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fix doc statement. test=develop

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revert-16190-refine_parallel_executor
dengkaipeng 7 years ago
parent b399ee2a23
commit abb5a9c726
2 changed files with 38 additions and 34 deletions
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65
paddle/fluid/operators/detection/yolo_box_op.cc
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@ -75,25 +75,25 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X",
"The input tensor of YoloBox operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimension(C) stores"
"box locations, confidence score and classification one-hot"
"keys of each anchor box. Generally, X should be the output"
"This is a 4-D tensor with shape of [N, C, H, W]. "
"H and W should be same, and the second dimension(C) stores "
"box locations, confidence score and classification one-hot "
"keys of each anchor box. Generally, X should be the output "
"of YOLOv3 network.");
AddInput("ImgSize",
"The image size tensor of YoloBox operator, "
"This is a 2-D tensor with shape of [N, 2]. This tensor holds"
"height and width of each input image using for resize output"
"This is a 2-D tensor with shape of [N, 2]. This tensor holds "
"height and width of each input image using for resize output "
"box in input image scale.");
AddOutput("Boxes",
"The output tensor of detection boxes of YoloBox operator, "
"This is a 3-D tensor with shape of [N, M, 4], N is the"
"batch num, M is output box number, and the 3rd dimension"
"This is a 3-D tensor with shape of [N, M, 4], N is the "
"batch num, M is output box number, and the 3rd dimension "
"stores [xmin, ymin, xmax, ymax] coordinates of boxes.");
AddOutput("Scores",
"The output tensor ofdetection boxes scores of YoloBox"
"operator, This is a 3-D tensor with shape of [N, M, C],"
"N is the batch num, M is output box number, C is the"
"The output tensor ofdetection boxes scores of YoloBox "
"operator, This is a 3-D tensor with shape of [N, M, C], "
"N is the batch num, M is output box number, C is the "
"class number.");
AddAttr<int>("class_num", "The number of classes to predict.");
@ -107,30 +107,31 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"and thrid YoloBox operators.")
.SetDefault(32);
AddAttr<float>("conf_thresh",
"The confidence scores threshold of detection boxes."
"boxes with confidence scores under threshold should"
"The confidence scores threshold of detection boxes. "
"Boxes with confidence scores under threshold should "
"be ignored.")
.SetDefault(0.01);
AddComment(R"DOC(
This operator generate YOLO detection boxes from output of YOLOv3 network.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimension(the channel dimension), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimension, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if :math:`tx, ty, tw, th`, the box
predictions correspnd to:
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors, In the second dimension(the channel
dimension), C should be equal to S * (class_num + 5), class_num is the object
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
box.
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
predictions should be as follows:
$$
b_x = \sigma(t_x) + c_x
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \sigma(t_y) + c_y
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
@ -139,14 +140,14 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
b_h = p_h e^{t_h}
$$
While :math:`c_x, c_y` is the left top corner of current grid and
:math:`p_w, p_h` is specified by anchors.
in the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
The logistic scores of the 5rd channel of each anchor prediction boxes
represent the confidence score of each prediction scores, and the logistic
scores of the last class_num channels of each anchor prediction boxes
represent the classifcation scores. Boxes with confidence scores less than
conf_thresh should be ignored, and box final scores is the product of
The logistic regression value of the 5rd channel of each anchor prediction boxes
represent the confidence score of each prediction box, and the logistic
regression value of the last :attr:`class_num` channels of each anchor prediction
boxes represent the classifcation 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.
)DOC");

7
python/paddle/fluid/layers/detection.py
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@ -628,10 +628,12 @@ def yolo_box(x,
class_num (int): ${class_num_comment}
conf_thresh (float): ${conf_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
name (string): the name of yolo box layer
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Variable: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
and a 3-D tensor with shape [N, M, C], the classification scores
of boxes.
Raises:
TypeError: Input x of yolov_box must be Variable
@ -640,6 +642,7 @@ def yolo_box(x,
TypeError: Attr conf_thresh of yolo box must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')

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