Fix Normalizaiton Normalization

python/paddle/audio/functional/functional.py

@@ -313,7 +313,7 @@ def create_dct(
     Args:
         n_mfcc (int): Number of mel frequency cepstral coefficients.
         n_mels (int): Number of mel filterbanks.
-        norm (Optional[str], optional): Normalizaiton type. Defaults to 'ortho'.
+        norm (Optional[str], optional): Normalization type. Defaults to 'ortho'.
         dtype (str, optional): The data type of the return matrix. Defaults to 'float32'.
 
     Returns:

python/paddle/utils/cpp_extension/cpp_extension.py

@@ -480,7 +480,7 @@ def unix_custom_single_compiler(
                 # shared library have same ABI suffix with libpaddle.so.
                 # See https://stackoverflow.com/questions/34571583/understanding-gcc-5s-glibcxx-use-cxx11-abi-or-the-new-abi
                 add_compile_flag(cflags, ['-D_GLIBCXX_USE_CXX11_ABI=1'])
-                # Append this macor only when jointly compiling .cc with .cu
+                # Append this macro only when jointly compiling .cc with .cu
                 if not is_cuda_file(src) and self.contain_cuda_file:
                     if core.is_compiled_with_rocm():
                         cflags.append('-DPADDLE_WITH_HIP')
@@ -829,7 +829,7 @@ def load(
     If the above conditions are not met, the corresponding warning will be printed, and a fatal error may
     occur because of ABI compatibility.
 
-    Compared with ``setup`` interface, it doesn't need extra ``setup.py`` and excute
+    Compared with ``setup`` interface, it doesn't need extra ``setup.py`` and execute
     ``python setup.py install`` command. The interface contains all compiling and installing
     process underground.
 
@@ -850,7 +850,7 @@ def load(
         from paddle.utils.cpp_extension import load
 
         custom_op_module = load(
-            name="op_shared_libary_name",                # name of shared library
+            name="op_shared_library_name",                # name of shared library
             sources=['relu_op.cc', 'relu_op.cu'],        # source files of customized op
             extra_cxx_cflags=['-g', '-w'],               # optional, specify extra flags to compile .cc/.cpp file
             extra_cuda_cflags=['-O2'],                   # optional, specify extra flags to compile .cu file

python/paddle/utils/cpp_extension/extension_utils.py

@@ -585,7 +585,7 @@ def normalize_extension_kwargs(kwargs, use_cuda=False):
             # See _reset_so_rpath for details.
             extra_link_args.append(f'-Wl,-rpath,{_get_base_path()}')
             # On MacOS, ld don't support `-l:xx`, so we create a
-            # liblibpaddle.dylib symbol link.
+            # libpaddle.dylib symbol link.
             lib_core_name = create_sym_link_if_not_exist()
             extra_link_args.append(f'-l{lib_core_name}')
         # -----------------------   -- END --    ----------------------- #

python/paddle/utils/inplace_utils.py

@@ -39,7 +39,7 @@ def __impl__(*args, **kwargs):
                 for arg in args:
                     if hasattr(arg, "is_view_var") and arg.is_view_var:
                         raise ValueError(
-                            f'Sorry about what\'s happend. In to_static mode, {func.__name__}\'s output variable {arg.name} is a viewed Tensor in dygraph. This will result in inconsistent calculation behavior between dynamic and static graphs. You must find the location of the strided API be called, and call {arg.name} = {arg.name}.assign().'
+                            f'Sorry about what\'s happened. In to_static mode, {func.__name__}\'s output variable {arg.name} is a viewed Tensor in dygraph. This will result in inconsistent calculation behavior between dynamic and static graphs. You must find the location of the strided API be called, and call {arg.name} = {arg.name}.assign().'
                         )
 
             origin_func = f"{func.__module__}.{origin_api_name}"

python/paddle/vision/datasets/folder.py

@@ -130,7 +130,7 @@ class DatasetFolder(Dataset):
             ...             dirname = list(subpath.keys())[0]
             ...             make_directory(root / dirname, subpath[dirname])
 
-            >>> directory_hirerarchy = [
+            >>> directory_hierarchy = [
             ...     {"class_0": [
             ...         "abc.jpg",
             ...         "def.png"]},
@@ -146,7 +146,7 @@ class DatasetFolder(Dataset):
             >>> # You can replace this with any directory to explore the structure
             >>> # of generated data. e.g. fake_data_dir = "./temp_dir"
             >>> fake_data_dir = tempfile.mkdtemp()
-            >>> make_directory(fake_data_dir, directory_hirerarchy)
+            >>> make_directory(fake_data_dir, directory_hierarchy)
             >>> data_folder_1 = DatasetFolder(fake_data_dir)
             >>> print(data_folder_1.classes)
             ['class_0', 'class_1']

python/paddle/vision/models/shufflenetv2.py

@@ -330,7 +330,7 @@ def _shufflenet_v2(arch, pretrained=False, **kwargs):
 
 def shufflenet_v2_x0_25(pretrained=False, **kwargs):
     """ShuffleNetV2 with 0.25x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -365,7 +365,7 @@ def shufflenet_v2_x0_25(pretrained=False, **kwargs):
 
 def shufflenet_v2_x0_33(pretrained=False, **kwargs):
     """ShuffleNetV2 with 0.33x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -400,7 +400,7 @@ def shufflenet_v2_x0_33(pretrained=False, **kwargs):
 
 def shufflenet_v2_x0_5(pretrained=False, **kwargs):
     """ShuffleNetV2 with 0.5x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -435,7 +435,7 @@ def shufflenet_v2_x0_5(pretrained=False, **kwargs):
 
 def shufflenet_v2_x1_0(pretrained=False, **kwargs):
     """ShuffleNetV2 with 1.0x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -470,7 +470,7 @@ def shufflenet_v2_x1_0(pretrained=False, **kwargs):
 
 def shufflenet_v2_x1_5(pretrained=False, **kwargs):
     """ShuffleNetV2 with 1.5x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -505,7 +505,7 @@ def shufflenet_v2_x1_5(pretrained=False, **kwargs):
 
 def shufflenet_v2_x2_0(pretrained=False, **kwargs):
     """ShuffleNetV2 with 2.0x output channels, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
@@ -540,7 +540,7 @@ def shufflenet_v2_x2_0(pretrained=False, **kwargs):
 
 def shufflenet_v2_swish(pretrained=False, **kwargs):
     """ShuffleNetV2 with swish activation function, as described in
-    `"ShuffleNet V2: Practical Guidelines for Ecient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
+    `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
 
     Args:
         pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained

python/paddle/vision/ops.py

@@ -112,13 +112,13 @@ def yolo_loss(
     box coordinates (w, h), sigmoid cross entropy loss is used for box
     coordinates (x, y), objectness loss and classification loss.
 
-    Each groud truth box finds a best matching anchor box in all anchors.
+    Each ground truth box finds a best matching anchor box in all anchors.
     Prediction of this anchor box will incur all three parts of losses, and
     prediction of anchor boxes with no GT box matched will only incur objectness
     loss.
 
     In order to trade off box coordinate losses between big boxes and small
-    boxes, box coordinate losses will be mutiplied by scale weight, which is
+    boxes, box coordinate losses will be multiplied by scale weight, which is
     calculated as follows.
 
     $$
@@ -134,10 +134,10 @@ def yolo_loss(
     While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
     target will be smoothed when calculating classification loss, target of
     positive samples will be smoothed to :math:`1.0 - 1.0 / class\_num` and target of
-    negetive samples will be smoothed to :math:`1.0 / class\_num`.
+    negative samples will be smoothed to :math:`1.0 / class\_num`.
 
     While :attr:`gt_score` is given, which means the mixup score of ground truth
-    boxes, all losses incured by a ground truth box will be multiplied by its
+    boxes, all losses incurred by a ground truth box will be multiplied by its
     mixup score.
 
     Args:
@@ -146,7 +146,7 @@ def yolo_loss(
                       and the second dimension(C) stores box locations, confidence
                       score and classification one-hot keys of each anchor box.
                       The data type is float32 or float64.
-        gt_box (Tensor): groud truth boxes, should be in shape of [N, B, 4],
+        gt_box (Tensor): ground truth boxes, should be in shape of [N, B, 4],
                           in the third dimension, x, y, w, h should be stored.
                           x,y is the center coordinate of boxes, w, h are the
                           width and height, x, y, w, h should be divided by
@@ -163,7 +163,7 @@ def yolo_loss(
         ignore_thresh (float): The ignore threshold to ignore confidence loss.
         downsample_ratio (int): The downsample ratio from network input to YOLOv3
                                 loss input, so 32, 16, 8 should be set for the
-                                first, second, and thrid YOLOv3 loss operators.
+                                first, second, and third YOLOv3 loss operators.
         gt_score (Tensor, optional): mixup score of ground truth boxes, should be in shape
                             of [N, B]. Default None.
         use_label_smooth (bool, optional): Whether to use label smooth. Default True.
@@ -313,7 +313,7 @@ def yolo_box(
     The logistic regression value of the 5th channel of each anchor prediction boxes
     represents the confidence score of each prediction box, and the logistic
     regression value of the last :attr:`class_num` channels of each anchor prediction
-    boxes represents the classifcation scores. Boxes with confidence scores less than
+    boxes represents the classification scores. Boxes with confidence scores less than
     :attr:`conf_thresh` should be ignored, and box final scores is the product of
     confidence scores and classification scores.
 
@@ -340,7 +340,7 @@ def yolo_box(
                              be ignored.
         downsample_ratio (int): The downsample ratio from network input to
                                 :attr:`yolo_box` operator input, so 32, 16, 8
-                                should be set for the first, second, and thrid
+                                should be set for the first, second, and third
                                 :attr:`yolo_box` layer.
         clip_bbox (bool, optional): Whether clip output bonding box in :attr:`img_size`
                           boundary. Default true.
@@ -1356,7 +1356,7 @@ def decode_jpeg(x, mode='unchanged', name=None):
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
     Returns:
-        Tensor: A decoded image tensor with shape (imge_channels, image_height, image_width)
+        Tensor: A decoded image tensor with shape (image_channels, image_height, image_width)
 
     Examples:
         .. code-block:: python
@@ -1809,18 +1809,18 @@ def forward(self, x, boxes, boxes_num, aligned=True):
 
 class ConvNormActivation(Sequential):
     """
-    Configurable block used for Convolution-Normalzation-Activation blocks.
+    Configurable block used for Convolution-Normalization-Activation blocks.
     This code is based on the torchvision code with modifications.
     You can also see at https://github.com/pytorch/vision/blob/main/torchvision/ops/misc.py#L68
     Args:
         in_channels (int): Number of channels in the input image
-        out_channels (int): Number of channels produced by the Convolution-Normalzation-Activation block
+        out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
         kernel_size: (int|list|tuple, optional): Size of the convolving kernel. Default: 3
         stride (int|list|tuple, optional): Stride of the convolution. Default: 1
         padding (int|str|tuple|list, optional): Padding added to all four sides of the input. Default: None,
-            in wich case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``
+            in which case it will calculated as ``padding = (kernel_size - 1) // 2 * dilation``
         groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
-        norm_layer (Callable[..., paddle.nn.Layer], optional): Norm layer that will be stacked on top of the convolutiuon layer.
+        norm_layer (Callable[..., paddle.nn.Layer], optional): Norm layer that will be stacked on top of the convolution layer.
             If ``None`` this layer wont be used. Default: ``paddle.nn.BatchNorm2D``
         activation_layer (Callable[..., paddle.nn.Layer], optional): Activation function which will be stacked on top of the normalization
             layer (if not ``None``), otherwise on top of the conv layer. If ``None`` this layer wont be used. Default: ``paddle.nn.ReLU``
@@ -1887,7 +1887,7 @@ def nms(
 
     If category_idxs and categories are provided, NMS will be performed with a batched style,
     which means NMS will be applied to each category respectively and results of each category
-    will be concated and sorted by scores.
+    will be concatenated and sorted by scores.
 
     If K is provided, only the first k elements will be returned. Otherwise, all box indices sorted by scores will be returned.
 

python/paddle/vision/transforms/functional.py

@@ -314,7 +314,7 @@ def hflip(img):
         img (PIL.Image|np.array|Tensor): Image to be flipped.
 
     Returns:
-        PIL.Image|np.array|paddle.Tensor:  Horizontall flipped image.
+        PIL.Image|np.array|paddle.Tensor:  Horizontally flipped image.
 
     Examples:
         .. code-block:: python
@@ -966,7 +966,7 @@ def normalize(img, mean, std, data_format='CHW', to_rgb=False):
         data_format (str, optional): Data format of input img, should be 'HWC' or
             'CHW'. Default: 'CHW'.
         to_rgb (bool, optional): Whether to convert to rgb. If input is tensor,
-            this option will be igored. Default: False.
+            this option will be ignored. Default: False.
 
     Returns:
         PIL.Image|np.array|paddle.Tensor: Normalized mage. Data format is same as input img.

python/paddle/vision/transforms/functional_cv2.py

@@ -245,7 +245,7 @@ def center_crop(img, output_size):
         img (np.array): Image to be cropped. (0,0) denotes the top left corner of the image.
         output_size (sequence or int): (height, width) of the crop box. If int,
             it is used for both directions
-        backend (str, optional): The image proccess backend type. Options are `pil`, `cv2`. Default: 'pil'.
+        backend (str, optional): The image process backend type. Options are `pil`, `cv2`. Default: 'pil'.
 
     Returns:
         np.array: Cropped image.
@@ -269,7 +269,7 @@ def hflip(img):
         img (np.array): Image to be flipped.
 
     Returns:
-        np.array:  Horizontall flipped image.
+        np.array:  Horizontally flipped image.
 
     """
     cv2 = try_import('cv2')
@@ -681,7 +681,7 @@ def to_grayscale(img, num_output_channels=1):
 
 
 def normalize(img, mean, std, data_format='CHW', to_rgb=False):
-    """Normalizes a ndarray imge or image with mean and standard deviation.
+    """Normalizes a ndarray image or image with mean and standard deviation.
 
     Args:
         img (np.array): input data to be normalized.

python/paddle/vision/transforms/functional_pil.py

@@ -268,7 +268,7 @@ def center_crop(img, output_size):
         img (PIL.Image): Image to be cropped. (0,0) denotes the top left corner of the image.
         output_size (sequence or int): (height, width) of the crop box. If int,
             it is used for both directions
-        backend (str, optional): The image proccess backend type. Options are `pil`, `cv2`. Default: 'pil'.
+        backend (str, optional): The image process backend type. Options are `pil`, `cv2`. Default: 'pil'.
 
     Returns:
         PIL.Image: Cropped image.
@@ -292,7 +292,7 @@ def hflip(img):
         img (PIL.Image): Image to be flipped.
 
     Returns:
-        PIL.Image:  Horizontall flipped image.
+        PIL.Image:  Horizontally flipped image.
 
     """
 
@@ -520,7 +520,7 @@ def to_grayscale(img, num_output_channels=1):
 
     Args:
         img (PIL.Image): Image to be converted to grayscale.
-        backend (str, optional): The image proccess backend type. Options are `pil`,
+        backend (str, optional): The image process backend type. Options are `pil`,
                     `cv2`. Default: 'pil'.
 
     Returns:

python/paddle/vision/transforms/functional_tensor.py

@@ -186,8 +186,8 @@ def to_grayscale(img, num_output_channels=1, data_format='CHW'):
     """Converts image to grayscale version of image.
 
     Args:
-        img (paddel.Tensor): Image to be converted to grayscale.
-        num_output_channels (int, optionl[1, 3]):
+        img (paddle.Tensor): Image to be converted to grayscale.
+        num_output_channels (int, optional[1, 3]):
             if num_output_channels = 1 : returned image is single channel
             if num_output_channels = 3 : returned image is 3 channel
         data_format (str, optional): Data format of img, should be 'HWC' or
@@ -585,7 +585,7 @@ def hflip(img, data_format='CHW'):
             'CHW'. Default: 'CHW'.
 
     Returns:
-        paddle.Tensor:  Horizontall flipped image.
+        paddle.Tensor:  Horizontally flipped image.
 
     """
     _assert_image_tensor(img, data_format)