Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix english docs typo errors #48599

Merged
merged 2 commits into from
Dec 8, 2022
Merged

fix english docs typo errors #48599

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3bd0530
fix english docs typo errors
enkilee Dec 1, 2022
b338e80
update docs; test=docs_preview
Ligoml Dec 7, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
77 changes: 34 additions & 43 deletions python/paddle/vision/ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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,
Expand Down Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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]].
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down