fix english docs typo errors (#48599)

* fix english docs typo errors the errors in docs as same as chinese pr 5468 * update docs; test=docs_preview Co-authored-by: Ligoml <39876205+Ligoml@users.noreply.github.com>

Author: enkilee

python/paddle/vision/ops.py

@@ -160,14 +160,14 @@ def yolo_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.
- name (string): The default value is None. Normally there is no need
- for user to set this property. For more information,
- please refer to :ref:`api_guide_Name`
- gt_score (Tensor): mixup score of ground truth boxes, should be in shape
+ gt_score (Tensor, optional): mixup score of ground truth boxes, should be in shape
  of [N, B]. Default None.
- use_label_smooth (bool): Whether to use label smooth. Default True.
- scale_x_y (float): Scale the center point of decoded bounding box.
- Default 1.0
+ use_label_smooth (bool, optional): Whether to use label smooth. Default True.
+ name (str, optional): The default value is None. Normally there is no need
+ for user to set this property. For more information,
+ please refer to :ref:`api_guide_Name`
+ scale_x_y (float, optional): Scale the center point of decoded bounding box.
+ Default 1.0.
 
  Returns:
  Tensor: A 1-D tensor with shape [N], the value of yolov3 loss
@@ -340,14 +340,6 @@ def yolo_box(
  score_{pred} = score_{conf} * score_{class}
  $$
 
- where the confidence scores follow the formula bellow
-
- .. math::
-
- score_{conf} = \begin{case}
- obj, \text{if } iou_aware == false \\
- obj^{1 - iou_aware_factor} * iou^{iou_aware_factor}, \text{otherwise}
- \end{case}
 
  Args:
  x (Tensor): The input tensor of YoloBox operator is a 4-D tensor with
@@ -369,15 +361,14 @@ def yolo_box(
  :attr:`yolo_box` operator input, so 32, 16, 8
  should be set for the first, second, and thrid
  :attr:`yolo_box` layer.
- clip_bbox (bool): Whether clip output bonding box in :attr:`img_size`
+ clip_bbox (bool, optional): Whether clip output bonding box in :attr:`img_size`
  boundary. Default true.
- scale_x_y (float): Scale the center point of decoded bounding box.
- Default 1.0
- name (string): The default value is None. Normally there is no need
- for user to set this property. For more information,
- please refer to :ref:`api_guide_Name`
- iou_aware (bool): Whether use iou aware. Default false
- iou_aware_factor (float): iou aware factor. Default 0.5
+ name (str, optional): The default value is None. Normally there is no need
+ for user to set this property. For more information,
+ please refer to :ref:`api_guide_Name`.
+ scale_x_y (float, optional): Scale the center point of decoded bounding box. Default 1.0
+ iou_aware (bool, optional): Whether use iou aware. Default false.
+ iou_aware_factor (float, optional): iou aware factor. Default 0.5.
 
  Returns:
  Tensor: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
@@ -902,8 +893,8 @@ def deform_conv2d(
 
  .. math::
 
- H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
- W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
+ H_{out}&= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\
+ W_{out}&= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
 
  Args:
  x (Tensor): The input image with [N, C, H, W] format. A Tensor with type
@@ -913,31 +904,31 @@ def deform_conv2d(
  weight (Tensor): The convolution kernel with shape [M, C/g, kH, kW], where M is
  the number of output channels, g is the number of groups, kH is the filter's
  height, kW is the filter's width.
- bias (Tensor, optional): The bias with shape [M,].
+ bias (Tensor, optional): The bias with shape [M,]. Default: None.
  stride (int|list|tuple, optional): The stride size. If stride is a list/tuple, it must
  contain two integers, (stride_H, stride_W). Otherwise, the
- stride_H = stride_W = stride. Default: stride = 1.
+ stride_H = stride_W = stride. Default: 1.
  padding (int|list|tuple, optional): The padding size. If padding is a list/tuple, it must
  contain two integers, (padding_H, padding_W). Otherwise, the
- padding_H = padding_W = padding. Default: padding = 0.
+ padding_H = padding_W = padding. Default: 0.
  dilation (int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must
  contain two integers, (dilation_H, dilation_W). Otherwise, the
- dilation_H = dilation_W = dilation. Default: dilation = 1.
+ dilation_H = dilation_W = dilation. Default: 1.
  deformable_groups (int): The number of deformable group partitions.
- Default: deformable_groups = 1.
+ Default: 1.
  groups (int, optonal): The groups number of the deformable conv layer. According to
  grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
  the first half of the filters is only connected to the first half
  of the input channels, while the second half of the filters is only
- connected to the second half of the input channels. Default: groups=1.
+ connected to the second half of the input channels. Default: 1.
  mask (Tensor, optional): The input mask of deformable convolution layer.
  A Tensor with type float32, float64. It should be None when you use
- deformable convolution v1.
+ deformable convolution v1. Default: None.
  name(str, optional): For details, please refer to :ref:`api_guide_Name`.
  Generally, no setting is required. Default: None.
  Returns:
- Tensor: The tensor variable storing the deformable convolution \
-  result. A Tensor with type float32, float64.
+ Tensor: 4-D Tensor storing the deformable convolution result.\
+ A Tensor with type float32, float64.
 
  Examples:
  .. code-block:: python
@@ -1145,7 +1136,7 @@ class DeformConv2D(Layer):
  dilation(int|list|tuple, optional): The dilation size. If dilation is a list/tuple, it must
  contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
  dilation_D = dilation_H = dilation_W = dilation. The default value is 1.
- deformable_groups (int): The number of deformable group partitions.
+ deformable_groups (int, optional): The number of deformable group partitions.
  Default: deformable_groups = 1.
  groups(int, optional): The groups number of the Conv3D Layer. According to grouped
  convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
@@ -1504,7 +1495,7 @@ def decode_jpeg(x, mode='unchanged', name=None):
  Args:
  x (Tensor): A one dimensional uint8 tensor containing the raw bytes
  of the JPEG image.
- mode (str): The read mode used for optionally converting the image.
+ mode (str, optional): The read mode used for optionally converting the image.
  Default: 'unchanged'.
  name (str, optional): The default value is None. Normally there is no
  need for user to set this property. For more information, please
@@ -1694,10 +1685,10 @@ def roi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
  2D-Tensor with the shape of [num_boxes,4].
  Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates,
  and (x2, y2) is the bottom right coordinates.
- boxes_num (Tensor): the number of RoIs in each image, data type is int32. Default: None
+ boxes_num (Tensor): the number of RoIs in each image, data type is int32.
  output_size (int or tuple[int, int]): the pooled output size(h, w), data type is int32. If int, h and w are both equal to output_size.
- spatial_scale (float, optional): multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
- name(str, optional): for detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default.
+ spatial_scale (float, optional): multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0.
+ name(str, optional): for detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Default: None.
 
  Returns:
  pool_out (Tensor): the pooled feature, 4D-Tensor with the shape of [num_boxes, C, output_size[0], output_size[1]].
@@ -1871,10 +1862,10 @@ def roi_align(
  Default: True.
  name(str, optional): For detailed information, please refer to :
  ref:`api_guide_Name`. Usually name is no need to set and None by
- default.
+ default. Default: None.
 
  Returns:
- The output of ROIAlignOp is a 4-D tensor with shape (num_boxes,
+ The output of ROIAlignOp is a 4-D tensor with shape (num_boxes,\
  channels, pooled_h, pooled_w). The data type is float32 or float64.
 
  Examples:
@@ -1971,10 +1962,10 @@ class RoIAlign(Layer):
  data type is int32. If int, h and w are both equal to output_size.
  spatial_scale (float32, optional): Multiplicative spatial scale factor
  to translate ROI coords from their input scale to the scale used
- when pooling. Default: 1.0
+ when pooling. Default: 1.0.
 
  Returns:
- The output of ROIAlign operator is a 4-D tensor with
+ The output of ROIAlign operator is a 4-D tensor with \
  shape (num_boxes, channels, pooled_h, pooled_w).
 
  Examples: