deafult is 1
+   * \param[in] stride limit image stride, default is 32
+   * \param[in] interp interpolation method, default is 1
    * \param[in] lib to define OpenCV or FlyCV or CVCUDA will be used.
    * \return true if the process successed, otherwise false
    */

libs/ultra-infer/ultra_infer/vision/common/processors/limit_short.h

@@ -19,7 +19,7 @@
 namespace ultra_infer {
 namespace vision {
 
-/*! @brief Processor for Limit images by short edge with given paramters.
+/*! @brief Processor for Limit images by short edge with given parameters.
  */
 class LimitShort : public Processor {
 public:
@@ -45,7 +45,7 @@ public:
    * \param[in] mat The input image data
    * \param[in] max_short target size of short edge
    * \param[in] min_short target size of short edge
-   * \param[in] interp interpolation method, deafult is 1
+   * \param[in] interp interpolation method, default is 1
    * \param[in] lib to define OpenCV or FlyCV or CVCUDA will be used.
    * \return true if the process successed, otherwise false
    */

libs/ultra-infer/ultra_infer/vision/common/processors/manager.h

@@ -30,7 +30,7 @@ public:
 
   /** \brief Use CUDA to boost the performance of processors
    *
-   * \param[in] enable_cv_cuda ture: use CV-CUDA, false: use CUDA only
+   * \param[in] enable_cv_cuda true: use CV-CUDA, false: use CUDA only
    * \param[in] gpu_id GPU device id
    * \return true if the preprocess successed, otherwise false
    */

libs/ultra-infer/ultra_infer/vision/common/processors/mat_batch.h

@@ -28,7 +28,7 @@ enum FDMatBatchLayout { NHWC, NCHW };
 struct ULTRAINFER_DECL FDMatBatch {
   FDMatBatch() = default;
 
-  // MatBatch is intialized with a list of mats,
+  // MatBatch is initialized with a list of mats,
   // the data is stored in the mats separately.
   // Call Tensor() function to get a batched 4-dimension tensor.
   explicit FDMatBatch(std::vector<FDMat> *_mats) {

libs/ultra-infer/ultra_infer/vision/common/processors/normalize.h

@@ -18,7 +18,7 @@
 
 namespace ultra_infer {
 namespace vision {
-/*! @brief Processor for Normalize images with given paramters.
+/*! @brief Processor for Normalize images with given parameters.
  */
 class ULTRAINFER_DECL Normalize : public Processor {
 public:
@@ -48,7 +48,7 @@ public:
   // auto norm = Normalize(...)
   // norm(mat)
   // ```
-  // There will be some precomputation in contruct function
+  // There will be some precomputation in construct function
   // and the `norm(mat)` only need to compute result = mat * alpha + beta
   // which will reduce lots of time
   /** \brief Process the input images

libs/ultra-infer/ultra_infer/vision/common/processors/normalize_and_permute.h

@@ -48,7 +48,7 @@ public:
   // auto norm = Normalize(...)
   // norm(mat)
   // ```
-  // There will be some precomputation in contruct function
+  // There will be some precomputation in construct function
   // and the `norm(mat)` only need to compute result = mat * alpha + beta
   // which will reduce lots of time
   /** \brief Process the input images

libs/ultra-infer/ultra_infer/vision/common/processors/proc_lib.cc

@@ -37,7 +37,7 @@ std::ostream &operator<<(std::ostream &out, const ProcLib &p) {
     out << "ProcLib::CVCUDA";
     break;
   default:
-    FDASSERT(false, "Unknow type of ProcLib.");
+    FDASSERT(false, "Unknown type of ProcLib.");
   }
   return out;
 }

libs/ultra-infer/ultra_infer/vision/common/processors/resize.h

@@ -52,10 +52,10 @@ public:
    * \param[in] mat The input image data, `result = mat * alpha + beta`
    * \param[in] width width of the output image.
    * \param[in] height height of the output image.
-   * \param[in] scale_w scale of width, deafult is -1.0.
-   * \param[in] scale_h scale of height, deafult is -1.0.
-   * \param[in] interp interpolation method, deafult is 1.
-   * \param[in] use_scale to define wheather to scale the image, deafult is
+   * \param[in] scale_w scale of width, default is -1.0.
+   * \param[in] scale_h scale of height, default is -1.0.
+   * \param[in] interp interpolation method, default is 1.
+   * \param[in] use_scale to define whether to scale the image, default is
    * true. \param[in] lib to define OpenCV or FlyCV or CVCUDA will be used.
    * \return true if the process successed, otherwise false
    */

libs/ultra-infer/ultra_infer/vision/common/processors/resize_by_short.h

@@ -49,8 +49,8 @@ public:
    *
    * \param[in] mat The input image data, `result = mat * alpha + beta`
    * \param[in] target_size target size of the output image.
-   * \param[in] interp interpolation method, deafult is 1.
-   * \param[in] use_scale to define wheather to scale the image, deafult is
+   * \param[in] interp interpolation method, default is 1.
+   * \param[in] use_scale to define whether to scale the image, default is
    * true. \param[in] max_hw max HW fo output image. \param[in] lib to define
    * OpenCV or FlyCV or CVCUDA will be used. \return true if the process
    * successed, otherwise false

libs/ultra-infer/ultra_infer/vision/common/result.h

@@ -126,7 +126,7 @@ struct ULTRAINFER_DECL DetectionResult : public BaseResult {
   /// The classify label for all the detected objects
   std::vector<int32_t> label_ids;
   /** \brief For instance segmentation model, `masks` is the predict mask for
-   * all the deteced objects
+   * all the detected objects
    */
   std::vector<Mask> masks;
   /// Shows if the DetectionResult has mask
@@ -435,7 +435,7 @@ struct ULTRAINFER_DECL MattingResult : public BaseResult {
   std::vector<float> alpha; // h x w
   /** \brief
   If the model can predict foreground, `foreground` save the predicted
-  foreground image, the shape is [hight,width,channel] generally.
+  foreground image, the shape is [height,width,channel] generally.
   */
   std::vector<float> foreground; // h x w x c (c=3 default)
   /** \brief

libs/ultra-infer/ultra_infer/vision/detection/contrib/fastestdet/fastestdet.h

@@ -45,7 +45,7 @@ public:
    *
    * \param[in] img The input image data, comes from cv::imread(), is a 3-D
    * array with layout HWC, BGR format \param[in] result The output detection
-   * result will be writen to this structure \return true if the prediction
+   * result will be written to this structure \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(const cv::Mat &img, DetectionResult *result);

libs/ultra-infer/ultra_infer/vision/detection/contrib/nanodet_plus.cc

@@ -49,14 +49,14 @@ void WrapAndResize(Mat *mat, std::vector<int> size, std::vector<float> color,
                    bool keep_ratio = false) {
   // Reference: nanodet/data/transform/warp.py#L139
   // size: tuple of input (width, height)
-  // The default value of `keep_ratio` is `fasle` in
+  // The default value of `keep_ratio` is `false` in
   // `config/nanodet-plus-m-1.5x_320.yml` for both
   // train and val processes. So, we just let this
   // option default `false` according to the official
   // implementation in NanoDet and NanoDet-Plus.
   // Note, this function will apply a normal resize
   // operation to input Mat if the keep_ratio option
-  // is fasle and the behavior will be the same as
+  // is false and the behavior will be the same as
   // yolov5's letterbox if keep_ratio is true.
 
   // with keep_ratio = false (default)

libs/ultra-infer/ultra_infer/vision/detection/contrib/nanodet_plus.h

@@ -48,9 +48,9 @@ public:
    *
    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
    * with layout HWC, BGR format \param[in] result The output detection result
-   * will be writen to this structure \param[in] conf_threshold confidence
+   * will be written to this structure \param[in] conf_threshold confidence
    * threashold for postprocessing, default is 0.35 \param[in] nms_iou_threshold
-   * iou threashold for NMS, default is 0.5 \return true if the prediction
+   * iou threshold for NMS, default is 0.5 \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, DetectionResult *result,
@@ -70,7 +70,7 @@ public:
   // downsample strides for NanoDet-Plus to generate anchors,
   // will take (8, 16, 32, 64) as default values
   std::vector<int> downsample_strides;
-  // for offseting the boxes by classes when using NMS, default 4096
+  // for offsetting the boxes by classes when using NMS, default 4096
   float max_wh;
   /*! @brief
   Argument for image postprocessing step, reg_max for GFL regression, default 7

libs/ultra-infer/ultra_infer/vision/detection/contrib/rknpu2/preprocessor.h

@@ -73,7 +73,7 @@ protected:
   // padding value, size should be the same as channels
   std::vector<float> padding_value_;
 
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad_;
 
   // while is_mini_pad = false and is_no_pad = true,
@@ -87,7 +87,7 @@ protected:
   // padding stride, for is_mini_pad
   int stride_;
 
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh_;
 
   std::vector<std::vector<int>> pad_hw_values_;

libs/ultra-infer/ultra_infer/vision/detection/contrib/rknpu2/rkyolo.h

@@ -34,7 +34,7 @@ public:
    *
    * \param[in] img The input image data, comes from cv::imread(), is a 3-D
    * array with layout HWC, BGR format \param[in] result The output detection
-   * result will be writen to this structure \return true if the prediction
+   * result will be written to this structure \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(const cv::Mat &img, DetectionResult *result);

libs/ultra-infer/ultra_infer/vision/detection/contrib/scaledyolov4.h

@@ -45,9 +45,9 @@ public:
    *
    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
    * with layout HWC, BGR format \param[in] result The output detection result
-   * will be writen to this structure \param[in] conf_threshold confidence
+   * will be written to this structure \param[in] conf_threshold confidence
    * threashold for postprocessing, default is 0.25 \param[in] nms_iou_threshold
-   * iou threashold for NMS, default is 0.5 \return true if the prediction
+   * iou threshold for NMS, default is 0.5 \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, DetectionResult *result,
@@ -61,7 +61,7 @@ public:
   std::vector<int> size;
   // padding value, size should be the same as channels
   std::vector<float> padding_value;
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad;
   // while is_mini_pad = false and is_no_pad = true,
   // will resize the image to the set size
@@ -71,7 +71,7 @@ public:
   bool is_scale_up;
   // padding stride, for is_mini_pad
   int stride;
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh;
 
 private:

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolor.h

@@ -42,7 +42,7 @@ public:
   /** \brief Predict the detection result for an input image
    *
    * \param[in] im The input image data, comes from cv::imread()
-   * \param[in] result The output detection result will be writen to this
+   * \param[in] result The output detection result will be written to this
    * structure \param[in] conf_threshold confidence threashold for
    * postprocessing, default is 0.25 \param[in] nms_iou_threshold iou threashold
    * for NMS, default is 0.5 \return true if the prediction successed, otherwise
@@ -60,7 +60,7 @@ public:
   // padding value, size should be the same as channels
 
   std::vector<float> padding_value;
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad;
   // while is_mini_pad = false and is_no_pad = true,
   // will resize the image to the set size
@@ -70,7 +70,7 @@ public:
   bool is_scale_up;
   // padding stride, for is_mini_pad
   int stride;
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh;
 
 private:

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov5/preprocessor.h

@@ -59,7 +59,7 @@ public:
   /// Get is_scale_up, default true
   bool GetScaleUp() const { return is_scale_up_; }
 
-  /// Set is_mini_pad, pad to the minimum rectange
+  /// Set is_mini_pad, pad to the minimum rectangle
   /// which height and width is times of stride
   void SetMiniPad(bool is_mini_pad) { is_mini_pad_ = is_mini_pad; }
 
@@ -84,7 +84,7 @@ protected:
   // padding value, size should be the same as channels
   std::vector<float> padding_value_;
 
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad_;
 
   // while is_mini_pad = false and is_no_pad = true,
@@ -98,7 +98,7 @@ protected:
   // padding stride, for is_mini_pad
   int stride_;
 
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh_;
 };
 

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov5/yolov5.h

@@ -46,9 +46,9 @@ public:
    *
    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
    * with layout HWC, BGR format \param[in] result The output detection result
-   * will be writen to this structure \param[in] conf_threshold confidence
+   * will be written to this structure \param[in] conf_threshold confidence
    * threashold for postprocessing, default is 0.25 \param[in] nms_threshold iou
-   * threashold for NMS, default is 0.5 \return true if the prediction
+   * threshold for NMS, default is 0.5 \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, DetectionResult *result,
@@ -58,7 +58,7 @@ public:
    *
    * \param[in] img The input image data, comes from cv::imread(), is a 3-D
    * array with layout HWC, BGR format \param[in] result The output detection
-   * result will be writen to this structure \return true if the prediction
+   * result will be written to this structure \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(const cv::Mat &img, DetectionResult *result);

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov5lite.h

@@ -45,9 +45,9 @@ public:
    *
    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
    * with layout HWC, BGR format \param[in] result The output detection result
-   * will be writen to this structure \param[in] conf_threshold confidence
+   * will be written to this structure \param[in] conf_threshold confidence
    * threashold for postprocessing, default is 0.45 \param[in] nms_iou_threshold
-   * iou threashold for NMS, default is 0.25 \return true if the prediction
+   * iou threshold for NMS, default is 0.25 \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, DetectionResult *result,
@@ -64,7 +64,7 @@ public:
   // padding value, size should be the same as channels
 
   std::vector<float> padding_value;
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad;
   // while is_mini_pad = false and is_no_pad = true,
   // will resize the image to the set size
@@ -74,13 +74,13 @@ public:
   bool is_scale_up;
   // padding stride, for is_mini_pad
   int stride;
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh;
   // downsample strides for YOLOv5Lite to generate anchors,
   // will take (8,16,32) as default values, might have stride=64.
   std::vector<int> downsample_strides;
   // anchors parameters, downsample_strides will take (8,16,32),
-  // each stride has three anchors with width and hight
+  // each stride has three anchors with width and height
   std::vector<std::vector<float>> anchor_config;
   /*! @brief
     whether the model_file was exported with decode module. The official
@@ -117,7 +117,7 @@ private:
 
   // the official YOLOv5Lite/export.py will export ONNX file without decode
   // module.
-  // this fuction support the postporocess for ONNX file without decode module.
+  // this function support the postporocess for ONNX file without decode module.
   // set the `is_decode_exported = false`, this function will work.
   bool PostprocessWithDecode(
       FDTensor &infer_result, DetectionResult *result,
@@ -129,7 +129,7 @@ private:
                  bool scale_fill = false, bool scale_up = true,
                  int stride = 32);
 
-  // generate anchors for decodeing when ONNX file without decode module.
+  // generate anchors for decoding when ONNX file without decode module.
   void GenerateAnchors(const std::vector<int> &size,
                        const std::vector<int> &downsample_strides,
                        std::vector<Anchor> *anchors, const int num_anchors = 3);

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov5seg/preprocessor.h

@@ -59,7 +59,7 @@ public:
   /// Get is_scale_up, default true
   bool GetScaleUp() const { return is_scale_up_; }
 
-  /// Set is_mini_pad, pad to the minimum rectange
+  /// Set is_mini_pad, pad to the minimum rectangle
   /// which height and width is times of stride
   void SetMiniPad(bool is_mini_pad) { is_mini_pad_ = is_mini_pad; }
 
@@ -84,7 +84,7 @@ protected:
   // padding value, size should be the same as channels
   std::vector<float> padding_value_;
 
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad_;
 
   // while is_mini_pad = false and is_no_pad = true,
@@ -98,7 +98,7 @@ protected:
   // padding stride, for is_mini_pad
   int stride_;
 
-  // for offseting the boxes by classes when using NMS
+  // for offsetting the boxes by classes when using NMS
   float max_wh_;
 };
 

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov5seg/yolov5seg.h

@@ -45,7 +45,7 @@ public:
    *
    * \param[in] img The input image data, comes from cv::imread(), is a 3-D
    * array with layout HWC, BGR format \param[in] result The output detection
-   * result will be writen to this structure \return true if the prediction
+   * result will be written to this structure \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(const cv::Mat &img, DetectionResult *result);

libs/ultra-infer/ultra_infer/vision/detection/contrib/yolov6.h

@@ -48,9 +48,9 @@ public:
    *
    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array
    * with layout HWC, BGR format \param[in] result The output detection result
-   * will be writen to this structure \param[in] conf_threshold confidence
+   * will be written to this structure \param[in] conf_threshold confidence
    * threashold for postprocessing, default is 0.25 \param[in] nms_iou_threshold
-   * iou threashold for NMS, default is 0.5 \return true if the prediction
+   * iou threshold for NMS, default is 0.5 \return true if the prediction
    * successed, otherwise false
    */
   virtual bool Predict(cv::Mat *im, DetectionResult *result,
@@ -67,7 +67,7 @@ public:
   // padding value, size should be the same as channels
 
   std::vector<float> padding_value;
-  // only pad to the minimum rectange which height and width is times of stride
+  // only pad to the minimum rectangle which height and width is times of stride
   bool is_mini_pad;
   // while is_mini_pad = false and is_no_pad = true,
   // will resize the image to the set size
@@ -77,7 +77,7 @@ public:
   bool is_scale_up;
   // padding stride, for is_mini_pad
   int stride;
-  // for offseting the boxes by classes when using NMS,
+  // for offsetting the boxes by classes when using NMS,
   // default 4096 in meituan/YOLOv6
   float max_wh;